03-About Data Types.ipynb

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    "<div dir=\"rtl\" style=\"text-align:center; font-family: 'B Nazanin'; font-size: 24px;\">\n",
    "    <div class=\"alert alert-block alert-info\">\n",
    "        <h1>» Chapter Four: Data Types «</h1>\n",
    "       <p>One Session</p>\n",
    "</div>"
   ]
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   "source": [
    "# Built-in Data Types"
   ]
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   "source": [
    "* Text Type:\t`str`\n",
    "* Numeric Types:\t`int`, `float`, `complex`\n",
    "* Sequence Types:\t`list`, `tuple`, `range`\n",
    "* Mapping Type:\t`dict`\n",
    "* Set Types:\t`set`, `frozenset`\n",
    "* Boolean Type:\t`bool`\n",
    "* Binary Types:\t`bytes`, `bytearray`, `memoryview`\n",
    "* None Type:\t`NoneType`"
   ]
  },
  {
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    "tags": [
     "a"
    ]
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   "outputs": [],
   "source": [
    "x = \"Hello World\"\t#str\n",
    "x = str(\"Hello World\") # casting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc72196-e2a0-493b-8edd-ea0593a8ce99",
   "metadata": {},
   "outputs": [],
   "source": [
    "str(20)"
   ]
  },
  {
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   "id": "5d4c377a-d6c1-45f6-b031-d9015249c0e7",
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   "source": [
    "x = 20\t#int\n",
    "x = int(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40501a59-7d85-4751-bcac-9f888412072d",
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   "source": [
    "int(20.9)\n"
   ]
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   "id": "ea77540c-c877-48c4-aa72-9149e53af333",
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   "source": [
    "round(20.5)"
   ]
  },
  {
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   "id": "fc46c887-0756-4ae0-9deb-9ad2e14641c6",
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   "source": [
    "int('20.6')"
   ]
  },
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   "execution_count": null,
   "id": "d5c51265-3066-4b24-8c48-a171ef8bd55f",
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   "outputs": [],
   "source": [
    "x = 20.5\t#float\n",
    "x = float(20.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e8ed120-1a65-4c19-aca5-8df8e10c6afe",
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   "source": [
    "float('20.6')"
   ]
  },
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   "id": "a204992c-6731-4416-9553-d8aa1bded71e",
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   "source": [
    "x = 1j\t#complex\n",
    "x = complex(1j)"
   ]
  },
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   "execution_count": null,
   "id": "ef6b03f4-fa85-4251-8803-aa3c90fa88fe",
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   "source": [
    "x = [\"apple\", \"banana\", \"cherry\",20, [2,5,8]]\t#list\n",
    "x = list((\"apple\", \"banana\", \"cherry\"))"
   ]
  },
  {
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   "source": [
    "x = (\"apple\", \"banana\", \"cherry\")\t#tuple\t\n",
    "x = tuple((\"apple\", \"banana\", \"cherry\"))"
   ]
  },
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   "id": "ede7622d-dec4-492a-bbcd-4cd5183549d8",
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   "source": [
    "x = range(6)\t#range\t\n",
    "x = range(6)"
   ]
  },
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   "execution_count": null,
   "id": "443d082c-cb26-48f2-b149-b3133370bcf9",
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   "outputs": [],
   "source": [
    "print(x)"
   ]
  },
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   "cell_type": "code",
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   "id": "be692d50-22a9-43e7-a959-51ce0f7eaf4c",
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   "outputs": [],
   "source": [
    "list(range(6))"
   ]
  },
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   "execution_count": null,
   "id": "c2c4ddd5-fa9d-44a2-911e-b8fc41a3026f",
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   "source": [
    "x = {\"name\" : \"John\", \"age\" : 36}\t# dict # key-value\n",
    "x = dict(name=\"John\", age=36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce6c4e66-9c59-4089-b5a9-0f6f40f76f47",
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   "source": [
    "x = dict(name=\"John\", age=36)\n",
    "x"
   ]
  },
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   "execution_count": null,
   "id": "968deaf0-ac3c-479c-ab74-d68a56ab5b93",
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   "outputs": [],
   "source": [
    "x = {\"apple\", \"banana\", \"cherry\"}\t#set\n",
    "x = set((\"apple\", \"banana\", \"cherry\"))\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329293e8-c82a-45e5-81ca-a2d52ea06888",
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   "outputs": [],
   "source": [
    "set({\"name\" : \"John\", \"age\" : 36})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f9e903-d18f-4e9d-a852-cf9ce6c371eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = frozenset({\"apple\", \"banana\", \"cherry\"})\t#frozenset\t\n",
    "x = frozenset((\"apple\", \"banana\", \"cherry\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4420780d-1b6d-4588-8305-344548e50ee9",
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   "source": [
    "x = False\t#bool\t\n",
    "x = bool(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cbcbc65-b2fe-4d6f-b502-bcf624ea9366",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "00569d14-3675-4fbf-9bca-822f2570601a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bin(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e230381-f138-4bf4-8e3e-bc25d9eff1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = b\"Hello\"\t#bytes\t\n",
    "x = bytes(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f92c39-5fdd-4f79-8a86-bd23b15b289c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9aa935-01b9-4383-9ddd-b0d1fa35baf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bytearray(5)\t#bytearray\t\n",
    "x = bytearray(5)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "77844828-89c4-4fa2-8546-aacb3bd3795d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
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   "id": "4b880c91-6020-49b0-bef8-932fbf9170ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = memoryview(bytes(5))\t#memoryview\t\n",
    "x = memoryview(bytes(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b1b222d-ac7a-4d66-afb8-02de37e46634",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "38406257-989d-49ee-936d-708a94176e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = None\t#NoneType\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "450c23f9-bb54-4362-a44e-257c1d833a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list()\n",
    "print(bool(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d33e79-b12e-4ca3-9de4-3e61176182c6",
   "metadata": {},
   "source": [
    "# Numbers"
   ]
  },
  {
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   "source": [
    "- int\n",
    "  - or integer, is a whole number, positive or negative, without decimals, of unlimited length.\n",
    "- float\n",
    "  - or \"floating point number\" is a number, positive or negative, containing one or more decimals, unlimited length.\n",
    "- complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4a9043-5374-4968-8499-3994070196e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1    # int\n",
    "y = 2.8  # float\n",
    "z = 1j   # complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9bfaf9-c907-41ef-9dc1-54b2f1db149b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Float can also be scientific numbers with an \"e\" to indicate the power of 10.\n",
    "z = -87.7e100\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3441c38f-c2d3-46da-a90b-c645fb6786d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "1e2 # 1.0 * 10 ^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c45c0a03-6775-4481-8ed7-9c06b4af5737",
   "metadata": {},
   "outputs": [],
   "source": [
    "2E-3 # 2.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f57328-b8a2-4c00-ab7e-8a2a198d99c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type Conversion\n",
    "#int(), float(), and complex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6029961c-37aa-4d87-a6f9-d950553e1df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = int(1)   # x will be 1\n",
    "y = int(2.8) # y will be 2\n",
    "z = int(\"3\") # z will be 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "772071ea-a6c0-46b4-9cc5-76c31af13b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "int(2.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fecf169b-bc44-4577-869a-9baa09b44be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = float(1)     # x will be 1.0\n",
    "y = float(2.8)   # y will be 2.8\n",
    "z = float(\"3\")   # z will be 3.0\n",
    "w = float(\"4.2\") # w will be 4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "664c9ded-b85d-44d0-8f16-e478009cf1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = str(\"s1\") # x will be 's1'\n",
    "y = str(2)    # y will be '2'\n",
    "z = str(3.0)  # z will be '3.0'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4bf11e-69cb-4c5c-81ba-e4756b90b902",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
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   "source": [
    "#### Random Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ca61b7-2d65-4740-bc66-55dffaec85c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "print(random.randrange(0, 20,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27fb8fb8-d87d-4e50-9bf7-d4529e0771a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "print(random.randrange(0, 20,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2fcdf91-91e3-4ff0-8f4e-9c2b434da9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "print(random.randrange(1, 20,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adf1db7d-6643-43a0-9792-1f1be3a94d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(random.randint(1,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0054db-49a0-428e-83cd-9590d45371eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(random.random()) # 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ec6dbe-a202-47c3-be54-75fa4bfd65bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Generate a random range\n",
    "start = random.randint(0, 50)\n",
    "end = random.randint(start, 100)\n",
    "step = random.randint(1, 10)\n",
    "\n",
    "random_range = range(start, end, step)\n",
    "random_range_list = list(random_range)\n",
    "\n",
    "random_range_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d314d6-9781-4f13-8f7f-0fb23ac8aeb2",
   "metadata": {},
   "source": [
    "##### Random Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec89ebfc-240d-4656-96d9-12b2dbdff179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seed()\tInitialize the random number generator.\n",
    "import random\n",
    "\n",
    "random.seed(12)\n",
    "print(random.random())\n",
    "\n",
    "# By default the random number generator uses the current system time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3aa647d-5fd9-49de-9eb3-9543d1efaf37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you use the same seed value twice you will get the same random number twice.\n",
    "import random\n",
    "\n",
    "random.seed(10)\n",
    "print(random.random())\n",
    "\n",
    "random.seed(8)\n",
    "print(random.randint(1,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66ac5aef-c4eb-45a1-a4a2-c6a91a223ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getstate()\tReturns the current internal state of the random number generator.\n",
    "import random\n",
    "\n",
    "print(random.getstate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa86c3e7-69af-4e48-b67d-c3d7f4d3e56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setstate()\tRestores the internal state of the random number generator.\n",
    "import random\n",
    "\n",
    "#print a random number:\n",
    "print(random.random())\n",
    "\n",
    "#capture the state:\n",
    "state = random.getstate()\n",
    "\n",
    "#print another random number:\n",
    "print(random.random())\n",
    "\n",
    "#restore the state:\n",
    "random.setstate(state)\n",
    "\n",
    "#and the next random number should be the same as when you captured the state:\n",
    "print(random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55ee3648-71a3-45dc-ab97-ff947db98ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getrandbits()\tReturns a number representing the random bits\n",
    "import random\n",
    "\n",
    "print(random.getrandbits(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd67d6c4-f5af-4f4b-a7f3-d2e61b68c776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# randrange()\tReturns a random number between the given range.\n",
    "# random.randrange(start, stop, step)\n",
    "\n",
    "# start\tOptional. An integer specifying at which position to start.\n",
    "# Default 0\n",
    "# stop\tRequired. An integer specifying at which position to end.\n",
    "# step\tOptional. An integer specifying the incrementation.\n",
    "# Default 1\n",
    "\n",
    "import random\n",
    "\n",
    "print(random.randrange(3, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4641dae8-6c22-4212-bc36-7149a8b2e82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# randint()\tReturns a random number between the given range.\n",
    "#  This method is an alias for randrange(start, stop+1).\n",
    "import random\n",
    "\n",
    "print(random.randint(3, 9))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06f325c-a08a-454e-949a-763f51b04fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choice()\tReturns a random element from the given sequence.\n",
    "import random\n",
    "\n",
    "mylist = [\"apple\", \"banana\", \"cherry\"]\n",
    "\n",
    "print(random.choice(mylist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab766bd-43e6-474f-b1be-1ebc1c27af0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "x = \"WELCOME\"\n",
    "\n",
    "print(random.choice(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6e773e-734a-4039-86a9-a361b59a12c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choices()\tReturns a list with a random selection from the given sequence.\n",
    "import random\n",
    "\n",
    "mylist = [\"apple\", \"banana\", \"cherry\"]\n",
    "\n",
    "print(random.choices(mylist, weights = [10, 1, 1], k = 14))\n",
    "\n",
    "# sequence\tRequired. A sequence like a list, a tuple, a range of numbers etc.\n",
    "# weights\tOptional. A list were you can weigh the possibility for each value.\n",
    "# Default None\n",
    "# cum_weights\tOptional. A list were you can weigh the possibility for each value, only this time the possibility is accumulated.\n",
    "# Example: normal weights list: [2, 1, 1] is the same as this cum_weights list; [2, 3, 4].\n",
    "# Default None\n",
    "# k\tOptional. An integer defining the length of the returned list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b15cfd6-0fd3-4afe-b040-765ac3d3305a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle()\tTakes a sequence and returns the sequence in a random order\n",
    "import random\n",
    "\n",
    "mylist = [\"apple\", \"banana\", \"cherry\"]\n",
    "random.shuffle(mylist)\n",
    "\n",
    "print(mylist)\n",
    "\n",
    "#  This method changes the original list, it does not return a new list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9929d2bf-ad1d-47e6-b41e-22078ec103e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample()\tReturns a given sample of a sequence\n",
    "# Return a list that contains any 2 of the items from a list:\n",
    "\n",
    "import random\n",
    "\n",
    "mylist = [\"apple\", \"banana\", \"cherry\"]\n",
    "\n",
    "print(random.sample(mylist, k=2))\n",
    "\n",
    "# Note: This method does not change the original sequence.\n",
    "# Note: The specified number (k=2) cannot be longer than the length of the original sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "903708e6-9fbe-417f-90cf-f59e741e547b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random()\tReturns a random float number between 0 and 1\n",
    "\n",
    "# Return random number between 0.0 and 1.0:\n",
    "\n",
    "import random\n",
    "\n",
    "print(random.random())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37dae619-3c62-42e4-a0e8-3ac540b70e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uniform()\tReturns a random float number between two given parameters\n",
    "# Return a random number between, and included, 20 and 60:\n",
    "\n",
    "import random\n",
    "\n",
    "print(random.uniform(20, 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1161fe82-a939-43ff-9ce4-8862b7745ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# triangular()\tReturns a random float number between two given parameters,\n",
    "# you can also set a mode parameter to specify the midpoint between the two other parameters\n",
    "\n",
    "# Return a random number between, and included, 20 and 60, but most likely closer to 20:\n",
    "\n",
    "import random\n",
    "\n",
    "print(random.triangular(20, 60, 30))\n",
    "\n",
    "# The triangular() method returns a random floating number between the two specified numbers (both included), \n",
    "#but you can also specify a third parameter, the mode parameter.\n",
    "# The mode parameter gives you the opportunity to weigh the possible outcome closer to one of the other two parameter values.\n",
    "# The mode parameter defaults to the midpoint between the two other parameter values, which will not weigh the possible outcome in any direction.\n",
    "\n",
    "# random.triangular(low, high, mode)\n",
    "\n",
    "# low\tOptional. A number specifying the lowest possible outcome.\n",
    "# Default 0\n",
    "# high\tOptional. A number specifying the highest possible outcome.\n",
    "# Default 1\n",
    "# mode\tOptional. A number used to weigh the result in any direction.\n",
    "# Default the midpoint between the low and high values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a7c1a0-1027-4fb7-896f-6c5277ab5a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# betavariate()\tReturns a random float number between 0 and 1 based on the Beta distribution (used in statistics)\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameters \n",
    "alpha = 5\n",
    "beta = 10\n",
    "\n",
    "# using the betavariate() method \n",
    "print(random.betavariate(alpha, beta)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b568aa86-5d2c-446a-a3cc-5ec78969f0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expovariate()\tReturns a random float number based on the Exponential distribution (used in statistics)\n",
    "\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameter \n",
    "lambda = 1.5\n",
    "\n",
    "# using the expovariate() method \n",
    "print(random.expovariate(lambda)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccf58f35-9051-46b8-acb6-b4f37162a945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gammavariate()\tReturns a random float number based on the Gamma distribution (used in statistics)\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameter \n",
    "alpha = 100\n",
    "beta = 2\n",
    "\n",
    "# using the gammavariate() method \n",
    "print(random.gammavariate(alpha, beta)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4bac748-ec76-44c3-98e6-6eff4574b38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gauss()\tReturns a random float number based on the Gaussian distribution (used in probability theories)\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameters \n",
    "mu = 100\n",
    "sigma = 50\n",
    "\n",
    "# using the gauss() method \n",
    "print(random.gauss(mu, sigma)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc048cd-476b-42bb-808d-eb655b72cc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lognormvariate()\tReturns a random float number based on a log-normal distribution (used in probability theories)\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameters \n",
    "mu = 0\n",
    "sigma = 0.25\n",
    "\n",
    "# using the lognormvariate() method \n",
    "print(random.lognormvariate(mu, sigma)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "082bd85d-7ac9-4bdf-8167-e7cbb84d25f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalvariate()\tReturns a random float number based on the normal distribution (used in probability theories)\n",
    "# import the random module \n",
    "import random \n",
    "\n",
    "# determining the values of the parameters \n",
    "mu = 100\n",
    "sigma = 50\n",
    "\n",
    "# using the normalvariate() method \n",
    "print(random.normalvariate(mu, sigma)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a94ae1-0df3-4ebd-b345-d915f0640548",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vonmisesvariate()\tReturns a random float number based on the von Mises distribution (used in directional statistics)\n",
    "# import the random module \n",
    "import random \n",
    "  \n",
    "# determining the values of the parameters \n",
    "mu = 0\n",
    "kappa = 4\n",
    "  \n",
    "# using the vonmisesvariate() method \n",
    "print(random.vonmisesvariate(mu, kappa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68427fdd-920b-4b98-9bae-8e742e9d340f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# paretovariate()\tReturns a random float number based on the Pareto distribution (used in probability theories)\n",
    "# import the random module \n",
    "import random \n",
    "  \n",
    "# determining the values of the parameter \n",
    "alpha = 3\n",
    "  \n",
    "# using the paretovariate() method \n",
    "print(random.paretovariate(alpha)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a74dfa61-fecd-4179-b4c4-8e75e129e69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weibullvariate()\tReturns a random float number based on the Weibull distribution (used in statistics).\n",
    "# import the random module \n",
    "import random \n",
    "  \n",
    "# determining the values of the parameters \n",
    "alpha = 1\n",
    "beta = 1.5\n",
    "  \n",
    "# using the weibullvariate() method \n",
    "print(random.weibullvariate(alpha, beta)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d85b68f-cb28-479c-9fd9-66764b7e230e",
   "metadata": {},
   "source": [
    "# strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b26f8d-c233-4045-8ec9-0201ac51f021",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"It's alright\")\n",
    "print(\"He is called 'Johnny'\")\n",
    "print('He is called \"Johnny\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d7f2368-2c40-4d07-9bd4-37aafef50799",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiline Strings.\n",
    "# the line breaks are inserted at the same position as in the code.\n",
    "a = \"\"\"Lorem ipsum dolor sit amet,\n",
    "consectetur adipiscing elit,\n",
    "sed do eiusmod tempor incididunt\n",
    "ut labore et dolore magna aliqua.\"\"\"\n",
    "\n",
    "'''sfdsjdcjkgsjh\n",
    "skfbsjjsjkj\n",
    "sfsjjkk\n",
    "foisokso'''\n",
    "\n",
    "'djfhjkjfkjhj\\\n",
    "dkjgndkkm'\n",
    "\n",
    "'djfbsjfjh\n",
    "djkfkj\n",
    "dgndkv'\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f57a66-6a7e-47cf-8f34-9aa4a7555cdf",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Strings are Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3a88614-5eb8-4264-8414-56813d1918ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Like many other popular programming languages, strings in Python are arrays of bytes representing unicode characters.\n",
    "# However, Python does not have a character data type, a single character is simply a string with a length of 1.\n",
    "\n",
    "a = \"He llo ,World!\"\n",
    "print(a[-2]) # Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10a49c2a-6bf8-4a7f-9169-3f9b4b09c961",
   "metadata": {},
   "outputs": [],
   "source": [
    "[1,'hi', True]\n",
    "# Array : [1,2,3,45,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a429b14-9ea7-4341-84ee-1ec9a3446b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "array((1,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "id": "40328643-41fd-478b-85fa-680a291b99cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 6)"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "id": "f5b71204-52bc-423a-80d0-ba734de24958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "id": "edb4b99b-9f59-4c05-a825-6ab47b02f531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "125b3247-b3a3-4b9e-9318-08c6960935e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= list(range(6)).__iter__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116bba46-b50f-4e32-a9d3-e1961335c83b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Looping Through a String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "id": "53f312a5-6304-4bd1-962c-e712f908dbe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\n",
      "a\n",
      "n\n",
      "a\n",
      "n\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "# x= 'b'\n",
    "# x= 'a'\n",
    "# .\n",
    "# .\n",
    "# .\n",
    "for x in \"banana\": # iterable\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "id": "346679d8-3ab1-4bac-99c9-5eee4b92b818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 'a', 'n', 'a', 'n', 'a']"
      ]
     },
     "execution_count": 672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list('banana') # iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "id": "6399e86d-6f27-4272-945f-3ba0d56a2329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('b', 'a', 'n', 'a', 'n', 'a')"
      ]
     },
     "execution_count": 673,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple('banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "id": "4fb21a0e-2221-4f3e-88a1-987843e1b44b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "a = \"Hello, World! \"\n",
    "print(len(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb3264c-a911-4090-a458-149b57152c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"The best things in life are free!\"\n",
    "if \"ar\" in txt:\n",
    "    print(\"Yes, 'ar ' is present.\")\n",
    "else:\n",
    "    print(\"No, 'ar ' is not  present.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5982bb16-34a4-4271-a756-fadf2e25b32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"ar \" in txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb9d724e-ea2d-4c78-a193-a0f4c1d7c168",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"The best things in life are free!\"\n",
    "if \"expensive\" not in txt:\n",
    "  print(\"No, 'expensive' is NOT present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeba7b73-4215-48c3-9bfc-334a373fa7c1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1c86d2b-e150-499f-906b-b2d3204ad757",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing\n",
    "b = \"Hello, World!\"\n",
    "print(b[2:18]) # end not exclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "210efdde-52b7-4207-a2ed-56814b61b3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(b[2:18])\n",
    "# Is return null?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb210b28-e4b6-4d34-a553-90ff76707317",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ed26d2-70b6-4560-843b-e8e55c81f1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[2:13]) #len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f414e9-c5a8-4635-90b4-0cc6b3b0ee3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[2:len(b)]) #len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6ff2a2c-03cb-4eb5-a724-82d6474d86df",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[:]) #len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29812f99-c3e8-48b8-abdc-200b1c65f671",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = \"Hello, World!\"\n",
    "print(b[-5:-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b906ec-6b71-4ba2-87ec-0114ef7524ca",
   "metadata": {},
   "source": [
    "### Modify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "879c514f-f450-47e8-9cfc-b0fa4e625f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "# a.upper()\n",
    "print(a.upper())\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec1b410-3c11-4ecd-a2fa-9911a20ed616",
   "metadata": {},
   "outputs": [],
   "source": [
    "a =\"Hello, World!\".upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1998272b-6554-44f2-9cc6-585dd1e01121",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5063736a-75be-4ed3-a990-01befe79beb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[2:4] = 'ee' # is wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e42dc921-9aa4-4df2-a1ce-c5e133296897",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "print(a.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b67182a-9ed6-45de-a7b5-ec46bbfeea6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"***Hello, World!    \"\n",
    "print(a.strip('* '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6470ead-67eb-43f6-a1fb-21ca7b8e4b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3768cd5-c068-4643-abb7-5e39482ca8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"****   ****Hello, HWorld! \" #exclamination \n",
    "print(a.strip('*H o')) # returns \"Hello, World!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a27955-b3f2-4905-ac4e-31322e5521a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\t****Hello, World! @\"\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b3ada9-6c48-4265-a8eb-7777bf7fb988",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\t@****Hello, World! @\"\n",
    "print(a.strip(' ')) # returns \"Hello, World!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb35519-0ec3-456a-ba70-44552350f754",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\t@****Hello, World! @\"\n",
    "print(a.strip('!*\\t')) # returns \"Hello, World!\"\n",
    "# \\m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0960cb0-8c5c-423f-abff-1c45540316f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bytearray('\\t', 'ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59f1e1a-b616-4b8a-8fe0-cd911b2da47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Escape Chracters \\t \\n \\r \\\\ \\' ...\n",
    "a = \"\\t@  ****Hello, World! @\"\n",
    "print(a.strip('!*\\'')) # returns Hello, World!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1de4a6-3747-4dfb-88a9-88b6d8959322",
   "metadata": {},
   "outputs": [],
   "source": [
    "'hi i\\'m mehrab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66db40f3-4628-4210-8a80-f008bc801385",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90eab8ae-f003-4fb9-a8ab-4a9c6953516a",
   "metadata": {},
   "outputs": [],
   "source": [
    "'hfhsg\\'fddgdg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcfbd324-81d8-49f7-b9b8-136e7934113e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "print(a.replace(\"H\", \"J\"))\n",
    "print(a.replace(\"W\", \"m\"))\n",
    "\n",
    "# inplace\n",
    "# copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cde95c2-7b05-42ae-ab6b-07e7724f8093",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef7d8c90-cfed-4a66-96be-213193de5961",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "b = a.replace(\"l\", \"J\",2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3414a54-0544-4578-a2ef-0c21ee14b07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1c77298-1526-4448-9cb8-ef985bf4da72",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "b = a.replace(\"l\", \"J\",2)\n",
    "print(a)\n",
    "print(b)\n",
    "# by slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7391e7db-d78b-4b74-96d5-f336a192dbf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(c:=b.replace(\"J\", \"l\",1)) #walrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed479f61-e981-431e-b91d-6c72201a0fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "275ce82a-78f1-4be1-8403-6dbc5172f254",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specific replace index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f59040-77c1-4a6b-ad88-a876144b9b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello, World!\"\n",
    "print(a.split(\",\")) # returns ['Hello', ' World!']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99fc5c04-ec1e-4566-997a-041b94fbba84",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hel,lo , Worl,d!\"\n",
    "print(a.split(\",\",2)) # returns ['Hello', ' World!']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f98390-dc4d-4f90-acf6-b5d3cfca43da",
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f1639c-7225-4306-a1d2-ae0d7884fa27",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### String Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a82b353-5d8f-4514-bfbd-92b0687d1283",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello\"\n",
    "b = \"World\"\n",
    "c = a + b\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c0a738-b6fc-46bd-ad35-b3fcc920d0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello\"\n",
    "b = \"World\"\n",
    "c = a + \" \" + b\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f50c1750-80b3-4bbe-a1f4-7dfe04a7a5b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "2 * a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcba5610-4090-446e-98b6-22fe4b3cc6d8",
   "metadata": {},
   "source": [
    "### String Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81cd8157-bc94-403c-9383-ac2fad5b6689",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = 36\n",
    "txt = f\"My name is John, I am  \" + age\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09a07c4d-04ed-412c-a8be-b0b5e6962231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# F-Strings\n",
    "age = 30\n",
    "txt = f\"My name is John, I am {age}\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "446fd7c7-8fd4-4cff-9577-c3932f9b4adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 'ali'\n",
    "m = 'morteza'\n",
    "suff = 'dear'\n",
    "print(f'salam khobi? {suff} {m}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b890a82b-249f-4967-8073-8a9254d22d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A placeholder can include a modifier to format the value.\n",
    "#A modifier is included by adding a colon : followed by a legal formatting type,\n",
    "#like .2f which means fixed point number with 2 decimals:\n",
    "\n",
    "price = 59\n",
    "txt = f\"The price is {price:.2f} dollars\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77182b47-137d-4848-a19d-46446c2cefcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = f\"The price is {20 * 59} dollars\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "b387544f-5dbc-4c27-8794-07ec99c38bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PYTHON is the best programming language\n"
     ]
    }
   ],
   "source": [
    "name = \"python\"\n",
    "text = f\"{name.upper()} is the best programming language\"\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "babf2a80-31b0-41f2-9062-d00fb7495b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 1,000,000\n"
     ]
    }
   ],
   "source": [
    "number = \"1-000-000\"\n",
    "text = f\"The number is {number.replace(\"-\",\",\")}\"\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "5624bcb1-ce7e-4c3f-a497-90054f9a636f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 1,000,000\n"
     ]
    }
   ],
   "source": [
    "number = \"1-000-000\"\n",
    "text = f'The number is {number.replace(\"-\",\",\")}'\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "63989818-915b-4120-a26f-5f38a70b620c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMW has 280km/h max speed.\n"
     ]
    }
   ],
   "source": [
    "car = {\"name\": \"BMW\", \"max_speed\": 280}\n",
    "text = f\"{car['name']} has {car['max_speed']}km/h max speed.\"\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "c5d30dce-e5d1-413b-a8df-889e30bc8ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line1\n",
      "line2\n"
     ]
    }
   ],
   "source": [
    "line_separated = \"line1-line2\"\n",
    "text = f\"{line_separated.replace('-','\\n')}\"\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "203cf034-c8b2-42f5-9cc5-13da9ca6bc8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line1\n",
      "line2\n"
     ]
    }
   ],
   "source": [
    "line_separated = \"line1-line2\"\n",
    "_ = '\\n'\n",
    "text = f\"{line_separated.replace('-',_)}\"\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "ade1746e-5106-4b56-aafe-a631d4a05ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2+2}\n",
      "{4}\n",
      "{{2+2}}\n"
     ]
    }
   ],
   "source": [
    "print(f\"{{2+2}}\")\n",
    "\n",
    "print(f\"{{{2+2}}}\")\n",
    "\n",
    "print(f\"{{{{2+2}}}}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1baec84-ebd7-4a63-ace9-044b45ed4a82",
   "metadata": {},
   "source": [
    "f'{var:format}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "e4216808-0e50-4475-a68b-bcbd9232852d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is test       string.\n"
     ]
    }
   ],
   "source": [
    "# Padding\n",
    "test = \"test\"\n",
    "print(f\"this is {test:10} string.\") # 6 space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "8c84f601-5afc-46b3-a8d0-7b08008d422f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is       test string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:>10} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "5159f89b-78b1-4aab-b7ec-98f1a5fd151d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is test>>>>>> string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:<10} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "7dcaabd2-55be-4ef6-8c88-89934874ad29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is test<<<<<< string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:<<10} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "244426cb-7ef3-4e20-ad6d-35fd4f2fcd81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is test###### string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:#<10} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "dc3ad70a-ae67-47b3-bfff-4c1b1e4950d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is ---test--- string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:-^10} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "902e9791-5441-49b9-a630-7767a10e9cf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is -test-- string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:-^7} string.\") # right is right and by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68c50521-13e5-400d-9b4e-a230ae838382",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:.2} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "d8f31806-d272-45a2-a8c0-494a1e52594c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is te string.\n"
     ]
    }
   ],
   "source": [
    "# Truncate\n",
    "test = \"test\"\n",
    "print(f\"this is {test:.2} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "6325ef93-dd1a-4e93-ada6-60d29184994a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is ++++++te string.\n"
     ]
    }
   ],
   "source": [
    "test = \"test\"\n",
    "print(f\"this is {test:+>8.2} string.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "7bf4336f-cc0e-4f94-a092-4a4bb04628e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is     21.\n"
     ]
    }
   ],
   "source": [
    "#Width\n",
    "number = 21\n",
    "print(f\"The number is {number:6}.\") # left and by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "7cacd98c-37ef-494b-ab5a-d67767a5169b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 21    .\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:<6}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "95492405-98f3-447d-8f8e-87275934549f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 000021.\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:06}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "f435b63c-c536-47e1-97d6-a77d08ce86d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is    +21.\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:+6}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "a46c5bcc-4f02-41c0-ae98-c3f7f3d0df7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is     21.\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:-6}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "6812af4d-4b0d-462b-a53a-51fa71306ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is   21  .\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:^6}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "42469bfe-bf44-4b4a-aaf1-bef19dc8cdd2",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated f-string literal (detected at line 2) (35256649.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[281], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m    print(f\"The number is {number:*>6}\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated f-string literal (detected at line 2)\n"
     ]
    }
   ],
   "source": [
    "number = 21\n",
    "print(f\"The number is {number:*>6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "0978c6b9-fed9-49e8-a928-18053d480f4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = 3.14\n"
     ]
    }
   ],
   "source": [
    "# Precision\n",
    "number = 3.1415926535\n",
    "print(f\"The Pi number = {number:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "93e24dbc-2dca-4324-a225-68892adee44d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = 3.142\n"
     ]
    }
   ],
   "source": [
    "number = 3.1415926535\n",
    "print(f\"The Pi number = {number:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "aee8e5f3-5655-4df8-8da0-965f8ec6f3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = ***3.142\n"
     ]
    }
   ],
   "source": [
    "number = 3.1415926535\n",
    "print(f\"The Pi number = {number:*>8.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "b6a2752e-81f5-49f2-b4b6-be69724c7c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = 66.667%\n"
     ]
    }
   ],
   "source": [
    "number = 2/3\n",
    "print(f\"The Pi number = {number:.3%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "a5b470b9-4fe8-498f-ab0d-d402f01ac500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = 10,856,747.000\n"
     ]
    }
   ],
   "source": [
    "number = 10856747\n",
    "print(f\"The Pi number = {number:,.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ea1864-a960-493f-9470-4ceecd8c9cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "number = 10856747\n",
    "print(f\"The Pi number = {number:.2e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "7402e3b0-1197-4212-bd36-0d436232d239",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = 51324453\n"
     ]
    }
   ],
   "source": [
    "number = 10856747\n",
    "print(f\"The Pi number = {number:o}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "009fa62a-120f-4c7c-b470-cdf220e337e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pi number = a5a92b\n"
     ]
    }
   ],
   "source": [
    "number = 10856747\n",
    "print(f\"The Pi number = {number:x}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "11284cf4-f4c3-4342-b46f-e5d288604ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020/01/0210:10\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "date = datetime(2020,1,2,10,10)\n",
    "print(f\"{date:%Y/%m/%d%H:%M}\")"
   ]
  },
  {
   "attachments": {
    "5a58e93d-f638-46c5-bff6-d37fe7fc488c.PNG": {
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CsfAXEYyZrOer0b4cHzyngt1QxLeE/JgwGNIojKssAE/SAite6Q4xZj5fh3APPsybrTDZgAnx38HwglH+EZfntkd5kXJcFge7Nmt0OEIK7iwLRwXlfpw12qw13TWiqKL5NigS1qGNer1Fzqoh5pd0WnsuUrzcPhJD/32gik5KX2zDH0NX4sFXDFWm3FmG8ArK8ZMcrNusxUeqJIwG8mPCYMhfYV595cRKDmKvMAyZuxlHL0bh7r27uHXtHA5vXYLJA5rC11o5JMUCDee/UK/sQBDGi0EFY+qjbfi9gVvupBZOWBmjtKyULI+/jPldArKXzGH78dx6Yp8JLyNCFc03QvEem352VA8GV05WGoGDMSXv10m/PAZBYo69p+UxYM8RjKosZoW3EBUijmosum0g5PG4PL8LAnJmPvLc0NOUX/rvEPJjwnDIcGdStaIneKoSB6HfYJwweIFDEIbHAIJRivfXN2Ji5xpw0ZwJytmg5qSryDcNIOM1zi0fikYeygG66v14Dmi+3LS/nUsVzbdD/moLuqoidtmFL8+qPOp3+wMzVmzBvmOncf7CBZw/ewpH92/H2kVTMDpyD17lvGyyWOwZEIyATmvxNFOG17v7I7hSZ6x7ZsjBRBl4fW45hjby0Kg8eHBovjx7TVDCaCA/JgyJ4tNJjKhqWXgWc27iYOn/C1Z+zS4NgjAgBhCMGYi9vA0L/voNfTu3xU+N66N2rdpoM+6oahZXLsm3sKxnKDysNcQiJ4Z/v73aP31kQlBF822RvTuP+X1rw12s0WApmDgBJNV6Y9W9AjOZFVJIc4cYKSDN+8MAJOPWsp4I9bDWEIscxP79sJc+/Wd0kB8ThkaRdA/b/+6L5qH+8HCSwMZGAmevSqjZvCfGLD2BaJq4TJgQBhCMeqLIQtK7l3hy7zZu3nqOT99BlIUqGuNAmvAYZ3eswKyJv2NwxK/o3as3fo0Yhj+nLcWOCy+Q/M00mgJZSe/w8sk93L55C8+/h5f+O4T8mCAIQjdlLxi/Q8hmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQuiHBaADIZgRh+pAfEwRB6IYEowEgmxGE6UN+TBAEoZtCgnHhnv8LSpQoUaJEiRIlSpRyEglGSpQoUaJEiRIlSkUmEoyUKFGiRIkSJUqUikyFBCON49EfshlBmD7kxwRBELohwWgAyGYEYfqQHxMEQeiGBKMBIJsRhOlDfkwQBKEbEowGgGxGEKYP+TFBEIRuSDAaALIZQZg+5McEQRC6IcFoAMhmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQuiHBaADIZgRh+pAfEwRB6MZ4BKM8BbH3o3A7OgFZ6k25ZCTi3dsPSCr0g3FAFQ1BmD7kxwRBELoxCsGoSDyLCbUdIODxweMsEfDrTsTJ1T/KY7G+gzPMg0biYqp6m5FBFc03JuM5dv4RgcX3ZeoNxoQUL7dG4OeRR/A+550uBUkHfkPziE14lqneUNYoknBtxs/oEHkJnxXqbd8ZpfZj6Xvc2DIDw3t2QIufWqJDjyGYvPwIniQbqaHkb3Fu2WSMHTsOkUuO4UWGensRyN6cwfLIceyYSVh+4QO+01eAIIgiMALBKMPDqaEQO3fAmlfpeLOlM9yEQRh/S8p+U+DN5nC4iithxLmU7N2NEBKM35DUm5j3kycCh55GknqTUZF+EaMqi8GzqoVpd5XvdCmRv8Sq1m7wCl+L519wmtKi+LALPTwE4Lt0xIbc1tz3Ran8OOkypjR0goAzAye0hpObM2yEHDgzDuY1p8EY2zCKt0sQJjKDmRlLnAjVJ99hzZoiULzBqlZ27J6Ux3AQt16Hz+qfCIL472AEgjEDu7vaQhy2FG9Zs1URtwiNxRJ035cBxbvt+MVdjIDfTiNZvbcxQoLxGyF/jc3hHhD5D8XpsnpBZPF4n5AtmGTx7xFfAkEgfXkYm06+xZfKLHncJoS7WqDKyPMG9QflfWTfkgzx7+PZ/2sn/eFB7L1tvA23L0V/P5bi1sRgCDk+3NsswHX1y6BIfYmz/w5Hq7bToGr3GhmK2EVoxASjoEIVBFpz4JcfgjNFRBllD6ciVMSDfZVgePLNIG65Gp/UvxEE8d/BCASjFJd/94WwfASOJckRv6cH3EXV8ffdN9jdnYkBv0E4UbA5K0vCi2tHsOXf+Zg1YybmrdiGM08/f3GFXFpIMH4L5IhZ1QoOfFd03RVfNl1k8ljs7O0H24BfsfPqTvQNsIV/7514XWYvnhyx7J4lQj/8drYUwk0Wh3sP8ttKHrsTvf1sVcNAru7siwBbf/Te+fqb+ZLyHt9fOYXbSWXf6am3H8ufYWYtIcwElTDmuhEqQx3kCEZhyFjM7OoCHs8Z3ffoihlm4MJwXwj4FTBw/h+oLCDBSBD/VYxAMLJ67OlytHIVQGTnCFuRJSpF7MODvX3gJfJBxNHEAmIgA/u6S1TdIxxPBBuJDcR8DpzABY1nXMe3GOZIgvEbkHoCEV48CKv/jXtl1e2nSMLVqY3g32gqrsRexYwm/mgQeblsx/RlXMBwHx7EdebgmV6qToa702rBs8depKm3KFEkXcXURv5oNPUKYq/OQBP/Boi8/LkUAlyBT+cj0bXrBGy7X5rjs0m7PAE/WPPh2HQxHpdxd67efix7iMgaTDDyPTHwVHEDAbNwZXYntGjzG7bFFHhwyobI8DZoHj4Tl3JPI8WdJT3QsmMkzqRkIubYbAxoEYpA/wD80KQb/tr1JN9z1IdcwRg6FbdODII3Kz9tW6/BO20P7fMedHdhflZtEqKujdMpGLPizmH56G5oVrMK/H39UaVmU3QdtRzn4grMVJTewZIeLdEx8gxSMmNwbPYAtAgNhH/AD2jS7S/selLauyII4mtjFIJRiTzxKS4cPoDjUbFITTiEfuVFKN//ED4VKsSkuLd7KXZcikZiVvaPGa8PYVh1C3CWTbEsrrRVVekhwVj2JG7vBHtOhPrzXpZxNEwGaY6QkUm1d98qEvHw2GasXrMdJ+6+h2HnqUgRNTYAPF4AxkbpE9WS4dHUUJhXHoebBQ/TuA9Z7s0VJB3P9k3DkP5DMfPIK5aLAqRdxaSatuCpxvK5of6wzXhQmt7rtDtY2K4uemyKLnyNr4z+fpyB04O8wDfjYFl1EPa8LOpJp2JTe7EqGjm2YDRSegsTqghgJm6NdbkDcTNxsJcDeMKa6DO0Phz5Qtj7hqJuSHnYKhvI/HLovPVNqYS5pmB8lHEDfwYKwJnXw9znBT1JgbgVzWHNs0CjhS+ReVObYFTg86VI1HfkgePEcK3SAM1bNkXNinYQcBz4rs2w4K6GmM48iF4OTIDW7IOh9R3BF9rDN7QuQsrbgq/cv1xnbH1T9mU4QRDFYzSCMRdW2R6L8IHIuw/2x5e04JDj9YKGEPK9MfRc2a+9Q4KxrMnCiYhy4AmqYvJXCS9m4e217Zj/1yiMnLgA+x4mlbxilsdgUxdvCJlwyp5UIICdTz10+m061h6+jucf079Y4GYe6QdnMz5Cpj1RbykG6TtcWTMGHYMk4Jk3x8qP+lbIcsRt7QIPAae6J86iFqY/KGD39Af4N7wCLKxsYSVU7seDdVAfrHuUrt7B+CmNH8vjdqJnRaFqkgvP2g+tRq7G5bfayqBSCkalvUUV8POSm+pIdhaiV7WFM0/ZpTwFD0vx+ucTjDIZHk8LhYgTInji7fwNINkDTAkRgSfpiE3snZFqE4ypZzDUlwlOYUV03/iMNSvUyD/i7LgfYcvjYFF7JruOertaMConz4gq/IwlN9XR6KxorGrrzO5XiJApD7U3xAiC+KYYnWBMOjkYfiIP9NzzHknPTmHLqlXYcvIJ8g9pkiPp+QXsXrMIc2ZMxZQpkfizgx8EfA8MPEWC8btH8QErmgpZBdsemw08BkEaewx/t/GBZY7gU1bYNjURebNkccKUoxGoXDUcY+f/i2UzR6Nv65qoKFEKihwByUFoYQsHJ2eUqxNZeFJE+l0s6xIEN7dAtJl2XkuEndXjj6eiBt8MDr0PqrfoJuPxevStKoG1dwP0HtsTIRbuiDihp48kH0H/Ct5oFbkHZw/ORBsPcX5xoUjAoYjqqD1sD56nK5DIbFBRZAlLsTLC1BJLHhoqxsrOfWk2utT/Ec0iVuGOgZ99af1YGncS08Irw46vFMpK4eiDFqO34WG+CGspBSNni4ZzH+aPUmeexP88+OAsO2JzKaK4+QUjK01fL0aYZeHJLxnnh8GHz4dHvyOqoT7aBGPSji6w5/FgH74FCQXf1cyLGOHH7ktQBRNuqd8WtWDkbBtiboH3IvPk/+DBbGjZcTO+36lVBGG6GJdgTD6DYQFieHTdjkeHB6OShSXc/H3hJLZA5RHqZVMU73FkZC04CHgwd/ZH9Zo/4scff0SojwQ8Eoz/DRRvsbSJoQWjAh/P/o0mXp6o3XsK1h+9jJs3TmLtmKbwEArg+/ulAl2kCnx6W7C7WYHk6Ed4XXA4mzwZT/aMRn1ngSoSxYkl8PCphJ9m3s5/TkUiTgwOgL1fPTQMtAef54E+BwtPRpC/mI0fWcVt132feosOUs5iRK1a6L/8Ct4rLyS7h8nVLFBzxlPtUU7FJ7x9X1jcpe3tgYpdtquXLVLgw+pWcO+2Fzm3KX+9CE08+uBgzoaMPehR7TccurEM4eVFsKw1HQUDkqVBkXgIv3oJIRQyAc4JEfDHxdw8GIIv82MZPt7YiPE/V4FEFYnlQcIaBFdzlU8pBSPfC0POFijT5E8xQznZRhSGpcqlJfSkoGCE4iM2dmDlJ88F3ffkZCARu7o5gycIwOir2dcvLBizcHGED/icCM1WaFubMQMHezsy0WuO1msTszepBSPfawgK39YM1BKaQaReMYMgCOPCiARjCi78URnmyjEsca+wqLEF7DtswAeFDNFz6kJsG45tyay8OTMY3nwBKvbejbjccpdVYiuaQUSC8T8Cq4h6OYIT1sTM4mZ+yGOwccIcXCkYskg7iGFDd6mXp5Hh9e4IhP7wCxZd+1Sg4kvBnm4uCBp3M7+4Y5X2rHYDcayY4Jn0/RWsHBaG8pYCSKp2xd9j26JaxPECQlMJE6z7+yKw5jhcUtbZ6Xcxo64N7DrtyP5Zg6zzQ+HF8eA/+rp6i3ZS93SHV+3h2HL2DqLffET8h+dY2V4Cm7brC0eDGPKns9Bu4LFCeUve3gkOQb/hlOqgTDyaWR9BIy4yuZCN/Ml01BS5oGnkSbxO+YCL05qiWr9DKttmPV6AMKdQTM3tkywB8pdYN34ebubrzZYjdmlrNPrrBpJlCbg0pREcXXphv7YGg9bji8cwfizD+zOT0ciJBzPOAvXmPlOLc0MKxmjMqaMUjI2xuBTj/QoJRkbyvp5w5eVNflHELkczaw6iWjPwRL1PYcGYgb3dJEy826HbHm3SXYqrowIgMBOi/rxX2ZuKEozRc1BHKRgbLwYNYyQI48NoBGPa5TEINndF+KY3UEivYXSAGIF/3lBV0hn7ukMiboIlb2SIW9QIIs4WXXdrFFDy9zgSwQomEoz/EVgDYU0rWHE2aLcxQUtkIw/p1VEIEAjg0XoOLqnCbEpScHtWY9g7tsCSJ6mI3d0fNeuPwvHcT7HIkZEYi0eXD2J1ZE+EuIdgcr6+4wy83NUXfkIXtF7yQNVdlw95GmKvbsP0iKbwtWGVo/0P6L3woirKl3VxOPwCBuDAW83zKZB8az6au/BhW70/VrH3Sflryu5f4Bq2NHuXXGR4NOUH8M0c0VOrWspBgXesEWWh0bWuXKTZ2788LOzCsOiZ5vUZGS+xq68fhC6tseRBgfMmn8WISmKIy4WieVhlOHmFY+MrDaEuf4UN4R7Zkxz4fCbk/dB73S28U7qoMhrcqiWW6jEZLfuZWaHxwhcakdAUbOvkgXbr3mc/76wrGFmpMkZfK3AfDO3HF4/h/FiOl//Ug5DZw7zdRnWjJA2bVYIxoPASPEYgGJFxBkPKs2dnXg//RGfifmQNln8btFiZN7GmsGDMxKE+TuAxYdxBa994Fk4P8gSfE6PF6oTsTSQYCcJkMQ7BmH4N46tbwKX9OsQqS3jFe6xuaQ2LmuNx/uUjbOruDZH/SFxh5WzG8Qh48HlwDvsLuy9ew7nd8zGwvjsszc0hJMH4n0GRsAOdnXiwbb0W74uqXDKfYEUbV9VMVp5YAidHJ7i727F3xRHenlbgCZgQEvJh5eQJ7/Le8CjnAomlssszR2hxELgEoV6dGvDxckeVoED4udtAYMaDk0c5iMzYsa6+CAz0grOLNwJ9yrHGDat42bGcwAkhfRbgvKY4TNqP3uV44EQSeAUEwtvBAhI3N9gKhPBs1A61nJlwYBWwZ6326NEyCJWGnFUfqEZ6GxOD2Pk9InC8uBVIlJ+s27UMM8aPwKD//Q/DZh1FTNwGdLBnFbZ1OQQE+qK6b2UEBfrB3YZdl+fE7l8EM74VXH0D4OVgCQcvf/h5OsJCNT4v2yYcE5527j6oFOiDwAr+CPT3gqMFyxMnhIt/AFzE6rF85g7w8HRC+a478EEfAaB+ZsLK45A3Efwz1re1YGJUAu/KVRHk7wGJyAZddmmJbGk9vngM6cfJm9ozsc7BokPOeLwM7P7FBhyvHCKOF4jhKsf6+X5jwciaKFHjKjPRL0TwqIUYWJEPnnM37NYYEVFYMMrwcEoIhGYCBIy+lj8Cr0SdT07ZrZ0j7EkwEoTJYgSCMQNRk0Jg6dQGqzXWJ0uLmouWnuaqMV9Cl3r48+TH7JauPA77R9SBi2omJqu8WAVSueMUHN46EN5CEoz/HbJwJzIE5sJqmHS76GeueMdEkpMdXD3sIOALIHKuhk5zLyM+7jgiO/8IPzcJrC3MYW5hBVt7N5QPDEVYeAR+H9EPHRpWRcVyDrAWC8BnQpIvMIfEoxpaDd+Ie4kvsHdsG1R1t1WtBcoX8CG2cYZ3lUb4ZeQCHHiobTF5BeLPz8YvNb1hbyHIXobGvBzqDFiPh0z7yD5ex5rfWyPIUQShW1usitY8gwKfdneHK88GYYuj9Yqe5SFDzJ5haOBlCxFfBHdzHgTmEnhUa4XhG+8h8cVejG1TFe62ouy88ZWC1h81W/2KSRsu4c651ZjQpyVq+rnCRiSEk7kQlg7lUaNlBGbseaCanJb14Tb2LhyNXq3D0LzPP7iorf+7GFTPzK22xpADBRJOTUSYjz0slc9J4giJlRU6btGumgsfXzz6+rH8zX5Mj9yGBwUXGc96isXN7FVj9+r981z9nGS4PbGKSlz5Dzuft4aiLBb7IoKyI8FfKBgV8VewesokzNn/TL1FO9oFI8vKkxmoJeJYmSpUfb2mwpAzrHTOQ9ukF2nUn6pt/HKdsCXfJyMV+Hh4AHwErPwOnpg3uas0glERjyurp2DSnP14VqSrp+Px/gWYPncH7uR+x1uKV8eXYMbsjbiW+x7K8ebsv5g5cyVrzJXOiwjiv4gRCMYikKXiQ9w7JGmJEsiS4vD00VPEfi57gVgQEozfiPQbmBxqBUmThXiqUfEVQh6D+WFN8I9mN6rRI4e8YHbTLmFkJSHsmy3BUz0iZyaJ8pk1ro5xhRaNzCELp4fUQP/DOgaRFnt8YfT1Y9m9yajGGq4CSQCa9hyBiVNnYOqEwWgfbK9aU9Cq+p+4qNFTK7sbiRCxUpC5o+Gg6Vg4fzJ+rVsOIpvKCFYuzfOFgjFje7hqdr9yuZ2i0CUYlQuILw2zyo6OC6virzv5nUqbYFSuWLC3T/ns7vfyYRg0ZQlWr16G6cNaws+K3atVVYw6k3tTpROMGdsRbsnBTBiCKUWtI5R5GH3VY0dzu8illzDCh68ajtFkibp7XXYff1dn9jMTInTqI9VuBEEUj3ELRhOBbPbtkL3chM7eDmgw92HuJIyCKOK3opNPN+w26Y9IpOLymGA4/jBSPfnk+0b5zMI9WmClrr7s5FMYVKkR5uloBBR7vBb09mNFAq4uG4SmAZK8dTeVXfGWnqjbbyEufSyYtww8WtcHVSXK2fJsX04A++CumHc5Dju62ICz7IBN2QMeGZk42s+FCUYfjLhYUFkxMdxABM68GVZojMdI29wBYnZ9m3Yb1Fu0o3i3HE2ZcBXWno38a3UrEL+1Exx5HKzDFuNlgewrBXJ1JpAt221Uz5hXk/UC+8a1RiWN5aM4ngXK1eqN+RfVPUM5ZB5FPxcmGH1GoPBtzUcDEROezVbkH2aSthkdxOycNu2woah3X3od4yoru8C9MPi0uiEhe4yZtS1YfpzQbbc614pYrGjO7M3Zoc3ad9nbCIIoFhKMBoBs9m3JfLoBPWs0wnRti3jL32JP74pwaLdev3F0Rkb6pfFo0XEWLmtbmPF7Q/3MrOrNRb4e+RwUH3BwgD+sa8/CU22/F3e8DkrvxzKkvHmMW9eu4ca9F0jQEfTMJeMDnt2Owu2nH/K6fKXpSE4rEA2VsW3JGVqHHiiyUpGkub8iHts6O4En9MewEnxnXJqWjFT1l7Lyo0BmagoytNqN/ZaShILZzEWaiJiHt3Aj6i6evU/LLxQ1kKUnI1nrBRTISi14fiZit3WGE08I/2Fn1eNBiyAzCfGfM/JfW5qChMQCC+bLUpHwKZU9OYIgSgoJRgNANjMCMlklVLCizozGjohgWAt9MOS0wRZs/CYoMtKR8R/QirnPjO+KLtsLRKeUpD/F9oHVYcN+/2WHlt+LO74ITNmPZfenIEQshN+gE2X7bfOvjew+poSIIfQbhBPf1Y0RhOlBgtEAkM2MDTk+3VyLQbUcweeEqNj/IEr8lUniG6H5zHhw7bAeGnPgGGl4dXoBegUrv1fNh3unzdkrKuRS3PHFY7p+nIQDfdwh8h2I49+ZqEo60AfuIl8MPK7+hCBBEN8MEowGgGxmLKTi/tYJ6B3mn/2ZNk4Al7CZuFF8Dx3xzSj4zPhwqDsR53K73hX4dGMlBtYrB5FynCD73anxdFzLHetX3PElx3T9mInpqIu49664vnDTI+1VFC7ee4fv784IwvQgwWgAyGZGhCILyR9eI/rxA9x/9BpJNEjJ+Mn3zGLwWeszUyAr+SPevH6DzwXH0JXo+OIhPyYIgtANCUYDQDYjCNOH/JggCEI3JBgNANmMIEwf8mOCIAjdkGA0AGQzgjB9yI8JgiB0Q4LRAJDNCML0IT8mCILQDQlGA0A2IwjTh/yYIAhCNyQYDQDZjCBMH/JjgiAI3WgVjJQoUaJEiRIlSpQoaSZNKMJYCshmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQuiHBaADIZgRh+pAfEwRB6IYEowEgmxGE6UN+TBAEoRsSjAaAbEYQpg/5MUEQhG5IMBoAshlBmD7kxwRBELohwWgAyGYEYfqQHxMEQeiGBKMBIJtpR/r6Gm7EytV/lZRkRB04iZgSHyZHyqurOHYtVv03QZQO8mOCIAjdkGA0AGQzLaQdx8CKXog4nqneUBIU+LS7G9wcemB/kYcpkPb6MrbOHIJ2Ie6w5Nug9Zr36t8IonSQHxMEQejGeASjPAWx96NwOzoBWepNuWQk4t3bD0gq9INx8J+oaDKeY+cfEVh8X6beUAwZO9HZxg5d92SoN5SMpHWtIBI1x6oE9QYNFGkxuLD+b/QN84dEwMGME8KhUhP0GLset5IV6r2+T5IO/IbmEZvwTB/9bUgUSbg242d0iLyEz9+pqb9bP1bE49zC3zH0z814YKRlqOmiQPy5hfh96J/YTMYlvnOMQjAqEs9iQm0HCHh88DhLBPy6E3E5XZLyWKzv4AzzoJG4mKrepibz0xu8jonB64+p0Lfj05B894Ix9Sbm/eSJwKGnkaTeVCyZxzGgnAXabSzw0Ioh83AfOIrq4p+XeU9U8ekqlg9ujgCJABzPAh6h7TF42nqcfPwJUvU+3z3yl1jV2g1e4Wvx/BvctOLDLvTwEIDv0hEbcp3z+6LUfix9jxtbZmB4zw5o8VNLdOgxBJOXH8ETY2nESG9hQhUBzMStsa7EDvy1kePtuWWYPHYsxkUuwbEXJWhYyt7gzPJIjGPHTFp+AR+MwrxS3JpQBQIzMVobj3EJ4qtgBIJRhodTQyF27oA1r9LxZktnuAmDMP6WslZU4M3mcLiKK2HEuZTs3XNgFei8+mJwZmbgew7EKf0CWQbluxaM8tfYHO4Bkf9QnE5WbysJ0sv4w88CzVZ8YE+x5Egv/w5fUQ1MeZQTyZTj2azaMJcEocPo5Tj+NMlwjQNZPN7HZ19HFv8e6n8aLfK4TQh3tUCVkeehz6MoDuW9J6iMKkP8+3j2/9pJf3gQe28X8MPviFL5cdJlTGnoBAFnBk5oDSc3Z9gIOVYucTCvOQ0lDch/VYxRMCreYkmYCGas/DZjthJVn4w7RTaEWF2wqhXsmJ2Vx3DKe/ms/umbQoKR+O9gBIIxA7u72kIcthRvmbJQxC1CY7EE3fdlQPFuO35xFyPgt9OFKkj5y3moL+bDzcMNfL4XBp/5Vn1137NglCOGFdIOfFd03RWvl/CD7DYmBlug/rxXegk82f1IVBdXxeS7OTVtGnZ0soHQoz1WPDJgq4AJ4Z19/GEX0Afbr+xA3wA7BPTeocdkm2+BHLHseUiEfvjtbCmEmywO9x7kf47y2J3o7Weriupf3dkXAbb+6L3zteFEud7I8f7KKdxOKvvwkf5+zMTCxGAIOT7c2yzAdXWLQ5H6Emf/HY5WbadB1e791hilYIzFokZMMAoqoEqgNTh+eQw5U4R/yx5iaqgIPPsqCPbks3tpidWf1L99U0gwEv8djEAwSnH5d18Iy0fgWJIc8Xt6wF1UHX/ffYPd3T0g8huEE4VaknK8nNcAYmEwxm2agOpCPsoPOYtvJRm/W8GYegIRXjwIq/+Ne/pGSuRPML2mBUKnPtIZsdKG/MVc1DGvgom3c46S4dE/DWHH4yD07oT1huqPVSTh6rQm8G8YiUuvr2BqY380irxs/OPzMi5guA8P4jpz8EwvVSfD3Wm14NljL5PgeSiSrmJqI3bvU68g9uoMNPFvgMjLn/VrHKhQ4NP5SHTtOgHb7pfm+GzSLk/AD9Z8ODZdjMf6vnNfiN5+LH+GmbWETPRUwpjrxqAMdWDMglEYgrEzu8KFx4Nz9z3QFTTMuDAcvgI+Kgycjz8qK++FBCNBlDVGIBhZVfZ0OVq5CiCyc4StyBKVIvbhwd4+8BL5IOJoYuHKR/4S8xuIIQj8E1HptzA+SAB+haE4940U4/cqGBO3d4I9J0L9eS/1jzjJX2FefQsEjb/JitSSI3+9APXNc4YkqJG9wqY+ofC048O88jCc/GQoVSeDNFeXSvUStt8OKaLGBoDHC8DYKH0sy4T31FBmv3G4WfAwjXuX5RqkIOl4tm8ahvQfiplHXhV+pmlXMammLXiqrlk31B+2GQ9K03uddgcL29VFj03Rer03hkBvP5Y9RGQNJhj5nhhY7JiYLFyZ3Qkt2vyGbQXD2PJY7BzeBs3DZ+JS7mmkuLOkB1p2jMSZlEzEHJuNAS1CEegfgB+adMNfu57kE/45SN+ew+Kh7VE3OAD+QbXRftgKXHl7Q6tgzIg5g5UT+6N9wxBU9vNFQHBttPr1b+x6lDfuWHZvGXq1bIFOU85oGb8sw+M1A9C6RRfMu6Hn08oVjKGYeusEBnnzwdm2xpp32nz7M/Z0dwFPWA2Toq5hnE7BmIW4c8sxulsz1KziD1//KqjZtCtGLT+HuALzUaR3lqBHy46IPJOCzJhjmD2gBUID/RHwQxN0+2sXnmg3Ls4tHor2dYMR4B+E2u2HYcWVt7ihVTBmIObMSkzs3x4NQyrDzzcAwbVb4de/dyHPvDLcW9YLLVt0wpQzWsSm7DHWDGiNFl3mQV/zEsTXwCgEoxJ54lNcOHwAx6NikZpwCP3Ki1C+/yFo0wbyV/PRUCyA/6irrFiV4vqYAAj4FTHswreZpfZ9CsYsnIgoB56gKibrHV5kKN5iaZgFfEZcLDzrvQiUgrFBQcGoJvnqFDSwF7L34jASDaUZi0CR/AyP48pSRmbh7bXtmP/XKIycuAD7HiZpjdRlHukHZzM+QqY9UW8pBuk7XFkzBh2DJOCZN8fKj/oaT464rV3goZyZrhw/ZlEL0x8UsEv6A/wbXgEWVrawEir348E6qA/WPUpX72D86O/HGTg9yAt8Mw6WVQdhz8uiWqyp2NRerIpGji0YjdQaAczEwV4OTCTVRJ+h9eHIF8LeNxR1Q8rDls+B45dD561v8r0f8tfb0LW8EBzHh613DdSpFQBnEQeRdyiquyq7cfPOr3i3Du0kPCbuHRBQrxXCu4SjRagHLJjg5zm1xir1pDPFx43oIGHXs2uPDQXfG+k1jA4QsPehMRbqO5ZDUzA+ysCNPwMh4MxRb+7zQo1TRdwKNLfmwaLRQrzMvKldMCo+41IksxOP5VXsiioNmqNl05qoaMfyx+zh2mwB7mpo+syDveDAE6Jmn6Go78iH0N4XoXVDUN6WCVe2f7nOW/Emv3GxrWt5CDkOfFtv1KhTCwHOInAib4RWd2XvgKZgVODdunaQKHtFHAJQr1U4uoQzQephAY75hVPrVcg2rwIfN3aAhJ3Trv0GFDbvaAQwn7NovNDIh8oQ/xWMRjDmokjEsQgfVsj1wf54bRWbHK8WNIRY4IPhF7OliPTyH/AT8OEz/IJe4sRQfJeCUfEBK5oKWcHcHpv1m+is5hNWt7SAe8QJ7c8k/S6WdQmCm1sg2kw7n9swKEowKgvY9/t6o7xlHczWrz8WioSTWLH5oR5RRCnuRobCo/O2QgV50SiQeGk2utSvhZ8GrsP9Euolaewx/N3GB5bqQf0qYWZTE5E3C4sQ2eOpqME3g0Pvg+otusl4vB59q0pg7d0Avcf2RIiFOyJO6OklyUfQv4I3WkXuwdmDM9HGQ4zgibfzbKlIwKGI6qg9bA+ep7P7PxqBiiJLWIpZ5eraEkseGir0n2PbH9EsYhXulOq91E1p/FgetxM9KzKRxkQjz9oPrUauxuW32uxbSsGofA9EFfDzkpvq4RJZiF7VFs48MwhDpuBh7kNIxsE+7uBzQvj324ucdk7aky2ICLZWTQ7MJxgTjmHmmCU481ozMpqEc8MqMeEmRMiUh7nbdndzAo+zRstV79gTyEN6ZST8maCx+mlFfnFVEvIJRhl7p6chlIlbYfBE5I5GUSHDgykhEPEk6LjpIxRS7YIx9cxQ+LK8CCt2x8ZneU4n/3gW435URr4tUHtm3vCYbMHIbMKJUOHnJbipHouSFb0KbZ15qq7yKXnGRfLBPnBnQl3o3w9784yLLRHBsFb5bH7BmHBsJsYsOYP85j2HYZWYgNU8d9JudHNiwt26JVbli65KcWWkP3sWVvhpRf6GAUF8K4xOMCadHAw/kQd67nmPpGensGXVKmw5+QS5Y+Dlr7CgoTn43kOQO88l6zx+q8CHwPd3XPoGivH7FIxvsbTJlwhGZQVpDvue+wuPLWWNghODA2DvVw8NA+3B53mgz8Hs0UuK2IVoqFMwMli+Vrd2RvN/9Zl9LWPi7ycMOKRHtIvl8ciA8hBYNMMK5WysEqJIPIRfvYQQCpWRHiECR19B0Z2VCnw8+zeaeHmidu8pWH/0Mm7eOIm1Y5rCQyiA7++X1PvlIX8xGz8KzGDXfZ96iw5SzmJErVrov/wK3ivNKbuHydUsUHPGU+1DDBSf8PZ9YXGXtrcHKnbZru6SVODD6lZw77Y3977krxehiUcfHMzZkLEHPar9hkM3liG8vAiWtaajYECyNBS0bcAfF4uxrX6U1o+lcScxLbwy7JigUM745Vn7oMXobXiYr0u+lIKRs0XDuQ/z+1DmSfzPgw/OsiM251wjdQ+6OTDhYdcO6wu0cOSxG9DRhYmgfOfXTta5ofDm81R+m0Pa0f7wYPdm2XQZ4nJPnYVLv/syQWOLtuv0WwlBRQHBqIzgLQ6zLDz5JeM8hvnwwffohyPKckirYEzCji724PHsEb4loVBeMi+OgB/zF0GVCbilfg+zBSMH24Zzkb89k4mT//NgwtsSHfOMiz3dlM/CDu3WM9Gq3qpCHosNHV2YsC/YJa2NLJwb6s3KO3v0zP0yQRqO9s++XtNlcXnnzrqE332ZuLRti3XGsX4QQRiZYEw+g2EBYnh03Y5HhwejkoUl3Px94SS2QOUR2WsAymMWopE5D86/bMDTmBjEqNIzbOrmCp7AD39cLnvF+F0KRlYVH+zlyFrDNTFTz2heNhnY0ckKlj9vQ36ZxgTS/r4IrDkOl5QPNP0uZtS1gV2nHdm/fliBphb+GHVNh2BURhwif0RjfWZfK96wysgTvQ/op3wV8Tvwi3sA/riiJS/yl1g3fh5u5rs5OWKXtkXjv64hKesDTo+tCTvnHtinKRzSDmLY0F3qWf8yvN4dgdAffsGia58KVHQprJJyQdCfUeq/88g6PxReHA/+o6+rt2gndU93eNUeji1n7yD6zUfEf3iOle0lsGm7Hgla6iD501loN/BYIYGfvL0THIJ+wynVQZl4NLM+gjSGGsifTEdNkQuaRp7E65QPuDitKar1O6S6x6zHCxDmpBYFJUWnbVuj0V83kCxLwKUpjeDo0gv7tT1SrccXz5f5sQwfb2zE+J+rZC8qb8aDpE4kruY++1IKRr4XhpwtUKbJn2KGcrKNKAxL1Y0Z2Z2/ECw0g7DeP3hV0DG0nj8PWeo7PL93E9cuX8K5VT1RnolD68471b8yMlmDvCITqOaNsCDn5FnKyVd88CTh2FqaMcUFBaOyXFB2z/J4cOm+R904ARJ3dYMzT4CA0Vez3zdtgjHrIkawvHAi1rjTJq4yDqK3IxPT5q2xNjF7U7Zg5MNryNnc9zgbOZ7OqAWhmQhhS99m+6TsDv4KZvYW1sM/hY1bzKQXGVLfPce9m9dw+dI5rOpZnolDa3TemSeKM8//horM5uaNFuQ+u6wLw+HDhLskfKvWYVkE8S0wIsGYggt/VIZ5uc7YGvcKixpbwL7DBnxQyBA9py7EtuHYlixHzMJGMNfotsufcsY1li3fp2BU4MOaVrDibNBuY+FWe/FkYn8PCQTl+2Df+5xCVoHkW/PR3IUP2+r9sYrZTfmsUnb/Atewpdm7ZB5BX2cRgkde1DJjOQsfolajV6AvBp0qHAnTzSesb2sFccX2mH7wERKL1S4KZMY/wbl1w1GnXBMsel2wkmDVxNVRCBBYofHCFxrCNQXbOnkjfJM64pJxFkN9meC8nPNGpuD2rMawd2yBJU9SEbu7P2rWH4XjufaRIyMxFo8uH8TqyJ4I8aiJKYUWp5Ph0ZQfwDdzRE+taikHBd6taKYak5brH5wI3v7lYWEXhkXPCpw34yV29fWD0KU1ljwocN7ksxhRSQxxuVA0D6sMJ69wbNSsOOWvsCHcAwLl+C4+q7iFfui97hbeKetEZaS6VUsszQtNFYtu23qg3br32bbNuoKRlSpjtJaGhfbji8cwfizD+zOT0ciJx+xtgXpzn6nzYEjBGI05dZSCsTEWq/uCs04PgiffDOL2m9iVCqD1/HJ8vLQQ/Rv5QKIac6rxnpgVEIzMS7PHGIpQZ072GMPMc7+hAhM0Tt3yxJ1eFBKMjOR96OnKhF3O5Be2z/Jm1kwI1sKMJ+p9tAnGjL3ophpn2Q1aPywlvYpRAewYYX3MU7+3RQnG6Dl1VIKx8WJ1V3DWaQxSLeXTHpsKG1e7YJR/xKWF/dHIRwJhwfqqgGCE9Ab+DBSw+6yDOc9V1sW53yqAz3NCtz2lsi5BfBWMRjCmXR6DYHNXVtkyJ1UNphYj8M8bKkGRsa87JOImWPL6JROS5uA5NcO4VWuxdq1GWv0XWrrxIQgYDZ3Bqa/E9ykYWXmdsAOdWcVn23ot3heo72Uf4/CuSM2WiaP9XMFjlQ/fygXu5dwQFOABW4EQno3aoZYzK8BZhepZqz16tAxCJVZwZ5OIvd1dwTHxb+vhh/IVPBFcJQiBARXgLjFnrXMe7JsswEO9nrECn08OQ6B5dpeh0M4TQT82Qcv24filW3d079YVv3T+Ge1ahqF+aBX4uNqoBreb8WxQfdQp7RNsMp9gRRtXCCuPQ95k5c/Y0M4SnMAWnpV/QEj1ALhZCiEpHwhvF2e4u9tByHeEt6cVeAImwIR8WDl5wru8NzzKuUDC9uVUlQvLo1t9/LE/hsmPAkhvY2IQE2UeETiubSanJsovkOxahhnjR2DQ//6HYbOOIiZuAzrY88C3LocAJryr+1ZGUKAf3G3Y82AVlEc5VonzreDKhK6XgyUcvPzh5+kIC1V3a3aFxzHhYOfug0qBPgis4I9Afy84WrA8cUK4+AfARZxtZ565Azw8nVC+6w79vsqhw7br21owMSqBd+WqCPL3gERkgy67tCgErccXj+H8WI6X/9RTvUPm7Taqo8lp2KwSjAGFl+AxhGA8P4wJODOIfvq38JhbLefPjJqMEEvmm851MHjJfly88xjPoqPxdN//VGMB8wtG5u8PIlGDCUth6DQ8lmXizBBl12o59D1UVKOlCLQJRmSw8yojcOao9080Mu9nX9Omxcq8MZLaBGPmIfRRjgO06JDXRa+JWvBx4hZYrf7kqH6C8TyGVWCCUfQT/i1sXC2CMRNRk0NgyfHhXGcwluy/iDuPnyE6+in2/U/ZjV9AMDIvfxBZg70vQoROewxZ5hkM8eaDV64vSmtegvgaGIdgTL+G8dUt4NJ+HWKVDSzFe6xuaQ2LmuNx/uUjbOruDZH/SFyMXoTG5qxV22MfCpcLaTjSTzmrt+zXRPteBSMrKXEnMgTmyuUsbmsUq/KX+LfvHzhapGDJwulB5eFdtyV+9LSBQCyE2NYLdQasx0NWVso+Xsea31sjyFHExFFbrIrObvkrUbw/hci2QXCxEoBvI4ZQaA4bR08E1mmPgbMP4XlxQkkrcsRfW4U/OoTCy1Y5SUGjxZ+TWAFvbu+FoDqt0PP3Wdh8Oa7IMXKKd0x8udXW6LJnwvTCNLSp5ARLoQACPp9Vqkw48VllxhdA5FwNneZeRnzccUR2/hF+bhJYW5jD3NIado7u8A2ug5bdhmHa+rN4kVqwYlKiwKfd3eHKhGzY4mhVpEd/ZIjZMwwNvGwh4ovgzvxJYC6BR7VWGL7xHhJf7MXYNlXhbiuCaokcvgUkbv6o2epXTNpwCXfOrcaEPi1R088VNiIhnMyFsHQojxotIzBjzwPVWOOsD7exd+Fo9GodhuZ9/sFFbf3fxaDNtgmnJiLMxx6WFlawlThCYmWFjlu0vwyFjy8eQ/px8qb2sGCC0aLDZnVZlYHdv9iAYyIr4niBllbmRYzw/TLBKH81D/WFZuBXHI5Ci0WoxwHmnT8LZ4d4MWFmhZar1RFbNdLrY1FJi2BUXnNuPTET7NUx+cZxDPJi77bnQJwsykGKQqtgZNePGofKyskrwaOwcGBF5j/O6LZbY3VGbYJR9hBTQpg9mBjXFnGWR89BHSY8NYMJeglG1RJh7Pz8ihhe2Lg4P8wn/yzprLMYwuzDWbE85mtpS3F9rHJSUUHBqMzjXNRjDS1h9cm4cXwQvFjZ4TnwZJHlD0GUNUYgGDMQNYm1xpzaYLXG2gFpUXPR0tNcNftQ6FIPf558h1eLGsOc54Cuuwt+9yWb1IO94cIT5EYmy4rvVzAy0m9gcqgVJE0W4qm6XFd+jaVxj52Fu77yIcWlEZUQtjA2X4VUGDnkpVM+pUeRgU+xz3D/1g1cv34DUbfu4tHz1/iQnFVMXgsgj8H8xtUxrtDChjkw0Ty4Bvod1qf7vAjSLmFkJSHsmy3B07JtE5U9JbHtkBror8u2xR5fGH39WP5mP6ZHbsODgl+lyXqKxc3smeBWRspylomR4fbEKkyICOA/7HzeGoqyWOyLCMoeOvAFglEZRVSuR6tcE7LPfo0hJNIY7OoXAHG+82eyxrWLakLNz1s0l8pOxc3pDVXLwRQSjOyMccuawYpj+a9fF+WYoFGuSlHI+op4XFk9BZPm7MezgtpKEx2CEbInmFFLpIpWC5nI41cYgnwfgNEmGFlZE/VnZQjM+CjXaQvyfepc8RGHB/gwkSZE8MRbufWCXoKRHXVrfJDq/J599muM/5UiZlc/BKgi6hqCMfMI+rkou9Z/Rn7z3sT0hhJm98KCEYo4LGtmBU7gj/p1y4HP92HitKB1FYi/shpTJs3B/iKNqyQdj/cvwPS5O3An95vmUrw6vgQzZm/EtdybkOPN2X8xc+ZKnH9b1gUxYWoYgWAsAlkqPsS9Q5KRV47ftWBkyF5uQmdvBzSY+1BVuCasaQO/QacLFLQFYa3pP39Ezz16zjwwIRTxWxHu0QIrdfW3Jp/CoEqNML/QQPnSkIrLY4Lh+MNI9eST75uS2jZnTFpBij1eC/r6sezeZFRTRq4kAWjacwQmTp2BqRMGo32wPfgcB6vqf+KiRleI7G4kQpi44ITuaDhoOhbOn4xfmTgQ2VRGsHJpni8RjKzij13XXrXcDk9SDV3H/4NF8yagdy1XCC39UdXfUuP8CnzcEq5as1BUsT0iNx3G8YPrMaV7VUjMLWGhLcLIUHxcj3Z26qEJyq51bWN/MrYj3JLtU2BZmkLoEozK+1gaxoSpMuIvRNW/7uQflqFVMLLTfdiLPqo1KM1RPmwQpixZjdXLpmNYSz92LvYsqo6C5trY+glGtjV2Hdorl9vhSVCt63j8s2geJvSuBVehJfyr+sOS0xCMTKRuCXdkwlCEiu0jsenwcRxcPwXdq0pgbmmhNcKoeibr2+V+K1sQMEbL0KoMbA9nz9FMc9kjHWQeRl/1ONoOOf300kuqyUHKscxNlqjvTXYff1dn7xI7p/KrXARRFMYtGE2E/4LNMp9uQM8ajTD9XhZezasPq1DNNeC0IH2GhT/Vw6Q7Re1kwsjfYk/virCqNxcavel5KN7jQH8/WP84E08NoBfTL41Hi46zcPm/MGWyWNt+wMEB/rCuPUu7bYs7Xgd6+7EiAVeXDULTAM2JDRx4lp6o228hLn0sePEMPFrXB1UlguwhEZwA9sFdMe9yHHZ0sQFn2QGbcjtPlGOAXZhg9MEI9XqzuSijpw1E4MybYYVml6fiEy7N7ogA5eLTqvML2fl/wdyLcTisPJfm+ZmNjo4Lg6dqXG92XuwCwzHj+Ab0deVD0n2vekdNPmNfT+W4ZDMt6yWqSduMDmLlGqLtsKGoho3iHZY3VXZx18Zs1USPPJRiv5NyVrN1GBarFxDPRbk0FBM4nGU7bNQQgEqyXuzDuNaVNCbxsGdhUQ61es/HxQJjDzOP9oMLE4yFPywgR8z8BhAx4dlshWZ3vQKfLs1GxwBb1hjIPrfQPhi/zL2IuMPKc1miQ97DY+Y9inFhnrkTNDmBHQLDZ+D4hr5w5UvQfa+WzubP2ZN+lOIt3zqnuaRhcwcxe1Y2aLdBPRhTF9LrTFgzOwm8MPi0OlIpe4yZtS3AKSfT7M4Rt7FY0Zy9e5wd2qx9l72NIHRAgtEA/GdslpmMZFb2pB3oBSe+DX4YvB1PCoy1kyY+w4Wt09G3liscmizU83vHJkJmNHYoF+zlu6LL9gLrsilJf4rt/6sOG/b7Lzu0/F4KFBnpyDDEiYydkth2YBG2Le74Iii9H8uQ8uYxbl27hhv3XiBBRy95Lhkf8Ox2FG4//ZA3Rk2ajuS0AiElGduWnMEkTGEUWalIKri/GkXaezy9cxN3nn3MOz87V4qW/WXJsXgYdR23WF7S1caSpacgXeupNSZnaP1GvALx2zrDiSeE/7CzWsaZF0CahuRU7cNAFJmpSMnQXngoMlN03rsSaWIMHt66gai7z/A+TdcbIEN6cjK0XkKRhdSktNzu63wo0vD+6R3cvPMMH/OMi/QUbfvLkBz7EFHXb+Hph3T1fSr3Tdd+btkD1acmuUJR12wU8dtUkxCF/sNwtljjMjKTEP85I799pSlISEzP/07JUpHwKVXL8ySI/JBgNAD/OZtlRCEyVPn1CA4CW29Urx+GsEa18UOAO2yUa9BxPNj9MBT78w0m+h6Q49PNtRhUy1E1W9u1w/oCn+xKw6vTC9ArWPllCT48Om3OnsRFlAD9bOteyLbFHV88VPYVQ9Zl/OEvACfOWf6lALL7mBIihtBvEE4UXhOLKIasy3+ovpwjrjMHhc0rw/0pIRAL/TDoxGe9GkIEYShIMBqA/6LNFAnXsHxwM1RyFLMKPLuLRrnkSfmaHTBs8RnEFhdlMSlScX/rBPQO88/+mgcTLA51J+JcbvewAolRqzCwXjmIlLZQLqcRNhM3ShIF+M9TvG0/3ViZz7ZOjafjWm7vX3HHlxwq+4om9Ug/1efxLBov0irGkw70gbvIFwOPk6DRn1Qc6af8tKMFGi+KKRxVTjqAPu4i+A48rmV9WoIoG0gwGoD/ts2kSIl/izdv45Giu5fI9FFkIfnDa0Q/foD7j2LwWWv/jQJZyfF48/otPn/PtjA0JbbtR2bbN4VtW6Lji4fKvqJIw74ejuDxJGi7Tv0FlAKkvYrCxXvvCs+cJoonbR96OPLAk7TFOm2fIk17haiL94pZ+5Ygvi4kGA0A2YwgTB/y46KRZyThU3ImRQ+/CnJkJH1CciZZlzBeSDAaALIZQZg+5McEQRC6IcFoAMhmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQuiHBaADIZgRh+pAfEwRB6EarYKREiRIlSpQoUaJESTNpQhHGUkA2IwjTh/yYIAhCNyQYDQDZjCBMH/JjgiAI3ZBgNABkM4IwfciPCYIgdEOC0QCQzQjC9CE/JgiC0A0JRgNANiMI04f8mCAIQjckGA0A2YwgTB/yY4IgCN2QYDQAZDOCMH3IjwmCIHRDgtEAkM0IwvQhPyYIgtANCUYDQDYjCNOH/JggCEI3JBgNANmMIMqI5CgcOBkDufpPQ0J+TBAEoRvjEYzyFMTej8Lt6ARkqTflkpGId28/IEn5gzQZH9++RULa16gySgdVNARRBig+YXc3Nzj02I9M9SZDQn6sH7KnWzHq136YuPfrCHiCIIwLoxCMisSzmFDbAQIeHzzOEgG/7kRcTgkkj8X6Ds4wDxqJi6lML14fi0oCIerPe2U0hRRVNIR+ZOD5zj8Qsfi++m/jQvpyKyJ+Hokj77/Ew5Jw4LfmiNj0zIDiLgnrWokgar4KCeothqT0fqxA/NkF+H3oaKy6wQopY0MWg2PzJ2LsmDEYUzCNHY9lFxLYHehP5sFecGBltteQs4Ub+QRBfHcYgWCU4eHUUIidO2DNq3S82dIZbsIgjL8lZb8p8GZzOFzFlTDiXIpqbxKMhGmTipvzfoJn4FCcTlJvMirScXFUZYh5Vqg17S6UXlha5C9XobWbF8LXPv+i8+SRicN9HCGq+w9efgXnL70fS3FrQhUIzIRoMN/4om2KN4vRRGQGMzNtSYCAMddL9XxIMBLEfwsjEIwZ2N3VFuKwpXjLmrmKuEVoLJag+74MKN5txy/uYgT8dhrJ6r1JMBKmixyvWQPIQ+SPoadz3uivjQzx7xOyfUUWj/fxMtXWIpG+xOFNJ/H2ix1MjrhNrMFnUQUjzxvifqW4/LsvRDWm4FEJbkNfvlvBGLsIjZhg5FeMwJ4n0YiO1kgvXuFDWmniiyQYCeK/hhEIxuxKQFg+AseS5Ijf0wPuour4++4b7O7uAZHfIJz4rN6VkSMYVQVz5jtE7VuF+XPmYfWhB0j8RiU1CUaiJMhjVqGVAx+uXXchvnR1tJ7IEbuzN/xsA/DrzqvY2TcAtv69sfN1GTqKPBarWkkg9PsNZ7M7Cb4AGe5HVoe46mTcJcFYYnIEoyBgFK4aJtSrggQjQfy3MALByKqBp8vRylUAkZ0jbEWWqBSxDw/29oGXyAcRRxPzja/JFowihE1chgHBtuBxAgiFPHCcCOV/2YRX36C0JsFIFE8qTkR4gSdkjaF7X0HtaEWBpKtT0ci/EaZeicXVGU3g3yASlz+XiVrNJePCcPjwxKgz59kXiik5XsytA/MqE3HbVARjRgzOrJyI/u0bIqSyH3wDglG71a/4e9cj9kZoIsWdJT3QsmMkzqRkIubYbAxoEYpA/wD80KQb/tr1BGnqPfVFb8GY/gJH/xmCjg2qI8DXB5VCmqLHX9twLyn/e5MnGM8g9c1ZLBjUBj9W9kdA1fr4+ffVuPGpbN8zgiC+LkYhGJXIE5/iwuEDOB4Vi9SEQ+hXngnA/odQsMzJFowchCILlAubiIPPU6HIiMaWHhUg4Lmh9/6y6urLgwQjUSyJ29HJnoOo/ryvMv6uKGTSHHUlQ+4/86FA4sNj2Lx6DbafuIv3hp6CLI3C2AAeeAFjEVXSCFd6Ij4XyoccrxfUh3nQeKiGOBsYgwtGxTusaydhjVohHALqoVV4F4QzEehhwcGM54TWq16qd1SSiYO9HFiDoib6DK0PR74Q9r6hqBtSHrZ8Dhy/HDpvfVOqySn6CEbF5wuYVFuZZwt41u6IPgP6IrxeeVhyHKyq/o6TGgVytmDkwal+BzRyE0BgVx4hdWohwEkIzoyDZfWxuFD2xTFBEF8JoxGMuSgScSzCByLvPtivpd8uWzDyYN9oNu6kqzcy5M9no7aQB7f+x77KkhtFQYKRKI6sExEoxxOg6uR7TLZ9BbLe4tr2+fhr1EhMXLAPDwtEg3QjR8ymLvAWMhGjmgTBsYrfB/U6/Ybpaw/j+vOPSP9igZuJI/2cYcYPwbQnJbh76XOsaVcO1r7tMevCRw2RpBSMDUxIMCbg2MwxWHLmNTLUm5QknRuW3egNmaLeokQtGNkz4EQV8POSm8gOBGchelVbOPPMVPs/LMXLU3LBmI7zw/0g5Nmh/pQbuePGgRRcn1STiUYhKo2+pt6WIxjZO8O2e7dfgOvqMUGKz5cxsZY1OM4cdWY/ybMHQRAmjdEJxqSTg+En8kDPPe+R9OwUtqxahS0nnyCn/tM56SXzKPq58iBST54pS0gwEkWjwIcVTSE0E6P9ZkMvuyJF7LG/0cbHElzuzFcONjUjcbMkLaeUo4ioXBXhY+fj32UzMbpva9SsKIGQyzsXJ7SArYMTnMvVQaQWpZZ+dxm6BLnBLbANpp3/pCUKJsPjqTXAN3NA74PFZUqKR3MbwbdyNXiI2fUtqmD4yZxzFi8Yi8+LbgwuGHWRdQ5Dvfng2fdUb1CiFoycLRrOfZi/0Zt5Ev/z4IOz7IjNpRgHmiMYOb4IVra2sNVIdvbVMS5HRabuRXdHHvgVhuJcwceUuBkdbVhjovI49YYcwcjetQazcF9TETNSD/WFG/tNWO/rzGgnCKLsMS7BmHwGwwLE8Oi6HY8OD0YlC0u4+fvCSWyByiNOQ7kKiU7BmHUag1ihKmq0CLEkGAmjQoG3S5sYXjAqPuLs303g5Vkbvaesx9HLN3Hj5FqMaeoBocAXv1/Kr6oUn94W6m5WJEfj0esCtT3zrOQnezC6vjMETDhynBgSDx9U+mkmbhcQaorEExgcYA+/eg0RaM9EkEcfHNSYpJaNHC9m/8gElR267ytaMMpjV6J1cF8cSJDj8435aOMpAD9gFK6prqtA7MKGOgVjyfKim68qGGWpePf8Hm5eu4xL51ahZ3kmAK07q39UohaMfC8MOVtgCon8KWbUEsJMFIalpWgN5whGnk0Awn4OR3i4RuoyAluis3MsvT4GAQIzCGsMwfqdO7FTM22bgjasQc7ZdVXtq6SoSS+K+JX4SXlNl344UlwbgSAIk8CIBGMKLvxRGeblOmNr3CssamwB+w4b8EEhQ/ScuhDbhmNbchGCMXED2lnxYN99b6kHh5cWEoxEcWSwytWRE6LmzOImfsgRs3EC5lwpHEpKOzgMQ3epOwplr7E7IhQ//LII1woO9E3Zg24uQRh3U1NVyfF0VjsMPFZM7S19jysrhyGsvCUEkqro+vdYtK0WgePaDmOCdX/fQNQcd0nVmEu/OwN1bezQaUfBvGfh/FAvcDx/jL6uRenlIsPdSTVQM/Jhbrd9xp2p+NGuLua+UFotO1Jr4Z8jIDUocV508zUEo/zjJSzs3wg+EuW4vpyobXYquWCMxpw6SsHYGIvflF4wFtclnXnkV7gou5gL5DMvcRB6D1HvXbRgRMZedLPjwFl2wo6C7RGCIEwSoxGMaZfHINjcFeGb3kAhvYbRAWIE/nmDFcWs7NnXHRJxEyxhhaV2wSjH65UtIOE7IHyz5pinsoEEI1Ecig9r0MqKg027jUgo6gWVXsWoAAEEHq0x59J71fuvIuU2ZjW2h2OLJXiSGovd/Wui/qjjyP0YizwDibGPcPngakT2DIF7yOR8UbiMl7vQ108Il9ZL8KBQkFOOtNir2DY9Ak19bZhosccPvRfi4nt2gqyLGO4XgAEH3ublRYkiGbfmN4cL3xbV+6/C9Q/KX1Ow+xdXhC19l71PDrJHmPIDH2aOPbG/qACr4i2WNrFA0MjL7EzZZL7eiV6VWmO1ejxz5pG+cBYFY+TFz3l+rk9eisDggjEzCpNDLMHxnVFn8BLsv3gHj59FI/rpPvzPV2B0gjHr1EB48M0gbrECccnJSC6YUlKQoTGGskjBmLIZHSw48Jz64jBFGAniu8A4BGP6NYyvbgGX9usQqwokvMfqltawqDke518+wqbu3hD5j8QVVthlC0Y+PH76Eyv3nsbF80exeWYPBNvwIWn0D+4XKrm+PiQYiWJRJGBHZyfwbFtj7fuiKv1MPFnRBq6s4uZ4Yth7lkewjx887YTgO3rD04oHgVgMId8KTp7eKO/tgXIuElgKubwIFieAS1A9/OjnDBvnCgj0c4eNwIxV3mxfJhz4Vq7wDQyEl7MLvAN8UE4iBl85ZpEd5xTSBwvOa4rDJOzvXQ48TgSJVwCCKnvD2dEDAR62EAg90ahdLTizc3MWnqjVvgdaBlUqJHiktyciiM/BI+J4MdH/z9jeScLuQwhHvxoIDS4PiZAHp44b88YlJ+5Fd1cOZgJbePiVRwVPH73yUhRfIhhvjK3EBKMIjRbF5grZrLND4MXu26rlauR75NLrGFvJ+ASj/MVc1BWy/Uo4qShbMPJQrt8RaMw/VCG9Oiq7e7vWDDzVDLkSBGGyGIFgzEDUpBBYOrXB6pi8kiUtai5aepqrlmcQutTDnyezI4fyN4cxqXN9+DuIWCWm7iaR+CFsyFrcSda/MDUEJBiJkpB1JxIh5kJUm3S7cERGE8U7bOjgBDtXD0hEIrhZWMK1WifMvRyPuOOR6PyjH9wkrEFlbg4LK1vYu5VHYGgYwiN+x4h+HdCwakWUc7CGSLkcC8eDucQD1VoNx8Z7iXixdyzaVHWHrZgPji8AX2QDZ+8qaPTLSCw48BCftVTuivjzmP1LTXjbWzChygOfHWfrVQcD1j9k3ivDx+tr8HvrIDiKhHBruwrqIXHZKD5hd3dX8GzCsDjfD9qRRu/AkHoesGSCWWhXAfX6LinQ5a7A+1ORaBvkAivWcLRh1yxxXopBXz9WKHLylZQtdDlrhG/L6wLPPNIPLjwOtj9vYVI4j9Sb09FQwvtiwaiIv4LVUyZhzv5n6i3aKalghOw+ImsI2XtRAf0OxWvpqVGwe1b/k5EtGJlAF/mj19YXee+04gN29/BgjRAhqv51J3d4Acswrqyegklz9uNZMTo+/fF+LJg+FzvuJOfmQ/rqOJbMmI2N1/K+fS1/cxb/zpyJlayRQxDE18UIBGMRyFLxIe4dkrQWcgpkpcTj3YfPyNCjUvgakGAkSkY6bkwOhZWkCRY+1ejbK4QcMfPD0OQf4/n8ZYmQywvlN+3SSFQS2qPZkqf5u7SLQcHOVViw6IGWvBSHXn6cdQ7Dq1ZGiwEjMSoiDN4iJs6tm2KZxld0FB+3INyRCUNRRbSP3ITDxw9i/ZTuqCoxh6XFl0cYM7aHw5I1mvMvz1OYEgtGZvGEI/+Dn/JerPzQathsrNq6E7u2r8fiyMHoGOqJyiMuqvfNEYx8lKvgDXOhE0K6/4m5C+dgbHgQbJlQFnj2xO4PGk8xYzvCLTmYCUMwpcj1gTJxuK8TeGYcLDpsVg9PkOLSCB/w2TZRkyXINoMM9/+uDqEZs0HoVNVeBEF8PYxbMJoIZDOixMheYlNnbzg0mIuHuqIsinhs7eSDbrvLevqWgUm9jDHBjvhh5Kmix20aCXr5sewJFjd3Vi8/xIFnHYCuqwosh8Mk69uj4xCm6inJ3k9gF4jwGcexoa8r+JLu6v2UZOJoPxcmGH0w4mJBwRiD+Q1E4MybYYVG33ba5g4Qs3PatNug3qIdxbvlaGrOrh00HvnmQWlFitijkfg52EFjaSUzcAJreNXsgpnnEtT7sRyrIqiW6LDhCY5NaokKlkwcq4ZECOEU+ivW3Cvw/qZtRgcxO5dNO2woeiAvro+rzK4vgNfg02qbyvB4Zm1YcDw4ddutmtikFLixK5rDhuNg12atagtBEF8PEowGgGxG6EXmU2zoWQONpt9Tb9CEiYw9vVHRoR3Wa0ZnTI50XBrfAh1nXS70tSZjRX8/zkLCi3u4decJ3qUVcZOyZMQ+jML1W0/xIV29nywdKekF1BvblpycoTUyqshKRVKaxv6sUbFNOSZW6I9hJfhItzQtCSmZ+jwIOVLePMGd61dx/fZTvE3RFhGUIT0lLTdyLEt6jftRUbj38pOWIRcKxG/rDCeeEP7DzuZOatJNJpLiPyMjX5alSElILLCQvAypCZ+QWlTAkiAIg0CC0QCQzQj9yWTioOD00UxE74hAsLUQPkNOF/jWsKmhQEZ6xpd1K5cxpuTHsvtTECIWwm/QCfUXYYwc2X1MCRFD6DcIJ0wiwwRBFIQEowEgmxFfivzTTawdVAuOfA7Civ1xUMtnMYmvi+n4cRIO9HGHyHcgjpuI+Eo60AfuIl8MPK6xHBJBECYFCUYDQDYjSkvq/a2Y0DsM/nZ81YoAApcwzLxRfIcdYXhMx4/T8CrqIu69M50FDtNeReHivXfq8YgEQZgiJBgNANmMKD0KZCV/wOvox3hw/xFeJ9FgrG8F+TFBEIRuSDAaALIZQZg+5McEQRC6IcFoAMhmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQuiHBaADIZgRh+pAfEwRB6EarYKREiRIlSpQoUaJESTNpQhHGUkA2IwjTh/yYIAhCNyQYDQDZjCBMH/JjgiAI3ZBgNABkM4IwfciPCYIgdEOC0QCQzQjC9CE/JgiC0A0JRgNANiMI04f8mCAIQjckGA0A2YwgTB/yY4IgCN2QYDQAZDOCMH3IjwmCIHRDgtEAkM0IwvQhPyYIgtANCUYDQDYjCNOH/JggCEI3JBgNANmMMHakr6/hRqxc/VdJSUbUgZOIKfFhcqS8uopj12LVfxue5KgDOFnyDOkF+TFBEIRujEcwylMQez8Kt6MTkKXelEtGIt69/YCkQj8YB1TREEZN2nEMrOiFiOOZ6g0lQYFPu7vBzaEH9hd5mAJpry9j68whaBfiDku+DVqvea/+zbAoPu1GNzcH9Cg6Q6WG/Pg7RPYUW0f9in4T9+rR8CEIQhtGIRgViWcxobYDBDw+eJwlAn7dibgc55bHYn0HZ5gHjcTFVPZ31me8fR2DmBiWXr9G7NuPSM5UZO/7jaCKhihLMp7vxB8Ri3Ffpt5QHBk70dnGDl33ZKg3lIykda0gEjXHqgT1Bg0UaTG4sP5v9A3zh0TAwYwTwqFSE/QYux63kpk/Jh3Ab80jsOmZAcVd0jq0EonQXFuGDEDp/ViB+LML8PvQ0Vh1Q1lIEfojQ8yx+Zg4dgzGjCmYxmL8sgtIKE0xn3kQvRx44HsNwVkjDTgQhKlgBIJRhodTQyF27oA1r9LxZktnuAmDMP6WlP2mwJvN4XAVV8KIcymqvaVXRsJPYAYzs7zECe1QsX5fLLryiR1R9pBgJMqK1Jvz8JNnIIaeTlJvKQGZxzGgnAXabdRPzGQe7gNHUV388zIvNKP4dBXLBzdHgEQAjmcBj9D2GDxtPU4+/gSlx+Yhx8tVreHmFY61z/P/UmoyD6OPowh1/3nJzm54Su/HUtyaUAUCMyEazI/5Knn77lG8weImonzlumYSBIzB9dK8RiQYCcJgGIFgzMDurrYQhy3FW6b2FHGL0FgsQfd9GVC8245f3MUI+O00ktV7ZwtGASr1W4UDBw/iwJ7NWDLuZwRac+C5dMGO+LKXjCQYibJA/nozwj1E8B+a5w8lQnoZf/hZoNmKD3o1qKSXf4evqAamPMoJZcrxbFZtmEuC0GH0chx/mlS0OJLHYVO4KyyqjMR5vTKsA3Yfv/uKUGPKI9bMNDwkGL8hilgsasQEI78iIvY8QXR0tEZ6gVcf0koXDCDBSBAGwwgEoxSXf/eFsHwEjiXJEb+nB9xF1fH33TfY3d0DIr9BOPFZvSsjWzAKUXv2c42CWV1g81zw65GvM76pKEgwEl8deQxWtXIA37UrdunbKJLdxsRgC9Sf90ovMSO7H4nq4qqYfDdHnqVhRycbCD3aY8WjknVvy2NXoZVECL/fziK7j+ALkN1HZHUxqk6+S4LxeyNHMAoCMOqqgSLSSkgwEoTBMALByOqBp8vRylUAkZ0jbEWWqBSxDw/29oGXyAcRRxPztSy1C0YZHk0JgZBfHkPPlX2pQIKR+NqknoiAF0+I6n/f018syZ9gek0LhE7VLzInfzEXdcyrYOLtnKOYn/3TEHY8DkLvTlhfoq7mDFwY7gOeuA7mPPtCKSV/gbl1zFFl4m3TEYwZMTizciL6t2+IkMp+8A0IRu1Wv+LvXY+Qf4CAFHeW9EDLjpE4k5KJmGOzMaBFKAL9A/BDk274a9cTJtc1ycKV2Z3Qos1v2FZwNoc8FjuHt0Hz8Jm4pKHrM2LOYOXE/mjfMASV/XwREFwbrX79G7seFRiqIL2DJd1boEPkGSTHX8Q/Peoh0DcANbv+i3u3l6FXyxboNOUMCg+KkOHxmgFo3aIL5t3QU/TpLRjT8eLoPxjSsQGqB/jCp1IImvb4C9vuJeWPRGoIxjOpb3B2wSC0+bEy/AOqov7Pv2P1jQLDmIq4d1WgPesKZndqgTa/bSs0iUYeuxPD2zRH+MxL7K3Pw6jtThB6YBSCUYk88SkuHD6A41GxSE04hH7lRSjf/xA+FQimFBaMmXh7aT7aeQhgWWMSbpR9gJEEI/GVScT2TvbgRPUxT2M8YYmRv8K8+hYIGn+TyZKSI3+9APXNc8YTq5G9wqY+ofC048O88jCcLOigWpBGjUUAj4eAsVElv356Ij4X9GX5ayyob87u45Ze91FSDC4YFe+wrp0EPOWEoIB6aBXeBeFMBHpYcDDjOaH1qpfqHZVk4mAvB/CENdFnaH048oWw9w1F3ZDysOVz4Pjl0HnrGw1xk4pN7cVMYFXC2IKD+6S3MKGKAGbi1linVheKd+vQTsIDJ3RAQL1WCO8SjhahHrDgzMBzao1Vmu8VE1k97VmjoM4ADAm1Bsczh8TBGkKXX3EodiM6SFh+7Npjw8eChfM1jA4QgLNojIX6TknWRzAqPuPCpNqQsIaLhWdtdOwzAH3D66G8JcuXVVX8flJDBKoFI8+pPjo0coNAYIfyIXVQK8AJQnbvnGV1jL2gMV6iiHtXdV6lbkJ7sRkElcYWGlMpvTUBVQRmELdelyvqjN7uBKEHRiMYc1Ek4liED0TefbBfS9dbtmDkIDC3ga2tLazNheA4HmyqDMDOV18j7lA8JBiJr0rWCUSU40FQdTLuleYVV7zF0jAL+Iy4WHjJqiJQCsYGBQWjmuSrU9DAXsgadYeRWNhN85N5BP2czcAPmYYnJcm/9DnWtCsHa9/2mHXhY17lrxSMDUxJMCbg2MwxWHLmdb6IU9K5YajEyjBhyBT1FiVqwWjGRIyoAn5echOfVTeehehVbeHMM1Pt/zDXfnoKxoRjmDlmCc68zpcTnBtWCQImaEOmPFRvY6iFi3KyCU/SAJPZM5Cz/6V8+sxymYTd3ZyYCLZGy1Xv8kXnlGWzP7svq59W4E1x70RB9BCM6eeHw0/Ig139KbihofVSrk9CTSYahZVG41rOi64WjGZmbLt3eyy4npj9fJjovDyxFqw5DuZ1ZuNJzkMr8t4Z+gpGY7c7QeiB0QnGpJOD4SfyQM8975H07BS2rFqFLSefIEntCNmCkY/ybf7ErNmzMWvqeAwJD4WbiGMiMxzrosteNJJgJL4mig8r0FTIKqL2mwt0Y5aUT1jd0gLuESe0C8b0u1jWJQhuboFoM+18blS/KMHIcoX3+3qjvGUdzC6uq1n2GFNr8GHm0BsHi+0BkOLR3EbwrVwNHqxiNrOoguE5EaNiBWM67i7rgiA3NwS2mYbzJYh+amJwwaiLrHMY6s0Hz76neoMStWDkbNFw7sNscZJD5kn8z4MPzrIjNucOBNVPMOoi69xQePN5sO+5X72FkSNceB7otut9PnGiJO1of3jwOVg2XYa43B+zcOl3XyaCbNF2nX6Tq1TkCEaOD5GVrSoYkJvs7FF93FX1M0/F3u6O4PErYOi5gi9TIjZ3tAEnqIxxN9U2UQtGzqYBZt3XFG2M1EPo66aM/tXLWwmgmHvXVzDqwmjsThB6YFyCMfkMhgWI4dF1Ox4dHoxKFpZw8/eFk9gClUecVjlh4S5pJXK82doZ5fh8Vikez9eaLwtIMBJfE8XbpWjyRYJRKS7MVZVTIb2mSMSJwQGw96uHhoH24LPKqs/B7FlmitiFaKhTMDIUb7G6tTOa/1tMRSV/gdk/MgFj1x37ihGM8tiVaB3cFwcS5Ph8Yz7aeArADxiFa8osMFGxsKEuwahA4onBCLD3Q72GgbBnlbFHn4PQmC9XLF9VMMpS8e75Pdy8dhmXzq1Cz/JMAFp3Vv+oRC0Y+V4YUnB2hvwpZtQSwkwUhqXKpSRUlF4wylLf4fm9m7h2+RLOreqJ8kyEWHfeqf6VoRYuPM9BOKOthZF5Hr9VZPk3b4QFr9R3m3UBw32YCJaEY6ueQl1FjmDk2SAg7GeEh4drpC4YsSU6267S6xgTwO5NWAND1u/Ezp2aaRumtHFloltjzVG1YNQ66UURj5U/Ka/pgn45kyWLu/cvEIxGaXeC0AMjEowpuPBHZZiX64ytca+wqLEF7DtswAeFDNFz6kJsG45tyboEIyNxDVoyRxY1W8GOUW8rI0gwEl+VDFbpOXIQ1pyJ0s0bycCOTlaw/Hkb0tVbslHg4/6+CKw5DpeUNVz6XcyoawO7Tjuyf1VGNi38MUql1rQhw4PIH9G4uNnXWecx1ItVhP6ji1lLT4a7k2qgZuTD3EktGXem4ke7upj7gl1B8QErmlrAf9S1QoJR8XE/+gbWxLjsG8HdGXVhY9cJO/SYmv01BKP84yUs7N8IPhIhOLP8awuWXDBGY04dpWBsjMW5fY76CkY5Pl5aiP6NfCARZnd75iVdwmWwjpnFUtz4MxACToQ6c7LL4cxzv6ECE+lO3fYUG13TSkm7pDOP4FcXZRezZv7zJ07ojSGn1BkvSjAyv9jbzQ4cZ4lOO/IEZpH3rrdgNHK7E4QeGI1gTLs8BsHmrgjf9AYK1SBeMQL/vKGqGDL2dYdE3ARLWGGpXTAqkHiwLzz5fFT47bxe47QMAQlG4qvChNKaVlbgbNphY+k+d4H9PSQQlO+Dfe9zvEaB5Fvz0dyFD9vq/bGKvcNKX0vZ/Qtcw5Zm78Iq577OIgSPvKgeT6dJFj5ErUavQF8MOlV02FD2aAp+4JvBsef+oiOkyrGWTSwQNPJy7hI8ma93olel1litGs+ciSN9nSEKHomLGhlSJN/C/OYu4NtWR/9V1/Eh+0bwi2sYlr4rub0MLhgzozA5xBIc3xl1Bi/B/ot38PhZNKKf7sP/fAVfKBjTsFklGAMwpgSCMTNqMkIsOfCd62Dwkv24eOcxnkVH4+m+/8FXoK9wYc/0QSRqMAEkDJ2Gx7JMnBniDT6vHPoeKl0MvMSCMesUBnrw2b21wIq4ZCQnF0wpSMnQGJZUpGBMweYOFuB4Tuh7uGCEUce9p23OFoxaFhLXJhiN3u4EoQfGIRjTr2F8dQu4tF+HWGVpq3iP1S2tYVFzPM6/fIRN3b0h8h+JK8xBswWjAD7hkVi6bBmWLpqDSYNao5ItD0KvX7DldZ6MLCtIMBJfFwUSdnSGE88WrdcWHtsk+xiHd0Vqtkwc7ecKnhmruKxc4F7ODUEBHrBlDS/PRu1Qy5mJC84CnrXao0fLIFQaclZ9XCL2dncFZyaArYcfylfwRHCVIAQGVIC7xBx8jgf7JgvwsIj6XSmmbk8MYvt6IOJ4/oVhCvMZ2ztJ2PWEcPSrgdDg8pAIeXDquFG1qL+SxL3d4cqxCtvWA37lK8DTxx8etgIIPRuhXS1nJtqUM2droX2PlgiqpN/ae18iGG+MrcSuLUKjRbG5zyfr7BB48TlYtVyN95oPTXodYyt9qWDMwO5fbJjYKVf4G+GZFzGCCdI8wZiFs0O82DOwQsvV+d8f6fWxqgk4+goXZZ7m1hODE1bH5BvHMciLD77nQJws7XigkgpG5dJKdZktBEUMldBELRh55frhSP7wOrv5qxil6t6uhRlP1fVGcfeewRoiNuz3chEobPYRTARqCkYTsDtB6IERCMYMRE0KgaVTG6zWWBIgLWouWnqas8qDtaZc6uHPk9mzJbMFozqkz3EQiG3h6vcjOgxbivNvS1CAfAVIMBJfnaw7iAwxh7DaJNzWrEzkL/Fv3z9wtEgtloXTg8rDu25L/Ohpw3xGCLGtF+oMWI+HrKKRfbyONb+3RpCjCEK3tlgVneeHivenENk2CC5WAvBtxBAKzWHj6InAOu0xcPYhPC9GAyo+7UZ3Vx5swhZD47Q6kUbvwJB6HrDkm0FoVwH1+i7BNc2xWawxeSqyLYJcrCDg20AktIFXnQFYn30juL7md7QOcmTb3dB2lXrcWwnR148Vipx8JWULXc4a4dvy+sAzj/SDC4+D7c9b8o2lTL05HQ2VS618kWCUMSFeBUIm5v2Hnc9bo1EWi30RQaplW/IEYyaO9HNRTaj5eUu+nODm9Iaq5Wn0Fi6sNI5b1gxWHLt+/bqq8eM+wy+wKxVAEY8rq6dg0pz9eKbzXIySCkZ23/cja0DI8VGh3yFoW8M+77kw1ILRjBPBv9dWvMjNgwIfdvdQTSIRVv0Ld3KCksXdu3IR/CpKweqPYefzXn5Z7D5EBFmohh3kCUYTsDvS8Xj/AkyfuwN3lN+AVyHFq+NLMGP2RlzL7dGQ483ZfzFz5kpWz+rjVcT3hBEIxiKQpeJD3DskfRsdWGJIMBJlQfqNyQi1kqDJwqe5Y/yUX2Np3GNn0V29rAK4NKISwhbmRb+0I4fcoHVBGi6NrAShfTMseaqfEytYRorOa9HIS3Ejevlx1jkMr1oZLQaMxKiIMHiLOCYAm2KZRg+H4uMWhDsyYSiqiPaRm3D4+EGsn9IdVSXmsLT40ggje/Z3IxEiZtcVuqPhoOlYOH8yfq1bDiKbygiuKASXKxgV+LglHI5MoIgqtkfkpsM4fnA9pnSvCom5JSxKE+liKD6uRzs79bg8Zde4trGuGdsRbsn2EYZgSt6aQIUpsWBkuyYcwf/8REygW8Gv1TDMXrUVO3dtx/rFkRjcMRSelUfgYk6+1YKRX64CvM2FcArpjj/nLsScseEIsmXPRuCJnrs1Jm0Ve+8y3I0MgZhjQtO9IQZNX4j5k39F3XIi2FQORkUhpyEYTcDumYfR10kpqC3QIWcKvvQSRvjwVSK7yRL12p+y+/i7OnsHzYSqDwAQ/02MWzCaCGQzomyQ4eWmzvB2aIC5D7NrlIQ1beA36HQx43aluP7nj+i5p2Cf3NclVTku2fEHjDyV8EXir6zQy49lT7C4ubNq8Wfl5AWedQC6riqwHA4T4G+PjkOYqqckez+BXSDCZxzHhr6u4Eu6q/dTohw24MIEow9G5KodNfIYzG/ABJJ5M6zI17edgUfr+jABysSnqsdFAPvgrph3OQ47utiAs+yATTnrFMrf4ui4MHiaq4UG29cuMBwzjm9AX1c+JN33qndkZB7Fr87snioOzxNeWvmMfT2VQx3MIAyeiNwPAmmSthkdxGaq8bcbihp/q3iH5U2ZnZRdzTlL4hSBNPYoIn8OhoPmRBJ2T9ZeNdFl5jnkXkq5BqgLD5YdNuDJsUloWcESPPUzEzqF4tc19/J/Qack957xCOv6MNHHBJ/yupzAHsFd5+Fy3A50seHYtTblfevd2O0uvY5xlVnjQuCFwafVb6/sMWbWzh7b2W13tvRVCvoVzdk7xdmhzdp32duI/xwkGA0A2YwoOzLxdENP1Gg0HfdkcryaVx9WoZoLOmtB+gwLf6qHSbn9bmVA+iWMb9ERsy4X+PSaEaO/H2ch4cU93LrzBO/SirhLWTJiH0bh+q2n+JCu3k+WjpT0AsKIbUtOztDaja7ISkVSmg4hlfEBz25H4fbTD0xCqpGyc2nZX5Yci4dR13GL7ZuXlRQUzkoykjOKi9IqZ8kru4ezo06F3y4F4rcpx94K4T+sBN8Sl6YhKSVTr/dFnvIGT+5cx9Xrt/H0bYqWPGTfX64pZEl4fT8KUfde4pMOUVaye1ea/RluR7HrfsgbQChlx2p7TEZt98wkxH/OyG93aQoSEtPzv4uyVCR8StVqY+K/AQlGA0A2I8qazORkVTQr7UAvOPFt8MPg7XiSmr+qlSY+w4Wt09G3liscmiws5ZI8pUSRgfQMfar+bw/5sZ5kXcYf/gJwyu+EP9fycsnuY0qIGEK/QThReJo9UVrI7sQ3ggSjASCbEd+MjChEKr87q+zutPVG9fphCGtUGz8EuMNG2WXG8WD3w1DsjytLtWiakB/rR+qRfnDnc7BovAjaPmGcdKAP3EW+GHj8s8lEmU0BsjvxrSDBaADIZsS3RJFwDcsHN0MlR3Hu+CxOKEH5mh0wbPEZxBaaQklog/xYH9Kwr4cjeDwJ2q57q1WYpL2KwsV77wqM6yS+DLI78e0gwWgAyGaEcSBFSvxbvHkbjxQt46iIoiE/1g95RhI+Jes35pD4csjuxLeCBKMBIJsRhOlDfkwQBKEbEowGgGxGEKYP+TFBEIRuSDAaALIZQZg+5McEQRC6IcFoAMhmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShG62CkRIlSpQoUaJEiRIlzaQJRRhLAdmMIEwf8mOCIAjdkGA0AGQzgjB9yI8JgiB0Q4LRAJDNCML0IT8mCILQDQlGA0A2IwjTh/yYIAhCNyQYDQDZjCBMH/JjgiAI3ZBgNABkM4IwfciPCYIgdEOC0QCQzQjC9CE/JgiC0A0JRgNANiMI04f8mCAIQjckGA0A2YwgTB/yY4IgCN2QYDQAZLOiSEbUgZOIkav/LFPK+topuLX0L6x+LFP/TZgS5McEQRC6MR7BKE9B7P0o3I5OQJZ6Uy4ZiXj39gOSCv2QhyL9E96+fV/kPl8Lqmh0ocCn3d3g5tAD+zPVm8qMsr62AvHbO8OR74M/rkjV2whTgvyYIAhCN0YhGBWJZzGhtgMEPD54nCUCft2JuJyokDwW6zs4wzxoJC6mqrcVQoEPK5pBxPfAwFNlrxi/SUWT8Rw7/4jA4vv6R7MUSdcw4+cOiLz0mVnu65K0rhVEouZYlaDeoIOkA7+hecQmPDOguCvptQ1F8vrWEJp5YsjZb9BqIb6Y0vuxAp/v78GcEb3QoUVTNG35M/qOXoSjz9PUv2tH/vEqVo6NQP9hK3EzQ73xS1B8xv09czCiVwe0aNoULX/ui9GLjkJ7NhRIiT6F1ZFD0Su8NZq3aI/uv03H1qiP+PL4uD72UCDp8REs/2sgfmn7E5o2b4uugyKx7vJbmE6zq7T3IMfHqysxNqI/hq28CUO8AgTxNTECwSjDw6mhEDt3wJpX6XizpTPchEEYf0vpagq82RwOV3EljDiXkr27Vv5jgjH1Jub95InAoaeRpN5UcpitdvWAh4APl44b8oT5VyLzcB84iurin5fFXEj+Eqtau8ErfC2eG6imKPG1DUTmkb5w4twQcZIEoylSOj/OwrN1XVBRzMGME8LWzQPlJCJwnBk462oYdvQj87iCJOPB5mGo7yoEZ2YGM1EL1qj5wqZb1jOs61IRYuV1hbZw8ygHiYhj5+dgXW0Yjn7UPH86rs8MQzkhyzO7Psex/dhxqn8LPdBm6QOUvt2mjz1YPqY3gAvLB8cTQ1LOAy422TbhBC5otuDeF+SjrCjlPSQ/wOZh9eGqfgaiFqvwpa8AQXxtjEAwZmB3V1uIw5biLXMYRdwiNBZL0H1fBhTvtuMXdzECfjvNitii+A8JRvlrbA73gMh/KE4XbZQiSMfDg3txuygNbiCkl3+Hr6gGpjwqPm4hj9uEcFcLVBl5vpjnXTL0ubYhkN4Yg0p8J/x61PirOaIwpfFj2YOZqG3FgefQAJPOvMuOKMkScG1uC7jyzcD3isAxDT9TvD+NKa0rwoKJKefQhqgq4RlAMMrwYGZtWHE8ODSYhDPvsltcsoRrmNvCFXwzPrwijiE3G/IXmFvPHgHtx2PjxRf4LJUj/e1VrOjqB5FS2Nm2wMq40uVHL3vIY7AgzAv1Bi3FqRcp2UIy6y1OT6wHCY/lQ9IRm41dRel9Dwq8Pz0FrStaMHHujNCGVVX7kWAkTAEjEIxSXP7dF8LyrCBJkiN+Tw+4i6rj77tvsLs7E0Z+g3Dis3pXTRQpiLl6CFvW/IuV63dg6x81IfzuBaMcMatawYHviq674jVa6saL7H4kqourYvLdkog2OWLZ/UmEfvjt7JerWf2urSfy97hy6jaSNB6CPGYe6grt0ftQSQWjHO+vnMJtzZP8Z1Ag/voVPCkimqxIiEFMctnZRn8/zsDJgZ7gcxao/89z9jQ1YEJiSZgVOM4O4VvzCrDEta1g5fwjIv69jvj0Mxjixf9ywZhxEgM9+eAs6uOf5/mj6fKYJQhjAo6zC0deNhRIjk8oHPlK2o1uTkzAcjbovDNdvVEf9LdHVmZm4XKM3c//PJhd+BUx/ILxR+v1u4dErG1lBecfI/Dv9XiknxkCLyakSTASpoARCEZWsT9djlauAojsHGErskSliH14sLcPvEQ+iDiaWMgZM59vx6BQJwhZi1poaQ0LgbLrxYw553cuGFNPIMKL3XP1v3GvbIJmX4z8xVzUMa+CibdLmOGMCxjuw4O4zhw8+8KeZL2vXWLScHnCD7DmO6Lp4sd5Y75SN6GdWIIeB0omGNMuT8AP1nw4Nl2MbzWxWpGcgE/fok5OO49hAZ749ZD2hoHiw3EMr24D+3qRuFxGglpvP5ZewUg/AThRIyyMLZhHOV7Nqw+hGR8e/zuVO5FP8fk1Yj6rX+ysswYRjNIrI+HHykBRo4UonI1XmFdfqCob/1dc2Si7i8nV2L6cOdptLEWDrRT20Ir0Mv5g5zETBGLsDQONTylrdN6DAp9fxyDvFSDBSJgORiEYlcgTn+LC4QM4HhWL1IRD6FdehPL9D+FTQSdKv4yxweYQerbDP+ffZA8UzvqIK+NDv/sIY+L2TrDnRKg/72X+1ru+pD/DvmlD0H/oTBx59XULZPnrBahvnjMmtSRIETU2ADxeAMZGlfSYdCR+LizS9L92yUm7sxDt6vbApmiNc2cexa8uEnTbV8IIY9odLGxXFz02RRczOP7roPh0DuN/dIT3z6vwuKxH3EtvYUKwEPbhW7RXlGl3sbCFJ3y6bMBzLS6tSHmBi/t3YM+Zx/hkILGttx8nrkNrcw58j0E4rSWPmUf7wUXZ3djsX+QbQpiDgQRj4rrWMOeYEBt0WosQy8TRfi7gmYnQ7N+P6m06SD+EPs687KjYxVKUo19qDxVMUJ0eAn+BGfgVhuBMaQKd35yS3wMJRsKUMBrBmIsiEccifCDy7oP98YU9KHFrOCR8B3TalqARefwvjGHMwomIcuAJqmLyl4QX5XHY2sUDAtUgdw4WtabjQYlPl4W317Zj/l+jMHLiAux7mKTxDLSjFG0NtIo2Gd7FvtM6IzPzSD84m/ERMu2JektRSPF8TTuUs/ZF+1kX8lVEuq/9lZDewJhKEnTdaxrzHRUJZzGulgM8Wi/GvTKvmOWIWRIGa+ufsOKNjrcoMxVpWl4Q+ett6OEjzu5V4Hiw9GqAX2fswp34L3vO+vqx7NFUhAjNIKw2WWvEX3rjTwQy0SAMmQqtw2gNIhhleDQ1BEIzIapNvqfFn6S48WcgBOz3kKmP1Nu0k7i3J9yYoFOKnLOleIVLa4/0uPu4fvUqLp3ZjzV/d0M1CQ98SS2MPWUaw26UlPYeSDASpoTRCcakk4PhJ/JAzz3vkfTsFLasWoUtJ5+ox4pl4ewQb/BFYVj6TtO7/gOCUfEBK5oKYSZuj806lxcqnuQj/VHBuxUi95zFwZlt4CEOLlGXrTT2GP5u4wNL9WxK1YxKm5qIvFl0NE2naEs+gN4BP2GZlhnMssdTUYMVog69D6q36Eb6aC4a+VZGNQ8xy5MFqgw/mRuVLlYwpt/Fsi5BcHMLRJtp5wtHs/VF/gJz6tjjlz2GEIwKJF6ajS71f0SziFW48wXPvCgUyR8RX8KAaElIv7sMXYLc4BbYBtPOfyqyslR82o7OjhaoP++FHhHzFByNCETltr8hctY0jOzeCL52AtVsYJ6FB2r9PBRT/92NM7ee412SlrFlRaCvH0ujxqGyUgDVnIGnWm5A9iASNZiAElSZgFtfTTBKETWuskoQ1pzxVIsdZXgQWYMJSgGqTLil3qaF9OsYX42JcJ4EzZbp8zzyKJ09krGpvUW2+Fcljgmtyugy6yRiTKPdxSj9PZBgJEwJ4xKMyWcwLEAMj67b8ejwYFSysISbvy+cxBaoPEK5hEwG9nS1BWfeEVvyRUT+C4LxLZY2+VLBmIa9PSqiy3b1YjxMhK5u5Y5uRUbEFPh49m808fJE7d5TsP7oZdy8cRJrxzSFh1AA398vqffTjiJ2IRpqEW2y25NQlR2vjIoURP5iNn5kFY9d933qLTqQx2Jl62D0PZAA+ecbmN/GEwJ+AEZdy76WrmurUCTixOAA2PvVQ8NAe/B5HuhzUNvsKn34jDUtHdBl95fXdIrEQ/jVSwihUAiOEyLgj4vZwy++MYpPb/Feh8BUJJ7A4AB7+NVriEB7PngefZBr0swUpBQ6Lg3HBnhAHDpNewRO8QlvC15MkYzoR6/z2UKR9hqXt87Cb+G14GGevUyJKnEWqD3zsXqv4tHXj2W3JyJYGVGrEYmHWvIvuzcZ1ZW//xAJrculGijCeHtisCrCWCPyoZYIowz3JldX/f5D5H31tgIoPuPcyGCYczw4tliOZ9ryWgJKZw8FEu4cxvZtW7Flw7+YN3EAmvlaM/HFg03VITiYLzBgrJT+HkgwEqaEEQnGFFz4ozLMy3XG1rhXWNTYAvYdNuCDQoboOXUhtg3HtuRMHB/gBp4oDMvea3qXHNGz63znYxgzcLCXIzhhTcwsdjaIHDEbJ2DOlYID15OxvZMDgn47lV04ZT7CzPpBGKE5XintIIYN3aVe1kaG17sjEPrDL1h0rWC0KAV7urkg6M8o9d/aUXxYgaYW/rkiLo9knPxfBVi226T+O4+s80PhxSov/9HX1Vu0I7s7CTVqalROGXcw9Uc71J37QvWn7mszEby/LwJrjsMlpXZOv4sZdW1g12lH9s8lRP5yHcbPu4m8touyQeOOLrv0kHbyl1g3fh5u5msAyRG7tDUa/XUDybIEXJrSCI4uvbBfW0NB6/GlR/5yDX6PPKdjfU85ns5qh4HHtChGxUfs7xuImuMuqY5NvzsDdW3s0GmH8h2U4e60AZilRRWmH+kHN2EljLlW2G/lT2eh3cBjxa/FJ/2AqC1/o3utchBzfFh7VkODn1qjXfeZuKDH3A19/Vj+bCZ+VEbM/EfhqpY2iWpZJ9bwETVaVHgyihKDCEY5ns38URVB9B91VctY2OxVKARmIjRaFKvepkkmnqxoCzc+B3FABA7lK1f144vtkUPqLUytZ8MEFx/uvx5WbzQxSngPJBgJU8JoBGPa5TEINndF+KY3UEivYXSAGIF/3lAVgBn7ukMiboIlb/4fXi9sBDHPGd335FVpaXfmIsxBOVj7exaMCnxY0wpWnA3abdQcv6kF6VWMChBA4NEacy69z1eJJJ8dgUpiMcqFNkdYZSd4hW/Eq1z9mYLbsxrD3rEFljxJRezu/qhZfxSOv8/ZQY6MxFg8unwQqyN7IsSjJqbc0VIzaJJ5BH2dRQgeeRGf82VaitjlzeESvk39dw4yPJryA/hmjuipVSHloMDbpU1gETQSl3NEQeZr7OxVCa1Xx6v/1nZtBZJvzUdzFz5sq/fHKvbslHeQsvsXuIYtzd6lREhxdVQABFaNsfBFjn2ycGpgJfyih2CUXh2FAIEVGi/U7AZMwbZOHmi37n32c866gpGVKmN0IeGr6/jSkoULwytCIPBA63+u4VOBE2a83IW+fkK4tF6CB5qPRpGMW/PZs+Tbonr/Vbj+QWVR7P7FFWFL37Hf47CoiRfCN7zO9y6q1ueb2gh2PB4kDabihuYSOhkvsauvH4QurbEk38VyyELC07PYGNkXDbwswXEW8Gr0P8w78hSlXYlHbz9O2YwOlsolazpjp5YspmzpqFob0bHPIe2i10CTXlI2d4Alx8Gu804UzkYKtnS0AsdzRJ9Cyz3JELO9J3xEHISeP2PN0y8sO7/UHhqk7OgEO84M/IrD1VtMj5LcAwlGwpQwDsGYfg3jq1vApf06xCorKcV7rG5pDYua43H+5SNs6u4Nkf9IKD/RK3/9L1rY8SD0Cces3cewb8kQ1HGxQVDdGpCwiu77FYzMLAk70NmJB9vWa1F0IEAZNWijWihX+fUBe8/yCPatjKBKfvB0tAA/dxwiK9xFdnD38YeXswPc3Gwh5DvC29MKPIEYYiEfVk6e8C7vDY9yLpBYKrtHs48TutXHH/tjWJVTHInY292VtbQFsPXwQ/kKnqgSFAhfDzsIOWs0XRaj3k+N9DYmBvHBeUTgeNFfVsPn7Z0gYfkROvqhRmgwykuE4Dl1xEblCvAqCl87KMADtgIhPBu1Qy1ngarb0rNWe/RoGYRKQ86qjysZmU9WoI2rEJXHRamFEBORo+vi1xKvw8jIfIIVbVwhrDwOeZPCP2N9W+XCvhJ4V66KIH8PSEQ22iOXWo/XhQzRhw7hbhH7ZT2Yg4bMv8w4AWzc/RHoUwkV/Csj0M8dNgIzZl8PlBOxStDKFb4BXnCwsIe7uy0EQk80alcLzmwfzsITtdr3QMugSurPJCZjd1cH8HjmcAkIQe26dVCrRmV4S0TgWVRFrwENIeFxEEncUNHLF0GBfnC3Yc+G58TeOxFrCFrB1TcQgV7OcPGujMoBFVDOjh3Lnj3Hs4HvT8Ow/Hxc8ZHIYtDbj2X3EfmDEGYC3/xRehUZOD3IS7UmYdNlrBGs3poPAwlG5XqjPygje74jUDgbpzGIXYOzaIpl+SYXKfDu0CAEWXDgu7XE4vsGCFF/qT00SNvbTbWgtYCV+6ZKSe6BBCNhShiBYMxA1KQQWDq1weqYvJBGWtRctPQ0Z5U9Eycu9fDnyZxPSsnwauf/UMNePdDd0hvNxh/Fy1ND4C38vgUjK15wJzIE5sJqmHS7mPtUvMOGDk6wc1WKDRHcLAQQWkrg5l8TrX6dhA2X7uDc6gno07Im/FytIeDzmfCqik5zLyM+7jgiO/8IPzcJrC3MYW5pDTtHd/gG10HLbsMwbf1ZvEgteemmeH8KkW2D4GIlAN9GBL5ABJtywWg9dj9i8ilOBT7t7g5XJgLCFkcXHzGTRmPHkHrwsGSVrtAOFer1xZICXecFry229UKdAevxkGkv2cfrWPN7awQ5ipgAbotV0frG6Filu6ED3GrPVK8ZKcOd2WOxTs9xV4p3G9DBrbbGUAMFEk5NRJiPPSwtrGArcYTEygodt2hX0IWP144ibi0TZ9uKqZikeDSzNruuBE525kwIOkHM48Nc4oFqrYZj471EvNg7Fm2qusNWxGM+yIO5Rx0MWP+QebIMH6+vwe+tg+AoEsKt7SrkmFQWsw+jmvlBovyEGmuscHxLuFXviMlHYyGVv8HhMU3haydm7yoPAnMJPKq1wvCN95D4Yi/GtqkKd1sxExt89p4KYWVfDn6hzdFr9ELsv/+pBI2WkqG/H8twSzl+kOWrXJdt0HzsshfL0cKeB56kDdbmNmAKUJxgVMTjyuopmDRnP54V5e6yW5gYzBpz/HLosu2dxvsvw4vlLWCvjOC2Wav6klY2zM/OjEGoDcufUxhm39QWwdWgpPnQ0x6Kz9ewa9edQpFsSF9iVVtn8Ni7Ve7XQ+qNjBLng5H+GPsXTMfcHXfyIs7SVzi+ZAZmb7yW5wPs3Tv770zMXHkebzXzUcJr6X0PBSDBSJgSRiAYi0CWig9x75CkLSKSEY+XT6PxLsVQ1UXpKVObpd/A5FArSJosxNMib12OmPlhaPLPKwN0VZYRaZcwspIQ9s2W4Gmx0TINFHLIv6iwZceX0kjymPloXH0cbqrzm5GSkr/btSTIYzC/cXWMyzlJIbJwekgN9D+sI4ZW7PFKZHg8vSE6rC1mOAND8W4Zmlf/E1+0ZjIzqFaTSlORmJCIlEzjqx1L48eKdzvQ1YMPjrOCf+vhmLZoKRZE/g9NPLNnHDecc489vTzkTABPGTEcw4YNw7Ch4ahuy4OZoDya/Y/9PWw4RkzYgoc5ds/YjnBLjjWGQjBF2yySXFjDZUdXePCZGLfyR+vh07Bo6QJE/q8JPMWsUS1piDn3NHKRfhT93ZlQZcLOsVprdO7SBV3ypV/QddCqvIkpJc6HfvbIngTDh51fGHqOnIoFy5Zj8czR6FbThYlODvxy7bE2d7gHQ498ZB7uCyeeMtrdAZvVQ1akl0bAR9nrIlIOb8p+/2T3/1ZNxDEThmKq5hjbEl5L73tgXvFi7xSMGK583sMwNLw6bJVRyPLN8D/lOzF8BCZseah/GUIQZYBxC0YToaxtJnu5CZ29HdBg7sN8lVE+WAt5aycfdNtdTL+u0ZCKy2OC4fjDSJwymaa2AvFbw+HRYiU+fEGWFfFbEe7RAit1nST5FAZVaoR5eYNN81Hs8UrY+7C6lYeWcWxayDyM/vXHQsuQSeMjMxnJ6luSJydBj8B3IUrrx6n31iKilqvqO8w5Qz1EziHotfg6EgvkR7nGqHLx6tyZ3AUSZxuOrTnDs9M2o4OYbbNphw3F+kQq7q2NQC1XkSqCqzofJ4JzSC8svl7ga1kpu9HNQRkdzn9tzcSz7459Ob3UeuVDD3vI3+DkjJ6oW95GvS5sduJETqjaYQL2PCvQTa5HPqTXx6GykIPAazBOq98P2eOZqG3BBLRTN+xW21gRuwLNbZTjLttgrWZItKTX0vcekIkjqoXU8/bNnzjYhm/VMemMIL4tJBgNwLewWebTDehZoxGma13EW463e3qjokM7rP8SJfPVSMWthRH4fWfeGMj0S+PRouMsXP7ixRDLDvnbPehd0Qr15pag+1wX8rfY07sirOrNze2+zYfiAw4O8Id17Vla17Yr9vgclFHI+pao/vf9XJtrR4FPp4bih1Yr8sbJZkVjy59/YV+sDFkvtmHcX3vxuuiTlA0Z97GEiWCPVotx5+kO9A5wQa1x50q9nuaX+bEMybEPEXX1KqIexCLpi+3DGiPbOsOJJ4T/sLMo8WRvWTJiH0bh6tUoPIhNKuZZl4RS5kMve0iRFPcU96KuI+ruM7xP0/YA9c9HZlI8PmfkP5c0JQGJ6fkdRZaagE+pmhkszT2X5B4IwrQhwWgAvpnNNKIreWQiekcEgq2F8BlyWsusyW+PPHohGlubgefWA3vVtbsiIx0FynajJjN6ByKCrcF37YLtur9zVjSZ0dgREQxrviu6bM8Zo6tB+lNsH1gdNuz3X3Zo+b244/ORgeMR7hBI6mLiuXitAlf68Ra2/dUe/tau6LxVPUNbWXnu6gpXjgeP3huwtqsrOJ47eh/40jUrDQAT08dG1EPtwQcQl3gdM34KQadVj9mdlg6jKvtk9zElRAyh3yCcyL+8QNnyX8yHsdwzQRgZJBgNgLHYTP7pJtYOqgVHPgdhxf44qOXTisaBHG+PLsLKq5+LETlGiPwTbq4dhFqOfCacXNFhfUwpootyfLq5FoNqOYLPhJhrh/XQmO/FSMOr0wvQK9gWPI4P906bs1cPyKW447WTdW8W6trywAnsUalpNwwaNQ4Tx4/BiP91R5u6AXAUceD4jqgz/hTyvTqKz7iycROuJynYP69i48br6i8vGQfy98cxZexaPP7CFocxlX1JB/rAXeSLgce/rY/8F/NhLPdMEMYGCUYD8K1tlnp/Kyb0DoO/HRMxZhwELmGYeaPknUdECUi9j60TeiPM346JNDMmrBxQd6K+3Z+puL91AnqH+cOOiXrlpAOHuhNxLvckCny6sRID65XLHv/FfndqPB3XsldRZxR3fHEo8PHCbHQKkuQbb6UcN8WzcEdIu2FYcjZO97hYo0SKO1NCYSnwQJ/9RX+KsDiMqexLexWFi/feffFSQV/KfzEfxnLPBGFskGA0AN/eZgpkJX/A6+jHeHD/EV5/+QAqQguKrGR8eB2Nxw/u41HM59KND1NkIfnDa0Q/foD7j2LwWetJlM/zI968foPPBSeelOj4YlCk4c2d09i3bTO27DqE01fuITZF/zip0SB/g8tnH+JLp3dR2UcQBKEbEowGgGxGEKYP+TFBEIRuSDAaALIZQZg+5McEQRC6IcFoAMhmBGH6kB8TBEHohgSjASCbEYTpQ35MEAShGxKMBoBsRhCmD/kxQRCEbkgwGgCyGUGYPuTHBEEQutEqGClRokSJEiVKlChR0kyaUISxFJDNCML0IT8mCILQDQlGA0A2IwjTh/yYIIj/z955QEVxvW2c2UoTkI4LqDQVsYElCTYwFoxiiMau0aBGRRON/a9BjSQao1hj7733diT2gu0IYv8UQQ8qemgH4cCe3TnPN7ssfRd2YcVd8/7OmcS9M8zc+868c5/73jKEZkgw6gGyGUEYP+THBEEQmiHBqAfIZgRh/JAfEwRBaIYEox4gmxGE8UN+TBAEoRkSjHqAbEYQxg/5MUEQhGZIMOoBshlBGD/kxwRBEJohwagHyGYEYfyQHxMEQWiGBKMeIJsRhPFDfkwQBKEZEox6gGymTzIRe/AUEuWqnwbNp82rTCZT/Us9stTbOLBqIRZEb8WFF7mqVGOiZu1LfkwQBKEZwxGM8iwkxcXi1pN3yFclFZGbhpRXb5BRbodhQBWNvmDxfk8/ONsOwIE8VZLBUgN5ZV9h54w5iElnVQnFsC/XIyxkPh6XEFOy+6swa+0jSLl/595ZhK+dBOCb2cPdox4kbl8h8rrBG7UENf8skB8TBEFoxiAEI5sWg+ltbCHg8cFjzOHzwy4kF1aE8iRsCHWAqe8EXMgGct4l4cWLVHxQ7S4mD2kvX+DFmyzUdMDHKCqa3MfY9Us4lsRVHJVSB5txFfO+DUXkxXSuGv+4ZKzvBpGoM1a/UyUYMB87r+nHfkRdPh91eizFnUxVooKcBKzsWQeW3dYitfCGsCnYEuYAfq02mH39Mf7pXAvmAb8i5q3qfrNS5CuUZAkyDo5F5/DNeGSgOrKmn4Wq+zGL9Li9mD9+EEK7BCO467cY+ms0jj0u/5ZSwqYjbu98jB8Uii7Bwej67VD8Gn0Mmg7XmiqeV/72ClZNDsfwiFW4YVSBaBZZT05jTeQYDAoLQecuPdF/7O/YFvsW6t5ybMZ9HF3xP4z8/ht8HdwZ3/Qdhcj1l/CqjF8QBKEeAxCMMtybEwCxQyjWPs/By6294Sz0xbSbCi9m8XJLGJzEDTD+3yzudy72fl8LJqKe2FL2JZh/BiMlPAjbL8HLj61qymDwgjH7BhZ+7YqGY84gQ5WkPSze7B4AiYAPx14bi4X8RyLvyBDYib7EX89qWvbrzsfNKydCToyEp5CBiQkDU9fW6PXDTxjzYxi+dDcHw7NB97UvuaNUyOLwe2sLMNyxovp+aFBLiOaRcWorziLkz7A6xBluYevw2AArzZp+Fqrmx/l4tL4P6ou5+8QIYeUsgYuNCAxjAsayKSKOvS2+RwryH2F9n/oQK/YLreAscYGNiFHeN8umETj2toovryqdNxPxWyIQ6CTkjjPh3qtdOHFewy/PKpODa1FBcFH6B1dmhisrV3blv4USdF8Wj5LtoJxrv6OtI1dOhgexjQskjrUgVBzPCODYaRHuGlPwnSA+EQYgGHOxp68VxEHL8Ip7V7HJ0eggtkH//blgU3bg+zpi+Iw9w73aCo4lwagj8hfYEiaByHsMzpSMUulEDu4d2odbCs3+kZFe+hmeohaYnaB7JFQzMqS+Ti0QT7JUvE7Vz7k/Tl5LwL7HwaGu4Csq85IbYwbvQdvwvMxl8+/MQSuzggpUITKFHmFYdOYx0nJzkf78Mrb+uR7XclQHq5Anb0aYkxkaTzin8rGqYKT2LUNV/FgWH4U2Fgx4tm0x82yKcjgAZO9wdUEXOPFNwHcLx/Eiv5EhPqoNLDjRYtt2Js6mFKh02burWNDFibvPfLiFH4fubqb7ednXZzA7pD7MOJHrENAOTWx4xiUY5U+x4Kva8Ok5DZsuPEW6VI6cV1ewsq8XRArRbNUFq5ILyyJH4qIguH01CstOP0WWMjkfr87MwFeKcjM26LXlXWlhTxBEOQxAMEpx6WdPCOtyL9YMOVL3DkAdUTP8ducl9vTnhI7XKJxMVx1KglFHuBfl6m6w5Tuh7+5Uo3ghyuIi0UzcBLPu6EskyPFi1xB4W/tgyI7L2DnUB9Y+g7FTDzMp9J/X8shf/IMuXKXGs26FIVMm4dfpUVhz6jGyVftLk41/I7whUEVaSotMAVy6ryo15rEAOZK4Z8RG6IWxMVVpERi3fUuiux/n4tRITtBzAj7wr8ecJUogT8TSIAswjDXCtqleYLmnMNKVD8YsEH+VuRHyxKUI4oQnYx2GwsO1pgrnTVvXDRYOrRH+zzWk5pzFaDe+kUUYWWSmvisVRVSSsQf97BUisBZ67yrROsrPQ165onH3b4REKajrjztffuw8QRClMADByFUMD1egm5MAIms7WInM0SB8P+L3DYGbyAPhx9JKCB0SjDqRfRLhbpxNmv2GuzVT51Yb+dMF+MK0MWbc0leGWWRcmYuO3u0QefEFLs/pAO/2kbikZiKJRuRcZaOmy1b/eVVHDv4d6wkBvy5+OlMmPKgG+YsV+LoWA0HdEIwZ0w8hQUHoEvYDpq29gteaup1zz2OcBw/iL+bjkc46r/r2leflFUTmylAz9i1GZz+WXsYELwEYUXssTipbXjmeLwyEkBMjkhGnlWJEenkCvAQMRO0Xo/zhz7EwUAgTvgQjTusmXapyXjb9BRLTVTc7P8YIBaMGZHcwqylXXsYUPTZV1gCS4tIvXhCYCNBw8nW1zyBBEMUYhGBUIE97iPNHDuJEbBKy3x3GsLoi1B1+GO9Lvb9IMOpC2o7vUJsRIXDhs9LRD13JeYT9c0dj+JgoHH3+cV+r8heLEGhaOIZVf8ikhaJDhqJ/VkbeU+yZGIx6lnwwQns0H7SiVLf8x8prWWT3f0dLESc8wk9yHlAZWTg01AU8QSNMvaFtvqSInewDHs8Hk2OrVpaq2Dfv6R5MDK4HSz4DoX1zDFpxq1S3aU3ZtxCd/ThtPUJMGfAlo3BGjcbLOzYMjjwTiDr9A8UQwrT1ITBluPs46oyaaFYejg1zBM9EhE7/vFWlaUe1z/s5CcacwxjiwOMEcn2Mu1Cx8GbTz2C0t4A7th5Gn628MUYQ/3UMRjAWwabheLgHRO5DcKBoCmghKsFoYsJVbrxym2LgNgnGQvJxMlwhHJpgVnXCi/JkbOsjUXVzMjBr9TvitT5dPl5d3YG//zcRE2Yswv57GZV2iytEQlu1IkGGlKQU7r81BFfuXQPrQ8i3grt/B3wd/BWa1rOH55B9yspfeYjGvGoDC1bb51SeiOgOphB4jEcldaCS3JgxqMcXwn/2Pa3tlXd0GBxM+PCf+0CV8nGRJ+/CwPpC8K3c4d/hawR/1RT17D0xZF/xJJEas68KXf1YljAH/kLundN0ltoIvvT6FDQUcPv95yBBJkPCHH8ITYRoOuuumvsixfUpDSHg9vvPSVClaYMezvsZCca0fQPhzIl0fr3RiCnbuspJRty1K7hy8SwOrP0N/ZragMe3QavJp4tXGyAIQiMGJxgzTv0EL5EEA/e+Rsaj09i6ejW2nnqADKVDqwSjwBf95v6JP/8ssc0bgS9sGBKMhbBvsDJYCBNxT2xRP+BNKzKPDkc9926I3BuDQ1HdIRH7adVFKE06jt+6e8C8xHg6plZLRN6oeDqiRpGQeRCDfb7G8kpnzGbi8pY9uK+tUlKLHMnre8K1STi2388sIXJZ5KRnFI2bqo6gkSX8gZ5DdmhZUcnxYnF7iIUt8fsDLWLFef9iTF0+zLqu0boilN2fgxZ8E9gOPqRK0YQe7MuJ8fU9XdEkfDvuZ5bIIJuD9Izi56Pm7FuArn4sjZ2KRgpB2HIeHqq5LbL4SLTgBKWg8XTclEkRO7WRUri1nPdQTcRfhvjIFpzwE6Dx9JuqNG3Qw3k/F8GYcw3TmoqVKwh0Wv60nC0yN/eEWcn3EScWG/X5A6cSjXFRe4KoeQxLMGaeRYSPGJK+O5Bw5Cc0MDOHs7cn7MVmaDResSQMdUlrDfsKyzpWVzB+wL4B9dFnh2oxHk6ErulWB/32VfSCZfE25jd0dHNFm8GzseHYJdy4fgrrJgVDIhTA8+eLquPUwyYtRjs1IkF2ayaacH+viKJUhOz+HwgZvl/NOp0VwSLtyl6cfKGqYuQPEdVKDNfw4xomlxSgKa9K5LIyFRYLedFzKcXViV7giYOwtGgmZ8UoolkBQmv02a1NyXJw5AcnCD1/xkUttZb86Z9ozYkf6/77VSnq0Yd95Q+j0ErsivDjFT+YNWlfBbr6sezWDPgpIowtInFPjYCW3Z2FZor9zSMRJ5Ph1gw/ZSSwRaS6yK8Md2c1U+5XLIekPXo47+cgGNl0/DvBD6YMD3ZdVuCRmvvBvruNIzu2Y9vWjfhn4Qz82MkTlpyA5NVqgtGHUrgniCCIijAgwZiF8780gqlLb2xLfo7oDmaoHboRb1gZnsz/EmKrMGzPJMGoPbk4NMgOjLAloiqdySBH4qbpmH+57CDxTOz4zha+Y09DWY/kJSAq0BfjS/aLfjiEiDG7VUuyyPBiTzgCmn+P6Kvvy7yAs7C3nyN8p8SqfquHfbMSwWbemHi1rEjIxKkR9WDeY7PqtzpYpCzvBPfhx7QY61cIi3cXZ6ODS3ssKhKMBc8fI/JA75W3VdHtAuRZL3D9dCxecodqzqscTxaOxJySs3vfb8aYSTFFY8zkTxejg6Uzfjyu5QJw+acwQiLGF38+KRc5KQ+L1NVdYGb1PfZoaYj8c2PgxlW23r9eU6WoQz/2lT+PRgczBiKP3lh5u+QwBTmyXlzH6diXyjLWqH05dPVj+aMotFZEEL0n4ooaTatcFogT4aL20Uhi5XgU1VoZ6fOeeIWTtGUpWC1CYCJC++gkVZo26OG8Ri8Y8/Bg5Tdw5jMQ+4Tj8Gtty5CNm3O+Qi1ONPLr/IAjVVkkgCD+QxiMYPxwaRL8TJ0QtvklWOlV/OojRsMpBTPXcvf3h424I5a+/ECCUWtYvFnbDRZMLfTYVMkaY9IrmOgjgEASgvkXX5eqdDJjxqOBWAyXgM4IamQPt7BNeF6kWLJw648OqG3XBUsfZCNpz3C0DJyIE68LD5AjNy0JCZcOYU3kQPhLWmL2bTU1a0nyjmKogwh+Ey6g9ERbKZJWdIZj2HbVb3WweLu6MyeUAjB2z0NkV1joXLy+uR9/hQeijogPvrAhxp8vXonww835CHLgg2FMIfHvhn5D+iOktSdqC3mwDFnLNWS4gzTkVf7mOEb5mMLnp5NIVZoiD4+XfA0bSW9seaEqf/ZpjPLwwQR1SkMdinXnvjSD77SbakRBeaQXObFiHlreT9QiQ8Ls5uCb2GHggQpjqnqy7wfcnB8EB66CZ0wl8O/WD0P6h6C1Z20IeZYIWfum4HmtSfty6OzHWVsQaq5YsqY3dqkxW9bWXsq1Ee2GHFYOY8jaEgpzhoF1711qItdZ2NrLAgzPDkMOay9yFVT7vEYtGGVI3DEQHiIGQtdvsfahFoN8S5K1E99ZM1pNkiGI/zqGIRhzrmJaMzM49lyPJGVo4TXWdLWEWctpOPcsAZv7u0PkPQGXpRRh1AX23U70tufBKmQdKm50K1ro3ZULDTM8MWq71oWfZyP4NvCCq50Z+EXjfrjKUWSNOh7ecHOwhbOzFYR8O7i7WoAnEEMs5MPC3hXudd0hcXGEjbniywoFfyd0DsQvBxLVdJmVJQ37+juBMRHASuKFuvVc0di3ITwl1hAylghenqg6Tj3sq634zkmxDhv3924tEBTaH8NHjcX4n3/GuLGj8OOg3vgmKAA+jubgcZUs36YJBi07iHkdzcAIbeDq5QHf+g3QuKEnnCwFBV/AUGxcxW/m5IsOfSKw/HLhUk9l8yqBL/d3jgoRYWkFS67c5k6e8KlrC5GJCLVqiTiB5AAPH29IrAXge4zDBa1DddnY9q01ms68o4UNOTskLUb7WqHYqs3kT+ktzPDlxLEkHCcqEZjVta+kjgPsXX3Q0NMJloLCRcYVz50ZnHw7oE/EclxOK3xYa9K+VfBjWRwimwthIvAsHXVXkoszo9yUazQGLy/4Io9iXcnmioikp5rJS7lnMIoTbYxZMJbr+AKr9nmNVjCySDk8Cr5m3HPm3BVL4qow0/nDPvRTLN4t8MaEy9o3Lgjiv4gBCMZcxM70h7l9d6wpsdjvh9gF6OpqylUWnNhw/ApTTilmT5Jg1I183I70h6mwKWbeqqT1zKZgY6g9rJ0ksBGJ4GwmgNDcBs7eLdHth5nYePE2/l0zHUO6toSXkyUEfD6Edk3w3YJLSE0+gcjereHlbANLM1OYmlvC2q4OPP2+QNd+EZi7IQZPKw5HlYJ9fRqR3/jC0YKr9DkRwBdwYsDFDyGTDyBRC7WUHb8Fv3RtCFvVZ8NKbwx4otrwaN0To6J24pbqqyQ58esRHlgftU3FcOTKbmplhzpenCAK+xGTF2zCiTspyFFThNJ5FYIvsoJrqyFYHvsMMXN6oJGDOUxt6iNo4n48ub8VI790h5WpOZyafo/F13X5tko+zo9riG6rVNG3ysg9hMFeg3Cg0mAVi/d7+sOJVwtBS7Tp7q6efW24RoWAa5RYcc+HV4sghP04GQs2ncCdlBy15ao5+1bFj2W4qRg/yPDh0mc7UkoUQPZ0BbrU5oFn0x3rFJ+wUibexAw/rhHFd0Gf7SXHzMnwdEUX1ObxYNN9nfKLV0rYVFxeMxsz5x/Ao4rcV9fzlqUywahtPjhy7h/Aot8XYOft4sli0ucnsHTen9h0tbinQ/4yBv9ERWHVuVeqFBVaX4t7bs9OQkAtzsb2QfjzRgWRcTYdV3fvxu33ZZ9uKZ6t/gYOPBPwXH7AYeqSJogKMQDBWAGybLxJTkGGgTf8DFcwcuRcx6wAC9h0XIyHFYotORL/DkLHv55rJRqMAXn2K9yPPY/Tx47gyNHjOPXvZdx6kIyMSio9QyX38U3Ea7sgNpuBuKv3UGkd+OEiJjQQonanpXioo599bvatih+zKTvRV6IYumAB75BxmBu9DIsiR6Cja8Fs3Xbz7xaNq1SInJSdfSFRdMVbeCNk3FxEL1uEyBEd4SrmhLZNO8y/W8J4uTsQZs6JcqE/ZqubVVOEjuflkD/dh9njxyEiIgIRY8LQzEoRZauLTiO43xHjMH76VtwrfB60zkcejgy1B49rNJiFblE9e1JcHO8BPpcm6rhU1ZiXIe43xUQcEwgD5iiPKkLba+Ucw/A6nMjlxLpd0xD07tMHfUpt36PvqNWIU5xCdhezmnGNDWsvBA2cgDmLlmPFkij82q8lHLlGj0Jo91xXflY1QRClMWzBaCQYus1kzzajt7st2i64V6LyKgPXst/2nQf67dFq0BvxWZCNS5P8YNd8Ak4b5WQH/VJVP86+uw7hrZyU3zAujLKKHPwxaMk1FPWuF5GNu+vC0cpJVGK4gwgO/oOw5FrJr1pxfNiCULFiOaoe2Fjp/dHhvByKdTcVi4oXR4ZLb4xVGLapFkfQPh9SXJvaCEJGALefzijHbSrE4f2oNjBjeLDvtwcFp2SRtLIzainGXXZfp0wpQttrZe1BP9uCtXfV5V+x8Wr3x35lL7UcL0/Nw8Av66JWiWEQCvvYNwnF9L2PQMt2E0TlkGDUA8Zgs7yHGzGwRXv8rnYRbzle7R2M+rY9sEE5m8PQyMbNxeH4eZc2YyAJbcm5OA1dev2BS6U/p/SfpXp+LENm0j3EXrmC2PgkZFT2oMoykXQvFleuxCI+KUPNc80idXtv2POE8I6IqTxSXEil59UVXfORh4zUdOSWeqSkyHqXhpxSITwZst+9R3apDFaxzDogzUjGw7uxuBZ7B49efygnpAmC0AwJRj1gNDbLy0RmuTFteXiyMxx+lkJ4jD7DSTPDQ/5EsUSKCXjOA7CPxI3eYHNzylTs/20Myo9lcZjtL4bQaxRO6vBdbr1Tk/kwlDITBKEWEox6wFhtJn9/A+tGtYKd4lu+9YfjkMF+H0uOV8eisepKOkUEiI+GIflxxsEhqCPyxMgTn/aZr8l8GEqZCYJQDwlGPWBsNsuO24bpg4Pgbc1XzkIXOAYh6vrH6AAiCOPBkPz4w/NYXLibohoH+OmoyXwYSpkJglAPCUY9YHw2Y5Gf+QYvntxHfFwCXlQ64IogPn/o3UcQBKEZEox6gGxGEMYP+TFBEIRmSDDqAbIZQRg/5McEQRCaIcGoB8hmBGH8kB8TBEFohgSjHiCbEYTxQ35MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmlErGGmjjTbaaKONNtpoo63kVhKKMFYBshlBGD/kxwRBEJohwagHyGYEYfyQHxMEQWiGBKMeIJsRhPFDfkwQBKEZEox6gGxGEMYP+TFBEIRmSDDqAbIZQRg/5McEQRCaIcGoB8hmBGH8kB8TBEFohgSjHiCbEYTxQ35MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmiHBqAfIZvokE7EHTyFRrvpp0BhTXmWQyVT/NBpq1r7kxwRBEJoxHMEoz0JSXCxuPXmHfFVSEblpSHn1BhnldhgGVNHoCxbv9/SDs+0AHMhTJRksNZhX+XOsHfcb/s1S/dYA+2onZsyJQTqrSiiCxcv1YQiZ/1j12xio+WeB/JggCEIzBiEY2bQYTG9jCwGPDx5jDp8fdiG5MKogT8KGUAeY+k7AhWzF7yy8eZGIxEQ124sUpH8CUWkUFU3uY+z6JRxL4nQPM7EZVzHv21BEXkznqvGPS8b6bhCJOmP1O1WCAVMzeZUjeWMvOPDN8MUfD6D57qXj2I91wefXQY+ld5CpSlWQk7ASPetYotvaVFVKARkHx6Jz+GY8MlBxXtPPQtX9mEV63F7MHz8IoV2CEdz1Wwz9NRrHHn9Q7VeP/O0VrJocjuERq3AjV5VYHdh0xO2dj/GDQtElOBhdvx2KX6OPQX02WGQ9OY01kWMwKCwEnbv0RP+xv2Nb7NsKnjHDRRtbsllPcHpNJMYM4hpPnbugZ/+x+H1bLN4aY4EJ4hNgAIJRhntzAiB2CMXa5zl4ubU3nIW+mHZTyu1j8XJLGJzEDTBeFV5hXy5BB5EJTEzUbIKGmBKr+LuaxeAFY/YNLPzaFQ3HnEGGKkl7WLzZPQASAR+OvTYWC/mPRN6RIbATfYm/nhl+P2/N5JVFCucDjjwT8N1H4XSOKrksnFg4MdITQobzA8YUrq174YefxuDHsC/hbs6AZ9Mda1+WkfvyZ1gd4gy3sHV4XPNuUyk1/SxUzY/z8Wh9H9QXM5zdhbBylsDFRgSGuw+MZVNEHHvL3cGyZCJ+SwQCnYRgFO8tURdOFFezKZb/COv71IdYcV2hFZwlLrARMdz5GVg2jcCxtyXPn4NrUUFwEXJ55q7PMNxxiudG8W+hBN2XxcPgA/xFaGfLnGtRCHJRHaMsb0HZFfdM0n0Z4o2nwATxyTAAwZiLPX2tIA5ahlecn7PJ0eggtkH//blgU3bg+zpi+Iw9UxwxyUvElSOHcOjQIeyd0RGWnMP7j92q/H3oyL94kFHNF28VMGjBKH+BLWESiLzH4EzJsJNO5ODeoX24VUmXqD6QXvoZnqIWmJ2gz2a/DKmvUwsiJ7JUvE7Vz7k/Tl7VIH+I+V+ageHZo+8ezZKffX8QQ135xQ0o1caYeWPQtudqI0fy5M0IczJD4wnnSkUldcPI7auiKn4si49CGwtOkNu2xcyzKVDqbtk7XF3QBU58TuS7heN4Cb9hX5/B7JD6MOPeWw4B7dDEhqcHwShDfFQbWDA82LadibMpBepf9u4qFnRxAt+ED7fw4yjKhvwpFnxVGz49p2HThadIl8qR8+oKVvb1gkghOK26YFVyzb9HdUV7W8rxdMFXqO3TE9M2XcDTdCnkOa9wZWVfeHGi2oSxQpdVyWqEPUEQJTEAwSjFpZ89IazLvVgz5EjdOwB1RM3w252X2NOfEzpeo3AyXXVoGXL3D4ANI8LXq1I/qbMbrmCUI3F1N9jyndB396e1kbbI4iLRTNwEs+7oSyTI8WLXEHhb+2DIjsvYOdQH1j6DsVMPMyn0n1fNpO8fBBceD7V774QGd+DgyvpPF9hwx1m3GoIpk37F9Kg1OPVYMZZDE3Ikcc+IjdALY2Oq0iL4POyrQHc/zsWpka7gM2YI/OsxZ4kSyBOxNMgCDGONsG3FdyxtXTdYOLRG+D/XkJpzFqPdOIFfXcGYewojuYYCYxaIvx6Xtrs8cSmCOEHLWIehOBssMlPflY8iZuxBP3tOdDG10HuXplC24aCLLdnMVLwrX2Ds6WcPngmDWr13cc1igiAqwgAEI1cxPFyBbk4CiKztYCUyR4Pw/YjfNwRuIg+EH0vTKHRIMFZC9kmEu/EgbPYb7tZMnVtt5E8X4AvTxphxS18ZZpFxZS46erdD5MUXuDynA7zbR+JS+ZkhmpHnIU9Nl63+81oBeRfxs6cAPMchOFzR0LicfzGWO45f9yec0bYGzD2PcR48iL+Yj0c667zq21eel1cQmStDjdqXQ2c/ll7GBC8BGFF7LE4qW145ni8MhNCED8mI00UT+dj0F0hMVxk5P0YvglF6eQK8BAxE7RejfDaeY2GgECZ8CUacrmSAt+wOZjXljmVM0WNTDXQnVJPq21KGO7OacveIgWmPTcURWIIg1GIQglGBPO0hzh85iBOxSch+dxjD6opQd/hhvK/A90kwVkzaju9Qm7NP4MJnpaMfupLzCPvnjsbwMVE4+vzjDnaTv1iEQNPCMaz6QyYtFB0yFP2zMvKeYs/EYNSz5IMR2qP5oBWluuU/Vl7VI0Xs1EYQCLzwy6WKrifD/d9bQsQJhPCT2s6k4M492Qc8ng8mV3EMcFXsm/d0DyYG14Mln4HQvjkGrbhVqtKuWftWwY/T1iPElAFfMgpn1GixvGPDlGNPRZ3+QakhhIXoSTCmrQ+BKcMJ01Fnyq8wgTwcG+YInokInf55q0rTQM5hDHHgceKyPsZd+ASzB6tDlWyZg8NDHDjb8FF/3AU1tiMIoiQGIxiLYNNwPNwDIvchOJBaseOTYKyIfJwMdwFP0ASzqhNelCdjWx8JBMpB8QzMWv2OeK1Pl49XV3fg7/9NxIQZi7D/Xkal90khEtqqFQkypCSlcP+tIbhy7xpYH0K+Fdz9O+Dr4K/QtJ49PIfsK6r8NedVG1iwOj60ikiSt0CEDtEVj7eSJ0ajg6kAHuO1rwTzjg6DA1dx+s99oEr5uMiTd2FgfSH4Vu7w7/A1gr9qinr2nhiyr3iSSE3bV1c/liXMgb/QBMKms9RG8KXXp6ChgNvvPwdqh2HqRTDKkDDHH0ITIZrOuqvGP6S4PqUhBNx+/zkJqjT1pO0bCGfF5Kp6oxGjj1nbNUlVbJm2DwOdFQK5HkYbXYEJouYxOMGYceoneIkkGLj3NTIencbW1aux9dQDqJvLQoKxAtg3WBkshIm4J7ZUNIStEjKPDkc9926I3BuDQ1HdIRH7adVFKE06jt+6e8BcNftSOfmiVktE3qh4OqJGkZB5EIN9vsbySmfMZuLylj24Xy1lKUfy+p5wbRKO7fczSzxbLHLSM4rGflVH0MgS/kDPITtQSZuoNHknEO4ihNtPZysWgvIXWNxeDGHL3/FAy9Cy7P4ctOCbwHbwIVWKJvRgX06Mr+/piibh23E/s4QB2BykZxQ/HzVtX139WBo7FY0UgrDlPDxUY2dZfCRacIJS0Hg6bn40waiKPHOCsOW8h2p6EmSIj2zBCUoBGk+/qUpTQ841TGsqBsOzQaflT6vXI/Ep0NmWObg2rSnEDA82nZbjqdEVmCBqHsMSjJlnEeEjhqTvDiQc+QkNzMzh7O0Je7EZGo0vvyQMCcYKYF9hWcfqCsYP2DegPvrsUFmeE6FrutVBv30VtcZZvI35DR3dXNFm8GxsOHYJN66fwrpJwZAIBfD8+aLqOPWwSYvRTo1IkN2aiSbc3yuiKBUhu/8HQobv53KuCyzSruzFyReqWkP+EFGtxHANP46KTKcpr0rksjKVLgt50UMqxdWJXuCJg7BUl9mo7Dus7WYGs56bKxlvpYg6BUBo3Qe7tTSE/OmfaM2JH+v++1Up6tGHfeUPo9BK7Irw4xU/mDVtX139WHZrBvwUEcYWkbinRhDK7s5CM8X+5pFQu/ypniKMt2b4KSOMLSLvqYkwynB3VjPl/uaRcaq0MrDp+HeCH0w58WTXZQUeVacx8KnQyZYs0v+dAD9TBjy7LlhhlAUmiJrHgARjFs7/0gimLr2xLfk5ojuYoXboRrxhZXgy/0uIrcKwvcy6HyQYKyIXhwbZgRG2RFSlMxnkSNw0HfMvl5UhmdjxnS18x56G8h2cl4CoQF+MLzm+6cMhRIzZrVqSRYYXe8IR0Px7RF99X+aeZGFvP0f4TolV/VYP+2Ylgs28MfFqWZGQiVMj6sG8x2bVb3WwSFneCe7Dj3Gl1xYW7y7ORgeX9lhUJBgLnj9G5IHeK2+Xim7Ls17g+ulYvOQO1ZxXOZ4sHIk5JWf3vt+MMZNiiiKD8qeL0cHSGT8erzjiWhopLv7sCbOg5Uip5IHPPzUCEvEX+PNJZfe+gPxzY+DGCQbvX6+pUtShH/vKn0ejgxkDkUdvrLxdcpiCHFkvruN07EulGKxp++rqx/JHUWitiCB6T8QVNZpWuSwQJ8JF7aPLT0ZRoBfBKMejqNbKCKL3xCvcE1KWglUoBCYitI9OUqWVJA8PVn4DZz4DsU84Dr+uaj4+MTrYMu/BSnzjzAcj9kH44dclnj+CICrCYATjh0uTuBafE8I2vwQrvYpffcRoOOW68gWYu78/bMQdsbTMwsMkGCuCxZu13WDB1EKPTe8qto/0Cib6CCCQhGD+xdelKp3MmPFoIBbDJaAzghrZwy1sE54XaZAs3PqjA2pzrfSlD7KRtGc4WgZOxInXhQfIkZuWhIRLh7AmciD8JS0x+7aamrUkeUcx1EEEvwkXynziToqkFZ3hGLZd9VsdLN6u7gwzqwCM3fMQ2RUWOhevb+7HX+GBqCPigy9siPHni1skH27OR5ADV6kwppD4d0O/If0R0toTtYU8WIas5Roy3EEa8ip/cxyjfEzh89NJpCpNkYfHS76GjaQ3trxQlT/7NEZ5+GCCOqWhETkeRrWChWrN0opQzDD+0kzb7lwZEmY3B9/EDgMPVBhT1ZN9P+Dm/CA4cCKFMZXAv1s/DOkfgtaetSHkWSJk7ZuC57WG7auzH2dtQai5Ysma3tilxmxZW3sp10a0G3K4aBhDKfQiGBXZCIU5w8C69y41EfEsbO1lAYZnhyGHy+ZChsQdA+EhYiB0/RZrH2o74tUA0dKWssQdGOgh4hrSrvh27UOtx/gSBGEogjHnKqY1M4Njz/VIUoYWXmNNV0uYtZyGc88SsLm/O0TeE3BZ8e7/EId9y6IRHR2NheEtuRelEA37zVP+jl66BZfVTkf8uBimYOTM+G4netvzYBWyDhUHDhRRhu7KhYYZnhi1XevCz7MRfBt4wdXODPyicYhc5SiyRh0Pb7g52MLZ2QpCvh3cXS3AE4ghFvJhYe8K97rukLg4wsZcqPqCBFchOQfilwOJarrMypKGff2dwJgIYCXxQt16rmjs2xCeEmsIGUsEL09UHace9tVWfOekWEuO+3u3FggK7Y/ho8Zi/M8/Y9zYUfhxUG98ExQAH0dz8LhKlm/TBIOWHcS8jmZcJWIDVy8P+NZvgMYNPeFkKSj4MoRi4yp+MydfdOgTgeWXC5d6KptXCXy5v3NUiAhLK1hy5TZ38oRPXVuITESoVYurqEwd4OHjDYm1AHyPcbig01h7OZ7M/wK1um+o/Is92dvwrXVTzNRmDUPpLczw5cSxJBwnKulrrq59JXUcYO/qg4aeTrAUqL62oRjfyjODk28H9IlYjstphQ9rzdpXZz+WxSGyuRAmAs/SUXcluTgzyk25RmPwcq4RrEothZ4Eo2K9yuaKSKfneJTPxhmM4q7BmAVjeakGN4uUw6Pga8bdI+euWBJn5KsQamFLNuUwRvlyzyHfGV2XxNG6iwShIwYgGHMRO9Mf5vbdsabEYr8fYhegq6spV1lwYsPxK0w5VTB7kj4NqAv5uB3pD1MhJxxuVdKWZlOwMdQe1k4S2IhEcDYTQGhuA2fvluj2w0xsvHgb/66ZjiFdW8LLyRICPh9Cuyb4bsElpCafQGTv1vBytoGlmSlMzS1hbVcHnn5foGu/CMzdEIOnFYejSsG+Po3Ib3zhaMFV+pwI4As4MeDih5DJB5Cohf7Jjt+CX7o2hK3q02elNwY8UW14tO6JUVE7cUv1VZKc+PUID6yP2qZiOHJlN7WyQx0vThCF/YjJCzbhxJ0U5KgpQum8CsEXWcG11RAsj32GmDk90MjBHKY29RE0cT+e3N+KkV+6w8rUHE5Nv8fi67p+W4Wr5Jd1QgNtZj/nn8e4ht2wShkKrQgW7/f0hxOvFoKWPOEkaeVUx742XKNCwDVKrLjnw6tFEMJ+nIwFm07gTkqOWlFVk/bV3Y9luKkYP8jw4dJne6lhArKnK9ClNk/5ScZ1msLBlYkcNhWX18zGzPkH8KiiGy67iRl+XOOM74I+21NK2FGGpyu6oDaPB5vu60pEpbl7fnYSAmpx+bMPwp83Kh5LqnU+OHLuH8Ci3xdg5+3iyWLS5yewdN6f2HS1uKdD/jIG/0RFYdW5V6oUFTpcqxSV2JJ9fxaTAmqBx7NH0J83KhybTBCEegxAMFaALBtvklOQUfMaUCcMVzBy5FzHrAAL2HRcjIcVii05Ev8OQse/nmslGowBefYr3I89j9PHjuDI0eM49e9l3HqQjAxdKiIDQ/7qJm4kaaGauYbY45vxZbr11fDhIiY0EKJ2p6V4qKOffW72rYofsyk70VeiGLpgAe+QcZgbvQyLIkego2vBjON28++WEvfyp/swe/w4REREIGJMGJpZ8biGbl10GsH9jhiH8dO34l7hfcjdgTBzTpQL/TFb3ayaIriGxM6+kCi6+C28ETJuLqKXLULkiI5wFSu+I94O8++WyEXOMQyvw4krTujaNQ1B7z590KfU9j36jlpdPFFH63zk4cjQgi+nmIVuUU3MkuLieA/wuTRRx6UoCHLKEPebYiKOCYQBc5RHFaH1tXSxZQ6ODa+jzAPfrilCepctbx9833cUVqudmUQQRCGGLRiNBEO3mezZZvR2t0XbBfc0R6a4lv227zzQb49u818JYyYblyb5wa75BJyuRpfo50JV/Tj77jqEt3JSfoe5MMoqcvDHoCXXUNS7rkKx3qViMe/ykdmCjbEKw7bC8QYftiBUrFiOqgc2Vnp/snF3XThaOYlKDKMQwcF/EJZcK/O1rKw96GfLKz5Ozcar3R/7C/tstc6HFNemNoKQEcDtpzOqcZsy3I9qAzOGB/t+e1RDKVgkreyMWopxl93XKVOK0KHM2tsyC3v62YJXYomvchuvNvoXFZggCHWQYNQDxmCzvIcbMbBFe/yudhFvOV7tHYz6tj2wodIuzE9BNm4uDsfPu7QZA0loS87FaejS6w9cquhzSv8hqufHMmQm3UPslSuIjU9CRrUfVBap23vDnieEd0SMKlqnBbJMJN2LxZUrsYhPytCDv+iajzxkpKYjt9QjJUXWuzTklOq6kCH73Xtkl8pgFctMEESNQIJRDxiNzfIykVluumYenuwMh5+lEB6jzxjk2B75E8USKSbgOQ/APhI3eoPNzSlTsf+3MSg/lsVhtr8YQq9ROKnDd7n1Tk3mw1DKTBCEWkgw6gFjtZn8/Q2sG9UKdopv+dYfjkM6fXakJpHj1bForLqSXrprjSD0iCH5ccbBIagj8sTIE5/2ma/JfBhKmQmCUA8JRj1gbDbLjtuG6YOD4G3NV85CFzgGIeo6dQAR/20MyY8/PI/FhbspqnGAn46azIehlJkgCPWQYNQDxmczFvmZb/DiyX3ExyXgRfUHXBGE0UPvPoIgCM2QYNQDZDOCMH7IjwmCIDRDglEPkM0IwvghPyYIgtAMCUY9QDYjCOOH/JggCEIzJBj1ANmMIIwf8mOCIAjNkGDUA2QzgjB+yI8JgiA0Q4JRD5DNCML4IT8mCILQjFrBSBtttNFGG2200UYbbSW3klCEsQqQzQjC+CE/JgiC0AwJRj1ANiMI44f8mCAIQjMkGPUA2YwgjB/yY4IgCM2QYNQDZDOCMH7IjwmCIDRDglEPkM0IwvghPyYIgtAMCUY9QDYjCOOH/JggCEIzJBj1ANmMIIwf8mOCIAjNkGDUA2QzgjB+yI8JgiA0Q4JRD5DNCML4IT8mCILQDAlGPUA2Iwjjh/yYIAhCMyQY9QDZjKiQzFgcPJUIueonUQ0+oi3JjwmCIDRjOIJRnoWkuFjcevIO+aqkInLTkPLqDTLK7TAMqKIhNMK+x55+zrAdcAB5qiSiinxkW5IfEwRBaMYgBCObFoPpbWwh4PHBY8zh88MuJBeGEORJ2BDqAFPfCbiQrUozMKii+UTkPsauX8KxJE6mStASNgNX532L0MiLSGdVaR+NDKzvJoKo82q8U6X8N8jAwbGdEb75kR7F3ce1ZdX9mEV63F7MHz8IoV2CEdz1Wwz9NRrHHn9Q7VeP/O0VrJocjuERq3AjV5VYHdh0xO2dj/GDQtElOBhdvx2KX6OPQX02WGQ9OY01kWMwKCwEnbv0RP+xv2Nb7Fvo6E0GgTa2ZLOe4PSaSIwZFIaQzl3Qs/9Y/L4tFm+NscAE8QkwAMEow705ARA7hGLt8xy83NobzkJfTLsp5faxeLklDE7iBhj/b1bB4QYICcZPQPYNLPzaFQ3HnOFkhG6wb3ZjgEQAvmMvbCxqmXws8nBkiB1EX/6FZ/+xPmn5s9UIcXZD2LrHUHhz9fm4tqyaH+fj0fo+qC9mYMIIYeUsgYuNCAxjAsayKSKOveXeYmXJRPyWCAQ6CcGYmMBE1AWr31Wz5ZL/COv71IdYcV2hFZwlLrARMdz5GVg2jcCxtyXPn4NrUUFwEXJ55q7PMNxx3N8p/y2UoPuyeCOKhmtny5xrUQhyUR2jLG9B2RX3TNJ9GeIp/E8QlWIAgjEXe/paQRy0DK84P2eTo9FBbIP++3PBpuzA93XE8Bl7hnstGC4kGGsY+QtsCZNA5D0GZ6r4YOTcO4R9t2qiESLFpZ89IWoxGwn/uUiGHMmbuQafWWNMOKcPD/64tqyKH8vio9DGggHPti1mnk0pEMayd7i6oAuc+Cbgu4XjeInHjH19BrND6sOMEyoOAe3QxIanB8EoQ3xUG1gwPNi2nYmzKQXyXPbuKhZ0cQLfhA+38OMoyob8KRZ8VRs+Padh04WnSJfKkfPqClb29YJIITitumBVcjUFbA2gvS3leLrgK9T26Ylpmy7gaboU8pxXuLKyL7w4UW3CWKHLqmQ1wp4giJIYgGAsqASEdbkXa4YcqXsHoI6oGX678xJ7+nOiwGsUTqarDmXTkXD6IC48y8WHx8fxz19LsDc+k3P0D3h4eBnmL9mH+Myad3sSjDWJHImru8GW74S+u1ON4CUvQ1xkM4ibzMIdAxCMee9SkaWl0djUa7j8oJqxQXkSVnezgdBrLGKqrc8/ri119+NcnBrpCj5jhsC/HpeeiCNPxNIgCzCMNcK2Fb7AgLR13WDh0Brh/1xDas5ZjHbjV18w5p7CSFc+GLNA/PW4dOhVnrgUQZygZazDUJwNFpmp78pHETP2oJ89J7qYWui9K0eVaLjoYks2MxXvyhcYe/rZg2fCoFbvXTD8EhPEp8UABCNXDTxcgW5OAois7WAlMkeD8P2I3zcEbiIPhB9LKxYF0lhMbiBC6wlRCHMxhZmYexG6j8aWFd1hLzZVdsc4DTpY49FIEow1SPZJhLvxIGz2G+4aRcROEd34AqaNZ+DWJ89vBnb1kaDxsB14WukEsg84F+ED1x8OF0em1CJHXl7FojL3/Dh48MT4Yv6j0qJKZz6uLXX2Y+llTPASgBG1x+KksiJFjucLAyE04UMy4nTRRD42/QUS01VWyI/Ri2CUXp4ALwEDUfvFKJ+N51gYKIQJX4IRpyu56bI7mNWUO5YxRY9NNRF9rx7Vt6UMd2Y15e4RA9Memyp5zgmCMAjBqECe9hDnjxzEidgkZL87jGF1Rag7/DDel/R9pWDkw7yWPVr+Fot3+wegNmMDW8mXmHv7HXb3sQavbgTO1/BsahKMNUfaju+4ey5C4MJn1RAfOXi0fy5GDx+DqKPP9TS+ThNyvFgUCFPfaVAOy/2IsJkPcGzVPPwWuRh776aXj77K7mJugBlMTISo22cTnlSYHyluTveDsHYYtqqtgPPwdM9EBNezBJ8Rwr75IKzQ1MWv8FsfHng+kxGrrQ1y0pBeLiL0cW2psx+nrUeIKQO+ZBTOqHnn5B0bBkeeCUSd/kGpIYSF6Ekwpq0PgSnDCdNRZ8qvMMHdp2PDHMEzEaHTP29VaRrIOYwhDjxOXNbHuAs1/BKtLlWyZQ4OD3HgbMNH/XEX1NiOIIiSGIxgLIJNw/FwD4jch+BAahnHVwpGBiZu4TiZzf28OB71GQb1x/7LuX4+zv4kAc/+Bxyr4QHMJBhrinycDHcBT9AEs6ocXpQjeVsfSASqAf9mrfB7vA7nyn+Fqzv+xv8mTsCMRftxL6OyykkhctqqFzmyFCSl6CdUlnVjMXq6iwoG9XMbz7oDFj0sL6nZ95cxv5sEQhNTNJl4vsJovLI709ISX698WUZ8cjbcNRD1hXxYufujw9fB+KppPdh7DsE+tcooD0eHOXBCxB9zH2hRXuljrO3hAkvPnvjjfMlJIxXYUg/o6seyhDnwF5pA2HSW2mi39PoUNBRw+/3nqB9zqRfBKEPCHH/ufgrRdNZd7ldZpLg+pSEE3H7/OQmqNPWk7RsIZ07g8uuNRow+Zm3XJFWxZdo+DHRWCOR6GG10BSaImsfgBGPGqZ/gJZJg4N7XyHh0GltXr8bWUw+grJeVgpEHs7CdULi39NLPqM+zRv/9CoWYjxiVYDxKgvHzhH2DlcFCmIh7YktVl1jKPIrh9dzRLXIvYg5FobtEDL8Zt9RUtGWRIun4b+juYV4kykwUY59aRuJGhc+bZpGTeXAwfL5eXu0Zv7LHa9GzjgXcg0bhj/X7cOTIPmz482fMPqDhuZQ+w86hPhALfTH5WgWZZ99jR287mAUuxNMSeZQnr0dP1yYI334fJYcMsznpyFB7Ohnuz2kBvoktBh+qzDmlSFjQHp6NmkIi5mxs1hjjTr1XicbKBWPOneXo4+sM54bdMfdc4d9ph65+LI2dikYKQdhyHtRoc8jiI9GCE5SCxtNx86MJRilipzZSCsKW8x5yFiqLDPGRLThBKUDj6TdVaWrIuYZpTcVgeDbotPypmvMYODrbMgfXpjWFmOHBptPyUs83QRDqMSzBmHkWET5iSPruQMKRn9DAzBzO3p6wF5uh0fgzyCDB+N+GfYVlHasnGD/sG4D6fXaoluJh8WZNN9Tpt0/5PGmEfYuY3zrCzbUNBs/egGOXbuD6qXWYFCyBUOCJny9WFO5ikbS4nRqRI8OtmU24v29ajWgphzQBC9rVQ8fZl0oP3ygBm5mGjLIVovQx/unmCKfBh1CwTF8esrLKO86H4z9ywi0Ac4tCZHI8jGoFsWs4jmt9D+R4+mdrTtQU+qpm5EmrEOI3FAffyZF+/W90dxWA7zMRV5W202TLAti0k/jJpza8vmqHhrX54EmG4FDxfJNK0dWPZbdmwE8RYWwRiXtqbqHs7iw0U+xvHgm1S4XqKcJ4a4afMsLYIvKemoaPDHdnNVPubx4Zp0orA5uOfyf4wZQTT3ZdVuBRNR7HT4ZOtmSR/u8E+Jky4Nl1wQqjLDBB1DwGJBizcP6XRjB16Y1tyc8R3cEMtUM34g0rw5P5X0JsFYbt70gw/rfJxaFBdmCELRH1qJKQgDwRm6bPx+Uyw+oyd3wHW9+xOK2sVPKQEBUI3/Glxy99OBSBMbtVnbWyF9gTHoDm30fjallFlrUX/Rx9MaXCgXmcKF0ZDDPvQtFTgsxTGFHPHD02Vz7cXvbyBm6WWzOSRer27xEw/DA015E5ODV2JDapWaxS0aXaqmWB2JHdmYsf/0goLzhyjmKYsxANJl1V2UiO59EdYMaI4NF7JW6X7JKXZ+HF9dOIfVk2n/k4N8YNDM8bv16ryFYy3JnZAi1LCJ/c23PQ2vpLLFCGgCqwJSfqDwxtiJZTLyobAzl35uHLWtb4bqf2Uxl09WP5oyi0VkQQufxcUVMsxfvJU2ACUfvo8pNRFOhFMMrxKKq1MoLoPfEKymejYBUKgYkI7aOTVGklycODld/Amc9A7BOOw6+rmo9PjA62zHuwEt8488GIfRB++DX3VBEEoQ0GIxg/XJrEtficELb5JVjpVfzqI0bDKdeVL8Dc/f1hI+6Ipc+ukWD8T8MJhrXdYMHUQo9N7yp80UuvTISPQABJyHxcfF2iGs2MwfgGYohdAtA5qBHs3cKw6XkJgZN1C390qA27LkvxIDsJe4a3RODEE3hdeIg8F2lJCbh0aA0iB/pD0nI2bqsRCyXJOzoUDiI/TLhQZiKKNAkrOjsibHvFXwSB7D7+7j8eR8sNOEzHll72CPzjvuYIqfwJ5n9VH99vec5JgxKwOXhxbAz8g5cgmWWRHN0RbmEb8aJUWWR4d3UO2lvzwLNpiznXFUtYcXy4iflBDuAzDEwl/ujWbwj6h7SGZ20heJYhWPumzJ2RJWB2c64ytxuIAxVFJZURZDP4TrhUNGM178UuDGoQgjWq8cxqbclm4ubfneHIt0Kz4atx7Y2iEFnY870TgpalFByjBTr7cdYWhJorlqzpjV1qypW1tZdybUS7IYdL274QvQhGRTZCYc7dC+veu1A+G1nY2suCE+t2GHK4bC5kSNwxEB4iBkLXb7H2oRFP+9DSlrLEHRjoIeIana74du3DUg1FgiAqxjAEY85VTGtmBsee65GkDCS8xpquljBrOQ3nniVgc393iLwn4PIHijD+12Hf7URvex6sQtahwmBI3gOs7K5YtJgBT2wDezt7uDVoAC9XO5jxVV954DaGEcG6jgcaNPRAw3oecLUWgm/nDlcLHgRiMYR8C9i7uqOuuwQujjYwF3ICQfG3jBDOgb/gQKIW3Vlp+9DfibumwAoSr7qo5+oB34aekHDXYiyDsTyxbESuNJmHhqPdr5fLR48UAitI8VURUzg1CUbfn2bg761n8ahU/3MWDgx04PIsRC0Xrpy+vmjoXQ/OVty1eY7otSlZOV4tc09f2PJ4MHX0gX+bL/FFqxZo5G4DEc8MTQb9iHY2PDAiGzh7uMOvcUN4OllCoPo6iGIsJ8/MCb4d+iBi+WWklbkv0lsz4MvZXBJ+QtX9rYl07PjORplXO68WCPCrCxshD/a9NikX9VeixpY+EisIOAHQvkcrOAgUE5lc0arnAHT1bYDRMdpLAp39WBaHyOZCLi+eGF9uVnEuzoxyU67RGLy87KQhFXoSjLK4SDRXRDo9x6N8Ns5gFHcNxox7zl6WvAaLlMOj4GvGgO/cFUvijHwVQi1syaYcxihfMzB8Z3RdEgdad5EgdMMABGMuYmf6w9y+O9aUqDg/xC5AV1dTrvLgWr+OX2HKqbdgaQwjwd3n25H+MBU2xcxbFYsBNmUjQu2t4SSxhoAvAF9gDhtnb7Ts9gNmbryI2/+uwfQhXdHSywm1RELYm5rBqel3WHApFcknItG7tRecbbiGi6kpzC2tYVfHE35fdEW/iLnYEPMU2VrX8Sxen47EN76OsBDwuWtxYlRUCy5+IZh8ILF8N3ApcnF0WAMM3K+uemORdnUlxvb6Cg2czTlxUiDe+LZt8eft4rPKk4/if6Et4G5rDpFQBPPaEvi274eZu+8XR6Rkidg/sRO8bDghqTgPw4e5czP0mnUMSVI5Xh6ZhGBPa4htOBFtasXZwgstgsLw4+QF2HTiDlJyNBiDfY89/Z3AqxWEJU8qFsYKpE92YvRXEpjzTSC0roevhi4tMxSgrC2FsHL7Aj9uuMdZSoa319bi5xBf2HHpzt+shhaXLEJ3P5bhpmL8IGcrlz7bkVIim7KnK9CltiIy2x3ritRuGSoTOWwqLq+ZjZnzD+BRRY+67CZm+HH3je+CPttTSohTGZ6u6ILaXEPApvu6YtHNHfH+7CQE1OLyZx+EP29UMhhV23xw5Nw/gEW/L8DO26poNIf0+QksnfcnNl0t7hWQv4zBP1FRWHXulSpFhQ7XKkUltmTfn8WkgFrg8ewR9OcNNZFYgiAqwwAEYwXIsvEmOQUZlXT5fWpIMNYwOdcxK8ACNh0X42FFakueiL+DOuKvkl3Oxob8BRZ1cMXwSteKkiH96WXsXTwKXzraYeCBqraapMhOe4e0rLwSwqPqfLg4AQ2EtdFp6UM14+s0w8rl1bs+9/e63vWq+DGbshN9JXxOZFvAO2Qc5kYvw6LIEejoWjDjuN38u6W6PeVP92H2+HGIiIhAxJgwNLPiwURQF51GcL8jxmH89K24V2io3B0IM2dgIvTHbHWzaopgkbKzLyR8BoyFN0LGzUX0skWIHNERrmKGE63tMP9uiVzkHMPwOpy44oSuXdMQ9O7TB31Kbd+j76jVxRN1tM5HHo4MLfhyilnoFtWwAikujvdQRvpFHZeiIMgpQ9xviok4XKMgYI7yqCK0vpYutszBseF1lHng2zVFSO+y5e2D7/uOwmq1M5MIgijEsAWjkUA2q3lkzzajt7st2i64p3EcEpu6Dd959MOeSoYIGjTKbmcz1BtxQjWzu2JkyTvQr0FX/FOq+/ETkX0Jk/zs0HzC6Qom5RgOVfXj7LvrEN7KSfkd5sIuepGDPwYtuVauez7vaMFi3oVDIspujFUYthXe6A9bECrm0mr1wMZKDZiNu+vC0cqpeC1OE0YEB/9BWHKtxNeyFGTtQT9bXvFxajZe7f4oCmprnQ8prk1tBCEjgNtPZ1TjNmW4H9UGZgwP9v32FK1OkLSyM2opxl12X6dMKUKHMmtvyyzs6WcLXtH9UbPxaqO/2ig+QRCFkGDUA2SzT0Pew40Y2KI9fle3LI38FfYOrg/bHhtQdg6GQZB9E4vDf8auSsdAynBHEY0ROCP4t2N4rqZOy097jluntmLB+FD42ZrBe+wZVXTnU5KDi9O6oNcfmpf7MTSq58cyZCbdQ+yVK4iNT0JGtYNVihnwvWHPE8I7Ikb7+ynLRNK9WFy5Eov4pAwuV9VF13zkISM1Hbml7rkUWe/SkFMq5CtD9rv3yC6VwSqWmSCIGoEEox4gm31C8jKRWbb3Ne8Jdob7wVLogdFnDHG0khxPFneApQkPzgP2VSqo2LQz+KWpRcF4Xis3NPmiA4KC2uOLgKbwcVVMTGGUURLGzA1BE/fgSQ2P4VUPi9ycXO6/xoNB+bEsDrP9xRB6jcLJ9E9oxZrMh6GUmSAItZBg1ANkM0NBjvc31mFUKzvl943rDz+Esl+XNBjkr3AsehWuaFkxsum3sGHCN2jmUji5hROIXBktHT0R8HV//PLXbsSm6DJLgCiLIflxxsEhqCPyxMgTar4JXoPUZD4MpcwEQaiHBKMeIJt9arIRt206Bgd5w1qxZA4jgGNQFK5/ln1acuSmv8Wbt++RmWvEk3kMEEPy4w/PY3Hhbor69RtrkJrMh6GUmSAI9ZBg1ANkMwOAzUfmmxd4cj8ecQkv9DCGjPivQX5MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmiHBqAfIZgRh/JAfEwRBaIYEox4gmxGE8UN+TBAEoRkSjHqAbEYQxg/5MUEQhGZIMOoBshlBGD/kxwRBEJpRKxhpo4022mijjTbaaKOt5FYSijBWAbIZQRg/5McEQRCaIcGoB8hmBGH8kB8TBEFohgSjHiCbEYTxQ35MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmiHBqAfIZgRh/JAfEwRBaIYEox4gmxGE8UN+TBAEoRkSjHqAbEYQxg/5MUEQhGZIMOoBshlBGD/kxwRBEJohwagHyGaE4ZKJ2IOnkChX/TRSWDlb+C8U/VPPkB8TBEFoxnAEozwLSXGxuPXkHfJVSUXkpiHl1RtklNthGFBFQxgmLN7v6Qdn2wE4kKdKMjq4MpybgbZNe2PdgxSc+19bNAlbjfu5qt16hPyYIAhCMwYhGNm0GExvYwsBjw8eYw6fH3YhuTAiIk/ChlAHmPpOwIVsIOddMl4kvsDbD2XCDDnvkPwiES/eZKOmgylU0fwXycXjXb8gfEmc6rf2sBlXMe/bUEReTOfk0MclY303iESdsfqdKkEDGQfHonP4Zjz6SMIy//EWTJ15AMmyfDzdNg0z9ydBptpXMflIWNEdEstGGLH3X6z4RgLLBj9i/yv9W67qfswiPW4v5o8fhNAuwQju+i2G/hqNY48/qPaXgU1H3N75GD8oFF2Cg9H126H4NfoYNB2uNVU8r/ztFayaHI7hEatw4yMI8Y9FXsIuzJkyGZMmTSqzTcaUOXvwsNwD9gFPTizFlGFhCOkUjM6hAzF+wX4kZH5sLySIzwMDEIwy3JsTALFDKNY+z8HLrb3hLPTFtJtSbh+Ll1vC4CRugPH/ZnG/c7GrtwUYEz7qR5xDcd0mxY1pvhCYmEDYdhFe1LBiJMH4XyMbNxZ+DdeGY3AmQ5WkNSze7B4AiYAPx14bixtGH4m8I0NgJ/oSfz2r5ELyZ1gd4gy3sHV4rHA9fcKmYldfZ/D4bhi2ZSMG1OGD7zoMR7S2XS5eJr0taAjmvULSW+2kpq5UzY/z8Wh9H9QXMzBhhLBylsDFRgSGMQFj2RQRx96WbhTkP8L6PvUhVuwXWsFZ4gIbEcO90xhYNo3AsbdVFC9VOm8m4rdEINBJyB1nAhNRF65hYSziSY6H81pCqMi3mo0x644NaapDFXD22djPG2YK+4ht4e7thTpWinIzMG80Agc+QgOEID43DEAw5mJPXyuIg5ZB4bNscjQ6iG3Qf38u2JQd+L6OGD5jz3CvNgVpWNtNDKGY29xG4UxhazjvPMZ5msHMTACh/2zc+zj1iUZIMP6XkOMF14iRiLwx5kzBU6k7Obh3aB9uKdpAHxnppZ/hKWqB2QmVO4U8eTPCnMzQeMI5lb/pDzbjGrbvvAVFkTNjd2DnzRoovI5UxY9l8VFoY8GAZ9sWM8+mcE1XReI7XF3QBU58E/DdwnG8qKgyxEe1gQXDg23bmTibUqDMZe+uYkEXJ/C5hrBb+HGljXRD9/Oyr89gdkh9TkAJ4RDQDk1seEYmGGV48HsAhHwX9F0Ti9u3b5fY7uDuw9dczVKIDAl/fKG0j03gjCL7IOchNvX3gJDhw2XgPrwnzUgQFWIAglGKSz97QliXe7FmyJG6dwDqiJrhtzsvsae/BCKvUTiZrjqUTcLidmI4BXVGc3EdDD9W0Nfy4fiPkFgH47uuNhD6TMI1fUdIKoEE438HeeJqdLPlw6nvbqQaQQUji4tEM3ETzLqjTStKjqTV3WAj9MLYmCoIOlky7sanfpxu9twnOLJwGqb9dRhPNHWb5r1DalbVr667H+fi1EhX8BkzBP71uPRQGHkilgZZgGGsEbZN9QLLPYWRrnwwZoH463HpiK88cSmCOOHJWIeh8HCtqcJ509Z1g4VDa4T/cw2pOWcx2o1vpILRDaNjKhncLr2GyQ0EMBFy9UpcaT9g327Gt7V5YGp9gw3G4NAE8QkxAMHIuf7DFejmJIDI2g5WInM0CN+P+H1D4CbyQPixtOIKSJaAOQFiuIYvxf9aiOA46ACy2Azs6ecAm29WYVu4BELXn3C2hifHkGD8r5CNk+Fu4Ckqnrs1HMauIvKnC/CFaWPMuKVlfnPPY5wHD+Iv5uNRJb3YpZHhztxWcB2wD9UdilcO9h0ODasLIWOi7PZ1H7wPb9TU7Rm7+kDSeBh2PK3aC0BnP5ZexgQvARhReyxOKpshOZ4vDITQhA/JiNPKiXzSyxPgJWAgar8Y5Q9/joWBQpjwJRhxWrf8V+W8bPoLJKarbnB+zOctGNPWopuYAb9OOE6VPZRNxeoupmB4zhh+3GhnhhFEjWAQglGBPO0hzh85iBOxSch+dxjD6opQd/jh0t0E0uuY3FCEehExuPdHG5ja9cWup5vQy8YOvXe8wYXxHhDaDsLBGvZ7Eoz/EdJ24LvaXMUcuBCVDQmskJxH2D93NIaPicLR5x83HC5/sQiBpoVjgrVBitjJPuDxfDA5Vpe8yZAwJwCmjabihqY/y3uCg7+P4cr9B44lan9u+eMFaOf9DSI3bceKn9vDSdwE/7tdVgDLcHduAMxMTCCs2webnuhuV539OG09Qkw5ISIZhTNqNEvesWFw5JlA1OkfKIYQpq0PgSnDCchRZ8qvBIE8HBvmCJ6JCJ3+eatK045qn/czF4zsm5XoJDIBzyUcJ8vVDfmIGeMOPmef4BVvPk50nCA+EwxGMBbBpuF4uAdE7kNwoGwXgfLFJoTPxCvIS4xGB3MbBIV2gLXzYBzM4iq6KY0gMPsW2/Qe4qgYEoz/DfJPhsOFJ0CTWXe56qqKyJOxrY8EAkW0zISBWavfEa/1yfLx6uoO/P2/iZgwYxH238uotIJTCMa2agWjDClJKWrLkXd0GBxM+PCf+0CVUgnSFFxeOwm9fG3AM+2MVeomWLCvsKu/W5XK/X7dN/D+6WyBGGLfY2cfZwTMSSifd27f5fndIBGawLTJRJzXcSCmrn4sS5gDf+5awqazoC7gLL0+BQ0F3H7/OUiQKQS1P4QmQjRV+/xIcX1KQwi4/f5c2bRHD+c1ZsHIiOHo7YdGPj5o1CQA7b4ZiikrziKx5LCFvJMId+HBRNgUM2+X9YPCCZNCtIp6WHpYAUEQpTA4wZhx6id4iSQYuPc1Mh6dxtbVq7H11ANkKN5jeYcw2E6IxtNvQsamYHVXS+WMadcRp5DDvUDifmsGoehrrKrhsSgkGP8LsHizMpirmMXouSVblaY7mUeHo557N0TujcGhqO6QiP206i6WJh3Hb909YK4UXKqZoLVaIvJGxeF0jYIx8yAG+3yN5WpCpbL7c9CCbwLbwYdUKZrJvb8BQ5vYwNK9LQZPHgh/szoIP1k+4pN1Ihz1XTvhf7vO4rCi3KLGmK5V1FOOxL8DITKvh/Z9fsCwQd+ibX1L2PTfX2JSQ0mkeLZzKHzEQvhOvlZiJYXK0dWPpbFT0UghCFvOw0M1SkMWH4kWnKAUNJ6OmzKuQTuVa9BywqTlPHXCRIb4yBbc8yVQvt+0Rw/nNUrByPnjznAENAvAlx2C0SmoHVo1lqCWQDFbnQebgF9xqqgsOYgZ68E1VhhYNh6Ivw9dRXzCbVw4uh3Rk3rB14bH1SOc/X5/QIKRICrAsARj5llE+Igh6bsDCUd+QgMzczh7e8JebIZG488gI2c7wsyFaBF5j3sNskjd3As2Qk/8fFFRQcnxKKo1hMKvKl9CRM+QYPwvwOLVso7VFIwfsG9AffTZoVpPhn2DNd3qoN8+TbM4FLB4G/MbOrq5os3g2dhw7BJuXD+FdZOCIREK4PnzRdVx6mGTFqOdGsEouzUTTbi/V0SlyiJ/+idac0LImhNlFZIVg/GtWmH4ist4rTi97C5mNTVTI1w+YP/A+vhum2qdE/Y1VqstN4v3r16XE3myByvQvY5q6ReFUOYqftMemwpm/bKZSMso6+9SPP6nGxydBuOQDr0Nuvqx7NYM+CkijC0i1a7MILs7C80U+5tHIk4mw60ZfspIYMH7qywy3J3FNXi5/c0jdVnbUw/nNUrBqA4W2Q93YmQT1dJrY/9VpXN7Mq5hYagnLEo0uBSRbr61FwJ87ZVd9h2WJHNnIAhCEwYkGLNw/pdGMHXpjW3JzxHdwQy1QzfiDSvDk/lfQmwVhq3/txqdRUK0+UM1I5HNx4csRWxRgRwvFrXjBGP5mXAfGxKM/w1yDw2CHSNEy6hHlUQi5EjcNB3zL5edaZyJHd/ZwnfsaSjr5bwERAX6YvyFEhG5D4cQMWa3alkbGV7sCUdA8+8RffV9mcosC3v7OcJ3Sqzqt3oU47eCzbwx8WrZaF4mTo2oB/Mem1W/i8k/NwZuDA/ev15Tpagne29/uLUZh60xt/Hk5VukvnmMVT1tUOubDQXlKyIDm0Nt0HD0qeJyt22GSWWXM5A/xB89RkLt3IP893ieEI+ERy/w9kk0QvrtQA6XnHNqLEZuUrOgo2KCXKuWiNRhjS1d/Vj+KAqtFRFE74m4oiZYqlzSiBPeovbRSGJVDVoTAbwnXuEkbVkKVosQcMKlfXSSKk0b9HDez0YwFvDhRDgkfO6+eP2iSikkH2/jz2DPxtVYtWYjdp+4gaTsPJyLqAc+rzYG7K+o4UYQhMEIxg+XJsHP1Alhm1+ClV7Frz5iNJxyXfkCzN3fHzbijoi+vACBQiHaLXqhpsJWdBl2gkjAVY7q3t4fERKM/w3YN2vRzYJBrR6bygiiMkivYKKPAAJJCOZffF2qEs+MGY8GYjFcAjojqJE93MI24XnRw5yFW390QG27Llj6IBtJe4ajZeBEnHhdeIAcuWlJSLh0CGsiB8Jf0hKzy43JKkPeUQx1EMFvwgWkl8qzFEkrOsMxbLvqdyEyJMxuDr6JHQYeqCiSyiKF8zfFQshFERtGBHfvujCzDkL0o5L5YvH+2Eh4i8So07ILghraoy4n+EovWp6LZ7uHwkvoiJCl8VB/ZTmynl/Aqh+awu/nC1z1L+cak1+h/vdb8LyUyGSR8+IYxvgHY0my9gJIZz/O2oJQc8WSNb2xS02Gs7b2Uq79ZzfksDJqmrUlFOYMA+veu9SULwtbe1mA4dlhyGF1ilkz1T7vZyYY5U/m4wtOyPNsB6lSKkCeiL8DRWCEgVhY7IgEQajBMARjzlVMa2YGx57rkaTwWfY11nS1hFnLaTj3LAGb+7tD5D0B528rxgRpns2WuaE7xIrlI8qtnfBxIcH4H4F9h5297cGzCsG61xVVrHl4sLK7cuFmhidGbde68PNsBN8GXnC1MwO/SGRxYkNkjToe3nBzsIWzsxWEfDu4u1qAJxBDLOTDwt4V7nXdIXFxhI25UPkFEcXfCZ0D8cuBRDVdkGVJw77+TmBMBLCSeKFuPVc09m0IT4k1hIwlgpcnqo5TIb2FGb58MJJwnKisO1f6Gtd3L8e8aeMxasQIRPxxDInJGxFamwe+pQt8GnqimZdvQbltTTWWu453A3jVqQWBCQ/2EheITLhyO3miYUM3ODi6o3FjXzT0qQ+Jrbly0kzJsZtZBwbCgUsT1nKBRwPuWg29UU9hR06oOfbapNOXdHT2Y1kcIpsLYSLwLB0lVpKLM6PclGs0Bi/nGsFcimJNzOaKiKTneJQ//AxGcaKNMQvG8pe6ibZqn/czE4z5/45BXUWEsdFUVYpmpPFzECBmIG7zh9pxqARBFGMAgjEXsTP9YW7fHWsSiz32Q+wCdHU15So6rnJ0/ApTTr1F3tVf4cNVpCHr1K9sm7sjDOa8gq/E1CQkGP875HONFn9TIZrOvFUwa1cTbAo2htrD2kkCG5EIzoqvEJnbwNm7Jbr9MBMbL97Gv2umY0jXlvBysoSAz4fQrgm+W3AJqcknENm7NbycbWBpZgpTc0tY29WBp98X6NovAnM3xOBptvYVO/v6NCK/8YWjhQD8WiLwBSLUcvFDyOQDSCylOFm839MfTrxaCFryRE0UXxtkSNwbgbZuVhDxRahjxpXL1BJ27k3Qsd8UrD1/p1y5Gb4YNpKm6DZuE+6mPcW+yd3RpI4V1/hjuLxy4lPA2U5kzh3TCG37TMGmWyW+wS1PxtH/haKFuy3MRUKIzGtD4tse/Wbuxv2KAqRq0N2PZbipGD+o+FJIn+1IKXFLZE9XoAsnnHk23bGu8LNzspuY4ceJfr4L+mxPKdHoleHpii6ozePBpvs65RevlLCpuLxmNmbOP4BHFT1sup63LJUJRm3zwZFz/wAW/b4AO29nFuVD+vwEls77E5uuvitKk7+MwT9RUVh17pUqRYW218pNwoNnahaXz3uApZ3twGOEaDztuipRPez7C5jRxho8fh0M2FucN4Ig1GMAgrECZNl4k5yCjJrtYdYZEoz/JXJwfVYALGw6YvHDiuJ7itm9Qej41/MqCq9PwIeLmNBAiNqdluKhgfvcx6Aqfsym7ERfCSd6GQt4h4zD3OhlWBQ5Ah1dxWC4xmu7+XdLNCxYpOzsCwknhBkLb4SMm4voZYsQOaIjXMUMJy7bYf7dEiopdwfCzBmYCP0xu8KxmDqel0P+dB9mjx+HiIgIRIwJQzMrHkwEddFpBPc7YhzGT9+Ke4XPgNb5yMORoYoJJAzMQregQM5JcXG8B/hcmqjjUhQEOVUrWpiYQBgwR3lUEVpeS6oIHpjaw6/bcEyetxgrVi7DX7/9hB6+NuArZkM3n4SYojEYeTg+/gt06DsOkYtXY/3aFVgwdQgCFfeIMUfjMUfVLgRPEERpDFswGglks/8YsmfY3Nsdtm0X4J6mKAibim3feaDfnhpeFLTKZOPSJD/YNZ+A059Bt2RVqKofZ99dh/BWThCV6HIXOfhj0JJrSCtnymzcXReOVk6iolnfirGfDv6DsORaia9aKfiwBaFiRRd8D2ys9J7ocF4OxVqbikXFC/JbfmOswrCtcC6R1vmQ4trURhAyArj9dEY1212G+1FtYMbwYN9vDwpOySJpZWfUUoy77L5OmVKEttfKvIboQV+hvnXx7HnlUA0bH3SOWIdbpQbsynBnYWe4miuWz1Edy+XRyjMIo1fHqrlHBEGogwSjHiCb/QfJe4iNA1ug/e/ll6VRRBdf7R2M+rY9sMEgQxfZuLk4HD/vKh4DmXNxGrr0+gOXSn1aqWbIf7IVU/63H0myfDzdPhX/2/eiKF81SfX8WIbMpHuIvXIFsfFJyKisALJMJN2LxZUrsYhPylBTXhap23vDnieEd0SMKlqnBZWeV1d0zUceMlLTkVvqMZIi610ackqF2mXIfvce2aUyWIUyc+V9+fAuYq9dx+0HL5FVQTifzU3F07hYXL12Cwkv0ktNRiMIonJIMOoBstl/lTxkZpadeZqHJzvD4WcphMfoM2pmrX565E8Wo4OlCXjOA7BPJRDZ3JwylXwNwaZid18nMDwJBm9ch75ODHh1BuOg+mHKHxWD8mNZHGb7iyH0GoWTpae31yw1mQ9DKTNBEGohwagHyGaEAvn7G1g3qhXs+AyE9YfjUA1/cUh75Hh1LBqrrpSYPPIJYdMvY9Pma8hgWaRf2YRN1yr/5OHHwJD8OOPgENQReWLkiU97j2oyH4ZSZoIg1EOCUQ+Qzf7bZMdtw/TBQfC25itn9QscgxB1XetORMJAMCQ//vA8FhfupqjGAX46ajIfhlJmgiDUQ4JRD5DNCML4IT8mCILQDAlGPUA2Iwjjh/yYIAhCMyQY9QDZjCCMH/JjgiAIzZBg1ANkM4IwfsiPCYIgNEOCUQ+QzQjC+CE/JgiC0AwJRj1ANiMI44f8mCAIQjMkGPUA2YwgjB/yY4IgCM2oFYy00UYbbbTRRhtttNFWcisJRRirANmMIIwf8mOCIAjNkGDUA2QzgjB+yI8JgiA0Q4JRD5DNCML4IT8mCILQDAlGPUA2Iwjjh/yYIAhCMyQY9QDZjCCMH/JjgiAIzZBg1ANkM4IwfsiPCYIgNEOCUQ+QzQjC+CE/JgiC0AwJRj1ANiMI44f8mCAIQjMkGPUA2YwgjB/yY4IgCM2QYNQDZDOiesjx8uxSzJo8GZMmTcIk7v9TZ0Tiz2Xb8e+TLLCqo4qQv8K/y2dh8uSpiFx6HE9zVekVIHt5Fisip3J/MxMrzr8pf061ZCL24CkkylU/q40MT/fPRsSonzD+j2NIruC88lf/YvmsyZg8NRJLjz9F5UWUcTZcgcip3N/MXIHzb7QrYUnIjwmCIDRjOIJRnoWkuFjcevIO+aqkInLTkPLqDTLK7TAMqKIhqgX7Eks7imBiYlJuYwQ28BuwCnc+qI7lYF8tRZBIdQwjQrNZtyFV7VMLd/7V3azBKM/JQByyHumqXZph8X5PPzjbDsCBPFVSdck7j3Ee/IJ8Cxtjxi2ZakdZWLxaGgRRoQ1EzTDrdoUl5Iq4Gt2sGZVNxAhZX3kJy0J+TBAEoRmDEIxsWgymt7GFgMcHjzGHzw+7iqMP8iRsCHWAqe8EXMhWpRkYVNEQ1YJNQnR7TjAKPDH62Eu8efMayU/v4uzGaQipJwbDCFB/xAlkFB0ejfacYBTUa4yGlgz4dUfjbAUhONm9OQgQ8VC7sR9c+SYQd12D96p9FZGxvhtEos5Y/U6VUE1yT43krs+Hi5sEQq5MPhOvaBC6LJKi23OCUYB6jRvCkuGj7uizFUQZZbg3JwAiXm009nMF30SMrmu0KWFpqu/Hhh1BVSB/eRZLFddVRLInKa4/A5F/LsP2f58gq3woG6/+Xa6MfE+NXIrj2oWycXZFJKZyfzNzxXlUMZsEQRggBiAYC172YodQrH2eg5dbe8NZ6ItpNxVVCYuXW8LgJG6A8f9mFRxugJBgJKpFkWD0wcQrpSVU7rXJ8BUyYKx6YUtaQVqhYBT6T0ZUX0fweA7ov1dTRC0X58d5QsCvh5F//4JGAu0FY96RIbATfYm/numjTzobR35wAU/oh+kHF6KDKSd060fgnNroZaFgFMJ/chT6OvLAc+gPzUU8j3GeAvDrjcTfvzSC4FMJRgOPoCqu+3Jpx6Lrlto4AW/jNwCrSoeysTSoMPLNQNRsFirOJnf+1d1gzRSckxGHoErZJAjCIDEAwZiLPX2tIA5ahldca5RNjkYHsQ36788Fm7ID39cRw2fsGWSqjjZESDAS1aICwYj8GIx240SIsAlm3ikQIEWCMWAObp4cBXc+A6uQtUhRF81J34v+nOASNp2J2KtTdRKM0ks/w1PUArMTNAkfHcjg8uHA5aPZb4jLT8byYHMwfAnCT+aoDihJsWAMmHMTJ0e5g89YIWRtCrenPOl7+8ORJ0TTmbG4OvXTCUZDj6CWvK7n6GN4+eYNXic/xd2zGzEtpB7EDANB/RE4URzKVj2X9dC4oSV3v+pidMWhbMwJEIFXuzH8XLlnVtwVVcomQRAGiQEIRiku/ewJYd1wHM+QI3XvANThWty/3XmJPf0lEHmNwskSrVQ2/R5OHziGW4q+jrwUxO5fjb/nL8Saw/FI09vgfN0gwUhUi4oEY95x/OjM4wRjK0Q9LHjASwrGhNzrmNJQAMb0Kyx4XNYBWCSv7AxLnhnaL36GvBu6CUZZXCSaiZtglkqoVof328NQmxN1/rPvcfKHxatVXWDB8OA85BDKjzQpKRgTkHt9ChoKGJh+tQDli5iMlZ0twTNrj8XP8nDjkwlGw4+gFl9XjZjNvYbJvkIwnDDvVRzKLnguhf6YHNWXE+U8OPTfq3H8a+75cfAU8FFv5N/4pZGABCNBfGYYgGDkKqaHK9DNSQCRtR2sROZoEL4f8fuGwE3kgfBjadxrrhjptcloILRC78WbEe5nBR7XkhcKedyLToS632/G808gGkkwEtVCo2BkkXZ4GNz5JuB7jMcF1aSvUoJRJsP9uQEQMUL4zbjFibESyOIx218Enk0vbH7LQqqjYJQ/XYAvTCvqWtUS9i029LACI2yJ3x+ooqRv1qF7LQY8+77YUxjRKqK0YJTJ7mNugIj7e79yeZHFz4a/iAebXpvxlpV+OsFoBBHU4uuqi37mI2a0G/hcnprMvFOQVCQYAzDn5kmMcueDsQrBWvWhbOzt78gJ5qaYGXsVU0kwEsRnh0EIRgXytIc4f+QgTsQmIfvdYQyrywnA4Yfxvsy7SSkYBQxEYnO4BM3AocfZYHOfYOuAehDwnDH4QM13XpNgJKpFkWD0wKjDiXj5MhmJD2NxdNlItLLlGkM8R/Rcn4jCtlBpwcj5zoslCDIvP/kl91wEPPh8SIYdVUbxdBaMLxYh0LRwPHHVYV/9g84WnM+2+QOPigqRik2h1lzZbPDt1ndlhFIZwciV/MWSIJiX67rNxbkID/A5YTbsqLKEn0wwGkMEtfi66gRjHo7/6Awel6dWUQ8LkkoKxoRcXJ/SEALGFF8teFz0LBbCJq9EZ0sezNovxrO8GyQYCeIzxGAEYxFsGo6He0DkPgQHUsu3ZAsEIw+12/+J2yUa7/LHf6KNkHtBDz/OvfpqFhKMRLUorJgLJyAUbZwItG6E76OvI72EK5QVjIoI3qZQG/A4Ydl/b2G4Lg27+zmAJ/DBr1cKQpNVEYxt1QpGGVKSUkpHMzUiR2J0B5gxYk5oPCklNN5vC4MNj0GtkHV4XcrVywpGRRE3IdSGB55jfxQXcTf6OfAg8PkVBUX8RILRSCKoxdctLxjZNK6R7s6HCd8D44tD2SUEowyy+3MRIGIg9JuB0tmUIX62P0Sc+O+1+S1YKQlGgvgcMTjBmHHqJ3iJJBi49zUyHp3G1tWrsfXUA2SoKpQCwShE4MLnpVu5eccwzIkHkWryTE1CgpGoFoUVM88OX/44A7Nm/YbI3xfin51n8TC9bCxHcXgZwciRuX8gnDjxVTj5hU1agU6WDESt5kGlYfQnGDMPYLD311imzexp+WPM/1KsnDARFrUBmzZtKt6W/4imnABhzDthRXJJpy0vGBWLiO8f6AReUdctd8yKTrBkRGg174HqmE8jGI0lglp8XQE8Rh1G4suXSE58iNijyzCylS1nWx4ce64vXqi9jGDkHggsCVJ0tZeZ/JJ7DhEefPAlw1CQTRKMBPE5YliCMfMsInzEkPTdgYQjP6GBmTmcvT1hLzZDo/FnlOvQaRSM+WcwypUPYdu/9fhlCu0gwUhUi8KKWd2kFzWoE4zIPYvRdfnKyS9/PclDXGQLCJla6LLqZZFY0VUwskmL0U6NYJTd+h/8hHw0mXlXJdQ0I0uYgwChajkYTRtjinaLSvqzOsGoKOJo1OUrum7/wpO8OES2EIKp1QWrXhaV8BMIRuOJoBZft/w9YPjWaPR9NK6XDmWXFozc37/dFMqViROW/fcWrQuatrsfHHgC+Px6peCjCyQYCeKzxIAEYxbO/9IIpi69sS35OaI7mKF26Ea8YWV4Mv9LiK3CsD2zAsGYthE9LHiw7rNbzZihjwsJRqJa6EMwcmIiViEmFJNfJi7GyPp88Bz6YU+JKa06C8Y3KxFs5o2JV8vmKRMnR9SFWY9NnNdWhAy3/9eEE65CNJ9wGNdjYxFbZru0JBR2nKgStY6CahI4h3rBCGmsUogoum4nLh6J+nweHPrtKTFr9xMIRiOKoBZflwe7L3/EjFmz8Fvk71j4z06cfZhe+n2qoJxg5Mjcj4FOvOLJL9wxKzpZghG1wrziUDYJRoL4DDEYwfjh0iT4mTohbPNLsNKr+NVHjIZTrnOvSCB3f3/YiDti6UtWg2DkWvn/dIENvza+VYyhUaXWFCQYiWqhF8HIybMH89BKIVCEQk6k8VGvzNp+ugpG5B3FUAcR/CZcKDWGUiFckpZ3hkPYNpRY5rk80muY1IATeKI2+KOor7YMWQcxWCFAhC0QGV9YGA2CkfvXg3mtIGIYCLkyMvx6ZdYFrHnBaEwR1OLrqpv0ogZ1gpF7os6Orgu+YvLLX0+QFxeJFlz5a3VZheJskmAkiM8RwxCMOVcxrZmZcvxMkuKtyr7Gmq6WMGs5DeeeJWBzf3eIvCfgMveGKxCMfEi+noo1+8/g4qXT2D6vLxpZ8mHT4S/EqcZr1yQkGIlqoSfBqPiM5rIgC+U3oxlhE/zvdsmdinpcR8GINOzr78SdTwAriRfq1pPAt1EDeNSxgtDEAsHLimduqyP/wnh4cAJI/NUCPNF44AccHVYHfO4avtNuqESMJsGoKOIyBFkoBBonGpv8D6WLWNOC0bgiqMXXrY5gVGRT8RwpJr9MxOKR9cHnOaBf6VA2CUaC+AwxAMGYi9iZ/jC37441JQYffohdgK6uplxlxb2YHL/ClFMFkcMCwVgQYeApP0HF/dvGG8Fj1uF2Zk3HFgsgwUhUCzYFK4K5Z13gi2k3tBCMKSsQLOae+zZ/llmGhUXqtu+UAsUyaAnKzkmR3Z2FZkIG5j02FY0/qwz29WlEfuMLRwsB+LU4n+MLYencGCGT9yOxtB4tgxRXf/WBgLFE0NIyw0fKkHvmJ+XXapRrGCrPySJlRTDEnBBr82eZJVzYVGz7zo7zfe68S56VOa8Md2c14wScOXps0raExejsx4YSQeVscnnNbMycfwCPKmww60cwQvYA81qJwHD3R8g9T/x6Zb5lToKRID5LDEAwVoAsG2+SU5BR4s1W1CX91xNkvU/BqzfpyK2oNqoBSDAS1Ub6ARlZecpGkTZIP2QiO1/d0SzysrM0+AS3LysDHyrXpPpBUabMnBKCSBMs8rPTkZFT8kgpPmRmQ30R85CdlatehHL7sjI+VC6G1KCrHxtMBDV3B8LMub8R+mP2vYqsrSfByFk+aVkQLBQNdk40Nvnf7dL3mAQjQXyWGLZgVIPGSS+fEBKMBGH86ObHBhRB/bAFoWITMLV6YOO7ipocBdc1ZUqK1wpQRr7FYIRt8GeZFcXZ1G34zo4HxjIIS8qHsjGrmRCMeQ9UIdBLEISBQoJRD5BgJP4r5N5fi0mRx/BanoeHGybjt8MpBuOH1UVnPzaICCqL1O29Yc8TwjsippJZ6wq462ZkIU/7UDYys/O5q5SHzctGlobuHTYvCxk1FsomCKImMDrBKLu3GN/6B+CHLa/UvsQ+BSQYif8EbAo2htaGCa8+ftr+D3rZmoBX9yecqel1rD4SRunHsjjM9hdD6DUKJ0tPZScIgtArRicYDRGyGfFfgU39F+u23cEHRWTr3Dpsvf2ZqEUOY/TjjINDUEfkiZEn0g2mAU0QxOcJCUY9QDYjCOPHGP34w/NYXLibUuPfzycI4r8HCUY9QDYjCOOH/JggCEIzJBj1ANmMIIwf8mOCIAjNkGDUA2QzgjB+yI8JgiA0Q4JRD5DNCML4IT8mCILQDAlGPUA2Iwjjh/yYIAhCMyQY9QDZjCCMH/JjgiAIzZBg1ANkM4IwfsiPCYIgNKNWMNJGG2200UYbbbTRRlvJrSQUYawCZDOCMH7IjwmCIDRDglEPkM0IwvghPyYIgtAMCUY9QDYjCOOH/JggCEIzJBj1ANmMIIwf8mOCIAjNkGDUA2QzgjB+yI8JgiA0Q4JRD5DNCML4IT8mCILQDAlGPUA2Iwjjh/yYIAhCMyQY9QDZjCCMH/JjgiAIzZBg1ANkM4IwfsiPCYIgNEOCUQ+QzQjjJhOxB08hUa76aRToP8/kxwRBEJoxHMEoz0JSXCxuPXmHfFVSEblpSHn1BhnldhgGVNEQxguL93v6wdl2AA7kqbTKuDgAAE07SURBVJIMngryzGYjI1Om+qEb5McEQRCaMQjByKbFYHobWwh4fPAYc/j8sAvJhZEDeRI2hDrA1HcCLmSr0pTIkfXiDi6dOYHjp87h+v2XyKpaPVFtqKIh1JOLx7t+QfiSONVv7WEzrmLet6GIvJjOyaOPS8b6bhCJOmP1O1WCBjIOjkXn8M14ZADCUm2e2TScm94K9h79se257i+Dqvsxi/S4vZg/fhBCuwQjuOu3GPprNI49/qDaXwY2HXF752P8oFB0CQ5G12+H4tfoY9B0uNZU8bzyt1ewanI4hkeswo1cVaIRkJewC3OmTMakSZPKbJMxZc4ePCz3CHzAkxNLMWVYGEI6BaNz6ECMX7AfCZkf28MI4vPAAASjDPfmBEDsEIq1z3PwcmtvOAt9Me2mlNvH4uWWMDiJG2D8v1kFh3NpadeWYWgrZ4gZE5iYFG4MxF/Ox+NP0K1GgpEoTzZuLPwarg3H4EyGKklrWLzZPQASAR+OvTYWN54+EnlHhsBO9CX+elbJheTPsDrEGW5h6/BY4Z41Sd4TnL/wHIWXVZ9nKZ5tHYSWnebgSobuIqBqfpyPR+v7oL6YgQkjhJWzBC42IjDcu4mxbIqIY29LC/78R1jfp77y3cUIreAscYGNiAHDvb8sm0bg2NsqipcqnTcT8VsiEOgk5I7j3qGiLpwANxbxJMfDeS0hLHr/l94Ys+7YkKY6VAFnn439vGGmsI/YFu7eXqhjpSg3A/NGI3DglbGUmyA+HQYgGHOxp68VxEHLoPBZNjkaHcQ26L8/F2zKDnxfRwyfsWe4V1sB8hfr0MOeB6Hr15i0+jAu3LiNW9fO4+Tetfh7zb+o6vu2OpBgJEojxwuuoSMReWPMmcInV1dycO/QPtwqbCd9RKSXfoanqAVmJ1QelZMnb0aYkxkaTzhX5JMfHTYFuwe5QyCsj+EH3iqTyuc5E9f+noA/L7wHm/cImybPwL4k3aKMVfFjWXwU2lgw4Nm2xcyzKQWCVvYOVxd0gRPfBHy3cBwvuocyxEe1gQXDg23bmTibUiB/Ze+uYkEXJ/BN+HALPw7db7nu52Vfn8HskPqcgBLCIaAdmtjwjEwwyvDg9wAI+S7ouyYWt2/fLrHdwd2Hr7mapRAZEv74Qmkfm8AZRfZBzkNs6u8BIcOHy8B9eE+akSAqxAAEoxSXfvaEsC73Ys2QI3XvANQRNcNvd15iT38JRF6jcDJddaiiIl7cDiKeDb7brnPY5qNBgpEoiTxxNbrZ8uHUdzdSjaASksVFopm4CWbd0UZgyZG0uhtshF4YG1MFNStLxt341NJRt0ph8ebYOHz51S84oWoRls0z+3IjQu0YWAUtxq3jP8FDKEKjyZeV+7RFdz/OxamRruAzZgj86zFnmRLIE7E0yAIMY42wbaoXWO4pjHTlgzELxF9lukLkiUsRxAlPxjoMhYdrTRXOm7auGywcWiP8n2tIzTmL0W58IxWMbhgdU8ngduk1TG4ggImQq1fiSj/j7NvN+LY2D0ytb7DBGJyVID4hBiAYOdd/uALdnAQQWdvBSmSOBuH7Eb9vCNxEHgg/llaicpHjyfwvuBahGdovesq9MgwDEoxEMdk4Ge4GnqJyumsoT2jFyJ8uwBemjTHjlpb5zT2PcR48iL+Yj0eV9GKXRoY7c1vBdcA+VHe4nro8Z949jxtKwZOHJxcv40Vh/7WW6OzH0suY4CUAI2qPxUllxYYczxcGQmjCh2TEaeVEPunlCfASMBC1X4zyhz/HwkAhTPgSjDit2+y+qpyXTX+BxHTVzcuP+bwFY9padBMz4NcJx6myh7KpWN3FFAzPGcOPG82sL4L4JBiEYFQgT3uI80cO4kRsErLfHcawuiLUHX64XDeB/FE0gmy4FqGpB76ZtRtx7z99pUyCkSgibQe+q81V3oELUdmQwArJeYT9c0dj+JgoHH2uo/LREfmLRQg0LRw3rA1SxE72AY/ng8mxuuRNhoQ5ATBtNBU3qlkk3fNcOTr7cdp6hJhyQkQyCmfUaJa8Y8PgyDOBqNM/yqEyaetDYMpwAnLUmfIrQXAi99gwR/BMROj0T0G3u7ZU+7yfuWBk36xEJ5EJeC7hOFlOE+YjZow7+Jx9gle80THyTRD/LQxGMBbBpuF4uAdE7kNwQG0XAYvU8/PxXUNr8BkGPIu6aPfDPOyN//izSTVBgpEoJP9kOFx4AjSZdbfqEXB5Mrb1kUCgnNTFwKzV74jX+mT5eHV1B/7+30RMmLEI++9lVOoXCvHVVq34kiElKUVtOfKODoODCR/+cx+oUipBmoLLayehl68NeKadsUrnwcal81KVPFeGrn4sS5gDf6EJhE1nQV0wWXp9ChoKuP3+c5AgU4hlfwhNhGiq9tmQ4vqUhhBw+/3nJKjStEEP5zVmwciI4ejth0Y+PmjUJADtvhmKKSvOIrHkbO+8kwh34cFE2BQzb5d9XqS4Mc1XaZ9WUQ9LDysgCKIUBicYM079BC+RBAP3vkbGo9PYuno1tp56gHKTHuXvcXfPPPwY5AkrPgNG6IKOv/2LT/G+I8FIFMDizcpgrvIWo+eWUmtA6UTm0eGo594NkXtjcCiqOyRiP626i6VJx/Fbdw+YK4WmarZorZaIvFFxV5tG8ZV5EIN9vsZyNaFS2f05aME3ge3gQ6oUzeTe34ChTWxg6d4WgycPhL9ZHYSfrDgqVI7MAxjs/TWWqfKiOc+lj9MFXf1YGjsVjRSCsOU8PFRzOVl8JFpwglLQeDpuyqSIndpIKUxazlMnTGSIj2zBPTsCNJ5+U5WmDXo4r1EKRs7XdoYjoFkAvuwQjE5B7dCqsQS1BIrZ6jzYBPyKU0VlyUHMWA+uAcbAsvFA/H3oKuITbuPC0e2IntQLvooeK4X9fn9AgpEgKsCwBGPmWUT4iCHpuwMJR35CAzNzOHt7wl5shkbjz0D9NBc53t9cgyG+FmD4EvxwuMbmbhZBgpEogMWrZR2rKRg/YN+A+uizQ/W0s2+wplsd9NtX0QJ5LN7G/IaObq5oM3g2Nhy7hBvXT2HdpGBIhAJ4/nxRdZx62KTFaKdGfMluzUQT7u8VkauyyJ/+idacWLLuv1+VooGsGIxv1QrDV1zGa8XpZXcxq6mZBnHDwb7Hq9flBa7s1v/gJ+SjycyCvGjOc/FxukYZdfVj2a0Z3LU4wdgiEvfUXEx2dxaaKfY3j0ScTIZbM/yUkcAWkffU5E2Gu7OaKfc3j9Rl3U49nNcoBaM6WGQ/3ImRTbi6wISP+mP/VaVzezKuYWGoJyxKNKYU0Xu+tRcCfO2VXfYdliRzZyAIQhMGJBizcP6XRjB16Y1tyc8R3cEMtUM34g0rw5P5X0JsFYbtFWjBvLOjUZdfeeX4MSDBSBSSe2gQ7BghWkY9qiRaIUfipumYf7nsTONM7PjOFr5jTxdEy/MSEBXoi/EXSkTkPhxCxJjdqmVtZHixJxwBzb9H9NX3ZSq8LOzt5wjfKbGq3+pRjPEKNvPGxKtlonXcFU6NqAfzHptVv4vJPzcGbgwP3r9eU6WoJ3tvf7i1GYetMbfx5OVbpL55jFU9bVDrmw1qewPkD/9Aj5HHUV4yZuLkiLow67FJ+auiPBcep+scbl39WP4oCq0VEUTvibhSNhscyqV/OFEtah+NJFaOR1GtOeEmgPfEKyh/eMFqEQJOuLSPTlKlaYMezvvZCMYCPpwIh4TP3RevX1QpheTjbfwZ7Nm4GqvWbMTuEzeQlJ2HcxH1wOfVxoD9RrRqOUF8AgxGMH64NAl+pk4I2/wSrPQqfvURo+GU68oXYO7+/rARd8TSl5pfZmm7+8KBp+v4H/1AgpEohH2zFt0sGNTiBEuFda/0Cib6CCCQhGD+xdelKvrMmPFoIBbDJaAzghrZwy1sE54Xqc8s3PqjA2rbdcHSB9lI2jMcLQMn4sTrwgPkyE1LQsKlQ1gTORD+kpaYXW7cVhnyjmKogwh+Ey4gvVSepUha0RmOYdtVvwuRIWF2c/BN7DDwQEWRVBYpKzspF0suiuowIrh7c4LOOgjRj8rkK/cZdg/1gtAxBEvjy56Xy8vyznAI21bws6I8q47TdSa2zn6ctQWh5oola3pjlxozZG3tpVz7z27IYaUAztoSCnOGgXXvXSh/eBa29rIAw7PDkMMVDyEoS7XP+5kJRvmT+fiCE/I820GqlAqQJ+LvQBEYYSAWFjsZQRBqMAzBmHMV05qZwbHneiQpfJZ9jTVdLWHWchrOPUvA5v7uEHlPwGVpPmJmf49hk6OwfMMO7D1wEPt3bUT09H5obssH36kXNryoeacnwUgUwb7Dzt724FmFYN3riirfPDxY2V25uDPDE6O2a134eTaCbwMvuNqZgV8ksjhBIrJGHQ9vuDnYwtnZCkK+HdxdLcATiCEW8mFh7wr3uu6QuDjCxlyo/MqI4u+EzoH45UCimm7KsqRhX38nMCYCWEm8ULeeKxr7NoSnxBpCxhLByxNVx6mQ3sIMXz4YSThOVKbKpK9xffdyzJs2HqNGjEDEH8eQmLwRobV54Fu6wKehJ5opyt3QC3VqCWDCs+fKIYIJ3wJOnj5wszXjbOMNjzpcuU0sELysMC9l8yyBb6MGpY7T9U2gsx/L4hDZXAgTgWfpCLCSXJwZ5aZcozF4OdcI5lIUa0c2V0QkPcej/OFnMIoTbYxZMJZX0DBWR7XP+5kJxvx/x6CuIsLYaKoqRTPS+DkIEDMQt/lD7ThUgiCKMQDBmIvYmf4wt++ONYnFHvshdgG6upoqP90kdPwKU04pPrGVj7MT/WBnyr0AS4xD4Zk6o1noZOy8X93V3aoGCUaiJPm3I+FvKkTTmbfULHNSAjYFG0PtYe0kgY1IBGczAYTmNnD2boluP8zExou38e+a6RjStSW8nCwh4PMhtGuC7xZcQmryCUT2bg0vZxtYmpnC1NwS1nZ14On3Bbr2i8DcDTF4mq195c++Po3Ib3zhaCEAv5YIfIEItVz8EDL5ABJLKU4W7/f0hxOvFoKWPNFZlBUgQ+LeCLR1s4KIL0IdUx4EpjaQNO2GcZvuIu3pPkzu3gR1rETgKcQvI4Clc2MuL/tL5aV0noXg8YVqj9MW3f1YhpuK8YOKL4X02Y6UEuaWPV2BLpwo5tl0x7rCz87JbmKGHyfo+S7osz2lxPABGZ6u6ILaPB5suq9TfvFKCZuKy2tmY+b8A3hU0YOk63nLUplg1DYfHDn3D2DR7wuw83ZmUT6kz09g6bw/senqu6I0+csY/BMVhVXnXqlSVGh7rdwkPHimZtBB3gMs7WzHPTdCNJ52XZWoHvb9BcxoY809N3UwYG9x3giCUI8BCMYKkGXjTXIKMsr2qMk+IPXFYyTExyPhyUtkVPIS+9iQYCRKk4PrswJgYdMRix9WpFzkSPw7CB3/el5F4fUJ+HARExoIUbvTUjyspKfb2KiKH7MpO9FXwjVgGQt4h4zD3OhlWBQ5Ah1dxWB4Nmg3/26JRgOLlJ19IVGs6mDhjZBxcxG9bBEiR3SEq5hr+Nq0w/y7JV5muTsQZs7AROiP2epm1RSh43k55Jwonz1+HCIiIhAxJgzNrHgwEdRFpxHc74hxGD99K+4V3l+t85GHI0MVE0gYmIVuUY0hleLieA/wuTRRx6UoCHLKEPebYiKOCYQBc5RHFaHltaRXf4WPqT38ug3H5HmLsWLlMvz120/o4WujXG7NsvkkxBSNVcjD8fFfoEPfcYhcvBrr167AgqlDEKi4R4w5Go85ijekFgmiUgxbMBoJZDOiHLJn2NzbHbZtF+CepgYNm4pt33mg355PExnXnWxcmuQHu+YTcPoz6LosS1X9OPvuOoS3coKoxDACkYM/Bi25hrRyZsrG3XXhaOUkKu4lYURw8B+EJddKftWK48MWhIoVSyP1wMZK7a3DeTkU62gqFhUvyG/5jbEKw7bCZSm0zocU16Y2gpARwO2nM6qJSzLcj2oDM4YH+357VCtdsEha2Rm1FOMuu69TphSh7bUyryF60Feoby0s1dsktPFB54h1uFVqYKsMdxZ2hqu5Yvkc1bFcHq08gzB6dayae0QQhDpIMOoBshmhlryH2DiwBdr/Xn5ZGkV08dXewahv2wMbDDK8kY2bi8Px867iMZA5F6ehS68/cKns55eqTT6ebJ2C/+1Pgiz/KbZP/R/2vagokvVxqJ4fy5CZdA+xV64gNj4JGZVlX5aJpHuxuHIlFvFJGUU2LoZF6vbesOcJ4R0Ro/2M70rPqyu65iMPGanpyC31iEiR9S4NOaXC6DJkv3uP7FIZrEKZufK+fHgXsdeu4/aDl8iqIFTP5qbiaVwsrl67hYQX6VyuCILQBRKMeoBsRmgmD5mZZWen5uHJznD4WQrhMfqMmpmtnx75k8XoYGkCnvMA7FMJRDY3p4wQ0A9s6m70dWLAkwzGxnV94cTwUGfwQaSr9tcUBuXHsjjM9hdD6DUKJ0tPA69ZajIfhlJmgiDUQoJRD5DNCG2Rv7+BdaNawY7PQFh/OA6p/fylISDHq2PRWHWlJj65ySL98iZsvpYBlk3HlU2bcK3cp50+PobkxxkHh6COyBMjT3y6T54qqMl8GEqZCYJQDwlGPUA2IyojO24bpg8Ogre1YoY/A4FjEKKua93RSNQAhuTHH57H4sLdFNU4wE9HTebDUMpMEIR6SDDqAbIZUTks8jPf4MWT+4iPS8CLSge5ETUN+TFBEIRmSDDqAbIZQRg/5McEQRCaIcGoB8hmBGH8kB8TBEFohgSjHiCbEYTxQ35MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmiHBqAfIZgRh/JAfEwRBaEatYKSNNtpoo4022mijjbaSW0kowlgFyGYEYfyQHxMEQWiGBKMeIJsRhPFDfkwQBKEZEox6gGxGEMYP+TFBEIRmSDDqAbIZQRg/5McEQRCaIcGoB8hmBGH8kB8TBEFohgSjHiCbEYTxQ35MEAShGRKMeoBsRhDGD/kxQRCEZkgw6gGyGUEYP+THBEEQmiHBqAfIZgRh/JAfEwRBaIYEox4gmxkQmbE4eCoRctVPQ4KVs4X/QtE/iRLI8OrmTSQX3bxMxB48hcQaupnkxwRBEJoxHMEoz0JSXCxuPXmHfFVSEblpSHn1BhnldhgGVNEYCOx77OnnDNsBB5CnSjIMWLw/NwNtm/bGugcpOPe/tmgSthr3c1W7CQ4Wr/b+AC+Lxph2Q6r8/X5PPzjbDsCBGrqZ5McEQRCaMQjByKbFYHobWwh4fPAYc/j8sKs4yiBPwoZQB5j6TsCFbFWagfGfqGhyH2PXL+FYEidTJWgJm4Gr875FaORFpH/0qFoG1ncTQdR5Nd6pUgyDfCSs6A6JZSOM2PsvVnwjgWWDH7H/lTGGGTNwcGxnhG9+pGdRLkPCHH8ITfhwH3UGOVxKxvpuEIk6Y3UN3cyq+zGL9Li9mD9+EEK7BCO467cY+ms0jj3+oNpfBjYdcXvnY/ygUHQJDkbXb4fi1+hj0HS41lTxvPK3V7BqcjiGR6zCDWNrxHx4ghNLp2BYWAg6BXdG6MDxWLA/AZlqXesDnpxYiinDwhDSKRidQwdi/IL9SFB/MEEQZTAAwSjDvTkBEDuEYu3zHLzc2hvOQl9Mu1kQZXi5JQxO4gYY/29WweEGyGcvGLNvYOHXrmg45gwnF3SDfbMbAyQC8B17YWNxX+NHIg9HhthB9OVfeGZwfdK5eJn0tqCrPO8Vkt7qKLwNCPmz1QhxdkPYusdQeKm+SF8XAtPafbEzo8A2eUeGwE70Jf6qoZtZNT/Ox6P1fVBfzMCEEcLKWQIXGxEYxgSMZVNEHHvLvcVKkP8I6/vUh1ixX2gFZ4kLbEQMGBMGlk0jcOxtFcVLlc6bifgtEQh0EnLHmcBE1IUT58YjnvIfbUQ/bzNlGcW27vD2qgMroeI+mKPRiAMo1R7j7LOxnzfMFPYR28Ld2wt1rBTlZmDeaAQOGGXjjSBqFgMQjLnY09cK4qBlSgdnk6PRQWyD/vtzwabswPd1xPAZe4Z7tRkun7VglL/AljAJRN5jcKaKNyHn3iHsu1UTgl+KSz97QtRiNhKMV48ZAXIkb+YacmaNMeGc/jwz78hQ2Jv3wlZVREx66Wd4ilpgdg3dzKr4sSw+Cm0sGPBs22Lm2ZQCAS17h6sLusCJbwK+WziOFz36MsRHtYEFw4Nt25k4m1Igt2XvrmJBFyfwTfhwCz8O3T1F9/Oyr89gdkh9TkAJ4RDQDk1seMYlGGUJ+OMLCzA8GwTOOAtVkZHzcBP6e3BCkO+Cgfveq8S6DAl/fKG0j03gjCL7cAdjU38PCBk+XAbuw3vSjARRIQYgGAsqeWFd7sWaIUfq3gGoI2qG3+68xJ7+nFDxGoWT6apDZcm4dmg/DpxJUNO9mYdnFw9g/8EreKF6H9QUn69glCNxdTfY8p3Qd3dq6UiJQSJDXGQziJvMwh1DFIy5T3Bk4TRM++swnhhU1x+L1GuX8UAXv5EnYXU3Gwi9xiJGT20B6aVf4GX2Nf5RRcNkcZFoJm6CWTV0M3X341ycGukKPmOGwL8el55oJU/E0iBO0DDWCNumeoHlnsJIVz4Ys0D89bh01FSeuBRBnPBkrMNQeLjWVOG8aeu6wcKhNcL/uYbUnLMY7cY3KsEovTYZDQQmEDb7DaVHybB4u/lb1OYxqPXNBqQqiiO9hskNBDARcvVKmSE17NvN+LY2D0ytb7BBeTBBEJowAMHIVQwPV6CbkwAiaztYiczRIHw/4vcNgZvIA+HH0oqFCvcSju5oDsa0LRY+LdNNlbEX/e15ELWOwsOa6cEq4rMVjNknEe7GU76U7xqiACuHHE8XfAHTxjNwS4/5ZTPf4X11J1yx73BoWF0IGRNl16X74H14U5X6ic3Eu2pnpgwfziHCxxU/HNZN+eWeHwcPnhhfzH9UWixVEdm92fA3/wp/PS84m/zpAnxh2hgz9HkzK0BnP5ZexgQvARhReyxOKnsz5Xi+MFA5JlMy4rRyIp/08gR4CRiI2i9G+cOfY2GgECZ8CUac1u3+VuW8bPoLJKar7lp+jNEJxrS13SBm+KgTfqrcJEk2dTW6mDLgOQ/HccVA27S16CZmwK8TjlPlD8bqLqZgeM4YrjyYIAhNGIRgVCBPe4jzRw7iRGwSst8dxrC6ItQdfrhMNwGL1M29YMMTokVkPEpWI2k7vuNaleYIWvpCL5WXLnyuglFpU0aEwIXPqmHTHDzaPxejh49B1NHneh3zVh45XiwKhKnvNCiHwOoB9v2/mNbaDu7fVm9Ws/zxArTz/gaRm7Zjxc/t4SRugv/d1lEIse/x77TWsHP/Fqv1OcVaehPT/YSoHbYVOukFaSwm+/DA85mMWD3YW/5iEdqZt0DkvQK7KH4HmhaOZ/746OzHaesRwgkTvmQUzqjReHnHhsGRZwJRp3+gCJqmrQ+BKSdyJKPOlF8JAnk4NswRPBMROv3zVpWmHdU+r9EJRhZvVnaCyIQHl/CT5SdfceUZ464oTzBWcK0y9s1KdBKZgOcSjpPlD0bMGHfwOfsEr3hTHJwgCKIcBiMYi2DTcDzcAyL3ITigrosg6zB+cOFD0GASrhXWI1wrcVOoNXhW32B9lcI21ePzFIz5OBnuAp6gCWZVObwoR/K2PpAIGJiYmIAxa4Xf43U4V/4rXN3xN/43cQJmLNqPexmV3VuFYGyrXjDKUpCUons52HcxmNrKFpKQJbirmLpbRd6v+wbeP50tqNA54bezjzMC5iSUavRUCvsOMVNbwVYSgiXVyUw55EhcGgRLy6+x8qUaG2u0XR6ODnOACd8fcx9U9Rkpwfs16GreCFNU6lMhGNtWWTByQkHHV4GufixLmAN/oQmETWepjcBLr09BQ0W3qf8cJMgKZ4EL0XTWXTX3XYrrUxpCwO33554L7dHDeY0wwph3MhwunBgXNp2J22UfD+kNTPNVdEG3QpSiuynvJMJdeNzvpphZ/mDcmOartE+rqIfVaBgTxOePwQnGjFM/wUskwcC9r5Hx6DS2rl6NraceoFgr5OHiz14Q8N3x05mCKAv7ei261eLD7vvd0HX4jz74LAUj+wYrg4UwEffElqouZ5R5FMPruaNb5F7EHIpCd4kYfjNuaSGSpEg6/hu6e5gXzN5UbgxqtYzEjQp7jTQLxsyDg+Hz9fIqzZ5mM98itVq9VZwg+zsQIvN6aN/nBwwb9C3a1reETf/90DlOyGbirVaZYZF28U/0CWyNTuGrcbuSe8i+34HedmYIXPi0XKWp2XYy3J/TAnwTWww+VC0DFZCzDb0sPDDuQkGcrDqCUZbwB3oO2VEwhk1LdPVjaexUNFIIwpbz1A6DkcVHogUnKAWNp+OmTIrYqY2UwqTlPHXCRIb4yBac8BOg8fSbqjRt0MN5jVAwIicGYz0EYBhLNB74Nw5djUfC7Qs4uj0ak3r5wobHNVKFLfH7A4VFchAz1gMChoFl44H4+9BVxCfcxoWj2xE9qRd8bXjce4az3+8P1NiPIIhCDEswZp5FhI8Ykr47kHDkJzQwM4eztyfsxWZoNL54SZeCFzEPToMPIourGF+u6ARzvgTDj32ahRo/T8H4Css6Vk8wftg3APX77FDdNxZv1nRDnX77KhZJ7FvE/NYRbq5tMHj2Bhy7dAPXT63DpGAJhAJP/HyxIvHAImlxOzWCUYZbM5twf9+0GtHS6iF7sALd66iWL1FEW7nKy7THpirMiNUONu0wfnATQijkrskI4fPLhWK752Uhq5y++4DjP0ogDphbZoZ5RbaT4+mfrTmxYo3++/UgGHP3oq+NBCNVY+3YpMVop0kwymVlKveSX8+R4upEL/DEQViarL0A0tWPZbdmwE8RYWwRCVUveilkd2ehmWJ/80jEyTg7zvBTRgJbRN7jrFoWGe7Oaqbc3zwyTpWmDXo4rzEKRu5+Z1xbiFBPixKNSm5j+LD2CoCvvWLWdwcsUd1/NuMaFoZ6wkIxhrjoeAZ8ay8E+Noru+w7LEnmzkoQhCYMSDBm4fwvjWDq0hvbkp8juoMZaoduxBtWhifzv4TYKgzbC1fwUE1+4dUOw9a3z7G4vSkEHuNwXg91VlX4LAUjJy8ODbIDw7XSox5V0u7m7sem6fNxuYz6ydzxHWx9x+K0shLKQ0JUIHzHXyg1zurDoQiM2a26sbIX2BMegObfR+Nq2TUusvain6NvUXelehRjm4Jh5j0RV8selnkKI+qZo8fm6ks0WcICDJhwSsNST3IkbpqO+WWNoSD/PZ4nxCPh0Qu8fRKNkH47lAtUVwtZAhYMmIBTpTIjR9KyELT/33Vkyt7h4uz2sHMchANK4S/Dnbk/4g81S9XkHB0GZ2EDTLpaZiScRtvl49wYNzA8b/xaND6kGuQdwmB7Zww7VuDIirFnwWbemFjuZsrxZOFIzCk5e/r9ZoyZFFP0bMmfLkYHS2f8qMNEBl39WP4oCq0VEUTuebuipvjKZYEEJhC1j0YSK8ejqNbKSJ/3xCucpC1LwWoRAk64tI9OUqVpgx7Oa5SCUUX+W8Sf2YONq1dhzcbdOHEjCdl55xBRj8/VDwOwv1TrNB9v489gz8bVWLVmI3afuIGk7Dyci6gHPq82BpQ+mCCIMhiMYPxwaRL8TJ0QtvklWOlV/OojRsMp15UvwNz9/WEj7oilReOrCie/WKNH5FS0EQvhO+2GmpdlzfB5CkZOfK3txrXIa6HHpncVtrylVybCRyCAJGQ+Lr4ucRcyYzC+gRhilwB0DmoEe7cwbFLNgFWSdQt/dKgNuy5L8SA7CXuGt0TgxBN4XXiIPBdpSQm4dGgNIgf6Q9JydvnxSmXIOzoUDiI/TLiQXjrP0iSs6OyIsO3V/ZyGFDemNoLQ7CssUCekpVcw0UcAgSQE8y++VvNMypH1/AJW/dAUfj+XFs9VQXpjKhoJzfDVgpIzlbOw/TsJeqx/XWCD/MuY0KARflUILzaZa2y5IWzji9J5U6wdOKc9rHk82LSdg+slv36hyXacWJ3dnBMadgNVYrSa5B3HcBeH4u7tvKMY6iCC34QLpZbRkr85jlE+pvD56SRSlYXOw+MlX8NG0htbCtfUyj6NUR4+mKBOyWlAZz/O2oJQc8WSNb2xS035s7b2Uq79ZzfksHJiRtaWUJgzDKx770L5w7OwtZdiXUE7DDmsW8u32uc1ZsGoBnni3wgUMRAGLkTJ141auMbu34EirmEciIWVHkwQ/20MQzDmXMW0ZmZw7LkeSQqfZV9jTVdLmLWchnPPErC5vztE3hNwueS7XzX5RSQWgSdqid/vq+kTqiE+T8HI3YZ3O9HbngerkHV4XVE9kvcAK7srFghmwBPbwN7OHm4NGsDL1Q5m/IIJL4qNYUSwruOBBg090LCeB1ytheDbucPVggeBWAwh3wL2ru6o6y6Bi6MNzIWKL1UoupmEcA78BQcStbjHafvQ34m7psAKEq+6qOfqAd+GnpBw12Isg7E8sbJKQYYnhw/jTgU6Q568Eb0cRWj2m7pJBnn/3955AEV1/HGcd5Xe+yEqzYjEgho12NCIggZjidgTgyEqkujfKBINlmA0xpaYWLDEHls0ligjJmosWDKAWIITBXVQyiA4CAPM3Zvvf99xwAF3HE8POHQ/My/x7pZ9u7+3+/a7u7/dxe2fQpSbNjMCKWzcWsPPsz06dPDFWz5tIbM1hYg7bcKiG2Lrd8hUIk8/iqP1JwbbhjtCUmPro2fYOtSENILWcG//Nny9ZbCWWGD0r9wISiH2j7GFQGAMRx9/9OjVE927tIe7NalHJm9jwid9lP5fEmtntG3lWa/tyq/HwJc8X1n4SeiW4XJkP8xSCietlJ3CVJmaYEQ+Do51ImVABEuZF1q3kSnT48iJNHNLmHOndDh5wqe1LSRGElhYkIbf2AEePt4kzSIIPWbiHI9BI971WJ6C2M5iUtY8EaXyu6ymBAkRrZR7NA74kXSCyTfcvpKduRFJzyjUDZ6ACCLaGBNiZ00Lj+rhleN9rQRjOVK5k8MYKXos072IpTx1MbpKGUh7LGvy7dgolJaGAQjGEiQt8IepfQg2qTVIL5JWYLCbMWksSE/R8V3MPVXriC3S9CgXv5Dfzd9bXyE0m4nXVTByUzg3Yv1hzK0uvF7/WBibtQ2h9lZwkllBJCSNtcgU1s7e6DbkIyzYdh43zm7C/EmD0c3LCRYSMeyNTeDUcSRWXMjBw5OxGPWOF5ytSSfB2Bim5lawc/WEX8/BCItcgq2J91DU4HaMxZPTsRjq6wgzkZDci4hRIpZc/ILx5eEHGgReTdiH8UQ07daxvYwC/67sDev+a6HRRY7NwrZQe1g5cUJNAmcTIbGHCGIJsYmsPXqPnovt12uNgGqCfYj4CZ9gt45GXPHvSvS27l/lr8XZIPd0DAI9bGBqYgZLaztYm5lhuOoIFfmDQ5g90AvWKkHOCE3h3Gk4Fh7PRLniEX6fMwCeVlKS9npsx+ZhPxFzAotArE3XXfkU//2MyV8cr19Ylp3BZ+5OmPx7taxkn5xG7FBfOJqRMmVBOhgSS7h1n4Qfk/5D4uJhaO9gCmPrtgicfQjpt3bi017usDQ2JWXrQ6y+wu8UGv71WI5rnP8gd1LI6N3IUntM8nvrEWQjgMA6BPGVx87JryHGr+IUktG7s9Sevxz31gfBhhvdDYmvPtKOzcHfmxZhwfLDuFtf9eMbb210CcaGpoNQfOswVi1dgb03CqvSUX7/JNZ98y22X6qeqVA8SsTPcXHY8Odj1TcqeNyrLizyzsWgh5UAQtdxOKCj3rB55xDTwwoCoSvGHah/FoVCoRiEYKwHeRGePsxCgaYBFjYfRybLIBS6Ycoxvicc65fXVzASiq9gYVczIo5W4059aoub2gnsX7XpcstEjltL+yA0XnfjwT7ZgCE+05CosVHjVkUHov9393WOcNSH/NZS9AmN1703IvsEG4b4YJrmxBDKkDCtC6aoCTEl5UXIz83H81L+TeWL87PQTmyDgevuNMAVhDuBpx/GaZq3VYcTLm3cEaFpU8Mm4GXqMZu1F2NkQjCMGbyDZ2LJmh+wKnYq+rtJwR1b12d5sprbAYusvWMgExKhbuaN4JlLsOaHVYid2h9uUoaIyz5YnqyW95I9GGHKrfb1xyJNq2qq4BkvQXHvIBZFzURkZCQip49AJ0sBjEStMXAq+Rw5E1Hzd+Jm5YNtcDpK8ftkbgEJA5PQHaoFXeU4H+WhnH2Q9F+HikFOUh6+5hbiGEHcdbEyVBUNvVfpCUT17IcxM2OxeuMWbF6/AvMmBSjzy5h2wPRj6nsqluJEVE/0GzMTsas3Ysvm9VgxbxICuGfEmKLD9GMvt4k+hfKGYdiCUStyPNw/GZ5iAWyHbNDtp9LIvNaCkSD/7xeMcrdF7xU3tfrcsTm7MNIjDPt1z00aLmwONg2RNcyHjIibGT0j6p4cwUHi2TXSA2GvZAwWOZuGQKbyf6ufMiTO6IkIjYkhFJ5GRLu++vPRKrqAOX526DzrdAM3+s7F5hAv3UKw7CxmeHVrsrOja/Oy9bgoOR7h3Z0gqVqBS8SRgz8mrL2M/Dr2KUJyfDi6O0mqV/cyEjj4T8Day2qnWnG82IFQKee+MAzbdBqaR7yE0mMVm4pXuovUvhjLEdhV2Q9vcDrKcZnz72VEaPVZgqrckk5YXA+YMALYh+2v2jEh86dBsOD8LkPild9U0dB7yf/BykFuMOW2z6lMs8gSnoHTsDGpdn7l+GflILiZctvnqPJI0mjpGYhpG5M0PCMKhaKJFiUY2azfsSj8I4QN6gA7EenF+nyM/Q+bf0TrdReMHKV3tmF8l75YqmlbGsVjHJjYFrbDthpmT73oGlaHf459unwglQ7wpuj0dQppYuqDRd7p6eg8aJ2GKWkFHh+YiLa2w7D1lYxRsXejaZ2zcuvC5p3G9M6DNG8hwz7FkU+8Yf6yPlp1bFeM89FBGL7sQq1TmOpBeTydGbou0rTtSzXF/yzGu+2m4UwD9Hpj8Gr1WI7CzJtIungRSamZKNDxzCAvRObNJFy8mITUzAINdiEdht2jYC8QwzsyUTVa1wB0xssXvukoRUHOM5TUKBvleJ6bj+Ia5U+Ootw8FNVIIN97sSjJuYeUpEu4fD0NGc/qH+tmS3JwLyUJly5fR1rGswaMjFMoFHValGBU3N+AkW95o33XQITN24qrDW6xGpc3QTAqKS1EYe3GvDQde8P9YC72wLQEfSyV1TcKpK/uB3MjAZzHHdQhckpwMtwVIuteiDmbo3E6uTz7GnZ/9T68zZ0wapdqFXIVpUjfGw4/czE8piVoWLHKj5KT4XAVWaNXzFnVauBalGfj2u6v8L63OZxG7aq7MKn4DvZ82gkWQid8uLe2D3BD0GQ70kgXl/CM6wV+m+gAoUVnfLbndh1/1JLHSdi3OAwdbczQbUlyszXkBlWP5SlY5C+F2CsCf6gvEW9qmjIdhpJnCoWikRYlGA2VN9NmCuRdjUdEdzsIGTHaTjnC61SNJkXxGMfXbMDFBjRCZcnL0MtSAEZkg3YDwhAxex5ioucgaupYhPTygZ2EASO0Q8/o0zXyq8i7iviI7rATMhC3nYIj+jBGWTKW9bKEgBHBpt0AhEXMxryYaMyJmoqxIb3gYycBwwhh1zMap2vc7wXuJ6zCBD/ub4VwHbnj5ReF8bBdfZReXYTuFkzFVKB7JwQEDkRg3x7o5OkAE25akZGizYhNuKVj1roxMaR6XPDbJLhKPPHpyQYsjmpEmjIdhpJnCoWiGSoY9cCbZbMipOyaj4mB3rDitswhAsAxMA5XGjxnZuiwyP7rW4z0tVZuf1PpH8X5pQlMXOE/LBLrEh9W+XIWpezC/ImB8LYSKlf0ixwDEadHY7DZf+Hbkb6wVp3HXXkxAhO4+g9D5LpEPKwSWSzyrmzAp++6VPjTEbFo328pLvFbLNxIsMhP2oSZwR3hYiaq8CVjiLi2cEWHwPH4asf1hk9xNxKGVI9f3E/CuWQd2xA1AU2ZDkPJM4VC0QwVjHrgjbMZW4bCpxlIv5WKlLQM3f5aLRH2BR7dSMCh3Tuw89ejSPg7GZnPNQ3TsSgrfIqM9FtITUlDRqMYg8WLRzeQcGg3duz8FUcT/kZy5nONU+YVcGnKxqOMR9Dh1tU8kPJTlJ+L3IKSevLQ9NB3H4VCoWiHCkY9QG1GobR8aD2mUCgU7VDBqAeozSiUlg+txxQKhaIdKhj1ALUZhdLyofWYQqFQtEMFox6gNqNQWj60HlMoFIp2qGDUA9RmFErLh9ZjCoVC0Q4VjHqA2oxCafnQekyhUCja0SgY6UUvetGLXvSiF73oRS/1Sx06wvgSUJtRKC0fWo8pFApFO1Qw6gFqMwql5UPrMYVCoWiHCkY9QG1GobR8aD2mUCgU7VDBqAeozSiUlg+txxQKhaIdKhj1ALUZhdLyofWYQqFQtEMFox6gNqNQWj60HlMoFIp2qGDUA9RmFErLh9ZjCoVC0Q4VjHqA2oxCafnQekyhUCjaoYJRD1CbUSgtH1qPKRQKRTtUMOoBarM3lUIk/XYKDxSqj4YEqwBb9c/Kf+mgMAm/nXqA5suOHI+vXcPDqgQ0rX1pPaZQKBTtGI5gVDxHZkoSrqfnokz1VRUl+ch6/BQFdX4wDGhD8ybCIm9/GJxtx+FwqeorA4HN+xMxvTtiVPxtZP35FXq/PQIbb5WoftUCm4f9Yc6wHXcYzZMdFo8PfAQvsw6Ivlqu/NzU9qX1mEKhULRjEIKRzU/E/B62EAmEEDCm8PloX/UogyITW0MdYOw7C6ceZCIjIxM5L7SMmJQXICvjATKePm/SURLa0OibEvy77wuEr01RfW44bMElfPNBKGLPP6saYWssCrYMgUQyCBtzVV8YCGVp6xEiM0f7qQdwdv1QyMzb4ZNDj3XYowBbhkggGbQR9WenAL/NGITwX+7qWVjKkbbYH2IjIdwjElBMvmlq+758PWbxLOUAlkdNQGjQAAwY/AEm/28Njv/7QvV7LdhnSDmwHFETQhE0YAAGfzAZ/1tzHJqD84xbhSL7IjZ8GY4pkRtwVUdfwbBg8Tz9NDbFTseEEcEYFPQ+xs5Yil1J2aSEaIZ9loIDy6MwITQIAwYMxgeT/4c1x/+FJguxz9NxelMspk8YgeBBQXh/7Aws3ZWEbC2R84nbUHmRfhLr5n6MEcEDMWBQKMZHrcChtEKt7wNe4V+k4+S6ufiYPKuBAwYhdHwUVhxKQ6GWyPmmhWJYGIBglOPm4q6QOoRi8/1iPNo5Cs5iX0RfqxhleLRjBJyk7RB1tgBPdo2AA8PAYdQeZNcpYXLcWtYDxowZAr67rfXl0hhQwahPinB15Xtwe2s6EgpUXzUYFk9/HQeZSAjH4dvUpjYbh9LfJ8FO0gvf/deU3ZOGUfIoE9nKZJXicab2xraaUvw+yQ6SXt9BV3YU/21EsHMrjIj/F1wt1RfP4oNhbDMGewsqUtvU9n25elyGu1tGo62UgREjhqWzDC7WEjCMERjzjog8nl2zMSy7iy2j20LK/S62hLPMBdYSBowRA/OOkThe48XGM24lhUjdEYkAJzGJ0whGkiAiuFtKc1yMy3GBcBGT/JK0M+Rdz+VV+W+xDCE/pNbppJTd3YLRbaVK+4ktnSFzsYaE/J0RY46OkcdrtBPFl+MQ6KKyizLuivtwtpWF/IDUWpHzidswIeVnWxi8Tbg0S2Hr7g0vV0uIufJj2h5TD9fuRPILX3Z3G8K8TZT2kdq6w9vLFZbcs2NMSWf1MB7XiJxvWiiGiAEIxhLsH2MJaeAPygLGPlyDflJrjD1UAjZrDz50lcJnRgJ5DRLkd/BdgBmMRD744jw3BlENm7MPHzoJIG43Cxdq/tToUMGoLxTIIB0EmcQb0xOUT/wlKMbNIwdx/bnqYyNSfuFzeEq6YFFaU3ZPGotyXPjcE5Iui6A7Owo8/IV05Ew6YNafL/uc6lL6+2TYmw7HTtXwTVPb92XqsTw1Dj3MGAhse2PBmawKAS3PxaUVQXASGkHYKhwnqsqiHKlxPWDGCGDbewHOZFXIbXnuJawIcoLQSIhW4SdQGZxf3OQd+CQBi4LbwoQIIIeuffC2taBlCUbFPax41wY+70dj+7l7eFauQPHji/hpjBcRakRYWAZhw0O1vMhTEdfDDIzAFr0XnEGFOeXIvbQCQU5CGAlbIbzKQArcW/EubHzeR/T2c7j3rByK4se4+NMYeBHBbsRYImjDw2rRwituw0Setgw9ufJjHYCYyvJD3o93to+FBxF2QpfxOJhXbU9e4eVpWNaTs481AmIq7UNC39mOsR5ElAtdMP5gXpU9+aaFYpgYgGCsaKjErcnLr0CBnAPj4CrphK//eYT9Y2WQeEXgj2eqoITiC7PQTmwE876r8G/VwEMpkuZ1gFjggrEHcpq8p0IFo35QPNiIIbZCOI35FTkt4N0hT4lFJ+nbWPiPIQrGEqT/vhLR0d/haHpD5iTlSIntBOnbC9Gg7CgysXGINcReM5Cop3az/MIX8DJ5Dz+rhm6a2r7863EJTn3qBiFjgoDv/q3pBqN4gHWBpEFlrDBil+oFVnIKn7oJwZgE4Lvql5cSxYN1CCQNKmM1AhXBecZNyI8fAjOHdxD+82XkFJ/BtFZE2LSoEUYWhTm5dV0dCvYjzJ6IX8YCo/ZVjwaUnPoUbkIGJgHfqbUFHAo8WBdIhDkDqxG7UGkhtjAHuXUjx/4wewiMGFiM2kckTAV84zY8ynH5y3YQGYnR6euUmjMMbDZ++cAGAmLPoVsr20t+4csvf4l2IiOIO32NlJqBkf3LB7AREHsO3ap6j/NNC8VQMQDBSBqGO+sxxEkEiZUdLCWmaBd+CKkHJ6GVxAPhx/NrFiI2FwfGukBAxOG4g7nK3xSZmzCE9KYtAtfiXs33cJNABaM+KMIf4a0gEJPOQnLTCIRXRXFvBXoad0DMdT2mly1Ebt6rru5ikXvkY7RWTu0xELtPxMGnul7F3AhMTxh3iEFDs1Py10x4CKToufxuTUHzkshvLoK/6bv47n5FbI1i33rgXY/L/8YsLxEYSV+szqxtXwXurwxQ+mTKpp4G90TL/54FLxEDSd/VqBv8PlYGiGEklGHqaRKaZ9wc7LMMPHimehJliS1QMGpB/g8WdiS2YYwxbHtl76Qcf8/ygoiRoO/qzDpCQ3F/JQLERhDKpoIzp3bk+GdhR2JLBsbDtqtGd/UVd3OSj81DpGCErgg/VTuRLHI2BsGYEcB5ygmVQOcXPn/zEEgZIVzDT1WVv0rYnI0IMmYgcJ6CE8rI+aaFYqgYhGDkUOTfwV+//4aTSZkoyj2Kj1tL0HrKUWgapVbcI71xC9IQ+kXjamkRznzWFkJxB8y70jzFjQpGPZC/ByNtSGMasFKnD129FN/FoSXTMGV6HI7dV82TNBKKjFUIMK70t9UDbB7ORr8DO/cPdK9qrg/Fv1jRxxtDY7dj9/rP0ddJire/ulGzZ18HBTJWBcDYNxoNzk55Er70EUDg8yWS9GACzp59TLsg9mZFSvVuXx3wrsf5WxBMGkahLAIJGoRD6fGP4SgwgmTgz0p/t/wtwaRhJCIvIqFOI8vNkhz/2BECIwkG/pzNO+46vE6CsfgoJjkIiJhui5nnKo2Rjy3BxkSEyBCh2UD42JGbkh9YNWKtmWIcneRA7C5E25nnVM9FX3E3I+xT/DRQAiOBC8L/qNsuliVOh7uQlJ8B66HsS/IKz+LpTwMhMRLAJfyPuiKPlL3p7lzZG4D1XOR800IxWAxGMFbB5uNEuAck7pNwWOu8ZCmuRPtBzNgidOlC9DMnL+HJR5HfTIWNCsZXp+yPcLgIRHh7YbIOYVMPiofYNVoGkdJRnoFJ96VIbXBkZXh8aQ++/2o2ZsWswqGbBXVGFmrDCZreGgWNHFmZWfzzweYicV532MqCsTb5FRxx8+Ix1PsznFG2dSzy9o6Gc9fFOnwTOcHYW7NglGchM0vTH5fi2McOpCH3x5LbL/3UqsnbhMGm7TFXpT6127chsCDtGi/41mN52mL4i40g7rgQmgbFy6/MxVvctJ0/Z/vKVeBidNRYxstxZe5bymk7/8VpPONWfanOayQY8w+OhzMRx8I205BY2Y+Sp2GxvxhG4o5YqNlAmPuWiPzuj8X1Ffz8gxjvzInRNphWGbm+4m5WSvFHuAsRwqS8LbhBSldNyq9Gw5crP93jcEfZQecXvlT5vubK5wLcqBsY0b6cfbojThk537RQDBWDE4wFpz6Dl0SG8QeeoODuaezcuBE7T91GQa13Hpt/FB+5cX4tRmCsg7ChGXdPpoLxVeF6rANIYyrF+zuKVN/xp/DYFLRxH4LYA4k4EhcCmdSvQdOZ5Zkn8HWIB0yVQlO1KtOiG2Kv1j9irVXQFP6GiT7v4ceXGSplC5GdU/99daF48D0CJKZo03c0Pvp4Aj7o3Rbm1mNxqN5BS+2CsfC3ifB570cNI79y3FrcBUIjW0w88mppVlK8C8PNPKpGkV5FMHJO9u9P2sPLF5ZvPS5Pmof2XEPX7RuNDZ08NRZdiOgTdZiPa/JyJM1rrxSE3b65Q6xdGzlSY7uQOiBCh/nXeMat+lKd10UwFl9GdEepcnHFwB/vVdutPAnz2nOipBu+0WwgxHYhok/UAfM1GoijGJejO0LKCGA98Mdqdya9xN38FCfOgIeIAWPeAeO/P4JLqWm4ce4Ydq+Zg+G+1hCQ952421LcVmWRV/jiRMzwEIFhzNFh/Pc4cikVaTfO4djuNZgz3BfWAkZpv6WqyPmmhWKYGJZgLDyDSB8pZGP2IO33z9DOxBTO3p6wl5qgfVQCau6yosCDNX1hbCSE79wkDVM8TQcVjK8Ki8c/9H9FwfgCB8e1xeg9qlLCPsWmIa4IO1ifSmKRnfg1+rdyQ4+Ji7D1+AVcvXIK8XMGQCYWwfPz86pwmmEzV6OPBkEjv74Ab5O/50aSmgX5bawPcVVuWVGxbQh5URsPQ5X7l0ZYZK7uo0EwynF9wdsQizSNtihw79t3iAiywthDehCMJQcwxlqGT1WOYdrsq0QhryW6WFQfaFOOS7O9IJAGYp36qlod8K3H8usx8ONGAbvEQjWLXgN58kJ04n7vHIsUObFjjJ9yhLFL7E0NI4xyJC/spPy9c2wKz7hVX6rzOghG9hnOzvJT+rfZBa3HXfV8yq8jxo8bBax2YaiBPBkLO3G/d0asRgOxeHZ2Fvw4Xzu7IKxXj/yV4zYQ2AJcXhkKTzPOl1mtMyy0gldXX9hzLg391qKqivAKz6Lg8kqEeppVbFNUeTFCWHl1hS+3SEnSD2srI+ebFopBYkCC8Tn++qI9jF1GYdfD+1jTzwQ2odvwlJUjfXkvSC1HYHetHTxKDo2FNXnBDtzQvKurqGB8dUqOTIAdI0a3OF0LKEhHYft8LP+7tvopxJ6RtvCdcRrK9rE0DXEBvoiq8nkivDiCyOm/VmzRRBrojP3h6Nr5Q6y5VL39QwXPcSDMUdkRqQ/26U8YYOKN2ZdqC5pCnJraBqbDflF9fgXkaVgxbhZOadu9RvEA2+cvRx1zkC5U3v00pKbdRUZ2OtYEh2FPvbPcFaO8Jt6zUTc7pzC1jSmG/VL7JmX4c3orMAJv/O+yBlHHl9IjmGjvjI+PV4hP7fZVIH3lp1isvno67xdMn5NY1XFU3FuNfubO+KTC675B8K3HirtxeIcb5SM2u6gh+8ptgUSkIey7BpmsAnfj3lGOIHrPvlhnWo4TudxuESIjCfquyeQZt+pLdVq8YCzF7Z+GwlnIQOoTjqNPauVBcRdx73CjfKR8aDYQPvcUkfz3xRoNBiq9/ROGOgvBSH0QfvRJzfr/inEbGmXZqUjYvw0bN2zCtl9P4mpmEUr/jEQboQA24w6hdpeaV/iybKQm7Me2jRuwaduvOHk1E0WlfyKyjRACm3F1ZjX4poViWBiMYHxxYQ7p7TlhxC+PwJZfwv98pHhr7hXli1UpDKX9se5RzcpJBePrA/t0M4aQ3qfFsO0Vgk8b5Rcx20cEkSwYy88/qdHwFiZGoZ1UCpeugxDY3h6tRmyHasEt4TmuL+sHG7sgrLtdhMz9U9AtYDZOPqkMoEBJfibSLhzBptjx8Jd1w6I6zjm1KD2GyQ4S+M06h2c10lyOzPWD4Dhit+rzy1N+dR7ai03w7grNQrr84mz4iESQBS/H+Sd106t4fh/nNnyEjn6fQ107a6L02GQ4SPww61ytU3LKM7F+kCNG7K51vgURs4s6E1FiNx6HX96ToJrSE5ji4lA9va3FvoqnJxDhYwyfz/5AjtIopfh37Xuwlo3CjgyVDYpOI8LDB7M0Nfha4F2Pn+9AqCm3Fc4o7NOQ/+c7hyv3XLSbdJSkkAseClNuO5ZR+1A3+HPsHM7ta2eHSUdJaJ5x16FFC0Y5HuwZDw8JA7HbB9h8R1PBfY4doabKrYVGaTYQhpP3icBuEjhzqiN/sAfjPSRgxG74YPMdDbNTLx93y4B0ur8PgIR00ANW3tf4XqkJv/AVLjHk2QWsVHv/aoNvWijNiWEIxuJLiO5kAsf3tyCTKzHsE2wabA6TbtH48780/DLWHRLvWfi71rufCsbXCDYXe0fZQ2AZjPjaowk14EYeQpQbFzMCKWzcWsPPsz1823nBzc4Ewio/RNLYSqzg6uGNVg62cHa2hFhoB3c3MwhEUkjFQpjZu8G9tTtkLo6wNhWTBqLi78TOAfji8AMN04a1ycfBsU5gjESwlHmhdRs3dPB9C54yK4gZcwz48YEqnHbk6Udx9J96RI3iIbYNd4Sk09caFz+g9DZ+CuE2fSYNmNQa9nZEKL/VAb5v+aCtzBamIka5f1232KuahYU6+Qcx1omEF1lC5tUabdw8SDyekFkR25gPwI+1/ITLr8fAV8hAFn6yAUelyZH9MKv+NJSdwlSZmmCsY1+ZMj2OnJAyt4Q5ybOpkyd8WttCYiSBhQURAcYO8PDxJmkWQegxE+d4DFnwrsfyFMR25kaiPGuOZCspQUJEK+U+igN+JJ1g8g23r2RnbtTQM6queC9JQAQReIwJsTPXMeYZdx1arGBkkXU0Ar4mDITOg7E2RduwOLdvaGfliK1nVOXq5mpKEiLQittHccCPUB9nYLOOIsLXBIzQGYPXplTtu1iTl4u7xVCeisVdpWCkPbBMk49mbXiFL0cqd3IbI0WPZZp8dWvBNy2UZsUABGMJkhb4w9Q+BJvUGqQXSSsw2M244lgmx3cx91TdY7CoYHy9KLsRC39jbiXddQ29fjXYLGwLtYeVkwzWEgmcTUQQm1rD2bsbhny0ANvO38DZTfMxaXA3eDmZQyQUQmz3NkauuICchycRO+odeDlbw9zEGMam5rCyc4WnX08MDovEkq2JuFfU8NLEPjmN2KG+cDQjAoUIFqGICBcXPwR/eRgPdClO9iHiJ3yC3ToadMW/K9Hbun+1P1At2KxtCLW3ghMRqiKhCAKBCCKxBKbWMrTvPRpzt1+vNQKqDRZPTsdiqK8jzERCWEgEEEks4OIXjC9rC2g2D/uJmBNYBGJtuu4XveK/nzH5i+P1C8uyM/jM3QmTf6+WlTXtK4ZQYgm37pPwY9J/SFw8DO0dTGFs3RaBsw8h/dZOfNrLHZbGpnDq+CFWX+F3Cg3/eizHNc4vkRHCZfRuZKnZWH5vPYJsBBBYhyC+8ow0+TWlbxx3Csbo3Vlq7yw57q0Pgo1AAOuQeNWRajzjro0uwcjm4O9Ni7Bg+WHcrbeyEYpv4fCqpVix90b1GcHl93Fy3Tf4dvul6hkBxSMk/hyHuA1/4rF6kWjwvVjknZmDrhYkb/aB+PaqhtE9NeTXOD9P7pSQ0dhd00BYH2RD6oE1QuKrj5xj885gTlcL8r09Ar+9qmGUtxq+cRumPTVA6u25mB6wEgjhOu5A/bM5HLzCk+d3LgY9rAQQuo7DAV2R800LpdkxAMFYD/IiPH2YhYKGzyo1C1Qw6otiXFnYFWZEHK2+U5/a4qYxAtH/u5Y9hSG/tRR9QuMb8NJ+gg1DfDAtUUvroHiA7wP7V2143RS8OM+duGSDgevu1HAL0Aw3YtMP4zRN76nDiZw27pr3vmsCXqYes1l7MUYmBMOYwTt4Jpas+QGrYqeiv1vFyt4+y5PVOj8ssvaOgUzIgDHzRvDMJVjzwyrETu0PNylDBGAfLE9WC80rblIM7h3EoqiZiIyMROT0EehkKYCRqDUGTiWfI2ciav5O3Kx8WCV7MMKUAbc1zCJNCzvUUB7ZKDACYxKKHSo31vLzUfDgRvkl1a5C8pSvlQtxjMRda24309B7FR/HFFcicolItusYjFGjR2N0jetDjInYWL3Ih3Qc946RQcgwMPMOxswla/DDqlhM7e9WsfK5z3JUm7MYx6e4KkfihXYdETyqdtyj8eGYCGysjJxX3AQDtGfpiSj07DcGM2NXY+OWzVi/Yh4mBXDpZ2DaYTqO1dr0kFf40hOI6tkPY2bGYvXGLdi8fgXmTQpQlmPGtAOmH3taLaYJfNNCMUwMWzC2EKjN9Ij8P/wyyh22vVfgpvoLWR3Sw9410gNh+3VPhBouLHI2DYFMmw9aDcqQOKMnIuqcklABm7MLIz3C0GTmKLqAOX526DxLtcBIJ7nYHOKlWwiWncUMr27Ndjb3y9bjouR4hHd3Up53XOkOIXHwx4S1lzXsDVuE5PhwdHeSVK8uZSRw8J+AtZdrnWpF4BN36bGKzbwrwtW9GMsR2FW51cSLHQiVku8shmGbjodYfpnzo2UgavUZElSFVX4rDj1MiMi1J+WucmOCzJ8wyILzuwxBvPqoXEPv9Xw/wmwFNVfd1roENmNxSH0euSgZ8eHd4cSdB60Kw0gc4D9hLS7XMNBz7A+zVW7foh5fjUtgg7HqkTc4boIB2lP+z0oMcjNVyzO5p6UnAqdtRJKGTYt5hZf/g5WD3GDKbZ9TaRuRJTwDp2FjUt1yzDctFMOECkY9QG2mZ0rvYNv4Lui7VNO2NAo8PjARbW2HYatB9kqLcG11OD7fp8sHssLZ27TOWax1YfNOY3rnQZq3iFE8xoGJbWE7bGvjnJJQdA2rwz/Hvqr59WKcjw7C8GUXNJ7CpBHlsXdm6LpI03Yy1RT/sxjvtpuGM7oVdKPwavVYjsLMm0i6eBFJqZko0KV55YXIvJmEixeTkJpZoKOs8IxbJ6SzsnsU7AVieEcmEimlm9KCHDwrqfnAy5/nIr+45qi2vCgXeUXqCeR/r5dBXpiJm0kXcTEpFZmvbqAa6I7bgO3JliDnXgqSLl3G9bQMPNM1HcArPIuSnHtISbqEy9fTkKErcr5poRgcVDDqAWqzxqAUhYW1lUMp0veGw89cDI9pCfX6IDUXinRuOxcjCJzH4aAORVVyMhyuImv0ijmrWu1bi/JsXNv9Fd73NofTqF2osxaoNB17w/1gLvbAtITGsIYC6av7wdxIAOdxB1UCkTQSxSXkv3x4gd8mOkBo0Rmf7bmN2i6iJY+TsG9xGDramKHbkuQGTHE3Dm9MPZanYJG/FGKvCPzRMOfWl6cp79VcUHtS3hCoYNQD1GaNjyLvKuIjusNOyEDcdgqO8DnCo0lR4PHxNdhwsdbWNJooS8ayXpYQMCLYtBuAsIjZmBcTjTlRUzE2pBd87CRgOH+untE4XSO/CuRdjUdEdzsIGTHaTjnC60QTXige4/iaDbj4io1T6dVF6G7BwIjk1dK9EwICByKwbw908nSACTetxUjRZsQm3NIxa92YvCn1uOC3SXCVeOLTkw0oo69IU96ruaD2pLwpUMGoB6jNGo+ilF2YPzEQ3lZC5Yp5kWMg4q401sRW08Nm/4VvR/rCmtv+RuULpPQHEpjA1X8YItcl4mGViCpCyq75mBjoDSsinDnx5RgYh5ZhDhb5SZswM7gjXMxEFX5qDBH/Fq7oEDgeX+243vAp7kbiTanHL+4n4Vyyji2O9ERT3qu5oPakvClQwagHqM0aExZlhU+RkX4LqSlpyNCzf5JhwOLFoxtIOLQbO3b+iqMJfyM58zk0rnlmy1D4NAPpt1KRkpahB3+2ZoDkoSg/F7kFJZrz2EzQekyhUCjaoYJRD1CbUSgtH1qPKRQKRTtUMOoBajMKpeVD6zGFQqFohwpGPUBtRqG0fGg9plAoFO1QwagHqM0olJYPrccUCoWiHSoY9QC1GYXS8qH1mEKhULRDBaMeoDajUFo+tB5TKBSKdjQKRnrRi170ohe96EUvetFL/VLHSPV/CoVCoVAoFApFI1QwUigUCoVCoVDqhQpGCoVCoVAoFEq9UMFIoVAoFAqFQqkXKhgpFAqFQqFQKPVCBSOFQqFQKBQKpV6oYKRQKBQKhUKh1APwf8+GX+Q7eEKsAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "927e6442-c074-4d69-a3fa-f54b283efc8f",
   "metadata": {},
   "source": [
    "![Captسسسure.PNG](attachment:5a58e93d-f638-46c5-bff6-d37fe7fc488c.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "240a3f9f-67be-4d22-890a-8a182025e5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Siavash |25 |1.85\n",
      "Soroush |16 |1.72\n",
      "Guido   |64 |1.80\n"
     ]
    }
   ],
   "source": [
    "users = [\n",
    "{\"name\": \"Siavash\", \"age\": 25, \"height\": 1.85},\n",
    "{\"name\": \"Soroush\", \"age\": 16, \"height\": 1.72},\n",
    "{\"name\": \"Guido\", \"age\": 64, \"height\": 1.8}\n",
    "]\n",
    "for user in users:\n",
    "    print(f\"{user['name']:<8}|{user['age']:<3}|{user['height']:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf202e02-5139-4db6-ac7d-9010e0850c9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bb1433a-cf5a-4066-ae56-d1e2f80726b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27ad9fbc-7e21-4168-b95c-eef64d192f0f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Escape Character"
   ]
  },
  {
   "attachments": {
    "8001fb57-d09e-46d1-a909-ae2d102d96f6.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ef8dd5bb-6cff-4cbf-b736-5d746e11dfe9",
   "metadata": {},
   "source": [
    "![Captur111111e.PNG](attachment:8001fb57-d09e-46d1-a909-ae2d102d96f6.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff029bf-9b1f-4014-8cdb-a91ebf783837",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To insert characters that are illegal in a string, use an escape character.\n",
    "# An escape character is a backslash \\ followed by the character you want to insert.\n",
    "txt = \"We are the so-called \"Vikings\" from the north.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ce31ae-5278-455c-ac8c-aec4b24dec81",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"We are the so-called \\\"Vikings\\\" from the north.\"\n",
    "txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ca0857b-a71f-470e-a8e4-304a25d1633a",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = 'It\\'s alright.'\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e0743e1-41d6-4d1a-a3db-fe535c1b313a",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"This will insert one \\\\ (backslash).\"\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db4afe10-8096-4a28-bbea-194df4bf3ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello\\nWorld!\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e90fcaaf-0b51-4da8-95cf-7f6454f91020",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello\\tWorld!\"\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1584135f-f8bd-468c-b31d-b3c9af7a1442",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This example erases one character (backspace):\n",
    "txt = \"Hello \\bWorld!\"\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96158b5d-6742-4ac0-aa1c-cd7b9ae72f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carriage Return\n",
    "txt = \"Hellpqoeqyy\\rWorld!\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ec2eee6-b883-497c-b465-5a85b139a671",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"HI    World!\\rHELLO\"\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904c6a1f-fbc9-4271-9975-bfc3a7817577",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A backslash followed by three integers will result in a octal value:\n",
    "txt = \"\\110\\145\\154\\154\\157\"\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccdf3f15-17b0-4bc6-a868-9c5e19dadf65",
   "metadata": {},
   "outputs": [],
   "source": [
    "'\\108'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33cdf3d6-e067-4d39-a51e-c903a897c3e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A backslash followed by an 'x' and a hex number represents a hex value:\n",
    "txt = \"\\x48\\x65\\x6c\\x6c\\x6f\"\n",
    "print(txt) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cf71cc-a781-4362-aaf0-98dce0fc0100",
   "metadata": {},
   "source": [
    "### String Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c455f671-fc50-4e2e-a64f-d9eddd648d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converts the first character to upper case\n",
    "# capitalize()\tConverts the first character to upper case\n",
    "txt = \"hello, and welcome to my world.\"\n",
    "\n",
    "x = txt.capitalize()\n",
    "\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95c6ea24-fa94-4dd9-8aae-fa4c78d2f1f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"36 is my age.\"\n",
    "\n",
    "x = txt.capitalize()\n",
    "\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233d8ea8-3322-4e69-b650-9e5f140bc31f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converts string into lower case. like lower() but more powerfull.\n",
    "# casefold()\tConverts string into lower case\n",
    "txt = \"Hello, And Welcome To My World!\"\n",
    "\n",
    "x = txt.casefold()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769d3aad-f0f0-4830-a0c5-0448c65aafb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns a centered string.\n",
    "txt = \"banana\"\n",
    "\n",
    "x = txt.center(20,'-')\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b500a8c8-9487-4a9a-beea-953b4f7faf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns the number of times a specified value occurs in a string.\n",
    "txt = \"I love apples, apple are my favorite fruit\"\n",
    "\n",
    "x = txt.count(\"apple\")\n",
    "\n",
    "print(x)\n",
    "\n",
    "\n",
    "# string.count(value, start, end)\n",
    "# value\tRequired. A String. The string to value to search for\n",
    "# start\tOptional. An Integer. The position to start the search. Default is 0\n",
    "# end\tOptional. An Integer. The position to end the search. Default is the end of the string\n",
    "\n",
    "txt = \"I love apples, apple are my favorite fruit\"\n",
    "\n",
    "x = txt.count(\"apple\", 10, 24)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aa91f09-a780-4ddf-a5ec-aa0fcfcf569f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode()\tReturns an encoded version of the string.\n",
    "# UTF-8 encode the string:\n",
    "\n",
    "txt = \"My name is Ståle\"\n",
    "\n",
    "x = txt.encode()\n",
    "\n",
    "print(x)\n",
    "\n",
    "# string.encode(encoding=encoding, errors=errors)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8519606a-05c7-41d8-910f-3ca06007bf34",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"My name is Ståle\"\n",
    "\n",
    "print(txt.encode(encoding=\"ascii\",errors=\"backslashreplace\"))\n",
    "print(txt.encode(encoding=\"ascii\",errors=\"ignore\"))\n",
    "print(txt.encode(encoding=\"ascii\",errors=\"namereplace\"))\n",
    "print(txt.encode(encoding=\"ascii\",errors=\"replace\"))\n",
    "print(txt.encode(encoding=\"ascii\",errors=\"xmlcharrefreplace\"))"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "aa8de524-ba14-4461-b1f4-424dd40eadc2",
   "metadata": {},
   "source": [
    "![fgdf.PNG](attachment:f00aaa65-2353-497d-843c-64b7a6162b40.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "effea244-0639-448b-8fa2-5c8349ba566e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# endswith()\tReturns true if the string ends with the specified value.\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.endswith(\".\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0db2d6-4efb-4bca-824d-811d4e473bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.endswith(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to check if the string ends with\n",
    "# start\tOptional. An Integer specifying at which position to start the search\n",
    "# # end\tOptional. An Integer specifying at which position to end the search\n",
    "\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.endswith(\"my world.\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7a923cf-3e4e-493a-b5d6-51fba294cc4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.endswith(\"my world.\", 5, 11)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2fb5028-195f-475d-8248-4ea7ad8caca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expandtabs()\tSets the tab size of the string.\n",
    "# Set the tab size to 2 whitespaces:\n",
    "\n",
    "txt = \"H\\te\\tl\\tl\\to\"\n",
    "\n",
    "x =  txt.expandtabs(2)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ed87a0-11f0-45a1-9646-3bc1e2b79019",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"H\\te\\tl\\tl\\to\"\n",
    "\n",
    "print(txt)\n",
    "print(txt.expandtabs())\n",
    "print(txt.expandtabs(2))\n",
    "print(txt.expandtabs(4))\n",
    "print(txt.expandtabs(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7578f466-6ace-491c-9edc-0b9c644e70c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find()\tSearches the string for a specified value and returns the position of where it was found.\n",
    "# Where in the text is the word \"welcome\"?:\n",
    "\n",
    "\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.find(\"welcome\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e99fb2-e1a2-4cf8-a935-2353f9e70c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The find() method finds the first occurrence of the specified value.\n",
    "\n",
    "# The find() method returns -1 if the value is not found.\n",
    "\n",
    "# The find() method is almost the same as the index() method,\n",
    "# the only difference is that the index() method raises an exception if the value is not found. (See example below).\n",
    "\n",
    "# string.find(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to search for\n",
    "# start\tOptional. Where to start the search. Default is 0\n",
    "# end\tOptional. Where to end the search. Default is to the end of the string.\n",
    "\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.find(\"e\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9d4b7b2-9a7b-4081-a06e-e0e7db251ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.find(\"e\", 5, 10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76d1818d-de61-471e-b998-e3f3e328718b",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "print(txt.find(\"q\"))\n",
    "print(txt.index(\"q\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9023086a-9f57-4856-96e3-d1d65ecbe1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# format()\tFormats specified values in a string.\n",
    "# Insert the price inside the placeholder, the price should be in fixed point, two-decimal format:\n",
    "\n",
    "txt = \"For only {price:.2f} dollars!\"\n",
    "print(txt.format(price = 49))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16bdd1be-0d9c-4db4-bf38-bdfe2ea96651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The format() method formats the specified value(s) and insert them inside the string's placeholder.\n",
    "\n",
    "# The placeholder is defined using curly brackets: {}. Read more about the placeholders in the Placeholder section below.\n",
    "\n",
    "# The format() method returns the formatted string.\n",
    "\n",
    "\n",
    "# string.format(value1, value2...)\n",
    "\n",
    "# value1, value2...\tRequired. One or more values that should be formatted and inserted in the string.\n",
    "\n",
    "# The values are either a list of values separated by commas, a key=value list, or a combination of both.\n",
    "\n",
    "# The values can be of any data type.\n",
    "\n",
    "txt1 = \"My name is {fname}, I'm {age}\".format(fname = \"John\", age = 36)\n",
    "txt2 = \"My name is {0}, I'm {1}\".format(\"John\",36)\n",
    "txt3 = \"My name is {}, I'm {}\".format(\"John\",36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80b03813-f003-44b1-954f-295aa4670ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To demonstrate, we insert the number 8 to set the available space for the value to 8 characters.\n",
    "\n",
    "#Use \"<\" to left-align the value:\n",
    "\n",
    "txt = \"We have {:<8} chickens.\"\n",
    "print(txt.format(49))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d702fb9-0784-4662-9853-a1e3a1cfa43f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To demonstrate, we insert the number 8 to set the available space for the value to 8 characters.\n",
    "\n",
    "#Use \">\" to right-align the value:\n",
    "\n",
    "txt = \"We have {:>8} chickens.\"\n",
    "print(txt.format(49))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05827055-592c-44b7-b861-b3dcba95c7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To demonstrate, we insert the number 8 to set the available space for the value to 8 characters.\n",
    "\n",
    "#Use \"^\" to center-align the value:\n",
    "\n",
    "txt = \"We have {:^8} chickens.\"\n",
    "print(txt.format(49))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ea632af-5602-4594-84ef-5ba4f546ae35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To demonstrate, we insert the number 8 to specify the available space for the value.\n",
    "\n",
    "#Use \"=\" to place the plus/minus sign at the left most position:\n",
    "\n",
    "txt = \"The temperature is {:=1} degrees celsius.\"\n",
    "\n",
    "print(txt.format(-5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4e2c95a-5cae-4870-8ae2-566fd8f12291",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"+\" to always indicate if the number is positive or negative:\n",
    "\n",
    "txt = \"The temperature is between {:+} and {:+} degrees celsius.\"\n",
    "\n",
    "print(txt.format(-3, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbe2e0f8-95b5-4c8e-8767-e8a9d9ca23d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"-\" to always indicate if the number is negative (positive numbers are displayed without any sign):\n",
    "\n",
    "txt = \"The temperature is between {:-} and {:-} degrees celsius.\"\n",
    "\n",
    "print(txt.format(-3, 7))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e444b07-4cf4-485a-ae51-44722fc3d609",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \" \" (a space) to insert a space before positive numbers and a minus sign before negative numbers:\n",
    "\n",
    "txt = \"The temperature is between {: } and {: } degrees celsius.\"\n",
    "\n",
    "print(txt.format(-3, 7))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c472134-1879-48c5-9ca2-2409ba229645",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \",\" to add a comma as a thousand separator:\n",
    "\n",
    "txt = \"The universe is {:,} years old.\"\n",
    "\n",
    "print(txt.format(13800000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6ac45e2-8856-4fe1-a118-36385e455c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"_\" to add a underscore character as a thousand separator:\n",
    "\n",
    "txt = \"The universe is {:_} years old.\"\n",
    "\n",
    "print(txt.format(13800000000))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54c5112f-1895-407c-a568-63a9a9e470fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"b\" to convert the number into binary format:\n",
    "\n",
    "txt = \"The binary version of {0} is {0:b}\"\n",
    "\n",
    "print(txt.format(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156c59fb-2873-46c2-8a3b-920a5a0ce7b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"d\" to convert a number, in this case a binary number, into decimal number format:\n",
    "\n",
    "txt = \"We have {:d} chickens.\"\n",
    "print(txt.format(0b101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "793dc422-21d0-4673-ad4d-103cedf5b1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"e\" to convert a number into scientific number format (with a lower-case e):\n",
    "\n",
    "txt = \"We have {:e} chickens.\"\n",
    "print(txt.format(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fea2327-4840-4988-97ef-591c62b7ca25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"E\" to convert a number into scientific number format (with an upper-case E):\n",
    "\n",
    "txt = \"We have {:E} chickens.\"\n",
    "print(txt.format(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6190082e-1d41-4304-b916-c30b2902b67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"f\" to convert a number into a fixed point number, default with 6 decimals, but use a period followed by a number to specify the number of decimals:\n",
    "\n",
    "txt = \"The price is {:.2f} dollars.\"\n",
    "print(txt.format(45))\n",
    "\n",
    "#without the \".2\" inside the placeholder, this number will be displayed like this:\n",
    "\n",
    "txt = \"The price is {:f} dollars.\"\n",
    "print(txt.format(45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c080e66-3293-405a-81de-81685580d1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"F\" to convert a number into a fixed point number, but display inf and nan as INF and NAN:\n",
    "\n",
    "x = float('inf')\n",
    "\n",
    "txt = \"The price is {:F} dollars.\"\n",
    "print(txt.format(x))\n",
    "\n",
    "#same example, but with a lower case f:\n",
    "\n",
    "txt = \"The price is {:f} dollars.\"\n",
    "print(txt.format(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a94deae-a449-4db8-a722-5ef8f25e1065",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"o\" to convert the number into octal format:\n",
    "\n",
    "txt = \"The octal version of {0} is {0:o}\"\n",
    "\n",
    "print(txt.format(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec5f3f6-6647-4c02-95a1-9df33873619b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"x\" to convert the number into Hex format:\n",
    "\n",
    "txt = \"The Hexadecimal version of {0} is {0:x}\"\n",
    "\n",
    "print(txt.format(255))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6388d0ba-1e5a-4ccb-a960-629b8704bdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use \"%\" to convert the number into a percentage format:\n",
    "\n",
    "txt = \"You scored {:%}\"\n",
    "print(txt.format(0.25))\n",
    "\n",
    "#Or, without any decimals:\n",
    "\n",
    "txt = \"You scored {:.0%}\"\n",
    "print(txt.format(0.25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d10649ba-cac0-4b07-b861-eb025ebd7e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input dictionary \n",
    "profession = { 'name':['Barry', 'Bruce'], \n",
    "\t\t\t'profession':['Engineer', 'Doctor'], \n",
    "\t\t\t'age':[30, 31] } \n",
    "\t\t\t\t\t\n",
    "# Use of format_map() function \n",
    "print('{name[0]} is an {profession[0]} and he'\n",
    "\t' is {age[0]} years old.'.format_map(profession)) \n",
    "\t\t\n",
    "print('{name[1]} is an {profession[1]} and he'\n",
    "\t' is {age[1]} years old.'.format_map(profession))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0dfbc3b-23ce-4e11-a43e-d4a19cf08c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# index()\tSearches the string for a specified value and returns the position of where it was found.\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.index(\"welcome\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a323b316-8c4f-46b7-92b0-25a1cbb4090c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.index(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to search for\n",
    "# start\tOptional. Where to start the search. Default is 0\n",
    "# end\tOptional. Where to end the search. Default is to the end of the string.\n",
    "\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.index(\"e\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "931a74e3-e6d8-472f-baac-c663860b3efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.index(\"e\", 5, 10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c2bfec-822c-4ab5-abfd-e977302e2e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the value is not found, the find() method returns -1, but the index() method will raise an exception:\n",
    "\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "print(txt.find(\"q\"))\n",
    "print(txt.index(\"q\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "944b9727-c8ae-4df9-8385-6c94add297e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isalnum()\tReturns True if all characters in the string are alphanumeric.\n",
    "txt = \"Company12\"\n",
    "\n",
    "x = txt.isalnum()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c0f067a-33d4-4ef0-9e34-ad903a24dd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Company 12\"\n",
    "\n",
    "x = txt.isalnum()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fec1e51-a4cd-4f0b-ae47-2dcbe4479deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isalpha()\tReturns True if all characters in the string are in the alphabet.\n",
    "txt = \"CompanyX\"\n",
    "\n",
    "x = txt.isalpha()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be0dd19d-6d0e-4843-9d50-bb98bacbe018",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Company10\"\n",
    "\n",
    "x = txt.isalpha()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7c33ca9-27aa-4518-b424-4a0ea4c37ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isascii()\tReturns True if all characters in the string are ascii characters.\n",
    "txt = \"Company123\"\n",
    "\n",
    "x = txt.isascii()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b094d8d-48fa-46bf-8a00-f8e138b6989c",
   "metadata": {},
   "source": [
    "##### ASCII"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f9a7716-97f2-4496-b6b3-785cfd254040",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ASCII stands for the \"American Standard Code for Information Interchange\".\n",
    "\n",
    "# It was designed in the early 60's, as a standard character set for computers and electronic devices.\n",
    "\n",
    "# ASCII is a 7-bit character set containing 128 characters.\n",
    "\n",
    "# It contains the numbers from 0-9, the upper and lower case English letters from A to Z, and some special characters.\n",
    "\n",
    "# The character sets used in modern computers, in HTML, and on the Internet, are all based on ASCII.\n",
    "\n",
    "# The following tables list the 128 ASCII characters and their equivalent number.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d50a742-24ac-4d3f-a07c-61867633140d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ASCII Device Control Characters\n",
    "# The ASCII control characters (range 00-31, plus 127) were designed to control hardware devices.\n",
    "\n",
    "# Control characters (except horizontal tab, line feed, and carriage return) have nothing to do inside an HTML document."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "303581bd-3c15-49d1-913a-bdab89451e03",
   "metadata": {},
   "source": [
    "![Capt123ure.PNG](attachment:08258848-be66-4432-8cd9-23aa9fafb229.PNG)"
   ]
  },
  {
   "attachments": {
    "a5532325-6283-453f-99ce-5a8f3b2a44c5.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "41d47310-d36b-49c6-88a0-3af95c4df292",
   "metadata": {},
   "source": [
    "![Captur353e.PNG](attachment:a5532325-6283-453f-99ce-5a8f3b2a44c5.PNG)"
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "00d98259-891c-428f-bc39-e80c76390ff2",
   "metadata": {},
   "source": [
    "![C11apture.PNG](attachment:0db39692-47d4-4b04-a1af-de4a79550561.PNG)"
   ]
  },
  {
   "attachments": {
    "51c56585-a151-40b4-850a-1395c7cebcfe.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a57f2d80-85f7-4cf8-8558-8ffefbc64811",
   "metadata": {},
   "source": [
    "![Cap2222ture.PNG](attachment:51c56585-a151-40b4-850a-1395c7cebcfe.PNG)"
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"
    }
   },
   "cell_type": "markdown",
   "id": "f30fee8f-ea47-4ed9-b95a-c350d087ef09",
   "metadata": {},
   "source": [
    "![Captu333re.PNG](attachment:20e59f4d-c49e-4bd5-b265-f20c45509d35.PNG)"
   ]
  },
  {
   "attachments": {
    "0e3f2940-5d01-42b4-8fa5-6b7eda5f5f9b.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0fc636f6-4488-4715-90af-c343b19282f1",
   "metadata": {},
   "source": [
    "![Captur4444e.PNG](attachment:0e3f2940-5d01-42b4-8fa5-6b7eda5f5f9b.PNG)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "f363e755-511b-4a82-b345-bfa3e2229163",
   "metadata": {},
   "source": [
    "![Captur8888e.PNG](attachment:c04c97ce-1248-48be-8f21-e4ae1754f50d.PNG)"
   ]
  },
  {
   "attachments": {
    "c3364ad3-66fa-4310-803b-1d3f08c97113.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b8b57218-ad01-4761-8017-c9ce5444adf0",
   "metadata": {},
   "source": [
    "![Captur9999e.PNG](attachment:c3364ad3-66fa-4310-803b-1d3f08c97113.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38be123-ec70-46b5-899f-2a9143b1ac56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isdecimal()\tReturns True if all characters in the string are decimals.\n",
    "txt = \"1234\"\n",
    "\n",
    "x = txt.isdecimal()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42f34c56-89b0-4312-ab95-db16f35f1f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\u0030\" #unicode for 0\n",
    "b = \"\\u0047\" #unicode for G\n",
    "\n",
    "print(a.isdecimal())\n",
    "print(b.isdecimal())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb9e725d-872e-44a3-b465-843d6a1f686c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isdigit()\tReturns True if all characters in the string are digits\n",
    "txt = \"50800\"\n",
    "\n",
    "x = txt.isdigit()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "319a3069-4710-4f01-bfb2-e2d8855958d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\u0030\" #unicode for 0\n",
    "b = \"\\u00B2\" #unicode for ²\n",
    "\n",
    "print(a.isdigit())\n",
    "print(b.isdigit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b467dffe-0d82-4c2b-9311-a6f3c6aedae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isidentifier()\tReturns True if the string is an identifier.\n",
    "\n",
    "txt = \"Demo\"\n",
    "\n",
    "x = txt.isidentifier()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "975d6d25-55b1-4f41-b112-c86a58ba9f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A string is considered a valid identifier if it only contains alphanumeric letters (a-z) and (0-9),\n",
    "# or underscores (_). A valid identifier cannot start with a number, or contain any spaces.\n",
    "\n",
    "a = \"MyFolder\"\n",
    "b = \"Demo002\"\n",
    "c = \"2bring\"\n",
    "d = \"my demo\"\n",
    "\n",
    "print(a.isidentifier())\n",
    "print(b.isidentifier())\n",
    "print(c.isidentifier())\n",
    "print(d.isidentifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cad80c2-f19f-4635-9aec-13c97591d98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# islower()\tReturns True if all characters in the string are lower case\n",
    "\n",
    "txt = \"hello world!\"\n",
    "\n",
    "x = txt.islower()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "404b465c-9934-4625-a4c9-d031dc5b2c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello world!\"\n",
    "b = \"hello 123\"\n",
    "c = \"mynameisPeter\"\n",
    "\n",
    "print(a.islower())\n",
    "print(b.islower())\n",
    "print(c.islower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa3774a3-558f-4dee-8b1b-9d7784771b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isnumeric()\tReturns True if all characters in the string are numeric\n",
    "txt = \"565543\"\n",
    "\n",
    "x = txt.isnumeric()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e601250-1d93-4e02-8bdf-993e0f9c5ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\\u0030\" #unicode for 0\n",
    "b = \"\\u00B2\" #unicode for &sup2;\n",
    "c = \"10km2\"\n",
    "d = \"-1\"\n",
    "e = \"1.5\"\n",
    "\n",
    "print(a.isnumeric())\n",
    "print(b.isnumeric())\n",
    "print(c.isnumeric())\n",
    "print(d.isnumeric())\n",
    "print(e.isnumeric())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "836a8a0b-127a-42bd-8ca9-492d98c91969",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isprintable()\tReturns True if all characters in the string are printable\n",
    "txt = \"Hello! Are you #1?\"\n",
    "\n",
    "x = txt.isprintable()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d5862f7-3bcc-4fc2-89dd-baef2c43fb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello!\\nAre you #1?\"\n",
    "\n",
    "x = txt.isprintable()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8e80b64-b91b-4067-ba0b-935fa3215ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isspace()\tReturns True if all characters in the string are whitespaces\n",
    "txt = \"   \"\n",
    "\n",
    "x = txt.isspace()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40bb402f-4a5e-4d84-bc2e-fe6a4aa4c71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"   s   \"\n",
    "\n",
    "x = txt.isspace()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "189c69d9-c19c-48ab-932e-d68aa567a34b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# istitle()\tReturns True if the string follows the rules of a title\n",
    "txt = \"Hello, And Welcome To My World!\"\n",
    "\n",
    "x = txt.istitle()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8032df3-a08b-4132-8c8e-3e880c241b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"HELLO, AND WELCOME TO MY WORLD\"\n",
    "b = \"Hello\"\n",
    "c = \"22 Names\"\n",
    "d = \"This Is %'!?\"\n",
    "\n",
    "print(a.istitle())\n",
    "print(b.istitle())\n",
    "print(c.istitle())\n",
    "print(d.istitle())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01694c4f-7eb9-4061-9ab2-d58eaa163fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# isupper()\tReturns True if all characters in the string are upper case\n",
    "txt = \"THIS IS NOW!\"\n",
    "\n",
    "x = txt.isupper()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d39c8c-ab8f-431b-a793-2f9461a3cb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"Hello World!\"\n",
    "b = \"hello 123\"\n",
    "c = \"MY NAME IS PETER\"\n",
    "\n",
    "print(a.isupper())\n",
    "print(b.isupper())\n",
    "print(c.isupper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f681227-f346-426c-8388-8946fb5d507b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# join()\tConverts the elements of an iterable into a string.\n",
    "myTuple = (\"John\", \"Peter\", \"Vicky\")\n",
    "\n",
    "x = \"#\".join(myTuple)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4439a720-96d4-4816-a982-1dd1346802ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "myDict = {\"name\": \"John\", \"country\": \"Norway\"}\n",
    "mySeparator = \"TEST\"\n",
    "\n",
    "x = mySeparator.join(myDict)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e37e02-403f-4a7c-94fa-9c65afe72486",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ljust()\tReturns a left justified version of the string\n",
    "\n",
    "txt = \"banana\"\n",
    "\n",
    "x = txt.ljust(20)\n",
    "\n",
    "print(x, \"is my favorite fruit.\")#  In the result, there are actually 14 whitespaces to the right of the word banana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b980951-cc60-4303-ab3b-bc181bddf957",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"banana\"\n",
    "\n",
    "x = txt.ljust(20, \"O\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee01552-97fd-4f34-b2d8-ed0c79d27230",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lower()\tConverts a string into lower case\n",
    "txt = \"Hello my FRIENDS\"\n",
    "\n",
    "x = txt.lower()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1310ab-7d64-4c54-a8f9-9af43b3033e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lstrip()\tReturns a left trim version of the string.\n",
    "txt = \"     banana     \"\n",
    "\n",
    "x = txt.lstrip()\n",
    "\n",
    "print(\"of all fruits\", x, \"is my favorite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4acafcaa-bd73-46d0-a056-34a46b9382d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \",,,,,ssaaww.....banana\"\n",
    "\n",
    "x = txt.lstrip(\",.asw\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e905a425-04c8-4388-8863-8d32ad443427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# maketrans()\tReturns a translation table to be used in translations.\n",
    "txt = \"Hello Sam!\"\n",
    "mytable = str.maketrans(\"S\", \"P\")\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8230af69-ffd2-4805-b0aa-f36670872297",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The maketrans() method returns a mapping table that can be used with the translate() method to replace specified characters.\n",
    "# str.maketrans(x, y, z)\n",
    "\n",
    "# x\tRequired. If only one parameter is specified, this has to be a dictionary describing how to perform the replace.\n",
    "# If two or more parameters are specified, this parameter has to be a string specifying the characters you want to replace.\n",
    "# y\tOptional. A string with the same length as parameter x. \n",
    "# Each character in the first parameter will be replaced with the corresponding character in this string.\n",
    "# z\tOptional. A string describing which characters to remove from the original string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f71d1a3d-0e02-4438-b618-f77936f4a80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hi Sam!\"\n",
    "x = \"mSa\"\n",
    "y = \"eJo\"\n",
    "mytable = str.maketrans(x, y)\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "593b91b4-ee31-4b32-9c4a-70293d4c3cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Good night Sam!\"\n",
    "x = \"mSa\"\n",
    "y = \"eJo\"\n",
    "z = \"odnght\"\n",
    "mytable = str.maketrans(x, y, z)\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a091000c-e51f-4dd5-943b-bc6e486eb958",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Good night Sam!\"\n",
    "x = \"mSa\"\n",
    "y = \"eJo\"\n",
    "z = \"odnght\"\n",
    "print(str.maketrans(x, y, z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02dfb6bf-c2e0-4157-ad09-d8c695e2a014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# partition()\tReturns a tuple where the string is parted into three parts.\n",
    "\n",
    "\n",
    "# Search for the word \"bananas\", and return a tuple with three elements:\n",
    "\n",
    "# 1 - everything before the \"match\"\n",
    "# 2 - the \"match\"\n",
    "# 3 - everything after the \"match\"\n",
    "\n",
    "txt = \"I could eat bananas all day\"\n",
    "\n",
    "x = txt.partition(\"bananas\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f0ce3a-bc1b-470e-b43f-9f225af04200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The partition() method searches for a specified string, and splits the string into a tuple containing three elements.\n",
    "\n",
    "# The first element contains the part before the specified string.\n",
    "\n",
    "# The second element contains the specified string.\n",
    "\n",
    "# The third element contains the part after the string.\n",
    "\n",
    "# Note: This method searches for the first occurrence of the specified string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88293d76-bd6b-4138-85e8-fe2d061e5c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"I could eat bananas all day\"\n",
    "\n",
    "x = txt.partition(\"apples\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562f73c7-f958-49d6-a06e-1ef4ea8cb382",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace()\tReturns a string where a specified value is replaced with a specified value.\n",
    "txt = \"I like bananas\"\n",
    "\n",
    "x = txt.replace(\"bananas\", \"apples\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646ea5fb-a1d2-4a57-b311-0b69e80c0c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The replace() method replaces a specified phrase with another specified phrase.\n",
    "\n",
    "# Note: All occurrences of the specified phrase will be replaced, if nothing else is specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a78cee37-0abf-4f2c-8360-9ed45395a4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.replace(oldvalue, newvalue, count)\n",
    "\n",
    "# oldvalue\tRequired. The string to search for\n",
    "# newvalue\tRequired. The string to replace the old value with\n",
    "# count\tOptional. A number specifying how many occurrences of the old value you want to replace. Default is all occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b3ec461-6b3d-49cf-b539-02c551eafdf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"one one was a race horse, two two was one too.\"\n",
    "\n",
    "x = txt.replace(\"one\", \"three\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df71c320-451c-406d-b1e8-9f23072c8d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"one one was a race horse, two two was one too.\"\n",
    "\n",
    "x = txt.replace(\"one\", \"three\", 2)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52b31708-8840-4203-aab9-7ec27fb1e57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rfind()\tSearches the string for a specified value and returns the last position of where it was found.\n",
    "txt = \"Mi casa, su casa.\"\n",
    "\n",
    "x = txt.rfind(\"casa\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f52b0091-1b41-4638-8056-c48da1f9598b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The rfind() method finds the last occurrence of the specified value.\n",
    "\n",
    "# The rfind() method returns -1 if the value is not found.\n",
    "\n",
    "# The rfind() method is almost the same as the rindex() method. See example below.\n",
    "\n",
    "\n",
    "# string.rfind(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to search for\n",
    "# start\tOptional. Where to start the search. Default is 0\n",
    "# end\tOptional. Where to end the search. Default is to the end of the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ed4e35a-8b51-4718-ad1a-7aee40c5f755",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.rfind(\"e\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4521d8d-49ed-444d-bab0-f264705b1bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.rfind(\"e\", 5, 10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00ee275-ed36-4d9d-8911-f4d3d8ca311f",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "print(txt.rfind(\"q\"))\n",
    "print(txt.rindex(\"q\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f92f73e-f867-4263-bffe-60b66f229550",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rindex()\tSearches the string for a specified value and returns the last position of where it was found.\n",
    "txt = \"Mi casa, su casa.\"\n",
    "\n",
    "x = txt.rindex(\"casa\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2fcf480-2706-4650-b958-2d36db15ca41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The rindex() method finds the last occurrence of the specified value.\n",
    "\n",
    "# The rindex() method raises an exception if the value is not found.\n",
    "\n",
    "# The rindex() method is almost the same as the rfind() method. See example below.\n",
    "\n",
    "\n",
    "# string.rindex(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to search for\n",
    "# start\tOptional. Where to start the search. Default is 0\n",
    "# end\tOptional. Where to end the search. Default is to the end of the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d7a9323-a424-4fbe-9b66-0aec36540d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.rindex(\"e\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0321dfeb-7dd6-438e-a105-85b53b8280e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.rindex(\"e\", 5, 10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52d24fd9-6353-44d0-ac9c-d9c3250812a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "print(txt.rfind(\"q\"))\n",
    "print(txt.rindex(\"q\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d419b137-e543-4900-b9c3-bc67102d76a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"banana\"\n",
    "\n",
    "x = txt.rjust(20)\n",
    "\n",
    "print(x, \"is my favorite fruit.\")\n",
    "\n",
    "#  rjust()\tReturns a right justified version of the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d6eccb8-3083-4c16-a265-f34b89c4f838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.rjust(length, character)\n",
    "\n",
    "# length\tRequired. The length of the returned string\n",
    "# character\tOptional. A character to fill the missing space (to the left of the string). Default is \" \" (space)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be6dd9e8-07eb-496f-85c6-beeae605583d",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"banana\"\n",
    "\n",
    "x = txt.rjust(20, \"O\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40d1d9f3-3eed-4378-8c87-15018556a079",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rpartition()\tReturns a tuple where the string is parted into three parts\n",
    "\n",
    "# Search for the last occurrence of the word \"bananas\", and return a tuple with three elements:\n",
    "\n",
    "# 1 - everything before the \"match\"\n",
    "# 2 - the \"match\"\n",
    "# 3 - everything after the \"match\"\n",
    "\n",
    "txt = \"I could eat bananas all day, bananas are my favorite fruit\"\n",
    "\n",
    "x = txt.rpartition(\"bananas\")\n",
    "\n",
    "print(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa34c52b-f131-4c65-963d-8e680f1c7592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The rpartition() method searches for the last occurrence of a specified string, and splits the string into a tuple containing three elements.\n",
    "\n",
    "# The first element contains the part before the specified string.\n",
    "\n",
    "# The second element contains the specified string.\n",
    "\n",
    "# The third element contains the part after the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a611cb-267e-4089-a917-aee8f707e2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"I could eat bananas all day, bananas are my favorite fruit\"\n",
    "\n",
    "x = txt.rpartition(\"apples\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe20d0c6-3401-4480-a938-1a6bfefda316",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rsplit()\tSplits the string at the specified separator, and returns a list.\n",
    "txt = \"apple, banana, cherry\"\n",
    "\n",
    "x = txt.rsplit(\", \")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "907a2108-2907-4345-a921-d12a939b6dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The rsplit() method splits a string into a list, starting from the right.\n",
    "\n",
    "# If no \"max\" is specified, this method will return the same as the split() method.\n",
    "\n",
    "# Note: When maxsplit is specified, the list will contain the specified number of elements plus one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1df24633-9ed7-4299-984c-9cfc54b9c8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.rsplit(separator, maxsplit)\n",
    "\n",
    "# separator\tOptional. Specifies the separator to use when splitting the string. By default any whitespace is a separator\n",
    "# maxsplit\tOptional. Specifies how many splits to do. Default value is -1, which is \"all occurrences\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011b87a3-dcb0-479e-836f-3dad2b47dbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"apple, banana, cherry\"\n",
    "\n",
    "# setting the maxsplit parameter to 1, will return a list with 2 elements!\n",
    "x = txt.rsplit(\", \", 1)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0577ce88-67a3-452c-87c6-f498f4cea05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rstrip()\tReturns a right trim version of the string\n",
    "txt = \"     banana     \"\n",
    "\n",
    "x = txt.rstrip()\n",
    "\n",
    "print(\"of all fruits\", x, \"is my favorite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aec8372-0d92-4a5c-800a-01a1d00e5cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"banana,,,,,ssqqqww.....\"\n",
    "\n",
    "x = txt.rstrip(\",.qsw\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "746c32c5-8232-490d-a1b6-cbfed0b88f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split()\tSplits the string at the specified separator, and returns a list\n",
    "\n",
    "txt = \"welcome to the jungle\"\n",
    "\n",
    "x = txt.split()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8d3ddc9-4f88-4451-b0ab-81bdc0e07de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The split() method splits a string into a list.\n",
    "\n",
    "# You can specify the separator, default separator is any whitespace.\n",
    "\n",
    "# Note: When maxsplit is specified, the list will contain the specified number of elements plus one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ec6f20c-b908-4ab0-971e-2c9098dfd7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"hello, my name is Peter, I am 26 years old\"\n",
    "\n",
    "x = txt.split(\", \")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce683089-f60d-44b5-ad5c-0af6b0f707b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"apple#banana#cherry#orange\"\n",
    "\n",
    "x = txt.split(\"#\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aa790f9-8f9e-457f-a400-07ad3f04a6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"apple#banana#cherry#orange\"\n",
    "\n",
    "# setting the maxsplit parameter to 1, will return a list with 2 elements!\n",
    "x = txt.split(\"#\", 1)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97b8bd19-f234-47c8-93b4-3271b2584d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitlines()\tSplits the string at line breaks and returns a list\n",
    "\n",
    "txt = \"Thank you for the music\\nWelcome to the jungle\"\n",
    "\n",
    "x = txt.splitlines()\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78154101-a3fa-4f69-8c91-0322f41ec443",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Thank you for the music\\nWelcome to the jungle\"\n",
    "\n",
    "x = txt.splitlines(True)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "454a028d-bd1b-4f01-8c59-788bd7322312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# startswith()\tReturns true if the string starts with the specified value.\n",
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.startswith(\"Hello\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52e217c8-8c11-4669-939a-6f8d3f17de6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string.startswith(value, start, end)\n",
    "\n",
    "# value\tRequired. The value to check if the string starts with\n",
    "# start\tOptional. An Integer specifying at which position to start the search\n",
    "# end\tOptional. An Integer specifying at which position to end the search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3083175-efab-4d3a-9f0d-37ba65da95b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello, welcome to my world.\"\n",
    "\n",
    "x = txt.startswith(\"wel\", 7, 20)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c669855e-2cd0-4ac0-9c20-c9656d16a136",
   "metadata": {},
   "outputs": [],
   "source": [
    "# strip()\tReturns a trimmed version of the string.\n",
    "txt = \"Thank you for the music\\nWelcome to the jungle\"\n",
    "\n",
    "x = txt.splitlines()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1346891e-7a53-4a1e-b911-e00cabdc7130",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keeplinebreaks\tOptional. Specifies if the line breaks should be included (True), or not (False). Default value is False\n",
    "\n",
    "\n",
    "txt = \"Thank you for the music\\nWelcome to the jungle\"\n",
    "\n",
    "x = txt.splitlines(True)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f1569f-eb60-4537-8100-ecd16ad8050d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# swapcase()\tSwaps cases, lower case becomes upper case and vice versa.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a1003e7-233b-4767-862c-9f93857d0c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# strip()\tReturns a trimmed version of the string.\n",
    "txt = \"     banana     \"\n",
    "\n",
    "x = txt.strip()\n",
    "\n",
    "print(\"of all fruits\", x, \"is my favorite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30e50a72-4ef3-49e2-abd3-50abeb1d659e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The strip() method removes any leading, and trailing whitespaces.\n",
    "\n",
    "# Leading means at the beginning of the string, trailing means at the end.\n",
    "\n",
    "# You can specify which character(s) to remove, if not, any whitespaces will be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3917c441-4f82-42f0-bca0-b58fd7a54035",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \",,,,,rrttgg.....banana....rrr\"\n",
    "\n",
    "x = txt.strip(\",.grt\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4da1e0d-498f-46da-af5c-a4b08d0130d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# swapcase()\tSwaps cases, lower case becomes upper case and vice versa.\n",
    "txt = \"Hello My Name Is PETER\"\n",
    "\n",
    "x = txt.swapcase()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9afc0633-b9ad-41de-bcfb-a0a9255163eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# title()\tConverts the first character of each word to upper case.\n",
    "txt = \"Welcome to my world\"\n",
    "\n",
    "x = txt.title()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "531e10a5-922d-433a-8db9-30de4263c05a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The title() method returns a string where the first character in every word is upper case. Like a header, or a title.\n",
    "\n",
    "# If the word contains a number or a symbol, the first letter after that will be converted to upper case.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3ea9a5-0d8f-4160-b465-0db783268206",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Welcome to my 2nd world\"\n",
    "\n",
    "x = txt.title()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e07c8c-4e4b-4346-98c3-f9dde51cc3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"hello b2b2b2 and 3g3g3g\"\n",
    "\n",
    "x = txt.title()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628c5158-1fb7-4ba8-b755-87524fdb624e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate()\tReturns a translated string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "071d213c-a3f0-4cdc-8184-fe8d943364b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use a dictionary with ascii codes to replace 83 (S) with 80 (P):\n",
    "mydict = {83:  80}\n",
    "txt = \"Hello Sam!\"\n",
    "print(txt.translate(mydict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b6888f7-7358-4ae5-bf1b-c2693f54280a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The translate() method returns a string where some specified characters are replaced with the character described in a dictionary, or in a mapping table.\n",
    "\n",
    "# Use the maketrans() method to create a mapping table.\n",
    "\n",
    "# If a character is not specified in the dictionary/table, the character will not be replaced.\n",
    "\n",
    "# If you use a dictionary, you must use ascii codes instead of characters.\n",
    "\n",
    "\n",
    "# string.translate(table)\n",
    "\n",
    "# table\tRequired. Either a dictionary, or a mapping table describing how to perform the replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b00497-b8af-4fb6-b65b-33090d4b54b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hello Sam!\"\n",
    "mytable = str.maketrans(\"S\", \"P\")\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f16a231-c04b-4e52-b170-9e0f69d18de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Hi Sam!\"\n",
    "x = \"mSa\"\n",
    "y = \"eJo\"\n",
    "mytable = str.maketrans(x, y)\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69c6efbb-b591-47bc-9859-55b3a6bb8a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Good night Sam!\"\n",
    "x = \"mSa\"\n",
    "y = \"eJo\"\n",
    "z = \"odnght\"\n",
    "mytable = str.maketrans(x, y, z)\n",
    "print(txt.translate(mytable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cdc23e3-c109-4d4d-97eb-b5cac85ec709",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"Good night Sam!\"\n",
    "mydict = {109: 101, 83: 74, 97: 111, 111: None, 100: None, 110: None, 103: None, 104: None, 116: None}\n",
    "print(txt.translate(mydict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d02eb6f-ad8b-4b42-8c2d-239d71bc64e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upper()\tConverts a string into upper case\n",
    "txt = \"Hello my friends\"\n",
    "\n",
    "x = txt.upper()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03598fe8-f120-4330-8ef2-072b8d93dd21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# zfill()\tFills the string with a specified number of 0 values at the beginning\n",
    "# The zfill() method adds zeros (0) at the beginning of the string, until it reaches the specified length.\n",
    "\n",
    "# If the value of the len parameter is less than the length of the string, no filling is done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15a2e0f8-7cc9-41a6-99bf-ce399a08df3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"hello\"\n",
    "b = \"welcome to the jungle\"\n",
    "c = \"10.000\"\n",
    "\n",
    "print(a.zfill(10))\n",
    "print(b.zfill(10))\n",
    "print(c.zfill(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efba6360-d9b9-4a0c-a4af-153c9e4ceb90",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Booleans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7749afe-79ac-4915-9a46-bb82d7fd423a",
   "metadata": {},
   "source": [
    "* In programming you often need to know if an expression is True or False.\n",
    "\n",
    "* You can evaluate any expression in Python, and get one of two answers, True or False.\n",
    "\n",
    "* When you compare two values, the expression is evaluated and Python returns the Boolean answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d000a70-765a-41ab-bf8c-1e798de50361",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(10 > 9)\n",
    "print(10 == 9)\n",
    "print(10 < 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a84ddb28-3231-453d-8c28-528965ac227e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The bool() function allows you to evaluate any value, and give you True or False in return.\n",
    "print(bool(\"Hello\"))\n",
    "print(bool(-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4460c56b-11d1-4e57-9bcb-e3634a67c5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most Values are True\n",
    "# Almost any value is evaluated to True if it has some sort of content.\n",
    "\n",
    "# Any string is True, except empty strings.\n",
    "\n",
    "# Any number is True, except 0.\n",
    "\n",
    "# Any list, tuple, set, and dictionary are True, except empty ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4259ca6e-5452-4951-8918-05976905cfa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "bool(\"abc\")\n",
    "bool(123)\n",
    "bool([\"apple\", \"cherry\", \"banana\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e2f6dff-854b-47af-b92a-36605b55fd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In fact, there are not many values that evaluate to False, except empty values, such as (), [], {}, \"\",\n",
    "# the number 0, and the value None. And of course the value False evaluates to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ac93172-1903-4aaf-8136-aac96e44441c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bool(False)\n",
    "bool(None)\n",
    "bool(0)\n",
    "bool(\"\")\n",
    "bool(())\n",
    "bool([])\n",
    "bool({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a7e12e8-a5d0-462a-bcb0-798ea562d27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myclass():\n",
    "  def __len__(self):\n",
    "    return 0\n",
    "\n",
    "myobj = myclass()\n",
    "print(bool(myobj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d30690b7-c13c-45e6-b42e-634c82ab505c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def myFunction() :\n",
    "  return True\n",
    "\n",
    "print(myFunction())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13298ff6-bc12-4609-90ed-a1771442a25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def myFunction() :\n",
    "  return True\n",
    "\n",
    "if myFunction():\n",
    "  print(\"YES!\")\n",
    "else:\n",
    "  print(\"NO!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f5f892-5624-4473-9d7f-98f3b8e8c7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 200\n",
    "print(isinstance(x, int))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028dfe63-f4a8-4b59-bc1c-51a7fa7292fe",
   "metadata": {},
   "source": [
    "# Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978d7955-f289-4df9-b86f-22719de384f5",
   "metadata": {},
   "source": [
    "- **Arithmetic operators**\n",
    "- **Assignment operators**\n",
    "- **Comparison operators**\n",
    "- **Logical operators**\n",
    "- **Identity operators**\n",
    "- **Membership operators**\n",
    "- **Bitwise operators**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d34fe2-9c4d-4927-9b57-0271dcdafe0b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "##### Arithmetic Operators"
   ]
  },
  {
   "attachments": {
    "00add506-0e72-48c1-8e7b-673507aa4ffb.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "98a4651b-5fe3-49d0-a354-4f0e9d4e8695",
   "metadata": {},
   "source": [
    "![Captu3433re.PNG](attachment:00add506-0e72-48c1-8e7b-673507aa4ffb.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5e6dea3-0125-4c8c-a295-f1600713bda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15a739a0-5213-4e37-a12a-d65086b665f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x - y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b300e7f-86dd-4762-9e4b-5d129c0cdc50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x * y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4b8150d-275f-4012-af01-56046cdbf180",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 12\n",
    "y = 3\n",
    "\n",
    "print(x / y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b62a10d4-4fcd-4a7c-b63a-41967daa9803",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 2\n",
    "\n",
    "print(x % y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e1e3c4-0686-478a-b21f-c3d24e2a28f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 2\n",
    "y = 5\n",
    "\n",
    "print(x ** y) #same as 2*2*2*2*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81a74127-b206-427c-8622-a9201c958c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 15\n",
    "y = 2\n",
    "\n",
    "print(x // y)\n",
    "\n",
    "#the floor division // rounds the result down to the nearest whole number\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa54f8c-5814-4059-a800-9dcebaa9eb78",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Assignment Operators"
   ]
  },
  {
   "attachments": {
    "5f4c5b02-484c-49e1-af1a-551cc038a665.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "762ed626-1832-4943-b6ed-62746bdafe26",
   "metadata": {},
   "source": [
    "![Captu111re.PNG](attachment:5f4c5b02-484c-49e1-af1a-551cc038a665.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20fe907-b0da-4eed-9733-b63d1abb245c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e36ca26-0271-4420-9a61-1667d9e82450",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x += 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f38b9e8-7842-44fa-9b35-70aaf1eacfa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "x = 5\n",
    "\n",
    "x -= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7fdc863-9fca-4ef1-98bb-867b59d8f7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x *= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28bc6986-0b6f-4060-83d6-e28a0446f92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x /= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19b85419-1645-402b-ab8a-c92e007e0716",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x%=3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3626b317-30db-4cc8-8972-978d0a32958b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x//=3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a8221b-2781-4dc2-8666-89d3d0289914",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x **= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f2311d-c596-4298-a9e8-03c502e90d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x &= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36bd9fe-b92f-4fe3-8f94-b30633065c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x |= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a2a7db-d3c4-45fa-a08c-05624182aacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x ^= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1162fc74-1dea-4542-aacd-557976c9bef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x >>= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36800f46-ea98-41ea-9a90-102e3fb81695",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x := 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "652f4709-8237-4736-9b34-42789681a6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "x <<= 3\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce37985f-1897-490d-a437-ebc9bb1590b2",
   "metadata": {},
   "source": [
    "#### Comparison Operators"
   ]
  },
  {
   "attachments": {
    "c8a1b454-55d8-4c1e-a228-b54fd8f2cf1c.PNG": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "60dcf9ea-05ca-4cc0-80a4-fe974d95cc8f",
   "metadata": {},
   "source": [
    "![Captur3333333e.PNG](attachment:c8a1b454-55d8-4c1e-a228-b54fd8f2cf1c.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e479bf64-dd24-40f3-881c-c35d333ea2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x == y)\n",
    "\n",
    "# returns False because 5 is not equal to 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55906581-c4a6-42e1-a1b2-f97d83170eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x != y)\n",
    "\n",
    "# returns True because 5 is not equal to 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "856fd481-b878-4a8f-b976-bd0f18299676",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x > y)\n",
    "\n",
    "# returns True because 5 is greater than 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dba435d-f626-42a7-aa47-0074f5d6744a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x < y)\n",
    "\n",
    "# returns False because 5 is not less than 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c416f04-3de1-4519-b07d-9b22d17382d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x <= y)\n",
    "\n",
    "# returns True because five is greater, or equal, to 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b45830-e26f-4b21-8795-50757e900a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "y = 3\n",
    "\n",
    "print(x >= y)\n",
    "\n",
    "# returns True because five is greater, or equal, to 3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6329e8de-4e0b-4b76-91d4-bbe081d03a22",
   "metadata": {},
   "source": [
    "##### Logical Operators"
   ]
  },
  {
   "attachments": {
    "3204f966-508a-4987-8f5e-6432405d8157.PNG": {
     "image/png": 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DuXmSvrfNND/jtaViDQiU5tt+Un1W81lhvePcnMuPdscLeW7wx4AJrq8DNhFA2c0BYGVYI5Yb3bj2LNaU8jqzLSNd5qB+rSpvfsMmHa9ty3Jy93375t2mzT2u3yBb/krumfhwHq/pk1L5ofi+of+qD/bZRGB5QMmPy7IO+Tio39tyasY1//6MRNKUf09871rUj7lxqL1eHxu5usZxWD+GG43zZSuuf3Fcqvs1o3Bftm0r5zlXqTAlwet1Z7kG7TLen4nxg1jHRUudEqLYalgXd8yT0yTSvSGWx3LGtlaXOfVNbo7vrRQHpc/y6x3n50wS6SlQ52o2u2XA5IMkuzkArAxrxLT+TFIb4LDOJA+yi827dFBer3uJzS5eq8qbX+7Qunz3pn3JDbZeXL9B6fCceyY6BJUOP7kD00Jcj8J4pdQmIIW6pvs+VffavS2vZlzz7w8/79sWmt+fG9/19eS7l4fe3DhUXm+IjVxd437I9X9dv5aNZS8F7yqNS32/ZtTG8cJQp8RnZzBXqbTuzzF+MuOWNsbKIu4ObZoryVhI2MytmZp5Ga+L2VguxWeToA9XUvMpazNuh54LcT/UfZZf7zg/h02kx0BfToJ4Ia+cVIWNI7s5AKwMa0R8UIw2pM26lTdt3lGZgdxBuX79G9QnDdO1bT17TQeDlbh+g4MczmcHxpTdB5K4HkP/Vbezdh8Z65pPJGrioP7AWLJrXHe+f/nZclxW0uM7P2QmbdqWG4fK6w2xUT8ncv0/2NWvOZvnZuWOfRVcy49LS79mFOL4osxV6k0xtVdiGO5Zu+JmH4VYm6tdb3LzcrkuFtbJsU6nT6R7Y30q1+OlYb71DjUnCu0uflZe7zgfB02ky5tdeL12Ug33pSZzdnMAWEkeaqfD4mxTyq8zS8kyR+v1L7HZ1a9/g/qkYWlcP1dltxw2cmvpQQ7nBzj8xPWoH6+12gSkUNdl3+fHonZvaxGPa+792XpFB9v8+K6vV9UzNw6V1xtiI1fXuL25/k+pnS87xjS4no+Lln7NqI3jhaFOic/OYK5SadqH1tLxUmUcs3U5VTFfaazfrlioX292zKFgXmTnycHiczvvT9tvw7zrnWIM1+J+CGXnd2FN4PwcMJHOB0K8kNdOqpYyAbZym/xwPVx/ypvYTCIhmeQ2u/r1b1AuZ/vuG+8n6rvHxppbS7Ob90qub+sPUPXiegxlVrcx2yfjuG/qlq9rVIdsHNTubXlV45p5f3bMEvfnxj07tpHcONRez/f3Uu2ciMd0a+/5Uh0/5b6r79ecXL8Wxn0l+94zmKtUmMXT2Oc1e0/JWOZpE8NJbazWrze52IrXxey7xzaeNpEO6zy861Bl9nb3WdqO/SG33+Suc64O+h3p1ASYAnU+2eon1RAoi0Afr+U2DoDSYWDa+DaH0ukgEm1k/ZoUlJE9VK8k1qq29W+QO5ws25O6r9TmnOGZ+H25eqyl+mGzLi/eP12P2tv3Q6b8QFyPof+SY5Ayje3i/UO56bqGZU9jONuDsnHQsLdlVI1r7v2JGNw1LvEhcqxrIo76emzfmRuHhuuVsZGLxVS85+6t6tek1NhNfbQS1L1cXm2/5uRjKNfmtXOcq+wyzIHZGGbWp700ltXHTRR3YwzsjseV1BqSjK2x3VG9UjGdune8tnxXo2F+zssY5s6uNaDO/mXl53b+81yfct4O/sfGpkCd9JNxuBYuwi2TamUzMUer5+IyAbaGNSK3qY1rzWwdCq4t1pvZs4v1KNyUT7f+DXKH4rg9qfq2r4m5tbR4OO8l1uXhWqrPtwehUM1hLa7HUFbVQa83JRIP52OTbdt0GN3IxFAyDhr3tqTKcc3FYcO4zON1/vkylgc147Df9eW7wvvq50Rv0X+bPj/NfFk+279zvBaMdbo+c7v7tWTRV+O7L8pcpWTq11wcrz6L1o0ztoyLUVOyWhVbY9uj9o1zKFoXlzG46rNxXW6qW2Dq4/z6U563ZyLT/724nfH6ZU5eDAdPpAEAAOCYSaQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGjzxjW9e6wAAAIA6EmkAAABoIJEGAACABhJpAAAAaCCRBgAAgAYSaQAAAGggkQYAAIAGEmkAAABoIJEGAACABhJpAAAAaCCRBgAAgAYSaQAAAGggkQYAAIAGEmkAAABoIJEGAACABhJpAC6d638+6U5ORp+91z2duOdYHKKtQxn3u+uJzwCAdhJp4Cg9fevzbfIxs38yMZT5effOc+nPL77Xu3vJPok9vPWrxPPnb0gA531+lZLCprY+9173MDN2F6rPCvWsdfnnYtqZjNPY3yd/fj39eZNxDTlIWTxe5f3g3u9Sz7Dxu/tDX5kLV5pEGjhK6YP2r7p3PusPCfsdwI/x8D606eImpXEiPRz+Lkqif7Ya2yqRvvQk0uyteZyv+FiOifBe69DU1+bClSeRBo5S9qB9ikOlRPoikEhnFRJUifTlcKHGKUkifeHsndRd0bGcvpO8st8aNPXbfXOBs0mkh40gEAVZMHnDBeDIf88NOD/tifT03eqtcJMdypt/3pt+/G297iXWsDhRLa9/24P0eN/i87Ds8EAwaf1xvFwiva1H2C9Df+aeGeqTSm4WbWmo5yyxCPursayd+9JYdj/my7FOl1+Ol9129ElrWzP3T+1siqteFFuJ8c5J1GXTNzvqmZ5n85hK37Psm90xt+6Tvv3LOk19suyDZF9VxMFY/q7YimJ0LWh7qV9Lgvdvr8f1zsbCRtyns+fC94R9tx7b4dm4vmM9lvPx1PProtn2XTSHp76K+mCHMB5an10b67TXs1vRfEzE0Xb9Ccc1tVcMNmVGZU3P55/NCmJy/1ga37+u12H6j8vtwIl0GGDTtTHQUtei6wCHMWzEic123Exnh5npQDLbEONNMlvmyuZQvrg+PBMmIeX1b3uYTjwT1i/RjlL9cuL6Dbb1iMvLPTPUaXH/WM/ZwSV5sE/bHsCma0Nf1B+EKvel4FAalp3s08p4yUq1P9VPrW0t9Gt1XG3unbe5OrYKbdvEaqGe/XuW11PvLtanMuY2fZKJg9T1VJk74yAoM3w+1YY43kep9y/7NSd6tjAndsZv4b6gnfHnuVge65Lqr1kZtfW7yMY2hOObbOsOxX5uMdVnoaHMOF7H8VzE8Hb9qVhDRsP8mMdMam3aaZwny7L2MW/vMcQkp3XQRDq7sUWLuOADzlZ6PRrXntkBctwcF9d6yzKya9xKuYzwUFFe/+Yb9daynNx93/jde02HjLh+g2z5K7lnhgNL2D+JQ/KoVH4ovm/ov9oDUXbMlvtStE9N4vet61QRL2mpJGYQP9/W1nwb8v0djeVYRpycleN2UurvdyoS6bS4H/J9XR9zuXGsvZ67L6pba2xlx2lHv+Ys35+tz6rvbu06lxXioDiuuViOx6u6Xy+jsY+G9uXXgrTx/lz/H8KUdNbUKfuFnDhGcnG9y/Dc+I7aLxxtjPVYScdkmyH+wjYU5gJXxkET6dziFweb4APO1rDpDZvoVuoQNqxHyY12PPRMG3fpIFc+/NVvvrkDx/Ldm/ZVH8LS4voNSgef3DNRIr3ov+K9GXE9CuOVUL0vZZOA5XjVx0ta4fnooNjW1nwbWuMqPe75vgyMbciVsVaoZ/j5dt4OwvuX9V4+WxNzufbUXW+Ig2x7l7GVH6eqfs2J3j++d6UcqylxnTey7ezl+muZSDf062W1GcvW8TyHRHplmFu7+zm/VsRfIMjG9U5Bm1fSsZWzjfO25xIWa8egMBe4Mg6YSMcTJ/+Z4APOVnTQHg9h0Ro1XS+YDhTZw/tK7vAdHzbK61/uwJF693BtW89e64Ehrt+gdPDJPRMdNmYHxpR0X4biehQO2pGGfSmbBCzGqyFekrLvSX3W0taVQtm1cTXcV5Dsy4XUuIfxvrOey8/ifkjNh7WGmFu/K9GequstcZBtb7wWDO1Pz7ud/ZqTfP/47pnMe2fiOm8UxjUfy+M8PNT8uiSmtXuvtoR9VDP+rYrjuFWK1eVnxbjeaYy5mrUnJZg3u9qUNsRoPFaFucCV4TvSwFFKHrSnDXW2TuUOeLHs4X0lt/4Nz4QHiPL6lztwlN49GA+kq7JbDmdx/Qalg0/umaF/gzqOB7LTHHzjetSPV696X8oeHpfj1fb+WOH5MT63/dX4rsIBuDausmN7CsO7g3rl6rljDMLr2fnQEHO52Ki73jA21bGVH6eUqF9zCnGxkVwbU+I6bxTfk+uvRSLd0q+XVZDYldf0Hcb+XpeTGo99RetQWn6tWHyRcqUlrue2+9qp2xn0e1t8LeqQcdQxS5bfkQaOUm49Gq6H60+86WetN+L0wSd3UIgPG+X1r1zO9t3XbyXqW3NgXojrNygdfHJ9G/fP6df6uB5tB+1sXZd9le27ZRsa4iUp/3xc17a2lu6vjavaQ3TO06u4jGNm2YeZembfnbg/OxeX78pb90liHOquN8RBdWxN45Eap5p+zUjEeur3qnMxMje2O/XObDt7ubouyzvt/LrgZn009slp2zqWOd/X9pddM5d2zNewLnWxFQufG+q1/9q0Mda7l47VWpXzj6N20ER6swDOJktqoRB8wNkqHQaGzXl+sFwfRKIDTb+mBWWUDoqJQ8W08SfXxMz6lztwLNuTuq/6ABQYnonfVzz4pPphczhZvH+6HrW374dM+YG4HkP/1R+AKvel7Ngmxqs2XnKm58Myx36av3/PtiYO5rVxtb03cWDt67hj307GYDQ3cvVM9PV0bdkPFXNxV8yt25nrq5rrtXHQEltRXw3q+jVj+f5kfVJ9n5aLpeKYrMTPTXNz8d7afr10xj5OxVBFv+/UWFY/HtFYjTGVG8OlYUzDMZnGdB4f2ZgpGGJ+Ht/x+06hsa2x+jnD8TpwIj0YAj0QBZngA85W8uC5kTrABddCy3VqOqSPwk1+2vgn/QY9XAsPEOX1L3fgiNuTqm/bQaUX12+w8+Cz6Id1e9bXUn0+tnmh5gAT12Moq/Xws3NfyiYBufGqjJesuE/ihGifti7KHetTH1ej5fiu1R1go76uiYlNvy37pa9zph8KczHVv72wjHU9axLm4vWKOGiMrfk6su27un5NSL0/Mb71cbZo89Qn2XZOln3V13+8Fs2b086vi2aKx9wcfAxtS87xii/MLMzjdSU3d0r7ycJUZnbO1Mb+mUrPX66WM0mkAQAA4FhJpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaPPHt7/+wAwAAAOpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQaAAAAGkikAQAAoIFEGgAAABpIpAEAAKCBRBoAAAAaSKQBAACggUQagIW3uvsnJ93JX95KfHb8Xnj/y+7k5Mvu9ivpz7mILlbMDjG0qs/ah92NxD27XO04vNpr0GGNfWlNO5ChP794/9XEZ1fPjb8M69xV7Y+Ln0i/crf7QsACjeYH2dB+h9pjlD+oS6Tn/XLR+uNYxufV7vbDaV6mtMzVx9Mn0zpz/+byWm3Skq93WznHJu6X9v4Yy3h4t3sh+fnlV9MnQ6Jz0fe98lpwsXKAIa4ufV5y88NV35bjYlrfttL3T/eF6+BVIZEGjlL6gDFt1lf1cDqXP4Q9nqTkooj75aL1x7GMzzgfD5LoPJ4+iQ+QY5uq65Gvd35+XgVxv7T3x1jGVU6k18nS40hwpr22NoE/5Fpw1oa4uhB5yTi+7XUZ50ZhfFKxlf+izGUav8OSSANHKXvAGNeUy5+EnF7+EJY/3F8Fcb9ctP44lvG5/Il0TCJ9GHG/XO3+SCv3yeNIbsZ39mNXnUT3LlMiNsTmY81LxgS6OYmezj8buTHKtbHQ9sf2RZvH67CJdJD0DpN7O1jpjh0Xytx90YCPLv3hAThr2QNGNpEODwCD2WZR2iSSn+0obyWs4/R7RrNygs0y+mxj93tSlmv0ZCg/OMQu6pArOyqv9kAU7Buzd4XjE/VDYvNP7Bezuo6fx/0XHwzmsRPvU2v7HvhO3d4d9dmzncv42362jK/CdyM296xU9c/4XPHeTHuj+Tvet7we9eXh5lCvJlbyZZXHsm0M0uXFbU3bfqcpfNfUrl5dH4VxNJjXdf15YryHtibifBzP4fOw3EG5faeJiTMQzP1le5Lvj9a0cDwq+iRca6Yy17ZxEr136ptofu0SxkcuPkvG56vWjbR0f8z7rLeN9cV8ybw7Kvfhl5l+XdjEWdwf0zxpjrsgdne+f4dtPyQ+H98T1680TuNnzbFzuZ1JIr0c4CEIF8GcmuDjwM2uZRcCgLzkutNLbRDT2jXbAJaHsPwGEm1IVeXNN+jcgSa8HrWp8j052T4KDxhhOcnNdTpAzTfk4iYdmtqQqfNQzryOuX5I7Sebuo73RP08trW8Z9X36U6HaG+pPs3tHOqyvH/7Wfje1BxIXavtr/yc2urLWsZRqvzEtccyh8Y2VTw7yL+rfgxWxrbue34a4m75rlFlH8VzPq7r+p7EeA9tDZ+Ny4/7epc9Y+KsBHO/vN6sZOtZX/dyu8a+Cfs8Oc67jGO8LKtZWE6guDbM9e1dxnqqD7axXo637b2LPmyYV9M4xnEcr7lFUzm1760Qz9etUuyUnit9dqzO7DvS88+GAN1ez2wEK9HgtQQswCi9EYyb5WLtWS/+FetRqcxwjTpdeYPshnTz7ub+2vfk5O9LHyo2a3d0ME2UUbt2l+4bP4sPHPP6Zduxev729OyOssL3x+Xl+mMPB2hvsT6naudW9rPleGfbs4qV93f1V+bwvJau12SoX/kg/HjmUGKOFOXHsnoMCu/M9sFC6b66Psq348b722fLZZXHs3ZMtvaLiTOTnStDPbfXC325qH+pT0pjujbWZxiPMYYSY5MzvHvHO05jqt+pyl/2bb5fohhsWEtLpn5a35+NgZwxFpqeqVOKj73jKnceOGLnmkhvF4VCEI5fddkEbnPQAYSbfCi1wBfWo2gjXa5lqQ2nvrzSZrWpf/Zg01LvtPz743YO4sPWelNN1rEyoSis8UP90hv27L3jvpHd3HvZPon7MTemO9tS4xDtLdXnVO3cyn623KOnuiTfuUv94X04vC2FfRX3ydCG1bVs+XGfbOw9hyrjfiM/ltVjUKpr5cE2fziu7aOx3bl7R7n1YmhrzXjubsvWPjFxhrJzf1HPwngu+6DUJ7m+ntmsnb3C+pmw6cvG55qM9SvF1MzYd9s2DcLnc7Fe37eFOZExW7+q14beGBuN76uRn/MVcZUb88r15pg8nkQ6e1/is9K9ABnRRjBtsMuDxXS9IDzQzDeRxKG5obzSZrX9fP7scm1cfh5KHqwDuw4K8Ya/THy2h+esXYeGwho/O3ykhGM5OxCOEuMS90l8KIr7JdcfezhIewv1OVU7t7Kfjf08L3+sz0zN4XoZTwmbOJ+XN9QvvJbuk+G+qU6Ds51DiTWhKD+W1WOQiv2Z9BiHsofjpj5KrQfzd6/fkxjvmvHM9kfWHjFxlrJzf1HP5BwbLT4r9Umur5em/tgV62nhmNfM+Vb5+bE0rZ/z/h2eD6/lYn3Zl9k5kShzt6mf9uyjYI4fKlbz7auIq1w71vVsmaOXn+9IA0cpuRFMm9HscNG4KY5r0nqNWq5Xa/XllTar2PbAMrxvn818Lv/+5Zo9GesQ9N96U604rGUV1vihfvsdPIbNPig3HLfZvXE/xv2S6489HKS9hfqcqp1b2c+SMb+QnGcpcTwt5Q5tcV/VjNF5zKHxHdWxkq939Rhkx7xe/nB8ij4a6xWWm1svasYz2x9Z+8TEGcrO/UU9C+O57INSn+THNDDN1bWWvl3a9uPOd7bI9tnCjr4Nr5fXlGXfptrSPieGd45Os18G49Xy/pRifGTX+R1r9vq508TR5fOYEun8QMSLQnvAAuQOGMP1uvUobXv/7eShsL68XB174e8VbszW2NZ6J2Q3veWaPYnfWWpDldJBKbuZz72w6qvcYWd5OM3tT+H1uE1ju6P+2MMB2lusz6nauZX9bFnH1fs2v4ceqDrE74zh/OdD/cLy45g9jzl0+ljJ3189Btn5Wi8/XrV99FZ3OxHTyzaUk5jyeObXq5x9YmLx2SHtmJvbeubHM+q/Qp9k42cyq8/4zlPMhcEYL31ZyXhqVLsmZu8b2hX2eTkGg/5qKLNkVu5Y5qljbSznNGWV1+hcG8ttL5d5nB5TIr29N14kl8+ffqMDrp7SIWJY7IO1ZlqPonWmX38SZezaxCrL213H+YYU3d9a76WWNXsttR6P1xLt6Oub7J9Qtg6Daayiw0w/BmP9kv04jtH2uVTdx3Yu3p8qr3hAmMah5gBxgPZu70u973Tt3PnZsl+T7cnFz1KqrnOpegzXlu2P33kecyiu39imnW3fyo1l9RgE1+L39v2yOy6r4rvYR6kxT4xvou6147lr7sT2jImzkq1/oq1jP8XzdTHupT4p9tf4znBspnGOYmgf49hXxF5v3bble5vqk4q/8dpK2Af18y0Rv5t25fp1oXYc95Uov1Zxzq+k5kX5mbFvDhI/l8fjS6TD64F0YC3uu2KDBLRLbQJb281wu54E13auN9OaVDok7C6vuo4bqfe11DthOoCPhjU4t2aP74oO1NvDwVypf0bFw95oUcdBaoPPfz5Y7jl9/YZr8UFn+fyinxPJQdVh5kDtLdbnVO3c8dlYt9lenahvVV8U4im0jK2+7OFaGF+pmE3NjcPOobifxrJq599aeiybxmBtOe6DmrHYdaiu6qMp8cl9Ptp/PFcWsVZORk4TE2eg9Ywc9Wd6rub7ZGxrNL+mOInbvRmbxLidqVTsrNStI5Nl/PftG66F5dQn0r1lvPSfx2UmTeMS9eW2zHL8noFMP/dS7VnO1eJanV2XjtthE2kAuGJ2JyEAj8EVTW44b7kv2hw/iTQA7K3yuxMAj4Ev9HHW0t/Nvxok0gCwr/V3fBxSgYtq+pHnq5nocLamX6u6ql9MlkgDAABAA4k0AAAANJBIAwAAQAOJNAAAADSQSAMAAEADiTQAAAA0kEgDAABAA4k0AAAANJBIAwAAQAOJNAAAADSQSANH6cZfTrqTkw+7G4nPAADgNCTSwFGSSAMAcFYk0sBRkkgDAHBWJNLA0RmS6KUvu9uv9J+/1d3v//2Xt7pvv3K3+2L6/OHd7oXxsy/ef3VR5qvd7YfjM7PrP+xeeP/L+XvW5czvAQDguEikgaOU/470mEgnk96WRHq8tniH74QDABw/iTRwlHYm0onvLjcl0jc/XJU/fZc7MH6XOy4DAIBjIZEGjtJZJ9Lr8pM/xp3/MXAAAI6DRBo4SmebSE8/1l0gkQYAOFoSaeAoPb7vSAMAcOwk0sBRGv6a9n6JdPxZnEgP5Sd+RxoAgKMnkQaO0/qPgZ10928uPysl0qnvZAc/xj17ZroeJ9N9ku2PjQEAHC+JNHC05v/Hc+L/kU48E//+c/9c/B3pSfT/SK+lvhMOAMCxkEgDAABAA4k0AAAANJBIAwAAQAOJNAAAADSQSAMAAEADiTQAAAA0kEgDAABAA4k0AAAANJBIAwAAQAOJNAAAADSQSAMAAEADiTQAAAA0kEgDAABAA4k0AAAANJBIAwAAQAOJNAAAADR44hvfvNYBAAAAdSTSAAAA0EAiDQAAAA0k0gAAANBAIg0AAAANJNIAAADQQCINAAAADSTSAAAA0EAiDQAAAA0k0gAAANBAIg0AAAANJNIAAADQQCINAAAADSTSAAAA0EAiDQAAAA0k0gAAANBAIg0AAAANJNIAAADQQCINAAAADSTSAAAA0EAiDQAAAA0k0gAAANBAIg0AAAANnvj293/YAQAAAHV8RxoAAAAaSKQBAACggUQaAAAAql3r/j/EoiommOgqKAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "45cf9d71-4362-42c8-9da9-59369ef707ca",
   "metadata": {},
   "source": [
    "![Captu66564re.PNG](attachment:3204f966-508a-4987-8f5e-6432405d8157.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "868f60d9-ec1b-4efd-ba8f-0bdfb15e60aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "print(x > 3 and x < 10)\n",
    "\n",
    "# returns True because 5 is greater than 3 AND 5 is less than 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204904d1-fd02-48e9-9bfd-a76dc432d36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "print(x > 3 or x < 4)\n",
    "\n",
    "# returns True because one of the conditions are true (5 is greater than 3, but 5 is not less than 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f68eaa5-7d8f-4654-873c-beec4fff55bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5\n",
    "\n",
    "print(not(x > 3 and x < 10))\n",
    "\n",
    "# returns False because not is used to reverse the result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95c23d7-54bd-4560-97ac-829f7cd2a8ba",
   "metadata": {},
   "source": [
    "#### Identity Operators"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "b350e36f-efc1-4bd1-86dd-d157a7a9eef4",
   "metadata": {},
   "source": [
    "![Capt4564ure.PNG](attachment:a94be7d4-630e-487d-8e57-d45fa204e4cb.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26455567-1e88-46fb-b90d-adf7fbdd54e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"apple\", \"banana\"]\n",
    "y = [\"apple\", \"banana\"]\n",
    "z = x\n",
    "\n",
    "print(x is not z)\n",
    "\n",
    "# returns False because z is the same object as x\n",
    "\n",
    "print(x is not y)\n",
    "\n",
    "# returns True because x is not the same object as y, even if they have the same content\n",
    "\n",
    "print(x != y)\n",
    "\n",
    "# to demonstrate the difference betweeen \"is not\" and \"!=\": this comparison returns False because x is equal to y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd54fc37-b2db-449e-99ed-5bba24c14fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"apple\", \"banana\"]\n",
    "y = [\"apple\", \"banana\"]\n",
    "z = x\n",
    "\n",
    "print(x is z)\n",
    "\n",
    "# returns True because z is the same object as x\n",
    "\n",
    "print(x is y)\n",
    "\n",
    "# returns False because x is not the same object as y, even if they have the same content\n",
    "\n",
    "print(x == y)\n",
    "\n",
    "# to demonstrate the difference betweeen \"is\" and \"==\": this comparison returns True because x is equal to y\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7318632a-c60b-4d6b-9e5b-56d7415d3689",
   "metadata": {},
   "source": [
    "##### Membership Operators"
   ]
  },
  {
   "attachments": {
    "9297f3ad-0d52-4a02-9d4a-0b70bb3f555e.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "bcb7e3c4-3667-4422-a79c-245257b01503",
   "metadata": {},
   "source": [
    "![Ca254756pture.PNG](attachment:9297f3ad-0d52-4a02-9d4a-0b70bb3f555e.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e862e2e-5f8c-4da5-b02f-c3471a56ce76",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"apple\", \"banana\"]\n",
    "\n",
    "print(\"pineapple\" not in x)\n",
    "\n",
    "# returns True because a sequence with the value \"pineapple\" is not in the list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b45407-a483-43df-bdac-cba337d56130",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"apple\", \"banana\"]\n",
    "\n",
    "print(\"banana\" in x)\n",
    "\n",
    "# returns True because a sequence with the value \"banana\" is in the list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45159770-8424-424c-b97f-809ab4500f2d",
   "metadata": {},
   "source": [
    "##### Bitwise Operators"
   ]
  },
  {
   "attachments": {
    "bf71a333-fd4d-487b-a138-5f12fff707ab.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "70d9b4b1-c76e-4c00-a0fb-b13bf302eeb4",
   "metadata": {},
   "source": [
    "![Captu63564re.PNG](attachment:bf71a333-fd4d-487b-a138-5f12fff707ab.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b08d86e-5bd3-4eb3-bc38-89c2abba93c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 & 3)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The & operator compares each bit and set it to 1 if both are 1, otherwise it is set to 0:\n",
    "\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "2 = 0000000000000010\n",
    "====================\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6027c693-acdd-48c7-8223-fe6fd2831b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 | 3)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The | operator compares each bit and set it to 1 if one or both is 1, otherwise it is set to 0:\n",
    "\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "7 = 0000000000000111\n",
    "====================\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bf4dadf-53f9-4e54-bb48-a5a1f605f4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 ^ 3)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The ^ operator compares each bit and set it to 1 if only one is 1, otherwise (if both are 1 or both are 0) it is set to 0:\n",
    "\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "5 = 0000000000000101\n",
    "====================\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eb2ff28-2b45-4c1b-9568-84f548d91e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(~3)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The ~ operator inverts each bit (0 becomes 1 and 1 becomes 0).\n",
    "\n",
    "Inverted 3 becomes -4:\n",
    " 3 = 0000000000000011\n",
    "-4 = 1111111111111100\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    " 4 = 0000000000000100\n",
    " 3 = 0000000000000011\n",
    " 2 = 0000000000000010\n",
    " 1 = 0000000000000001\n",
    " 0 = 0000000000000000\n",
    "-1 = 1111111111111111\n",
    "-2 = 1111111111111110\n",
    "-3 = 1111111111111101\n",
    "-4 = 1111111111111100\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a387f7ce-3a99-40e0-86e4-a93aabc3d562",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(3 << 2)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The << operator inserts the specified number of 0's (in this case 2) from the right and let the same amount of leftmost bits fall off:\n",
    "\n",
    "If you push 00 in from the left:\n",
    " 3 = 0000000000000011\n",
    "becomes\n",
    "12 = 0000000000001100\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    " 0 = 0000000000000000\n",
    " 1 = 0000000000000001\n",
    " 2 = 0000000000000010\n",
    " 3 = 0000000000000011\n",
    " 4 = 0000000000000100\n",
    " 5 = 0000000000000101\n",
    " 6 = 0000000000000110\n",
    " 7 = 0000000000000111\n",
    " 8 = 0000000000001000\n",
    " 9 = 0000000000001001\n",
    "10 = 0000000000001010\n",
    "11 = 0000000000001011\n",
    "12 = 0000000000001100\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10dc5195-f1db-467f-93e4-30b1cf3fe4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(8 >> 2)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The >> operator moves each bit the specified number of times to the right. Empty holes at the left are filled with 0's.\n",
    "\n",
    "If you move each bit 2 times to the right, 8 becomes 2:\n",
    " 8 = 0000000000001000\n",
    "becomes\n",
    " 2 = 0000000000000010\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    " 0 = 0000000000000000\n",
    " 1 = 0000000000000001\n",
    " 2 = 0000000000000010\n",
    " 3 = 0000000000000011\n",
    " 4 = 0000000000000100\n",
    " 5 = 0000000000000101\n",
    " 6 = 0000000000000110\n",
    " 7 = 0000000000000111\n",
    " 8 = 0000000000001000\n",
    " 9 = 0000000000001001\n",
    "10 = 0000000000001010\n",
    "11 = 0000000000001011\n",
    "12 = 0000000000001100\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fdf8f9-26e2-426e-b967-035bb350f5eb",
   "metadata": {},
   "source": [
    "##### Operator Precedence"
   ]
  },
  {
   "attachments": {
    "be2e6ad0-de9f-45fc-bba8-977ba36324e4.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "920f0776-8b89-4440-ac69-d6d1a792455f",
   "metadata": {},
   "source": [
    "![Captur22212e.PNG](attachment:be2e6ad0-de9f-45fc-bba8-977ba36324e4.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4158a77-1719-4812-a4d0-811f38a45f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((6 + 3) - (6 + 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6cb5f7e-f4d6-4b58-9920-d67d11bd0d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(100 + 5 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fe44189-026b-4f9a-a14f-0d05d6883381",
   "metadata": {},
   "outputs": [],
   "source": [
    "print((6 + 3) - (6 + 3))\n",
    "\n",
    "\"\"\"\n",
    "Parenthesis have the highest precedence, and need to be evaluated first.\n",
    "The calculation above reads 9 - 9 = 0\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "520bbbf1-1ffd-4e6f-b5bd-37e79ebf567a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(100 - 3 ** 3)\n",
    "\n",
    "\"\"\"\n",
    "Exponentiation has higher precedence than subtraction, and needs to be evaluated first.\n",
    "The calculation above reads 100 - 27 = 73\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6292d164-200c-44a4-8591-4eb71313dae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(100 + ~3)\n",
    "\n",
    "\"\"\"\n",
    "Bitwise NOT has higher precedence than addition, and needs to be evaluated first.\n",
    "The calculation above reads 100 + -4 = 96\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "319bb29a-6c7b-4f80-9879-ebb8de54587f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(100 + 5 * 3)\n",
    "\n",
    "\"\"\"\n",
    "Multiplication has higher precedence than addition, and needs to be evaluated first.\n",
    "The calculation above reads 100 + 15 = 115\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df5f4354-434a-4923-abd5-fdbb00915b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Result Size: 945 x 724\n",
    "print(100 - 5 * 3)\n",
    "\n",
    "\"\"\"\n",
    "Subtraction has a lower precedence than multiplication, and we need to calculate the multiplication first.\n",
    "The calculation above reads 100 - 15 = 85\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97499cba-2c09-43c0-9797-bada884febca",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Result Size: 945 x 724\n",
    "print(8 >> 4 - 2)\n",
    "​\n",
    "\"\"\"\n",
    "Bitwise right shift has a lower precedence than subtraction, and we need to calculate the subtraction first.\n",
    "The calculation above reads 8 >> 2 = 2\n",
    "​\n",
    "More explanation:\n",
    "The >> operator moves each bit the specified number of times to the right. Empty holes at the left are filled with 0's.\n",
    "​\n",
    "If you move each bit 2 times to the right, 8 becomes 2:\n",
    " 8 = 0000000000001000\n",
    "becomes\n",
    " 2 = 0000000000000010\n",
    "​\n",
    "Decimal numbers and their binary values:\n",
    " 0 = 0000000000000000\n",
    " 1 = 0000000000000001\n",
    " 2 = 0000000000000010\n",
    " 3 = 0000000000000011\n",
    " 4 = 0000000000000100\n",
    " 5 = 0000000000000101\n",
    " 6 = 0000000000000110\n",
    " 7 = 0000000000000111\n",
    " 8 = 0000000000001000\n",
    " 9 = 0000000000001001\n",
    "10 = 0000000000001010\n",
    "11 = 0000000000001011\n",
    "12 = 0000000000001100\n",
    "\"\"\"\n",
    "​\n",
    "2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe62d9c7-3f9b-43e5-9aab-5593ca3cc093",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Result Size: 945 x 724\n",
    "print(6 & 2 + 1)\n",
    "​\n",
    "\"\"\"\n",
    "Bitwise AND has a lower precedence than addition, and we need to calculate the addition first.\n",
    "The calculation above reads 6 & 3 = 2\n",
    "​\n",
    "More explanation:\n",
    "The & operator compares each bit and set it to 1 if both are 1, otherwise it is set to 0:\n",
    "​\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "2 = 0000000000000010\n",
    "====================\n",
    "​\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n",
    "​\n",
    "2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7188542-e54c-4ab5-9db6-4a9b076663eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 ^ 2 + 1)\n",
    "\n",
    "\"\"\"\n",
    "Bitwise XOR has a lower precedence than addition, and we need to calculate the addition first.\n",
    "The calculation above reads 6 ^ 3 = 5\n",
    "\n",
    "More explanation:\n",
    "The ^ operator compares each bit and set it to 1 if only one is 1, otherwise (if both are 1 or both are 0) it is set to 0:\n",
    "\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "5 = 0000000000000101\n",
    "====================\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb524343-0c4b-41d5-808d-2bd902ed70d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 | 2 + 1)\n",
    "\n",
    "\"\"\"\n",
    "Bitwise OR has a lower precedence than addition, and we need to calculate the addition first.\n",
    "The calculation above reads 6 | 3 = 7\n",
    "\n",
    "More explanation:\n",
    "The | operator compares each bit and set it to 1 if one or both is 1, otherwise it is set to 0:\n",
    "\n",
    "6 = 0000000000000110\n",
    "3 = 0000000000000011\n",
    "--------------------\n",
    "7 = 0000000000000111\n",
    "====================\n",
    "\n",
    "Decimal numbers and their binary values:\n",
    "0 = 0000000000000000\n",
    "1 = 0000000000000001\n",
    "2 = 0000000000000010\n",
    "3 = 0000000000000011\n",
    "4 = 0000000000000100\n",
    "5 = 0000000000000101\n",
    "6 = 0000000000000110\n",
    "7 = 0000000000000111\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8496f344-ddf7-4608-819c-041f91decf2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(5 == 4 + 1)\n",
    "\n",
    "\"\"\"\n",
    "The \"like\" comparison has a lower precedence than addition, and we need to calculate the addition first.\n",
    "The calculation above reads 5 == 5 = True\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31a69c3-7374-40ed-961b-ab5b0d4d0427",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(not 5 == 5)\n",
    "\n",
    "\"\"\"\n",
    "The logical NOT operator has a lower precedence than \"like\" comparison, and we need to calculate the comparison first.\n",
    "The calculation above reads: not True = False\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db2bd25-e4b5-48ec-970b-74f036ac0d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(1 or 2 and 3)\n",
    "\n",
    "\"\"\"\n",
    "The and operator has a higher precedence than or, and we need to calculate the and expression first.\n",
    "The calculation above reads: 1 or 3 = 1\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d742ab21-09f6-49e9-8809-8d35d6a58996",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 or 5 + 10 or 8)\n",
    "\n",
    "\"\"\"\n",
    "The or operator has a lower precedence than addition, and we need to calculate the addition first.\n",
    "The calculation above reads: 4 or 15 or 8 = 4\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255fcc61-c1c6-4971-b472-580842343341",
   "metadata": {},
   "source": [
    "### Walrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75288fde-8ff8-4464-88de-ebe068edf8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "users = list()\n",
    "while True:\n",
    "    user = input()\n",
    "    if user == \"end\":\n",
    "        break\n",
    "users.append(user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "57e82039-5ed2-4388-a935-1beaa62ccad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " end\n"
     ]
    }
   ],
   "source": [
    "users = list()\n",
    "while (user:= input())!= \"end\":\n",
    "    users.append(user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecaf10b7-f236-4637-93a4-72ca0c5923fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "927f9ad6-9167-4c0d-b71f-6f87c94fbaee",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212f878b-ca72-4169-baef-ab5e9d2f658e",
   "metadata": {},
   "source": [
    "to store collections of data, Python Collections (Arrays):\n",
    "* List\n",
    "  * a collection which is `ordered` and `changeable`. `Allows duplicate` members.\n",
    "* Tuple\n",
    "  * a collection which is `ordered` and `unchangeable`. `Allows duplicate` members.\n",
    "* Set\n",
    "  * a collection which is `unordered`, `unchangeable`(but you can remove and/or add items), and `unindexed`. `No duplicate` members.\n",
    "* Dictionary\n",
    "  * a collection which is `ordered` and `changeable`. `No duplicate` members."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847ed39b-3d7e-4564-9923-36e8d0481b32",
   "metadata": {},
   "source": [
    "Features:\n",
    "* ordered (indexed)\n",
    "  * When we say that lists are ordered, it means that the items have a defined order, and that order will not change.If you add new items to a list, the new items will be placed at the end of the list.\n",
    "* changeable\n",
    "  * The list is changeable, meaning that we can change, add, and remove items in a list after it has been created.\n",
    "* allow duplicate values.\n",
    "  * Since lists are indexed, lists can have items with the same value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77b15fee-bc73-4f22-8996-3c1c00f88520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "print(len(thislist))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e1e17b-aa08-47a0-966a-ea325009aaa5",
   "metadata": {},
   "source": [
    "### Access List Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac7f7f9c-41c2-46d1-80cb-32eeb4f11d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "banana\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "print(thislist[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb434652-afb1-400d-b4bc-aae9389c3c95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "print(thislist[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4874ba4-0751-4c00-84ce-8f058b53e5d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cherry', 'orange', 'kiwi']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\"]\n",
    "print(thislist[2:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2109aeda-c159-4d20-b4bd-d6c1ff09ac90",
   "metadata": {},
   "source": [
    "### Check if Item Exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5403491-8722-4c10-ae7e-c3e53f9c4412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, 'apple' is in the fruits list\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "if \"apple\" in thislist:\n",
    "  print(\"Yes, 'apple' is in the fruits list\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b824c1b-1b57-4602-b824-92f616fb8fa9",
   "metadata": {},
   "source": [
    "### Change List Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df431e9c-9178-4143-ac84-6c3e64d8abb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'blackcurrant', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist[1] = \"blackcurrant\"\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6d68d47-8cd8-464e-81f1-e3266952139e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'blackcurrant', 'watermelon', 'orange', 'kiwi', 'mango']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"mango\"]\n",
    "thislist[1:3] = [\"blackcurrant\", \"watermelon\"]\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1da095d-b3eb-4841-83af-2c1f3363ef46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'blackcurrant', 'watermelon', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist[1:2] = [\"blackcurrant\", \"watermelon\"]\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e31920a-cd94-4606-873a-23da961873ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'watermelon']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist[1:3] = [\"watermelon\"]\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41054fe-c4bb-4f8e-85c7-a872d634d338",
   "metadata": {},
   "source": [
    "### Add List Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26b8cc35-855a-4192-a749-e75938e8cc3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.append(\"orange\")\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdb41122-8881-4b9f-8de1-f1600b101dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'orange', 'banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.insert(1, \"orange\")\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f3b5b4e-92d5-407e-be98-f61789270d43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'mango', 'pineapple', 'papaya']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "tropical = [\"mango\", \"pineapple\", \"papaya\"]\n",
    "thislist.extend(tropical)\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "056d74ff-a213-4809-9884-d3ea86aad2bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'kiwi', 'orange']\n"
     ]
    }
   ],
   "source": [
    "# add any iterable object (tuples, sets, dictionaries etc.).\n",
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thistuple = (\"kiwi\", \"orange\")\n",
    "thislist.extend(thistuple)\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f42c78-76d4-4c8b-b319-f7b4940bc438",
   "metadata": {},
   "source": [
    "### Remove Specified Item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec6334dc-2c88-4733-99bd-45cd92cb2ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.remove(\"banana\")\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5921a028-7b60-42da-b941-f5d066298271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'cherry', 'banana', 'kiwi']\n"
     ]
    }
   ],
   "source": [
    "# If there are more than one item with the specified value, the remove() method removes the first occurrence:\n",
    "thislist = [\"apple\", \"banana\", \"cherry\", \"banana\", \"kiwi\"]\n",
    "thislist.remove(\"banana\")\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5223880e-fcc1-43d3-91dc-bc593b59592c",
   "metadata": {},
   "source": [
    "### Remove Specified Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c7d8e08-ef19-46ed-8a1e-ebb4c0ae50c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.pop(1)\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c991f99c-e689-484b-a939-2afebd1cff8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana']\n"
     ]
    }
   ],
   "source": [
    "# If you do not specify the index, the pop() method removes the last item.\n",
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.pop()\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0e89622-c189-4cbf-9e75-3a157a3897df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "del thislist[0]\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bb131da-a384-4cca-8e3c-61480bfe2cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "del thislist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96915589-a636-418e-ab97-09099c49c549",
   "metadata": {},
   "source": [
    "### Clear the List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c3a6c33-5317-496d-8e0d-a50f47b5c028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.clear()\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad58b02-c659-4afd-b08b-3fbd7a491577",
   "metadata": {},
   "source": [
    "### Loop Through a List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a94f2e45-3851-41bb-bc92-d1bc2bb9a0ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "for x in thislist:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dbdd5c62-b28e-4b2e-980d-df2cf4db7192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "for i in range(len(thislist)):\n",
    "  print(thislist[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600cf223-0f08-4b86-8fb8-76fa8952e921",
   "metadata": {},
   "source": [
    "#### Using a While Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "234e686f-a822-441b-9def-dc2628ce3fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "i = 0\n",
    "while i < len(thislist):\n",
    "  print(thislist[i])\n",
    "  i = i + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a17db0-c43a-43fc-b868-25cc2944d355",
   "metadata": {},
   "source": [
    "### Looping Using List Comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ee4751d-d424-47d2-b0d4-1172475ae6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "[print(x) for x in thislist]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdbb98a-015f-4c31-bd98-7377703a4e27",
   "metadata": {},
   "source": [
    "### List Comprehension\n",
    "\n",
    "* List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list.\n",
    "* syntax :\n",
    "  * **`newlist = [expression for item in iterable if condition == True]`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1efe1e69-a63e-4346-8dfa-00885c1688dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'mango']\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"apple\", \"banana\", \"cherry\", \"kiwi\", \"mango\"]\n",
    "newlist = []\n",
    "\n",
    "for x in fruits:\n",
    "  if \"a\" in x:\n",
    "    newlist.append(x)\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7dd0e415-9c95-412c-bd80-f0d84a5a6000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'mango']\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"apple\", \"banana\", \"cherry\", \"kiwi\", \"mango\"]\n",
    "\n",
    "newlist = [x for x in fruits if \"a\" in x]\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ded7c8cc-c329-417d-aaf0-4b2180552914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['banana', 'cherry', 'kiwi', 'mango']\n"
     ]
    }
   ],
   "source": [
    "newlist = [x for x in fruits if x != \"apple\"]\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e67d4ee-9447-44a1-bf77-0b9f991d4490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "# he iterable can be any iterable object, like a list, tuple, set etc.\n",
    "newlist = [x for x in range(10)]\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fdf93e30-bfa9-413c-a058-1a5d480a99e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['APPLE', 'BANANA', 'CHERRY', 'KIWI', 'MANGO']\n"
     ]
    }
   ],
   "source": [
    "# The expression is the current item in the iteration, but it is also the outcome, \n",
    "# which you can manipulate before it ends up like a list item in the new list:\n",
    "newlist = [x.upper() for x in fruits]\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2517a9e7-23ba-4541-bd77-07cdc039d5cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'orange', 'cherry', 'kiwi', 'mango']\n"
     ]
    }
   ],
   "source": [
    "# The expression can also contain conditions, \n",
    "# not like a filter, but as a way to manipulate the outcome:\n",
    "newlist = [x if x != \"banana\" else \"orange\" for x in fruits]\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "140e72b7-0605-4752-b8e7-b126586f2c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'orange', 'cherry', 'kiwi', 'mango']\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"apple\", \"banana\", \"cherry\", \"kiwi\", \"mango\"]\n",
    "newlist = []  # Initialize an empty list to store the new values\n",
    "\n",
    "for x in fruits:\n",
    "    if x != \"banana\":\n",
    "        newlist.append(x)  # If x is not \"banana\", add x to newlist\n",
    "    else:\n",
    "        newlist.append(\"orange\")  # If x is \"banana\", add \"orange\" to newlist\n",
    "\n",
    "print(newlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebab46d-dae3-4ed0-8776-fe7cdf176738",
   "metadata": {},
   "source": [
    "### Sort Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "098c0a94-5118-447a-a161-8b558bcedd5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['banana', 'kiwi', 'mango', 'orange', 'pineapple']\n"
     ]
    }
   ],
   "source": [
    "# List objects have a sort() method that will sort the list alphanumerically, ascending, by default:\n",
    "thislist = [\"orange\", \"mango\", \"kiwi\", \"pineapple\", \"banana\"]\n",
    "thislist.sort()\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b932695-3680-4af6-84de-7c2a0b55789c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23, 50, 65, 82, 100]\n"
     ]
    }
   ],
   "source": [
    "thislist = [100, 50, 65, 82, 23]\n",
    "thislist.sort()\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94e6f5c8-b76a-43ce-a2f0-fad81865654a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pineapple', 'orange', 'mango', 'kiwi', 'banana']\n"
     ]
    }
   ],
   "source": [
    "# o sort descending, use the keyword argument reverse = True:\n",
    "thislist = [\"orange\", \"mango\", \"kiwi\", \"pineapple\", \"banana\"]\n",
    "thislist.sort(reverse = True)\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bb443d-bcd8-4f05-a716-2d27bf397e8f",
   "metadata": {},
   "source": [
    "### Copy Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3bebcf53-9438-428c-b8cb-b0cfa91c6a28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# You cannot copy a list simply by typing list2 = list1, \n",
    "# because: list2 will only be a reference to list1,\n",
    "# and changes made in list1 will automatically also be made in list2.\n",
    "# There are ways to make a copy, one way is to use the built-in List method copy().\n",
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "mylist = thislist.copy()\n",
    "print(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "158e061d-310d-4b22-9f92-dc5f9517fa11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# Another way to make a copy is to use the built-in method list().\n",
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "mylist = list(thislist)\n",
    "print(mylist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74d61d3-2562-4e42-b158-4e69e56fcf40",
   "metadata": {},
   "source": [
    "### Join Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2c43b132-ff36-4909-8dc2-f6ba2abb1f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c', 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "list1 = [\"a\", \"b\", \"c\"]\n",
    "list2 = [1, 2, 3]\n",
    "\n",
    "list3 = list1 + list2\n",
    "print(list3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a07a7f2b-ed8c-4947-9123-121152788af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c', 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "list1 = [\"a\", \"b\" , \"c\"]\n",
    "list2 = [1, 2, 3]\n",
    "\n",
    "for x in list2:\n",
    "  list1.append(x)\n",
    "\n",
    "print(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e5bb45e3-6c69-47e4-a882-5a3ce6b4ee86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c', 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "list1 = [\"a\", \"b\" , \"c\"]\n",
    "list2 = [1, 2, 3]\n",
    "\n",
    "list1.extend(list2)\n",
    "print(list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc07d9de-e74b-4d3b-b518-a6c7ebd133dd",
   "metadata": {},
   "source": [
    "### List Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "29fd6384-9fbe-4ba0-afdb-cc8805c5758a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange']\n"
     ]
    }
   ],
   "source": [
    "# append()\tAdds an element at the end of the list\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "fruits.append(\"orange\")\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8f558891-1b29-4b77-896d-9b014271de97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', ['Ford', 'BMW', 'Volvo']]\n"
     ]
    }
   ],
   "source": [
    "a = [\"apple\", \"banana\", \"cherry\"]\n",
    "b = [\"Ford\", \"BMW\", \"Volvo\"]\n",
    "a.append(b)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0e08122f-a7d6-45c1-a306-d39eea213ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "# clear()\tRemoves all the elements from the list\n",
    "fruits = ['apple', 'banana', 'cherry', 'orange']\n",
    "fruits.clear()\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "351e16fe-5620-4cf0-a451-5829d693deda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange']\n"
     ]
    }
   ],
   "source": [
    "# copy()\tReturns a copy of the list\n",
    "fruits = ['apple', 'banana', 'cherry', 'orange']\n",
    "x = fruits.copy()\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c692596d-63c6-498f-bdd4-b1d3d291759e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# count()\tReturns the number of elements with the specified value\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "x = fruits.count(\"cherry\")\n",
    "print(x)\n",
    "\n",
    "points = [1, 4, 2, 9, 7, 8, 9, 3, 1]\n",
    "x = points.count(9)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b8a8cbc1-da2d-410c-8f64-1f0c5597efdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 1, 4, 5, 9]\n"
     ]
    }
   ],
   "source": [
    "# extend()\tAdd the elements of a list (or any iterable), to the end of the current list\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "points = (1, 4, 5, 9)\n",
    "fruits.extend(points)\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "415377c9-cd30-4dbe-be88-13d0840b921c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# index()\tReturns the index of the first element with the specified value\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "x = fruits.index(\"cherry\")\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4c149d57-6627-46c0-8773-27ea13c238ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'orange', 'banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# insert()\tAdds an element at the specified position\n",
    "# list.insert(pos, elmnt)\n",
    "# pos\tRequired. A number specifying in which position to insert the value\n",
    "# elmnt\tRequired. An element of any type (string, number, object etc.)\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "fruits.insert(1, \"orange\")\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fd493c22-2f04-4818-8aa0-f96e4f79c9a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "banana\n",
      "['apple', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# pop()\tRemoves the element at the specified position\n",
    "# list.pop(pos)\n",
    "# pos\tOptional. A number specifying the position of the element you want to remove,\n",
    "# default value is -1, which returns the last item\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "print(fruits.pop(1))\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e7c0e51f-f106-4bcf-bc54-eeace00d15d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# remove()\tRemoves the item with the specified value\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "fruits.remove(\"banana\")\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6bbc2604-bc6e-4f84-926a-4b0942a158b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cherry', 'banana', 'apple']\n"
     ]
    }
   ],
   "source": [
    "# reverse()\tReverses the order of the list\n",
    "fruits = ['apple', 'banana', 'cherry']\n",
    "fruits.reverse()\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "de20388b-2c88-42a2-857b-7200f559a5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Volvo', 'Ford', 'BMW']\n"
     ]
    }
   ],
   "source": [
    "# sort()\tSorts the list\n",
    "\n",
    "# The sort() method sorts the list ascending by default.\n",
    "# You can also make a function to decide the sorting criteria(s).\n",
    "\n",
    "# list.sort(reverse=True|False, key=myFunc)\n",
    "# reverse\tOptional. reverse=True will sort the list descending. Default is reverse=False\n",
    "# key\tOptional. A function to specify the sorting criteria(s)\n",
    "cars = ['Ford', 'BMW', 'Volvo']\n",
    "cars.sort(reverse=True)\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1205fd51-3e6a-420b-941b-b069e9b84f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VW', 'BMW', 'Ford', 'Mitsubishi']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function that returns the length of the value:\n",
    "def myFunc(e):\n",
    "  return len(e)\n",
    "\n",
    "cars = ['Ford', 'Mitsubishi', 'BMW', 'VW']\n",
    "cars.sort(key=myFunc)\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "897bdecc-1d24-46e4-8ef3-09c10b6b81a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'car': 'Mitsubishi', 'year': 2000},\n",
       " {'car': 'Ford', 'year': 2005},\n",
       " {'car': 'VW', 'year': 2011},\n",
       " {'car': 'BMW', 'year': 2019}]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function that returns the 'year' value:\n",
    "def myFunc(e):\n",
    "  return e['year']\n",
    "\n",
    "cars = [\n",
    "  {'car': 'Ford', 'year': 2005},\n",
    "  {'car': 'Mitsubishi', 'year': 2000},\n",
    "  {'car': 'BMW', 'year': 2019},\n",
    "  {'car': 'VW', 'year': 2011}\n",
    "]\n",
    "\n",
    "cars.sort(key=myFunc)\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "368bee7c-0575-447d-976b-72e0de29f4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mitsubishi', 'Ford', 'BMW', 'VW']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function that returns the length of the value:\n",
    "def myFunc(e):\n",
    "  return len(e)\n",
    "\n",
    "cars = ['Ford', 'Mitsubishi', 'BMW', 'VW']\n",
    "\n",
    "cars.sort(reverse=True, key=myFunc)\n",
    "cars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27d1202-1241-403b-a380-53ed703522dc",
   "metadata": {},
   "source": [
    "# Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948e730e-a19b-43b5-9a0f-19a832f5f2d9",
   "metadata": {},
   "source": [
    "* a collection which is ordered and unchangeable. Allows duplicate members.\n",
    "  \n",
    "* Ordered\n",
    "    * When we say that tuples are ordered, it means that the items have a defined order, and that order will not change.\n",
    "\n",
    "\n",
    "* Unchangeable\n",
    "    * Tuples are unchangeable, meaning that we cannot change, add or remove items after the tuple has been created.\n",
    "\n",
    "\n",
    "* Allow Duplicates\n",
    "    * Since tuples are indexed, they can have items with the same value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8f426b16-c1eb-4e8c-a8f8-8b16cc4f3174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('apple', 'banana', 'cherry', 'apple', 'cherry')\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\", \"apple\", \"cherry\")\n",
    "print(thistuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a1a7d0cf-3f3c-49c1-bb5f-fa482dff2add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(len(thistuple))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3117cd06-f1a3-433c-8c64-a6182c76c4e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\",)\n",
    "print(type(thistuple))\n",
    "\n",
    "#NOT a tuple\n",
    "thistuple = (\"apple\")\n",
    "print(type(thistuple))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9c9854fb-efdb-4218-9d59-6e5f52249947",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuple1 = (\"apple\", \"banana\", \"cherry\")\n",
    "tuple2 = (1, 5, 7, 9, 3)\n",
    "tuple3 = (True, False, False)\n",
    "tuple4 = (\"abc\", 34, True, 40, \"male\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "de8c188c-ed0d-4a47-99f9-56fda5670065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "mytuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(type(mytuple))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "457dda0a-3574-4a47-a740-5d2943a8de8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constructor\n",
    "thistuple = tuple((\"apple\", \"banana\", \"cherry\")) # note the double round-brackets\n",
    "print(thistuple)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80278675-c97f-4fb3-b93b-cb70d416f8a5",
   "metadata": {},
   "source": [
    "### Access Tuple Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f5e46271-e03f-4b39-a0f1-018b5be1f029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "banana\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(thistuple[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "db83ecc4-105b-4ff9-b31c-7127dab25a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(thistuple[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cc9f4b00-1cc2-4e37-be1d-0ba747ca19c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('cherry', 'orange', 'kiwi')\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\")\n",
    "print(thistuple[2:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e02426d5-df7e-4245-9f18-a602d26b598d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, 'apple' is in the fruits tuple\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "if \"apple\" in thistuple:\n",
    "  print(\"Yes, 'apple' is in the fruits tuple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b0c4e32e-a1b2-4dc5-b8d1-2292c15934f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('apple', 'kiwi', 'cherry')\n"
     ]
    }
   ],
   "source": [
    "# Once a tuple is created, you cannot change its values.\n",
    "# Tuples are unchangeable, or immutable as it also is called.\n",
    "x = (\"apple\", \"banana\", \"cherry\")\n",
    "y = list(x)\n",
    "y[1] = \"kiwi\"\n",
    "x = tuple(y)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6afb52b2-0c6d-4f95-81b6-0fe0fc317c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple', 'banana', 'cherry', 'orange')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "y = list(thistuple)\n",
    "y.append(\"orange\")\n",
    "thistuple = tuple(y)\n",
    "thistuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ce8e2430-2e2e-45b5-a00e-2e9b3546cd76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('apple', 'banana', 'cherry', 'orange')\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "y = (\"orange\",)\n",
    "thistuple += y\n",
    "\n",
    "print(thistuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0fe8132b-cd2c-4c4d-a43e-a5cb9ea7aefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('banana', 'cherry')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "y = list(thistuple)\n",
    "y.remove(\"apple\")\n",
    "thistuple = tuple(y)\n",
    "thistuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "510df5f0-afad-41f0-9fea-962cbf389aa1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'thistuple' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[84], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m thistuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapple\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbanana\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcherry\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m thistuple\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mthistuple\u001b[49m) \u001b[38;5;66;03m#this will raise an error because the tuple no longer exists\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'thistuple' is not defined"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "del thistuple\n",
    "print(thistuple) #this will raise an error because the tuple no longer exists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9452ea3f-9c46-43cb-bfd4-7ecf3965e8b6",
   "metadata": {},
   "source": [
    "#### Unpack Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a33f8b1f-9106-46a1-aafe-ab828b62f67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "fruits = (\"apple\", \"banana\", \"cherry\")\n",
    "(green, yellow, red) = fruits\n",
    "print(green)\n",
    "print(yellow)\n",
    "print(red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1f92646e-8d7e-4bdc-8fc1-a38a824b4976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "['cherry', 'strawberry', 'raspberry']\n"
     ]
    }
   ],
   "source": [
    "# The number of variables must match the number of values in the tuple, if not, \n",
    "#you must use an asterisk to collect the remaining values as a list.\n",
    "# If the number of variables is less than the number of values, \n",
    "# you can add an * to the variable name and the values will be assigned to the variable as a list:\n",
    "fruits = (\"apple\", \"banana\", \"cherry\", \"strawberry\", \"raspberry\")\n",
    "(green, yellow, *red) = fruits\n",
    "print(green)\n",
    "print(yellow)\n",
    "print(red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "dc05d08f-1b5d-4fa6-b714-da9b6cea6472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "['mango', 'papaya', 'pineapple']\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "# If the asterisk is added to another variable name than the last, Python will assign values to the variable until \n",
    "# the number of values left matches the number of variables left.\n",
    "fruits = (\"apple\", \"mango\", \"papaya\", \"pineapple\", \"cherry\")\n",
    "(green, *tropic, red) = fruits\n",
    "print(green)\n",
    "print(tropic)\n",
    "print(red)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562cc587-c1de-4e66-b363-b2cd750fbea7",
   "metadata": {},
   "source": [
    "### Loop Through a Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b88233bc-c80a-4de3-9c64-56028fe8ffe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "for x in thistuple:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5d7987d8-af07-48ed-a89f-3d92a455c38a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "for i in range(len(thistuple)):\n",
    "  print(thistuple[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e295770c-875b-4e98-b918-1b57f9703a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "i = 0\n",
    "while i < len(thistuple):\n",
    "  print(thistuple[i])\n",
    "  i = i + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d7b984f-909d-4e36-b8af-fc4c7a19cab1",
   "metadata": {},
   "source": [
    "### Join Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d7d05de9-a868-423a-b8e9-7ab67737b4e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', 'b', 'c', 1, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "tuple1 = (\"a\", \"b\" , \"c\")\n",
    "tuple2 = (1, 2, 3)\n",
    "tuple3 = tuple1 + tuple2\n",
    "print(tuple3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "aeee23c2-cbd8-4da4-bb2b-1e7e90fed976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'apple', 'banana', 'cherry']\n",
      "('apple', 'banana', 'cherry', 'apple', 'banana', 'cherry')\n"
     ]
    }
   ],
   "source": [
    "fruits = (\"apple\", \"banana\", \"cherry\")\n",
    "mytuple = fruits * 2\n",
    "print(list(mytuple))\n",
    "print(mytuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e7d7f5b1-1299-423b-aa73-d02ea1625c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "#count()\tReturns the number of times a specified value occurs in a tuple\n",
    "thistuple = (1, 3, 7, 8, 7, 5, 4, 6, 8, 5)\n",
    "x = thistuple.count(5)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d0eaefc6-3bcf-4c73-a31d-859fc8ae949c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# index()\tSearches the tuple for a specified value and returns the position of where it was found.\n",
    "# The index() method finds the first occurrence of the specified value.\n",
    "# The index() method raises an exception if the value is not found.\n",
    "thistuple = (1, 3, 7, 8, 7, 5, 4, 6, 8, 5)\n",
    "x = thistuple.index(8)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "11e0f8e2-5f56-431f-8c49-16f9e5951f02",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "tuple.index(x): x not in tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[97], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m thistuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mthistuple\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n",
      "\u001b[1;31mValueError\u001b[0m: tuple.index(x): x not in tuple"
     ]
    }
   ],
   "source": [
    "thistuple = (1, 3, 7, 8, 7, 5, 4, 6, 8, 5)\n",
    "x = thistuple.index(10)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbe07da-a367-460d-a77a-9a8bacce0014",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58fa1432-8ca4-4485-9e8f-a41d547f2578",
   "metadata": {},
   "source": [
    "- Set is a collection which is unordered, unchangeable, and unindexed. No duplicate members.\n",
    "- Set items are unchangeable, but you can remove items and add new items.\n",
    "- Sets are unordered, so you cannot be sure in which order the items will appear.\n",
    "  - Unordered means that the items in a set do not have a defined order.Set items can appear in a different order every time you use them, and cannot be referred to by index or key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0c0f0aa2-218d-443d-aa85-362ce7b472f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "55e9bfd3-dfad-431e-9a55-60790dda0c6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\", \"apple\"}\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "44c8b11a-af7d-4cdd-979d-a7d56d833a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{True, 2, 'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\", True, 1, 2}\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "22be48a0-07a9-4978-a006-df0ee4121105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{False, True, 'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\", False, True, 0}\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721095ed-d5fe-4097-8bc7-fa0093f8d0c4",
   "metadata": {},
   "source": [
    "### Access Set Items\n",
    "You cannot access items in a set by referring to an index or a key.\n",
    "\n",
    "But you can loop through the set items using a for loop, or ask if a specified value is present in a set, by using the in keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f4028596-45ad-437f-9f0d-b910c3480613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cherry\n",
      "banana\n",
      "apple\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "for x in thisset:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3b3af6f6-b6ae-4333-910a-997f1f8acd61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "print(\"banana\" in thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "bbdca3d1-0502-4858-88c2-2025099bee6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "print(\"banana\" not in thisset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3cb616-f63d-482e-a940-14ce4ca49b98",
   "metadata": {},
   "source": [
    "### Add Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "35da83a8-1d93-461c-8cf0-e3c281a3fcb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana', 'orange', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.add(\"orange\")\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "14932f7b-458a-41ac-9a84-a5072fd980b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'papaya', 'pineapple', 'cherry', 'mango', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "tropical = {\"pineapple\", \"mango\", \"papaya\"}\n",
    "\n",
    "thisset.update(tropical)\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b67451e4-e0cd-4ffe-a643-7a012caa21d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kiwi', 'cherry', 'orange', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "# The object in the update() method does not have to be a set,\n",
    "# it can be any iterable object (tuples, lists, dictionaries etc.).\n",
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "mylist = [\"kiwi\", \"orange\"]\n",
    "\n",
    "thisset.update(mylist)\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cbd0a8-cb14-4654-b4d8-c20e344f0a64",
   "metadata": {},
   "source": [
    "### Remove Set Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bce3cc28-2789-4247-979b-1ee02e83cbd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.remove(\"banana\")\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "3606d432-b61f-49fa-bd14-291c059ecfc9",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'potato'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[114], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# If the item to remove does not exist, remove() will raise an error.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m thisset \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapple\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbanana\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcherry\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m----> 4\u001b[0m \u001b[43mthisset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpotato\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(thisset)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'potato'"
     ]
    }
   ],
   "source": [
    "# If the item to remove does not exist, remove() will raise an error.\n",
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.remove(\"potato\") 😁 # potato is a fruit?\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "14dc4540-b6ed-430a-ad43-5b2fdda414a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.discard(\"banana\")\n",
    "\n",
    "print(thisset)\n",
    "# If the item to remove does not exist, discard() will NOT raise an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "fb36f6f8-9a4a-4697-8d99-d1c56ef05e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cherry\n",
      "{'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "# You can also use the pop() method to remove an item, but this method will remove a random item,\n",
    "# so you cannot be sure what item that gets removed.\n",
    "# Sets are unordered, so when using the pop() method, you do not know which item that gets removed.\n",
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "x = thisset.pop()\n",
    "\n",
    "print(x)\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6490bffe-6cad-415b-9278-30f7162d2931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.clear()\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1f110b3e-d432-491b-9763-8feb7b9fb154",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'thisset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[126], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m thisset \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapple\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbanana\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcherry\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m thisset\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mthisset\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'thisset' is not defined"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "del thisset\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113e6522-c174-4c01-bb34-03a6a045bcfe",
   "metadata": {},
   "source": [
    "### Loop Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4f96cc3d-388e-49c9-bd9c-91d0d742f5ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cherry\n",
      "banana\n",
      "apple\n"
     ]
    }
   ],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "for x in thisset:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b819695-fb60-46a8-9583-ae2475f6c52e",
   "metadata": {},
   "source": [
    "### Join Sets\n",
    "- There are several ways to join two or more sets in Python.\n",
    "\n",
    "- The `union()` and `update()` methods joins all items from both sets.\n",
    "\n",
    "- The `intersection()` method keeps ONLY the duplicates.\n",
    "\n",
    "- The `difference()` method keeps the items from the first set that are not in the other set(s).\n",
    "\n",
    "- The `symmetric_difference(` method keeps all items EXCEPT the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a8f4e199-edd8-4e3d-9001-044627d95e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c', 1, 2, 'a', 3, 'b'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"a\", \"b\", \"c\"}\n",
    "set2 = {1, 2, 3}\n",
    "\n",
    "set3 = set1.union(set2)\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c916b4d6-8d0c-4ce3-9116-790a215ac2a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c', 1, 2, 'a', 3, 'b'}\n"
     ]
    }
   ],
   "source": [
    "# You can use the | operator instead of the union() method, and you will get the same result.\n",
    "set1 = {\"a\", \"b\", \"c\"}\n",
    "set2 = {1, 2, 3}\n",
    "\n",
    "set3 = set1 | set2\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "349799f3-f925-4ff9-be94-e099e4bf5534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 'a', 'Elena', 'b', 'John', 'bananas', 'c', 'cherry', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"a\", \"b\", \"c\"}\n",
    "set2 = {1, 2, 3}\n",
    "set3 = {\"John\", \"Elena\"}\n",
    "set4 = {\"apple\", \"bananas\", \"cherry\"}\n",
    "\n",
    "myset = set1.union(set2, set3, set4)\n",
    "print(myset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b23ad385-455f-4e1f-906c-80f683426087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 'a', 'Elena', 'b', 'John', 'bananas', 'c', 'cherry', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"a\", \"b\", \"c\"}\n",
    "set2 = {1, 2, 3}\n",
    "set3 = {\"John\", \"Elena\"}\n",
    "set4 = {\"apple\", \"bananas\", \"cherry\"}\n",
    "\n",
    "myset = set1 | set2 | set3 |set4\n",
    "print(myset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "54349658-93a6-4061-a945-0eec12100db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 'a', 'b', 'c'}\n"
     ]
    }
   ],
   "source": [
    "# The union() method allows you to join a set with other data types, like lists or tuples.\n",
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = (1, 2, 3)\n",
    "\n",
    "z = x.union(y)\n",
    "print(z)\n",
    "# The  | operator only allows you to join sets with sets,\n",
    "# and not with other data types like you can with the  union() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9bf87725-60c2-48a4-a36a-c7d269d3140a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c', 1, 2, 'a', 3, 'b'}\n"
     ]
    }
   ],
   "source": [
    "# The update() method inserts all items from one set into another.\n",
    "\n",
    "# The update() changes the original set, and does not return a new set.\n",
    "set1 = {\"a\", \"b\" , \"c\",1}\n",
    "set2 = {1, 2, 3}\n",
    "\n",
    "set1.update(set2)\n",
    "print(set1)\n",
    "# Both union() and update() will exclude any duplicate items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c17ff196-22d8-4a35-af36-be0d0dba89fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'apple'}\n"
     ]
    }
   ],
   "source": [
    "# The intersection() method will return a new set, that only contains the items that are present in both sets.\n",
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1.intersection(set2)\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "137079e7-142f-4dcc-b83c-7713ab590657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'apple'}\n",
      "{'cherry', 'banana', 'apple'}\n",
      "{'google', 'microsoft', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1 & set2\n",
    "print(set3)\n",
    "print(set1, set2, sep='\\n')\n",
    "#  The & operator only allows you to join sets with sets,\n",
    "# and not with other data types like you can with the intersection() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ceda5298-4b71-404e-acfc-22a1a84d2811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{False, True, 'apple'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"apple\", 1,  \"banana\", 0, \"cherry\"}\n",
    "set2 = {False, \"google\",1,  \"apple\", 2, True}\n",
    "\n",
    "set3 = set1.intersection(set2)\n",
    "\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1b59dd08-331c-4733-ac8f-bbb7230cea01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana', 'cherry'}\n"
     ]
    }
   ],
   "source": [
    "# The difference() method will return a new set that will contain only the items from the first set that are not present in the other set.\n",
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1.difference(set2)\n",
    "\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ea6ed342-161b-4317-a4f2-ab29996b3e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana', 'cherry'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1 - set2\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c7cf7319-7b42-4f4d-8176-140d0c7ef86f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana'}\n"
     ]
    }
   ],
   "source": [
    "# The - operator only allows you to join sets with sets,\n",
    "# and not with other data types like you can with the difference() method.\n",
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set1.difference_update(set2)\n",
    "\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ccbfe6f7-81a2-43dc-8dd5-48b4fafc9043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'microsoft', 'google', 'cherry', 'banana'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1.symmetric_difference(set2)\n",
    "\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d0a67999-0f45-443a-94ca-8a4e4c871698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'microsoft', 'google', 'cherry', 'banana'}\n"
     ]
    }
   ],
   "source": [
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set3 = set1 ^ set2\n",
    "print(set3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "3d44af8f-b7bc-4564-b18a-f847bf483ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'microsoft', 'google', 'cherry', 'banana'}\n"
     ]
    }
   ],
   "source": [
    "# The ^ operator only allows you to join sets with sets,\n",
    "# and not with other data types like you can with the symmetric_difference() method.\n",
    "set1 = {\"apple\", \"banana\", \"cherry\"}\n",
    "set2 = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "set1.symmetric_difference_update(set2)\n",
    "\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "de51ca40-f778-49fb-96c6-c0be437a52ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "# add()\t \tAdds an element to the set\n",
    "# If the element already exists, the add() method does not add the element.\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "fruits.add(\"apple\")\n",
    "\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3afc8f43-7ea9-42eb-88e4-cbdb03104964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "# clear()\t \tRemoves all the elements from the set\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "fruits.clear()\n",
    "\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "bee82771-d33f-44c4-8dce-497e0ce8c376",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "# copy()\t \tReturns a copy of the set\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "x = fruits.copy()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "dffea6c9-22cc-471a-a84a-0cc46de2a05e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana', 'cherry'}\n"
     ]
    }
   ],
   "source": [
    "# difference()\t-\tReturns a set containing the difference between two or more sets\n",
    "# set.difference(set1, set2 ... etc.)\n",
    "#  set - set1 - set2 .... etc.\n",
    "\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x.difference(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "448cc2d7-fd7a-48fa-898b-b62200bb9b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana'}\n"
     ]
    }
   ],
   "source": [
    "a = {\"apple\", \"banana\", \"cherry\"}\n",
    "b = {\"google\", \"microsoft\", \"apple\"}\n",
    "c = {\"cherry\", \"micra\", \"bluebird\"}\n",
    "\n",
    "myset = a.difference(b, c)\n",
    "\n",
    "print(myset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "cf850d1a-3632-46d4-8fd9-4d581be2759d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'banana'}\n"
     ]
    }
   ],
   "source": [
    "# difference_update()\t-=\tRemoves the items in this set that are also included in another, specified set\n",
    "# set.difference_update(set1, set2 ... etc.)\n",
    "# set -= set1 | set2 ... etc.\n",
    "a = {\"apple\", \"banana\", \"cherry\"}\n",
    "b = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "a -= b\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "68e0eae9-867c-4631-a311-f11c337f6176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana'}\n"
     ]
    }
   ],
   "source": [
    "a = {\"apple\", \"banana\", \"cherry\"}\n",
    "b = {\"google\", \"microsoft\", \"apple\"}\n",
    "c = {\"cherry\", \"micra\", \"bluebird\"}\n",
    "\n",
    "a -= b | c\n",
    "\n",
    "print(myset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "594e89f0-0dcb-4b3c-a5f3-c693a9128fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'google', 'micra', 'cherry', 'bluebird', 'microsoft', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "d = b | c\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "de3f6d85-2c2f-477c-a776-c09f063c3d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'banana'}\n"
     ]
    }
   ],
   "source": [
    "print(a - d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d0f2c988-a567-446e-bca0-5688a6e44580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cherry', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "# discard()\t \tRemove the specified item\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "fruits.discard(\"banana\")\n",
    "\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "e4eb6e6a-536c-4256-b48a-0595d9141677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'apple'}\n"
     ]
    }
   ],
   "source": [
    "# intersection()\t&\tReturns a set, that is the intersection of two other sets\n",
    "# set.intersection(set1, set2 ... etc.)\n",
    "# set & set1 & set2 ... etc.\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x & y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1648055f-8991-4509-8b5e-8b7f39d496a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c'}\n"
     ]
    }
   ],
   "source": [
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"c\", \"d\", \"e\"}\n",
    "z = {\"f\", \"g\", \"c\"}\n",
    "\n",
    "result = x & y & z\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bcf89db1-799e-4ead-be08-027053a998a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c'}\n"
     ]
    }
   ],
   "source": [
    "# intersection_update()\t&=\tRemoves the items in this set that are not present in other, specified set(s)\n",
    "# set &= set1 & set2 ... etc.\n",
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"c\", \"d\", \"e\"}\n",
    "z = {\"f\", \"g\", \"c\"}\n",
    "\n",
    "x &= y & z\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "faad4975-7944-4f2e-ac0c-769eabe3f931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# isdisjoint()\t \tReturns whether two sets have a intersection or not\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"facebook\"}\n",
    "\n",
    "z = x.isdisjoint(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4e647962-2c3d-4807-8766-3e2468cfddd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x.isdisjoint(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d56823d-a7cc-4224-b712-0c51b8a21fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# issubset()\t<=\tReturns whether another set contains this set or not\n",
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "\n",
    "z = x.issubset(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a9590182-2ae5-4b28-947c-1ba65bc7b58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "\n",
    "z = x <= y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c092882e-6b46-454d-80b1-6d7a6af51720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "#  \t<\tReturns whether all items in this set is present in other, specified set(s)\n",
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "\n",
    "z = x <= y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa3df122-44b9-4bbe-b001-b319d08b1c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# issuperset()\t>=\tReturns whether this set contains another set or not\n",
    "x = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "y = {\"a\", \"b\", \"c\"}\n",
    "\n",
    "z = x.issuperset(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29b910f8-156f-4e58-857d-632d3a47e3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "y = {\"a\", \"b\", \"c\"}\n",
    "\n",
    "z = x >= y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9cb699b-61db-471c-a229-a3206fd28d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  \t>\tReturns whether all items in other, specified set(s) is present in this set\n",
    "x = {\"f\", \"e\", \"d\", \"c\", \"b\", \"a\"}\n",
    "y = {\"a\", \"b\", \"c\"}\n",
    "\n",
    "z = x > y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dfe1dac-1a98-461b-beb9-1d7cf85abd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pop()\t \tRemoves an element from the set\n",
    "# The pop() method removes a random item from the set.\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "fruits.pop()\n",
    "\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc7fa50-2952-4636-8104-fc0be832c5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove()\t \tRemoves the specified element\n",
    "# This method is different from the discard() method, because the remove() method will raise an error if the specified item does not exist,\n",
    "# and the discard() method will not.\n",
    "fruits = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "fruits.remove(\"banana\")\n",
    "\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a9c52b-f705-47de-a370-9391fb780229",
   "metadata": {},
   "outputs": [],
   "source": [
    "# symmetric_difference()\t^\tReturns a set with the symmetric differences of two sets\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x.symmetric_difference(y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8378788c-4aaa-48bb-8088-b8c8d77e0dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x ^ y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2159018-808a-4357-acd6-cef0d6e50faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# symmetric_difference_update()\t^=\tInserts the symmetric differences from this set and another\n",
    "# set ^= set1\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "x.symmetric_difference_update(y)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c605d536-c699-4ec1-8181-1666e41d4562",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "x ^= y\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2af155b-bade-434c-aeec-d6c3f1fbe943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# union()\t|\tReturn a set containing the union of sets\n",
    "# set | set1 | set2 ...\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "z = x | y\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd6e7eb8-09cd-4c6a-b274-1da132ca6889",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = {\"a\", \"b\", \"c\"}\n",
    "y = {\"c\", \"d\", \"e\"}\n",
    "z = {\"f\", \"g\", \"c\"}\n",
    "\n",
    "result = x | y | z\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a45fe52-bb89-4769-a1a5-9b779eb763ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# update()\t|=\tUpdate the set with the union of this set and others.\n",
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "\n",
    "x.update(y)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "64b97a82-06c7-4f96-8c48-d14894886568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bluebird', 'microsoft', 'google', 'micra', 'cherry', 'banana', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "x = {\"apple\", \"banana\", \"cherry\"}\n",
    "y = {\"google\", \"microsoft\", \"apple\"}\n",
    "z = {\"cherry\", \"micra\", \"bluebird\"}\n",
    "\n",
    "x |= y | z\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0d749b20-a47b-4920-9a53-1fb0427b0111",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = y | z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "b3c30480-e12f-4598-8bd7-937ae1064552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple', 'banana', 'bluebird', 'cherry', 'google', 'micra', 'microsoft'}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x | f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a499c6-99a5-404b-b5df-9a0db868ede5",
   "metadata": {},
   "source": [
    "# Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d99124-2b59-4c67-891e-44be0dc22322",
   "metadata": {},
   "source": [
    "- Dictionaries are used to store data values in key:value pairs.\n",
    "\n",
    "- A dictionary is a collection which is ordered, changeable and do not allow duplicates.\n",
    "- When we say that dictionaries are ordered, it means that the items have a defined order, and that order will not change.\n",
    "\n",
    "- Unordered means that the items do not have a defined order, you cannot refer to an item by using an index.\n",
    "- Dictionaries are changeable, meaning that we can change, add or remove items after the dictionary has been created.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "cd2b7b41-9c98-4646-80b4-16c378056e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6ce84cd8-b108-44a6-a901-e147bb65f443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ford\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "print(thisdict[\"brand\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "e187bf6b-0e13-45a2-81af-30c2f4754c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 2024}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964,\n",
    "  \"year\": 2020,\n",
    "  \"year\": 2024,\n",
    "}\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "39dfa6c5-e9c1-458c-b02b-e01be72f94a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(thisdict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f5b9a9ca-0c59-4a26-893c-581181615e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'John', 'age': 36, 'country': 'Norway'}\n"
     ]
    }
   ],
   "source": [
    "thisdict = dict(name = \"John\", age = 36, country = \"Norway\")\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11029977-4885-4fdc-817e-2f5e5c4da66e",
   "metadata": {},
   "source": [
    "### Access Dictionary Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4814371f-163e-4f3c-ad68-63096b7a7d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "x = thisdict[\"model\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ae21ab28-030b-4fec-a4dd-ebb7398ce958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mustang'"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "9d8397bc-b639-4086-b7b1-7928de835e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mustang'"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is also a method called get() that will give you the same result:\n",
    "thisdict.get(\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c8bd558a-4d40-43fc-b680-f8f5bb51a6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['brand', 'model', 'year'])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The keys() method will return a list of all the keys in the dictionary.\n",
    "thisdict.keys()\n",
    "# The list of the keys is a view of the dictionary,\n",
    "# meaning that any changes done to the dictionary will be reflected in the keys list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "360a4b0a-fe18-4075-a23b-9be9a47e39f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['brand', 'model', 'year'])\n",
      "dict_keys(['brand', 'model', 'year', 'color'])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "\"brand\": \"Ford\",\n",
    "\"model\": \"Mustang\",\n",
    "\"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.keys()\n",
    "\n",
    "print(x) #before the change\n",
    "\n",
    "car[\"color\"] = \"white\"\n",
    "\n",
    "print(x) #after the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "f28db683-82cc-49d4-bed9-bda1f06de362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Ford', 'Mustang', 1964])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The values() method will return a list of all the values in the dictionary.\n",
    "thisdict.values()\n",
    "# The list of the values is a view of the dictionary,\n",
    "# meaning that any changes done to the dictionary will be reflected in the values list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "6ae5e2a7-f227-4a40-b9e9-f617fd11a39d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['Ford', 'Mustang', 1964])\n",
      "dict_values(['Ford', 'Mustang', 2020])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "\"brand\": \"Ford\",\n",
    "\"model\": \"Mustang\",\n",
    "\"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.values()\n",
    "\n",
    "print(x) #before the change\n",
    "\n",
    "car[\"year\"] = 2020\n",
    "\n",
    "print(x) #after the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "8f613a5f-a6b9-4698-8cc8-62edaddede78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['Ford', 'Mustang', 1964])\n",
      "dict_values(['Ford', 'Mustang', 1964, 'red'])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "\"brand\": \"Ford\",\n",
    "\"model\": \"Mustang\",\n",
    "\"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.values()\n",
    "\n",
    "print(x) #before the change\n",
    "\n",
    "car[\"color\"] = \"red\"\n",
    "\n",
    "print(x) #after the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c8d4e3b7-4848-4499-9774-ccbdb48f63d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964)])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The items() method will return each item in a dictionary, as tuples in a list.\n",
    "\n",
    "thisdict.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "81bf8a25-1e9d-41df-950d-74603ba61909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(thisdict.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "d875d7b0-da55-445e-9225-41816030468c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964)])\n",
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 2020)])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "\"brand\": \"Ford\",\n",
    "\"model\": \"Mustang\",\n",
    "\"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.items()\n",
    "\n",
    "print(x) #before the change\n",
    "\n",
    "car[\"year\"] = 2020\n",
    "\n",
    "print(x) #after the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "1f6dbb91-fd87-416f-a2b7-3e1ea993bcb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964)])\n",
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964), ('color', 'red')])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "\"brand\": \"Ford\",\n",
    "\"model\": \"Mustang\",\n",
    "\"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.items()\n",
    "\n",
    "print(x) #before the change\n",
    "\n",
    "car[\"color\"] = \"red\"\n",
    "\n",
    "print(x) #after the change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "0119f767-c557-48af-bb71-440a89d3dc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, 'model' is one of the keys in the thisdict dictionary\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "if \"model\" in thisdict:\n",
    "  print(\"Yes, 'model' is one of the keys in the thisdict dictionary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "08b86537-283d-4c6f-8993-a28ee8b948a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ford', 'Mustang', 1964, 'red']\n"
     ]
    }
   ],
   "source": [
    "values_list = list(car.values())\n",
    "print(values_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "4fa4a791-82eb-4727-aa8f-3b28af00a89f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ford\n",
      "Mustang\n",
      "1964\n",
      "red\n"
     ]
    }
   ],
   "source": [
    "for value in car.values():\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e73443-d36c-4f4c-ae64-7b0406576f8f",
   "metadata": {},
   "source": [
    "### Change Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4ff8414d-31cc-4309-bc1b-d6121e521a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"year\"] = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "121929ea-7999-4d33-9f35-756a8fe03397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 2018}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "7f69cd41-21c5-453c-8062-cd3e0a91911a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The update() method will update the dictionary with the items from the given argument.\n",
    "# The argument must be a dictionary, or an iterable object with key:value pairs.\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict.update({\"year\": 2020})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "d57fe6ff-17bf-4d6a-82d4-2bafa8dbdbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 2020}"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisdict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e641da-eae7-4015-bf22-412c2f7bae25",
   "metadata": {},
   "source": [
    "### Adding Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "32230daa-7794-488d-9c5c-ebc1d2f5997e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'red'}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"color\"] = \"red\"\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "8c9a8431-1d1a-4e57-97ac-6a8c30763677",
   "metadata": {},
   "outputs": [],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict.update({\"color\": \"red\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "9b79cf4b-9caf-4c6b-9a59-578575049f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'red'}"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thisdict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0356f128-f62b-4620-85c8-7e060ccb13bd",
   "metadata": {},
   "source": [
    "### Removing Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d45bd658-aa04-4198-80e5-510ba6779574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict.pop(\"model\")\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "fc17d93e-f830-4d85-806f-a85b4ee89b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang'}\n"
     ]
    }
   ],
   "source": [
    "# The popitem() method removes the last inserted item \n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict.popitem()\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d54328b4-ba05-4234-8006-1ec2e8b4d537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "del thisdict[\"model\"]\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "be41cbc0-1bf0-47c2-8003-20d4bacb5983",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'thisdict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[206], line 7\u001b[0m\n\u001b[0;32m      1\u001b[0m thisdict \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m      2\u001b[0m   \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrand\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFord\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      3\u001b[0m   \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMustang\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      4\u001b[0m   \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1964\u001b[39m\n\u001b[0;32m      5\u001b[0m }\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m thisdict\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mthisdict\u001b[49m) \u001b[38;5;66;03m#this will cause an error because \"thisdict\" no longer exists.\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'thisdict' is not defined"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "del thisdict\n",
    "print(thisdict) #this will cause an error because \"thisdict\" no longer exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "d8e22986-e033-4340-8c8b-61df7044b81b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    }
   ],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict.clear()\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c8fd30-beeb-4c21-8311-d4ccd4831c37",
   "metadata": {},
   "source": [
    "###  Loop Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "ce9cda21-5484-4a87-b050-7c88d6adf1f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand\n",
      "model\n",
      "year\n"
     ]
    }
   ],
   "source": [
    "# When looping through a dictionary, the return value are the keys of the dictionary,\n",
    "# but there are methods to return the values as well.\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "for x in thisdict:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "b110f15e-ab0a-44f9-8dd7-7b8a3a9adbd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ford\n",
      "Mustang\n",
      "1964\n"
     ]
    }
   ],
   "source": [
    "for x in thisdict:\n",
    "  print(thisdict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "16412d01-a6a5-4c74-a3e7-1c2a504521a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ford\n",
      "Mustang\n",
      "1964\n"
     ]
    }
   ],
   "source": [
    "for x in thisdict.values():\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "f870e8af-1efb-4018-891b-72cadbdb0377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand\n",
      "model\n",
      "year\n"
     ]
    }
   ],
   "source": [
    "for x in thisdict.keys():\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "36db50bd-d2be-4b50-bf2f-d85d96d07cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand Ford\n",
      "model Mustang\n",
      "year 1964\n"
     ]
    }
   ],
   "source": [
    "# Loop through both keys and values, by using the items() method:\n",
    "\n",
    "for x, y in thisdict.items():\n",
    "  print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924ff004-5e40-4060-b072-24e8f7025c2a",
   "metadata": {},
   "source": [
    "### Copy Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "218eb839-026e-4269-8347-e25d728816e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "# You cannot copy a dictionary simply by typing dict2 = dict1, because: \n",
    "# dict2 will only be a reference to dict1, and changes made in dict1 will automatically also be made in dict2.\n",
    "# There are ways to make a copy, one way is to use the built-in Dictionary method copy().\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "mydict = thisdict.copy()\n",
    "print(mydict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6410c90a-15ce-4cfc-9ef3-4ecc075e65e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "# Another way to make a copy is to use the built-in function dict().\n",
    "\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "mydict = dict(thisdict)\n",
    "print(mydict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a26d9b9-ae0a-479e-8b26-b550e46eb56d",
   "metadata": {},
   "source": [
    "### Nested Dictionaries\n",
    "A dictionary can contain dictionaries, this is called nested dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "690d999e-ad9b-4ede-adb2-ae7e870ca3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "myfamily = {\n",
    "  \"child1\" : {\n",
    "    \"name\" : \"Emil\",\n",
    "    \"year\" : 2004\n",
    "  },\n",
    "  \"child2\" : {\n",
    "    \"name\" : \"Tobias\",\n",
    "    \"year\" : 2007\n",
    "  },\n",
    "  \"child3\" : {\n",
    "    \"name\" : \"Linus\",\n",
    "    \"year\" : 2011\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "7f858d8a-3008-4cee-85dc-24f8f6ae14d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "child1 = {\n",
    "  \"name\" : \"Emil\",\n",
    "  \"year\" : 2004\n",
    "}\n",
    "child2 = {\n",
    "  \"name\" : \"Tobias\",\n",
    "  \"year\" : 2007\n",
    "}\n",
    "child3 = {\n",
    "  \"name\" : \"Linus\",\n",
    "  \"year\" : 2011\n",
    "}\n",
    "\n",
    "myfamily = {\n",
    "  \"child1\" : child1,\n",
    "  \"child2\" : child2,\n",
    "  \"child3\" : child3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "298a5714-78b5-4779-b7b9-43eeacb24f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'child1': {'name': 'Emil', 'year': 2004},\n",
       " 'child2': {'name': 'Tobias', 'year': 2007},\n",
       " 'child3': {'name': 'Linus', 'year': 2011}}"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfamily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "2ed53f6d-6476-4b3c-bfae-a7e57b01bf4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tobias\n"
     ]
    }
   ],
   "source": [
    "print(myfamily[\"child2\"][\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "481843f4-2b6b-4308-9e9a-237433b2f6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "child1\n",
      "name: Emil\n",
      "year: 2004\n",
      "child2\n",
      "name: Tobias\n",
      "year: 2007\n",
      "child3\n",
      "name: Linus\n",
      "year: 2011\n"
     ]
    }
   ],
   "source": [
    "for x, obj in myfamily.items():\n",
    "  print(x)\n",
    "\n",
    "  for y in obj:\n",
    "    print(y + ':', obj[y])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280bff15-cf7a-42eb-86a5-2ceaecd1fce1",
   "metadata": {},
   "source": [
    "### Dictionary Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267bcd79-cbb8-4586-aaaf-3f88263e3c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clear()\tRemoves all the elements from the dictionary\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "car.clear()\n",
    "\n",
    "print(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af7b6eca-aa13-493a-bf73-c271ad7af5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy()\tReturns a copy of the dictionary\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.copy()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c761ac-da8e-41c3-9213-cf34cff26834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fromkeys()\tReturns a dictionary with the specified keys and value\n",
    "x = ('key1', 'key2', 'key3')\n",
    "y = 0\n",
    "\n",
    "thisdict = dict.fromkeys(x, y)\n",
    "\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "5ac20ec5-3305-4824-b91b-3f301111605a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'key1': None, 'key2': None, 'key3': None}\n"
     ]
    }
   ],
   "source": [
    "x = ('key1', 'key2', 'key3')\n",
    "\n",
    "thisdict = dict.fromkeys(x)\n",
    "\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "df8de53e-326a-4979-85ec-28ec527ae9be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mustang\n"
     ]
    }
   ],
   "source": [
    "# get()\tReturns the value of the specified key\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.get(\"model\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "d498afab-cb40-4484-aa97-295f7c443bdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15000\n"
     ]
    }
   ],
   "source": [
    "# dictionary.get(keyname, value)\n",
    "\n",
    "# keyname\tRequired. The keyname of the item you want to return the value from\n",
    "# value\tOptional. A value to return if the specified key does not exist.\n",
    "# Default value None\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.get(\"price\", 15000)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "01d296eb-55b8-4dbf-9c76-ef75e78ef43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1964\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.get(\"year\", 1500)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "f04978bd-dc54-46e9-88d0-05d59502ec99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 1964)])\n"
     ]
    }
   ],
   "source": [
    "# items()\tReturns a list containing a tuple for each key value pair\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.items()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "872c8f9e-fa54-4d0b-ba8a-364c492b9a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('brand', 'Ford'), ('model', 'Mustang'), ('year', 2018)])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.items()\n",
    "\n",
    "car[\"year\"] = 2018\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f8f0cd4-2f52-4ce5-8ad6-0f5782ca1613",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keys()\tReturns a list containing the dictionary's keys\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.keys()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "859a3235-1291-40f0-b86b-93117df005b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.keys()\n",
    "\n",
    "car[\"color\"] = \"white\"\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f6facb-c83f-451b-b682-4bc9035f1632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pop()\tRemoves the element with the specified key\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "car.pop(\"model\")\n",
    "\n",
    "print(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dc25248-86b4-4870-9727-c09c0d30c864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dictionary.pop(keyname, defaultvalue)\n",
    "# keyname\tRequired. The keyname of the item you want to remove\n",
    "# defaultvalue\tOptional. A value to return if the specified key do not exist.\n",
    "# If this parameter is not specified, and the no item with the specified key is found, an error is raised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "81d04d36-ada0-4088-b6f2-3596ecf6ed8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mustang\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.pop(\"model\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "06bf9b7c-8c0d-461d-88ce-3939c45927e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hi'"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.pop(\"name\", 'hi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "1f73e58e-7fd5-4577-b801-e40c1c19db42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang'}\n"
     ]
    }
   ],
   "source": [
    "# popitem()\tRemoves the last inserted key-value pair\n",
    "# Remove the last item from the dictionary by default\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "car.popitem()\n",
    "\n",
    "print(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "dd5a6fcd-059c-42ab-8ec6-e72e301c28fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('year', 1964)\n"
     ]
    }
   ],
   "source": [
    "# The popitem() method removes the item that was last inserted into the dictionary.\n",
    "# The removed item is the return value of the popitem() method, as a tuple, see example below.\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.popitem()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "4bfec0f9-57a3-4fa4-9a2e-c201abde3ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mustang\n"
     ]
    }
   ],
   "source": [
    "# setdefault()\tReturns the value of the specified key.\n",
    "# If the key does not exist: insert the key, with the specified value\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.setdefault(\"model\", \"Bronco\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "2c5c8f53-18c8-492e-ace2-0167dfb6732e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "57d3ff2a-915b-4a4d-80c4-8cca44245aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "white\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.setdefault(\"color\", \"white\")\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "04df44a6-6567-4bde-b33e-890018349387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'White'}\n"
     ]
    }
   ],
   "source": [
    "# update()\tUpdates the dictionary with the specified key-value pairs\n",
    "# dictionary.update(iterable)\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "car.update({\"color\": \"White\"})\n",
    "\n",
    "print(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "86a1a6e4-e6d6-4349-9d45-db3352c7d685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['Ford', 'Mustang', 1964])\n"
     ]
    }
   ],
   "source": [
    "# values()\tReturns a list of all the values in the dictionary.\n",
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.values()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "1846d208-3e3c-4e58-839d-162a5ec0aa6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['Ford', 'Mustang', 2018])\n"
     ]
    }
   ],
   "source": [
    "car = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "\n",
    "x = car.values()\n",
    "\n",
    "car[\"year\"] = 2018\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "2d5dbfb5-7207-4f41-a89e-005bfb00f694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5:5\n",
      "4:4\n",
      "3:3\n",
      "2:2\n",
      "1:1\n"
     ]
    }
   ],
   "source": [
    "# اجزاي واردشده به دیکشنري در همان ترتیبی که اضافه شدهاند  iterateمیشوند\n",
    "test_dictionary={}\n",
    "for i in range(5,0,-1):\n",
    "    test_dictionary[i] = i\n",
    "for k,v in test_dictionary.items():\n",
    "    print(f'{k}:{v}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ff84b96-9bbb-4352-af27-461b238f8e4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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