pandasIntro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "aList = [1,2,3,4]\n",
    "bList = ['a','b','c','d']\n",
    "s1 = pd.Series(data=aList, index = bList)\n",
    "s2 = s1[['a','c']]\n",
    "s2['a'] = 7\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(aList,bList)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myDict = {'a':1,'b':2,'c':3,'d':4}\n",
    "s3 = pd.Series(myDict)\n",
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-02-01 07:00:00+01:00', '2021-02-02 07:00:00+01:00',\n",
       "               '2021-02-03 07:00:00+01:00', '2021-02-04 07:00:00+01:00'],\n",
       "              dtype='datetime64[ns, Europe/Berlin]', freq='D')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myTimes = pd.date_range(start='2021-02-01 07:00',\n",
    "                    freq='d', periods=4, tz='Europe/Berlin')\n",
    "myTimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-02-01 07:00:00+01:00    1\n",
       "2021-02-02 07:00:00+01:00    2\n",
       "2021-02-03 07:00:00+01:00    3\n",
       "2021-02-04 07:00:00+01:00    4\n",
       "e                            5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s4 = pd.Series(data=aList,index=myTimes)\n",
    "s4['e'] = 5\n",
    "s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas and data sources\n",
    "\n",
    "### drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "### movies = pd.read_csv('http://bit.ly/imdbratings')\n",
    "### train = pd.read_csv('http://bit.ly/kaggletrain')\n",
    "### ufo = pd.read_csv('http://bit.ly/uforeports', parse_dates=['Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['john','anne','mary']\n",
    "emails = ['john@mit.edu','anne@mit.edu','mary@mit.edu']\n",
    "contacts = {'Name': names,\n",
    "            'Email': emails,\n",
    "            'Course': courses}\n",
    "df = pd.DataFrame(data=contacts, index = [1, 2, 3])\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Python and Pandas DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>Course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>john</td>\n",
       "      <td>john@mit.edu</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anne</td>\n",
       "      <td>anne@mit.edu</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mary</td>\n",
       "      <td>mary@mit.edu</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name         Email  Course\n",
       "1  john  john@mit.edu       1\n",
       "2  anne  anne@mit.edu       3\n",
       "3  mary  mary@mit.edu       6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "contact = {'Name':'john','Email':'john@mit.edu','Course':1}\n",
    "names = ['john','anne','mary']\n",
    "emails = ['john@mit.edu','anne@mit.edu','mary@mit.edu']\n",
    "courses = [1, 3, 6]\n",
    "contacts = {'Name': names,\n",
    "            'Email': emails,\n",
    "            'Course': courses}\n",
    "df = pd.DataFrame(index = range(1,4))\n",
    "df['Name'] = names\n",
    "df['Email'] = emails\n",
    "df['Course'] = courses\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slice and Dice Rows and Columns\n",
    "loc and iloc  pick out a row\n",
    ".loc refers to the label index.\n",
    ".iloc refers to the positional index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name              john\n",
       "Email     john@mit.edu\n",
       "Course               1\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts = df\n",
    "contacts\n",
    "df.loc[1]\n",
    "df.iloc[1:4]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>Course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anne</td>\n",
       "      <td>anne@mit.edu</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mary</td>\n",
       "      <td>mary@mit.edu</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name         Email  Course\n",
       "2  anne  anne@mit.edu       3\n",
       "3  mary  mary@mit.edu       6"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice = df[df['Course'] > 1]\n",
    "slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Pick out a column as Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "courses = df['Course']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3333333333333335"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Course'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations on Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>Course</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>john</td>\n",
       "      <td>john@mit.edu</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anne</td>\n",
       "      <td>anne@mit.edu</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mary</td>\n",
       "      <td>mary@mit.edu</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name         Email  Course  Price\n",
       "1  john  john@mit.edu       1     10\n",
       "2  anne  anne@mit.edu       3     30\n",
       "3  mary  mary@mit.edu       6     60"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price'] = df['Course']*10\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>Course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>john</td>\n",
       "      <td>john@mit.edu</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>anne</td>\n",
       "      <td>anne@mit.edu</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>mary</td>\n",
       "      <td>mary@mit.edu</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name         Email  Course\n",
       "A  john  john@mit.edu       1\n",
       "B  anne  anne@mit.edu       3\n",
       "C  mary  mary@mit.edu       6"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = ['A','B','C']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Course</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Email</th>\n",
       "      <th>anne@mit.edu</th>\n",
       "      <th>john@mit.edu</th>\n",
       "      <th>mary@mit.edu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anne</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>john</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Course                          \n",
       "Email anne@mit.edu john@mit.edu mary@mit.edu\n",
       "Name                                        \n",
       "anne           3.0          NaN          NaN\n",
       "john           NaN          1.0          NaN\n",
       "mary           NaN          NaN          6.0"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Stacking of Data\n",
    "Sun Exposure Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Non Mel Skin Cancer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Yes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">No</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun Exposure</th>\n",
       "      <th>Years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Sun</th>\n",
       "      <th>&gt;20 Yrs</th>\n",
       "      <td>30</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;20 Yrs</th>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90</td>\n",
       "      <td>91.0</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>300</td>\n",
       "      <td>99.7</td>\n",
       "      <td>301</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>40</td>\n",
       "      <td>9.0</td>\n",
       "      <td>400</td>\n",
       "      <td>91.0</td>\n",
       "      <td>440</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Non Mel Skin Cancer                            \n",
       "                                     Yes         No       Total     \n",
       "                                       N     %    N     %     N    %\n",
       "Sun Exposure Years                                                  \n",
       "Sun          >20 Yrs                  30  75.0   10  25.0    40  100\n",
       "             <20 Yrs                   9   9.0   90  91.0    99  100\n",
       "             Never                     1   0.3  300  99.7   301  100\n",
       "             Total                    40   9.0  400  91.0   440  100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "gt20 = [30,75.0,10, 25.0,40,100]\n",
    "lt20 = [9,9.0,90,91.0,99,100]\n",
    "nvr = [1,0.3,300,99.7,301,100]\n",
    "ttl = [40,9.0,400,91.0,440,100]\n",
    "cols = ['N','%','N','%','N','%']\n",
    "idx = ['>20 Yrs','<20 Yrs','Never','Total']\n",
    "multicol1 = pd.MultiIndex.from_tuples([('Non Mel Skin Cancer','Yes', 'N'),\n",
    "                                       ('Non Mel Skin Cancer','Yes', '%'),\n",
    "                                       ('Non Mel Skin Cancer','No', 'N'),\n",
    "                                       ('Non Mel Skin Cancer','No', '%'),\n",
    "                                       ('Non Mel Skin Cancer','Total', 'N'),\n",
    "                                       ('Non Mel Skin Cancer','Total', '%')])\n",
    "multirow1 = pd.MultiIndex.from_tuples([('Sun','>20 Yrs'), \n",
    "                                       ('Sun','<20 Yrs'),\n",
    "                                       ('Sun','Never'),\n",
    "                                       ('Sun','Total')],\n",
    "                                         names=['Sun Exposure', 'Years' ])\n",
    "df = pd.DataFrame([gt20,lt20,nvr,ttl], index = multirow1, columns = multicol1)\n",
    "tup = ('Non Mel Skin Cancer','Yes', 'N')\n",
    "dict = {('Non Mel Skin Cancer','Yes', 'N'):\"value\"}\n",
    "dict[('Non Mel Skin Cancer','Yes', 'N')]\n",
    "#df.loc[('Sun','Total'),[('Non Mel Skin Cancer','Yes', 'N'), ('Non Mel Skin Cancer','Total', '%')]]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## iloc and loc for picking out rows, columns and values\n",
    "## and understanding the types that are returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Non Mel Skin Cancer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>%</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun Exposure</th>\n",
       "      <th>Years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>&gt;20 Yrs</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>0.3</td>\n",
       "      <td>99.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Non Mel Skin Cancer      \n",
       "                                     Yes    No\n",
       "                                       %     %\n",
       "Sun Exposure Years                            \n",
       "Sun          >20 Yrs                75.0  25.0\n",
       "             Never                   0.3  99.7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets use iloc\n",
    "#      row  col\n",
    "df.iloc[0:3,0:2]\n",
    "\n",
    "df.iloc[[0,2],[1,3]]\n",
    "\n",
    "# now lets use loc that uses the tuples to get data\n",
    "type(df.loc[[('Sun','>20 Yrs'),('Sun','Never'),('Sun','Total')],\n",
    "    ('Non Mel Skin Cancer','Yes', 'N')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Non Mel Skin Cancer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Yes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">No</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "      <th>N</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Sun Exposure</th>\n",
       "      <th>&gt;20 Yrs</th>\n",
       "      <td>30</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;20 Yrs</th>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90</td>\n",
       "      <td>91.0</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>300</td>\n",
       "      <td>99.7</td>\n",
       "      <td>301</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>40</td>\n",
       "      <td>9.0</td>\n",
       "      <td>400</td>\n",
       "      <td>91.0</td>\n",
       "      <td>440</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Non Mel Skin Cancer                            \n",
       "                                     Yes         No       Total     \n",
       "                                       N     %    N     %     N    %\n",
       "first        second                                                 \n",
       "Sun Exposure >20 Yrs                  30  75.0   10  25.0    40  100\n",
       "             <20 Yrs                   9   9.0   90  91.0    99  100\n",
       "             Never                     1   0.3  300  99.7   301  100\n",
       "             Total                    40   9.0  400  91.0   440  100"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "gt20 = [30,75.0,10, 25.0,40,100]\n",
    "lt20 = [9,9.0,90,91.0,99,100]\n",
    "nvr = [1,0.3,300,99.7,301,100]\n",
    "ttl = [40,9.0,400,91.0,440,100]\n",
    "cols = ['N','%','N','%','N','%']\n",
    "idx = ['>20 Yrs','<20 Yrs','Never','Total']\n",
    "multicol1 = pd.MultiIndex.from_tuples([('Non Mel Skin Cancer','Yes', 'N'),\n",
    "                                       ('Non Mel Skin Cancer','Yes', '%'),\n",
    "                                      ('Non Mel Skin Cancer','No', 'N'),\n",
    "                                      ('Non Mel Skin Cancer','No', '%'),\n",
    "                                      ('Non Mel Skin Cancer','Total', 'N'),\n",
    "                                       ('Non Mel Skin Cancer','Total', '%')])\n",
    "multirow1 = pd.MultiIndex.from_tuples([('Sun Exposure','>20 Yrs'), ('Sun Exposure','<20 Yrs'),('Sun Exposure','Never'),('Sun Exposure','Total')],names=['first', 'second']) \n",
    "df = pd.DataFrame([gt20,lt20,nvr,ttl], index = multirow1, columns = multicol1)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2],[3,4]])\n",
    "a.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating data from normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., 55., 61., 51., 58., 57., 55., 54., 53., 65., 52., 57., 50.,\n",
       "       56., 33., 55., 54., 62., 59., 41., 48., 36., 50., 54., 35., 53.,\n",
       "       61., 42., 58., 40., 42., 65., 63., 45., 57., 45., 50., 53., 43.,\n",
       "       55., 50., 52., 67., 42., 47., 56., 41., 40., 54., 35., 53., 51.,\n",
       "       50., 43., 52., 57., 38., 54., 59., 37., 36., 48., 39., 40., 54.,\n",
       "       58., 58., 47., 53., 57., 43., 46., 38., 42., 75., 51., 46., 47.,\n",
       "       48., 79., 59., 62., 50., 55., 49., 49., 47., 47., 49., 39., 56.,\n",
       "       73., 48., 54., 47., 65., 52., 33., 58., 60.])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calories = np.round(np.random.normal(50,10,100), decimals = 0)\n",
    "calories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
       "               '2020-01-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "cols = list('ABCD')\n",
    "myArr = np.random.randint(0,100,size=(5,4))\n",
    "indx = pd.date_range(start='1/1/2020', freq='D', periods=5)\n",
    "indx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Rep</th>\n",
       "      <th>Items</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>37</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>62</td>\n",
       "      <td>37</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Region  Rep  Items  Total\n",
       "2020-01-01      40   44     28     26\n",
       "2020-01-02      93    9     30     63\n",
       "2020-01-03      43   43     37     93\n",
       "2020-01-04      62   37     51     17\n",
       "2020-01-05      25   81      7     93"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=myArr, index=indx, columns=['Region','Rep','Items','Total'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Rep</th>\n",
       "      <th>Items</th>\n",
       "      <th>Total</th>\n",
       "      <th>New</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>37</td>\n",
       "      <td>93</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>62</td>\n",
       "      <td>37</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Region  Rep  Items  Total  New\n",
       "2020-01-01      40   44     28     26   72\n",
       "2020-01-02      93    9     30     63   39\n",
       "2020-01-03      43   43     37     93   80\n",
       "2020-01-04      62   37     51     17   88\n",
       "2020-01-05      25   81      7     93   88"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New'] = df['Rep'] + df['Items']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Rep</th>\n",
       "      <th>Items</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>37</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>62</td>\n",
       "      <td>37</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>25</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Region  Rep  Items  Total\n",
       "2020-01-01      40   44     28     26\n",
       "2020-01-02      93    9     30     63\n",
       "2020-01-03      43   43     37     93\n",
       "2020-01-04      62   37     51     17\n",
       "2020-01-05      25   81      7     93"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('New',axis = 1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with NaN  - dropna and fillna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.417022</td>\n",
       "      <td>0.720324</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.302333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.146756</td>\n",
       "      <td>0.092339</td>\n",
       "      <td>0.186260</td>\n",
       "      <td>0.345561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.396767</td>\n",
       "      <td>0.538817</td>\n",
       "      <td>0.419195</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204452</td>\n",
       "      <td>0.878117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.670468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417305</td>\n",
       "      <td>0.558690</td>\n",
       "      <td>0.140387</td>\n",
       "      <td>0.198101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.417022  0.720324  0.000114  0.302333\n",
       "1  0.146756  0.092339  0.186260  0.345561\n",
       "2  0.396767  0.538817  0.419195       NaN\n",
       "3  0.204452  0.878117       NaN  0.670468\n",
       "4  0.417305  0.558690  0.140387  0.198101"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "cols = list('ABCD')\n",
    "np.random.seed(seed=1)\n",
    "myArr = np.random.ranf(size=(5,4))\n",
    "myArr[2,3] = np.NaN\n",
    "myArr[3,2] = np.NaN\n",
    "df = pd.DataFrame(data=myArr, columns=cols)\n",
    "df.fillna(value = 0.0)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.417022</td>\n",
       "      <td>0.720324</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.302333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.146756</td>\n",
       "      <td>0.092339</td>\n",
       "      <td>0.186260</td>\n",
       "      <td>0.345561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.396767</td>\n",
       "      <td>0.538817</td>\n",
       "      <td>0.419195</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204452</td>\n",
       "      <td>0.878117</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.670468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417305</td>\n",
       "      <td>0.558690</td>\n",
       "      <td>0.140387</td>\n",
       "      <td>0.198101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.417022  0.720324  0.000114  0.302333\n",
       "1  0.146756  0.092339  0.186260  0.345561\n",
       "2  0.396767  0.538817  0.419195  0.000000\n",
       "3  0.204452  0.878117  0.000000  0.670468\n",
       "4  0.417305  0.558690  0.140387  0.198101"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()\n",
    "df.fillna(value = 0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding of Catagorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>apple</th>\n",
       "      <th>orange</th>\n",
       "      <th>pear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.417022</td>\n",
       "      <td>0.720324</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.302333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.146756</td>\n",
       "      <td>0.092339</td>\n",
       "      <td>0.186260</td>\n",
       "      <td>0.345561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.396767</td>\n",
       "      <td>0.538817</td>\n",
       "      <td>0.419195</td>\n",
       "      <td>0.685220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.204452</td>\n",
       "      <td>0.878117</td>\n",
       "      <td>0.027388</td>\n",
       "      <td>0.670468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417305</td>\n",
       "      <td>0.558690</td>\n",
       "      <td>0.140387</td>\n",
       "      <td>0.198101</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D  apple  orange  pear\n",
       "0  0.417022  0.720324  0.000114  0.302333      1       0     0\n",
       "1  0.146756  0.092339  0.186260  0.345561      0       0     1\n",
       "2  0.396767  0.538817  0.419195  0.685220      0       1     0\n",
       "3  0.204452  0.878117  0.027388  0.670468      0       0     1\n",
       "4  0.417305  0.558690  0.140387  0.198101      1       0     0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "cols = list('ABCD')\n",
    "np.random.seed(seed=1)\n",
    "myArr = np.random.ranf(size=(5,4))\n",
    "df = pd.DataFrame(data=myArr, columns=cols)\n",
    "df['Fruit'] = ['apple', 'pear', 'orange', 'pear','apple']\n",
    "one_hot = pd.get_dummies(df['Fruit'])\n",
    "df = df.join(one_hot)\n",
    "df.drop('Fruit',axis = 1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "myArr = np.random.randint(0,100,size=(9, 4))\n",
    "indx = pd.date_range(start='1/1/2019',freq='D', periods=9)\n",
    "cols = list('ABCD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = myArr, index=indx, columns=['State','Rep','Items','Total'])\n",
    "df['Rep'] = ('Anne','Fred','Mary')*3\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   values   A\n",
       "1       9   3\n",
       "2       8   6\n",
       "3       7   9\n",
       "4       6  12\n",
       "5       5  15\n",
       "6       4  18\n",
       "7       3  21\n",
       "8       2  24\n",
       "9       1  27"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "dates = pd.dates \n",
    "df = pd.DataFrame(index = indx)\n",
    "df['values'] = range(9,0,-1)\n",
    "a = []\n",
    "for i in range(1,10):\n",
    "    a.append(i*3)\n",
    "df['A'] = a\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dates</th>\n",
       "      <th>agent</th>\n",
       "      <th>product</th>\n",
       "      <th>sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>anne</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>betty</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>fred</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>anne</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>betty</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>fred</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>anne</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>betty</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>fred</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates  agent product  sold\n",
       "0 2020-01-01   anne       A     1\n",
       "1 2020-01-02  betty       A     2\n",
       "2 2020-01-03   fred       A     3\n",
       "3 2020-01-04   anne       B     4\n",
       "4 2020-01-05  betty       B     5\n",
       "5 2020-01-06   fred       B     6\n",
       "6 2020-01-07   anne       C     7\n",
       "7 2020-01-08  betty       C     8\n",
       "8 2020-01-09   fred       C     9"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = list('AAABBBCCC')\n",
    "dates = ('1/1/2020', '1/2/2020', '1/3/2020', '1/4/2020','1/5/2020','1/6/2020',\n",
    "        '1/7/2020','1/8/2020','1/9/2020')\n",
    "df1 = pd.DataFrame({'dates': dates,\n",
    "                'agent': ('anne','betty','fred')*3,\n",
    "                'product':group,\n",
    "                'sold':range(1,10)})\n",
    "df1.dates = pd.to_datetime(df1.dates)\n",
    "df2 = pd.pivot_table(data = df1, index = ['agent','dates'], values = ['sold'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "indx = ('1/2/2020','1/3/2020','1/4/2020')*3\n",
    "group = list('AAABBBCCC')\n",
    "df = pd.DataFrame({'dates': indx,\n",
    "                'group':group,\n",
    "                'values':range(1,10)})\n",
    "df.dates = pd.to_datetime(df.dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>group</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group       A  B  C\n",
       "dates              \n",
       "2020-01-02  1  4  7\n",
       "2020-01-03  2  5  8\n",
       "2020-01-04  3  6  9"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='dates', columns = 'group', values='values')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>CType</th>\n",
       "      <th>USD</th>\n",
       "      <th>EU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Item0</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Item0</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Item0</td>\n",
       "      <td>Gold</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Item1</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Item   CType  USD  EU\n",
       "0  Item0    Gold    1   4\n",
       "1  Item0  Bronze    2   3\n",
       "2  Item0    Gold    3   2\n",
       "3  Item1  Silver    4   1"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = {\"Item\": ['Item0', 'Item0', 'Item0', 'Item1'],\n",
    "    'CType':['Gold', 'Bronze', 'Gold', 'Silver'],\n",
    "    'USD':[1, 2, 3, 4],\n",
    "    'EU': [4, 3, 2, 1]}\n",
    "d = pd.DataFrame(table)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">EU</th>\n",
       "      <th colspan=\"3\" halign=\"left\">USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CType</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EU                USD            \n",
       "CType Bronze Gold Silver Bronze Gold Silver\n",
       "Item                                       \n",
       "Item0    3.0  6.0    NaN    2.0  4.0    NaN\n",
       "Item1    NaN  NaN    1.0    NaN  NaN    4.0"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv = d.pivot_table(index='Item', columns='CType', aggfunc=sum)\n",
    "pv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Samples using Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruits</th>\n",
       "      <th>colors</th>\n",
       "      <th>tastes</th>\n",
       "      <th>sizes</th>\n",
       "      <th>calories</th>\n",
       "      <th>region</th>\n",
       "      <th>cost</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pear</td>\n",
       "      <td>green</td>\n",
       "      <td>sour</td>\n",
       "      <td>big</td>\n",
       "      <td>140.0</td>\n",
       "      <td>north</td>\n",
       "      <td>37.0</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plum</td>\n",
       "      <td>green</td>\n",
       "      <td>sweet</td>\n",
       "      <td>big</td>\n",
       "      <td>154.0</td>\n",
       "      <td>south</td>\n",
       "      <td>15.0</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pear</td>\n",
       "      <td>brown</td>\n",
       "      <td>bitter</td>\n",
       "      <td>big</td>\n",
       "      <td>150.0</td>\n",
       "      <td>south</td>\n",
       "      <td>12.0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plum</td>\n",
       "      <td>orange</td>\n",
       "      <td>sour</td>\n",
       "      <td>big</td>\n",
       "      <td>142.0</td>\n",
       "      <td>south</td>\n",
       "      <td>26.0</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pear</td>\n",
       "      <td>orange</td>\n",
       "      <td>sweet</td>\n",
       "      <td>small</td>\n",
       "      <td>139.0</td>\n",
       "      <td>north</td>\n",
       "      <td>20.0</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fruits  colors  tastes  sizes  calories region  cost  units\n",
       "0   pear   green    sour    big     140.0  north  37.0    192\n",
       "1   plum   green   sweet    big     154.0  south  15.0    210\n",
       "2   pear   brown  bitter    big     150.0  south  12.0    199\n",
       "3   plum  orange    sour    big     142.0  south  26.0    197\n",
       "4   pear  orange   sweet  small     139.0  north  20.0    204"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "N = 5\n",
    "fruits = np.random.choice(['apple','pear','plum'], N)\n",
    "colors = np.random.choice(['green','yellow','red','orange','brown'], N)\n",
    "tastes = np.random.choice(['sour','bitter','sweet'], N)\n",
    "sizes = np.random.choice(['small','big'], N)\n",
    "calories = np.round(np.random.normal(150,10,N), decimals = 0)\n",
    "region = np.random.choice(['north','south'], N)\n",
    "cost = np.round(np.random.normal(20,10,N), decimals = 0)\n",
    "units = np.random.normal(200,10,N).astype(int)\n",
    "period = pd.date_range('20130101', periods=3, freq='M')\n",
    "dates = np.random.choice(period, N)\n",
    "dict = {'fruits':fruits, 'colors':colors, 'tastes':tastes,'sizes':sizes, \n",
    "        'calories':calories, 'region':region, 'cost':cost, 'units':units}\n",
    "df = pd.DataFrame(dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tastes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">bitter</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sour</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sweet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sizes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">big</th>\n",
       "      <th colspan=\"2\" halign=\"left\">big</th>\n",
       "      <th colspan=\"2\" halign=\"left\">big</th>\n",
       "      <th colspan=\"2\" halign=\"left\">small</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruits</th>\n",
       "      <th>pear</th>\n",
       "      <th>plum</th>\n",
       "      <th>pear</th>\n",
       "      <th>plum</th>\n",
       "      <th>pear</th>\n",
       "      <th>plum</th>\n",
       "      <th>pear</th>\n",
       "      <th>plum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>north</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>south</th>\n",
       "      <td>199.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        units                                            \n",
       "tastes bitter        sour        sweet                   \n",
       "sizes     big         big          big         small     \n",
       "fruits   pear plum   pear   plum  pear   plum   pear plum\n",
       "region                                                   \n",
       "north     NaN  NaN  192.0    NaN   NaN    NaN  204.0  NaN\n",
       "south   199.0  NaN    NaN  197.0   NaN  210.0    NaN  NaN"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv = df.pivot_table(index = ['region','fruits'], columns = ['tastes','sizes'], \n",
    "                    values=['units'], aggfunc = sum)\n",
    "pv.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Exercise use this data to recreate my pivot and pivot_tables\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruits = ['apple', 'pear', 'pear', 'apple', 'pear']\n",
    "colors = ['red', 'yellow', 'brown', 'brown', 'green']\n",
    "tastes = ['sour','sour', 'sweet','sweet','bitter']\n",
    "sizes = ['small','big','big','big', 'small']\n",
    "calories = [155,130,129,143,148]\n",
    "region = ['north', 'north', 'south', 'north', 'north']\n",
    "cost = [35,27,40,8,16]\n",
    "units = [210,197,187,208,201]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>139.0</th>\n",
       "      <th>140.0</th>\n",
       "      <th>142.0</th>\n",
       "      <th>150.0</th>\n",
       "      <th>154.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>fruits</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>north</th>\n",
       "      <th>pear</th>\n",
       "      <td>204.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">south</th>\n",
       "      <th>pear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plum</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "calories       139.0  140.0  142.0  150.0  154.0\n",
       "region fruits                                   \n",
       "north  pear    204.0  192.0    NaN    NaN    NaN\n",
       "south  pear      NaN    NaN    NaN  199.0    NaN\n",
       "       plum      NaN    NaN  197.0    NaN  210.0"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv = df.pivot_table(index=['region','fruits'],columns=['calories'],values='units',\n",
    "                    aggfunc = sum)\n",
    "piv\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'piv' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-ff09b76fec73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Email'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'piv' is not defined"
     ]
    }
   ],
   "source": [
    "df2 = piv.pivot(index=['Name'],columns=['Email'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>fruits</th>\n",
       "      <th>sizes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">north</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">pear</th>\n",
       "      <th>big</th>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">south</th>\n",
       "      <th>pear</th>\n",
       "      <th>big</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plum</th>\n",
       "      <th>big</th>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     units\n",
       "region fruits sizes       \n",
       "north  pear   big    192.0\n",
       "              small  204.0\n",
       "south  pear   big    199.0\n",
       "       plum   big    407.0"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv.stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot_Table Explained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Style</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ShipDate</th>\n",
       "      <th>Units</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>12</td>\n",
       "      <td>24.318029</td>\n",
       "      <td>North</td>\n",
       "      <td>291.816354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>19</td>\n",
       "      <td>26.908958</td>\n",
       "      <td>South</td>\n",
       "      <td>511.270193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>18</td>\n",
       "      <td>25.577032</td>\n",
       "      <td>South</td>\n",
       "      <td>460.386582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>4</td>\n",
       "      <td>24.431483</td>\n",
       "      <td>North</td>\n",
       "      <td>97.725931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>16</td>\n",
       "      <td>28.555318</td>\n",
       "      <td>South</td>\n",
       "      <td>456.885087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>18</td>\n",
       "      <td>23.748531</td>\n",
       "      <td>South</td>\n",
       "      <td>427.473563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>17</td>\n",
       "      <td>21.848335</td>\n",
       "      <td>North</td>\n",
       "      <td>371.421703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>4</td>\n",
       "      <td>26.297559</td>\n",
       "      <td>South</td>\n",
       "      <td>105.190235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>26.512295</td>\n",
       "      <td>South</td>\n",
       "      <td>53.024590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Style Gender   ShipDate  Units      Price Region       Total\n",
       "1    Tee   Girl 2013-03-31     12  24.318029  North  291.816354\n",
       "2   Golf    Boy 2013-02-28     19  26.908958  South  511.270193\n",
       "3  Fancy   Girl 2013-01-31     18  25.577032  South  460.386582\n",
       "4    Tee   Girl 2013-01-31      4  24.431483  North   97.725931\n",
       "5   Golf    Boy 2013-01-31     16  28.555318  South  456.885087\n",
       "6  Fancy    Boy 2013-02-28     18  23.748531  South  427.473563\n",
       "7    Tee   Girl 2013-02-28     17  21.848335  North  371.421703\n",
       "8   Golf    Boy 2013-03-31      4  26.297559  South  105.190235\n",
       "9  Fancy   Girl 2013-01-31      2  26.512295  South   53.024590"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period = pd.date_range('20130101', periods=3, freq='M')\n",
    "dates = np.random.choice(period, 9)\n",
    "region = ['North','South','South']*3\n",
    "style = ['Tee','Golf','Fancy']*3\n",
    "gender = ['Girl','Boy','Girl','Girl','Boy','Boy','Girl','Boy','Girl']\n",
    "columns = ['Style','Gender','ShipDate','Units','Price','Region']\n",
    "values = range(1,10)\n",
    "df=pd.DataFrame(index = range(1,10), columns=columns)\n",
    "df['Style'] = style\n",
    "df['Gender'] = gender\n",
    "df['ShipDate'] = dates\n",
    "df['Units'] = np.random.randint(1,20, size=9)\n",
    "df['Price'] = np.random.uniform(20,30,size=9)\n",
    "df['Region'] = region\n",
    "df['Total'] = np.multiply(df['Units'],df['Price'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ShipDate</th>\n",
       "      <th>2013-01-31</th>\n",
       "      <th>2013-02-28</th>\n",
       "      <th>2013-03-31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th>Style</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North</th>\n",
       "      <th>Tee</th>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">South</th>\n",
       "      <th>Fancy</th>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golf</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ShipDate      2013-01-31  2013-02-28  2013-03-31\n",
       "Region Style                                    \n",
       "North  Tee           4.0        17.0        12.0\n",
       "South  Fancy        10.0        18.0         NaN\n",
       "       Golf         16.0        19.0         4.0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pt = pd.pivot_table(data=df, index=['Region','Style'], columns=['ShipDate'], values='Units')\n",
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Style</th>\n",
       "      <th>Fancy</th>\n",
       "      <th>Golf</th>\n",
       "      <th>Tee</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North</th>\n",
       "      <th>Girl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.963988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">South</th>\n",
       "      <th>Boy</th>\n",
       "      <td>427.473563</td>\n",
       "      <td>1073.345516</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Girl</th>\n",
       "      <td>513.411171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Style               Fancy         Golf         Tee\n",
       "Region Gender                                     \n",
       "North  Girl           NaN          NaN  760.963988\n",
       "South  Boy     427.473563  1073.345516         NaN\n",
       "       Girl    513.411171          NaN         NaN"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total'] = df['Price']*df['Units']\n",
    "pt = pd.pivot_table(data=df, index =['Region','Gender'], columns='Style', values='Total',aggfunc=sum)\n",
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Style</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ShipDate</th>\n",
       "      <th>Units</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>12</td>\n",
       "      <td>24.318029</td>\n",
       "      <td>North</td>\n",
       "      <td>291.816354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>19</td>\n",
       "      <td>26.908958</td>\n",
       "      <td>South</td>\n",
       "      <td>511.270193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>18</td>\n",
       "      <td>25.577032</td>\n",
       "      <td>South</td>\n",
       "      <td>460.386582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>4</td>\n",
       "      <td>24.431483</td>\n",
       "      <td>North</td>\n",
       "      <td>97.725931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>16</td>\n",
       "      <td>28.555318</td>\n",
       "      <td>South</td>\n",
       "      <td>456.885087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>18</td>\n",
       "      <td>23.748531</td>\n",
       "      <td>South</td>\n",
       "      <td>427.473563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tee</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>17</td>\n",
       "      <td>21.848335</td>\n",
       "      <td>North</td>\n",
       "      <td>371.421703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Golf</td>\n",
       "      <td>Boy</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>4</td>\n",
       "      <td>26.297559</td>\n",
       "      <td>South</td>\n",
       "      <td>105.190235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fancy</td>\n",
       "      <td>Girl</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>26.512295</td>\n",
       "      <td>South</td>\n",
       "      <td>53.024590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Style Gender   ShipDate  Units      Price Region       Total\n",
       "1    Tee   Girl 2013-03-31     12  24.318029  North  291.816354\n",
       "2   Golf    Boy 2013-02-28     19  26.908958  South  511.270193\n",
       "3  Fancy   Girl 2013-01-31     18  25.577032  South  460.386582\n",
       "4    Tee   Girl 2013-01-31      4  24.431483  North   97.725931\n",
       "5   Golf    Boy 2013-01-31     16  28.555318  South  456.885087\n",
       "6  Fancy    Boy 2013-02-28     18  23.748531  South  427.473563\n",
       "7    Tee   Girl 2013-02-28     17  21.848335  North  371.421703\n",
       "8   Golf    Boy 2013-03-31      4  26.297559  South  105.190235\n",
       "9  Fancy   Girl 2013-01-31      2  26.512295  South   53.024590"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using loc and iloc to slice data\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "array = df['D'].to_numpy()\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.hist([1,2.2,3,5,3,2], alpha=0.4, density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,['A','B']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['2013-01-01',['D']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,['D']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 4), columns=['A','B','C','D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[2:5,['A','D']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[2:5,[1,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.pivot_table(data=df, index = ['B'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DrinksByCountry Pandas Groupby "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  beer_servings  spirit_servings  wine_servings  total  \\\n",
       "0    Afghanistan              0                0              0    0.0   \n",
       "1        Albania             89              132             54    4.9   \n",
       "2        Algeria             25                0             14    0.7   \n",
       "3        Andorra            245              138            312   12.4   \n",
       "4         Angola            217               57             45    5.9   \n",
       "..           ...            ...              ...            ...    ...   \n",
       "188    Venezuela            333              100              3    7.7   \n",
       "189      Vietnam            111                2              1    2.0   \n",
       "190        Yemen              6                0              0    0.1   \n",
       "191       Zambia             32               19              4    2.5   \n",
       "192     Zimbabwe             64               18              4    4.7   \n",
       "\n",
       "         continent  \n",
       "0             Asia  \n",
       "1           Europe  \n",
       "2           Africa  \n",
       "3           Europe  \n",
       "4           Africa  \n",
       "..             ...  \n",
       "188  South America  \n",
       "189           Asia  \n",
       "190           Asia  \n",
       "191         Africa  \n",
       "192         Africa  \n",
       "\n",
       "[193 rows x 6 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read from link and write locally in JSON\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "# replace column heading total_litres with total\n",
    "drinks.columns = ['country','beer_servings',\n",
    "                  'spirit_servings','wine_servings','total','continent']\n",
    "drinks.to_json('drinksbycountry.json')  # with column headings modified\n",
    "drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  total continent\n",
       "0  Afghanistan              0                0              0    0.0      Asia\n",
       "1      Albania             89              132             54    4.9    Europe\n",
       "2      Algeria             25                0             14    0.7    Africa\n",
       "3      Andorra            245              138            312   12.4    Europe\n",
       "4       Angola            217               57             45    5.9    Africa"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json('drinksbycountry.json')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Africa</th>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">South America</th>\n",
       "      <th>Paraguay</th>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            mean\n",
       "                           total\n",
       "continent     country           \n",
       "Africa        Algeria        0.7\n",
       "              Angola         5.9\n",
       "              Benin          1.1\n",
       "              Botswana       5.4\n",
       "              Burkina Faso   4.3\n",
       "...                          ...\n",
       "South America Paraguay       7.3\n",
       "              Peru           6.1\n",
       "              Suriname       5.6\n",
       "              Uruguay        6.6\n",
       "              Venezuela      7.7\n",
       "\n",
       "[193 rows x 1 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt = pd.pivot_table(data=df, index = ['continent','country'],  values='total', aggfunc=['mean'])\n",
    "pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## groupby continent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='continent'>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "drinks.groupby('continent').mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>total</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0.7</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>4.9</td>\n",
       "      <td>8.65</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            min  mean   max\n",
       "          total total total\n",
       "continent                  \n",
       "Africa      0.7  3.30   5.9\n",
       "Asia        0.0  0.00   0.0\n",
       "Europe      4.9  8.65  12.4"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.columns = ['country','beer_servings',\n",
    "                  'spirit_servings','wine_servings','total','continent']\n",
    "\n",
    "df1 = pd.pivot_table(df, index=['continent','country'], \n",
    "                     values=['total'], \n",
    "                     aggfunc=['mean','sum','max'])\n",
    "\n",
    "df3=df.head()\n",
    "df4= pd.pivot_table(df3, index=['continent'], values=['total'], aggfunc=['min','mean','max'])\n",
    "df4.plot(kind='bar')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                      \n",
       "Africa             61.471698        16.339623      16.264151   \n",
       "Asia               37.045455        60.840909       9.068182   \n",
       "Europe            193.777778       132.555556     142.222222   \n",
       "North America     145.434783       165.739130      24.521739   \n",
       "Oceania            89.687500        58.437500      35.625000   \n",
       "South America     175.083333       114.750000      62.416667   \n",
       "\n",
       "               total_litres_of_pure_alcohol  \n",
       "continent                                    \n",
       "Africa                             3.007547  \n",
       "Asia                               2.170455  \n",
       "Europe                             8.617778  \n",
       "North America                      5.995652  \n",
       "Oceania                            3.381250  \n",
       "South America                      6.308333  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = drinks.groupby('continent').mean()\n",
    "bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.wine_servings >= 200) & (df.continent == 'Europe')].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Laos</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>247</td>\n",
       "      <td>326</td>\n",
       "      <td>73</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                country  beer_servings  spirit_servings  wine_servings  total  \\\n",
       "92                 Laos             62                0            123    6.2   \n",
       "94              Lebanon             20               55             31    1.9   \n",
       "141  Russian Federation            247              326             73   11.5   \n",
       "177        Turkmenistan             19               71             32    2.2   \n",
       "\n",
       "    continent  \n",
       "92       Asia  \n",
       "94       Asia  \n",
       "141      Asia  \n",
       "177      Asia  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.wine_servings >= 20) & (df.continent == 'Asia')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-69-405da82d3f12>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-69-405da82d3f12>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    bd = pd.pivot_table(data=drinks, index = ['continent','country'], columns ['beer_servings'])\u001b[0m\n\u001b[0m                                                                                               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "bd = pd.pivot_table(data=drinks, index = ['continent','country'])\n",
    "bd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drinks.groupby(\"continent\").mean().plot(kind = 'bar')\n",
    "type(drinks.groupby(\"continent\").mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Series - Handling Lists and Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Course</th>\n",
       "      <th>Dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Course  Dept\n",
       "1       1     2\n",
       "2       2     3\n",
       "3       3     8\n",
       "4       4     2"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "\n",
    "myArray = np.array([[1,2],[2,3],[3,8],[4,2]])\n",
    "myArray = {'Course':[1,2,3,4], 'Dept':[2,3,8,2]}\n",
    "#mySeries = pd.Series(myArray)\n",
    "myData = pd.DataFrame(myArray,  index=range(1,5))\n",
    "myData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## writing to json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"3\":{\"Course\":1},\"4\":{\"Course\":2},\"5\":{\"Course\":3},\"6\":{\"Course\":3}}'"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.to_json( orient = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_json(_, orient='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## datetime - epoch is from Unix time 0 (midnight 1/1/1970)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What day of the week was 1/1/1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 4, 5, 6, 0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myArray = np.array([1,2,3,4,5,6,7])\n",
    "mySeries = pd.Series(myArray)\n",
    "mySeries.index = pd.date_range(start='1/1/1970', periods=7)\n",
    "mySeries.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 20)\n",
    "pd.options.display.max_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unix time now: 1615565998.616631\n",
      "Local datetime now       : 2021-03-12 11:19:58.616991\n",
      "Local date_time from unix: 2021-03-12 11:19:58.616631\n",
      "UTC datetime now         : 2021-03-12 16:19:58.617235\n"
     ]
    }
   ],
   "source": [
    "import datetime as datetime\n",
    "import time\n",
    "# Unix time is independent of Time Zones \n",
    "unixtime = time.time()      # Unix, Epoch time from 1970/1/1\n",
    "print(f'unix time now: {unixtime}')  \n",
    "\n",
    "# create a datetime now or from unixtime\n",
    "# Notice that datetime knows about your machines \"local time zone\"\n",
    "dt_now = datetime.datetime.now()\n",
    "print(\"Local datetime now       :\",dt_now) \n",
    "dt_obj = datetime.datetime.fromtimestamp(unixtime) \n",
    "# Notice slight difference in Epoch time due to calculation time\n",
    "print(\"Local date_time from unix:\",dt_obj) \n",
    "\n",
    "utcNow = datetime.datetime.utcnow()  # UTC time now\n",
    "print(f'UTC datetime now         : {utcNow}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local date_time: 2021-03-12 11:19:58.809644-05:00\n",
      "EST\n"
     ]
    }
   ],
   "source": [
    "from tzlocal import get_localzone\n",
    "\n",
    "# Time zone aware \n",
    "local_tz = get_localzone()\n",
    "local_datetime = datetime.datetime.now(local_tz)\n",
    "print(\"Local date_time:\",local_datetime)\n",
    "\n",
    "localtime = reference.LocalTimezone()\n",
    "print(localtime.tzname(today))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"1970-01-01T00:00:00.000Z\":1,\"1970-01-02T00:00:00.000Z\":2,\"1970-01-03T00:00:00.000Z\":3,\"1970-01-04T00:00:00.000Z\":4,\"1970-01-05T00:00:00.000Z\":5,\"1970-01-06T00:00:00.000Z\":6,\"1970-01-07T00:00:00.000Z\":7}'"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mySeries.to_json( orient = 'index',date_format='iso')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00+00:00</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-02 00:00:00+00:00</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-03 00:00:00+00:00</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-04 00:00:00+00:00</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-05 00:00:00+00:00</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-06 00:00:00+00:00</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-07 00:00:00+00:00</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0\n",
       "1970-01-01 00:00:00+00:00  1\n",
       "1970-01-02 00:00:00+00:00  2\n",
       "1970-01-03 00:00:00+00:00  3\n",
       "1970-01-04 00:00:00+00:00  4\n",
       "1970-01-05 00:00:00+00:00  5\n",
       "1970-01-06 00:00:00+00:00  6\n",
       "1970-01-07 00:00:00+00:00  7"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_json(_, orient='index')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 4, 5, 6, 0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UFO Data - Handling Time Day of Week etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>2000-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>2000-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>2000-12-31 23:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City Colors Reported Shape Reported State  \\\n",
       "0                    Ithaca             NaN       TRIANGLE    NY   \n",
       "1               Willingboro             NaN          OTHER    NJ   \n",
       "2                   Holyoke             NaN           OVAL    CO   \n",
       "3                   Abilene             NaN           DISK    KS   \n",
       "4      New York Worlds Fair             NaN          LIGHT    NY   \n",
       "...                     ...             ...            ...   ...   \n",
       "18236            Grant Park             NaN       TRIANGLE    IL   \n",
       "18237           Spirit Lake             NaN           DISK    IA   \n",
       "18238           Eagle River             NaN            NaN    WI   \n",
       "18239           Eagle River             RED          LIGHT    WI   \n",
       "18240                  Ybor             NaN           OVAL    FL   \n",
       "\n",
       "                     Time  \n",
       "0     1930-06-01 22:00:00  \n",
       "1     1930-06-30 20:00:00  \n",
       "2     1931-02-15 14:00:00  \n",
       "3     1931-06-01 13:00:00  \n",
       "4     1933-04-18 19:00:00  \n",
       "...                   ...  \n",
       "18236 2000-12-31 23:00:00  \n",
       "18237 2000-12-31 23:00:00  \n",
       "18238 2000-12-31 23:45:00  \n",
       "18239 2000-12-31 23:45:00  \n",
       "18240 2000-12-31 23:59:00  \n",
       "\n",
       "[18241 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "ufo = pd.read_csv('http://bit.ly/uforeports', parse_dates=['Time'])\n",
    "ufo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo['Time'].dt.dayofweek.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## write ufo data to json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>2000-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>2000-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>2000-12-31 23:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City Colors Reported Shape Reported State  \\\n",
       "0                    Ithaca             NaN       TRIANGLE    NY   \n",
       "1               Willingboro             NaN          OTHER    NJ   \n",
       "2                   Holyoke             NaN           OVAL    CO   \n",
       "3                   Abilene             NaN           DISK    KS   \n",
       "4      New York Worlds Fair             NaN          LIGHT    NY   \n",
       "...                     ...             ...            ...   ...   \n",
       "18236            Grant Park             NaN       TRIANGLE    IL   \n",
       "18237           Spirit Lake             NaN           DISK    IA   \n",
       "18238           Eagle River             NaN            NaN    WI   \n",
       "18239           Eagle River             RED          LIGHT    WI   \n",
       "18240                  Ybor             NaN           OVAL    FL   \n",
       "\n",
       "                     Time  \n",
       "0     1930-06-01 22:00:00  \n",
       "1     1930-06-30 20:00:00  \n",
       "2     1931-02-15 14:00:00  \n",
       "3     1931-06-01 13:00:00  \n",
       "4     1933-04-18 19:00:00  \n",
       "...                   ...  \n",
       "18236 2000-12-31 23:00:00  \n",
       "18237 2000-12-31 23:00:00  \n",
       "18238 2000-12-31 23:45:00  \n",
       "18239 2000-12-31 23:45:00  \n",
       "18240 2000-12-31 23:59:00  \n",
       "\n",
       "[18241 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.to_json('ufoepoch.json', date_format='iso')  \n",
    "ufo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1930-06-01T22:00:00.000Z\n",
      "1        1930-06-30T20:00:00.000Z\n",
      "2        1931-02-15T14:00:00.000Z\n",
      "3        1931-06-01T13:00:00.000Z\n",
      "4        1933-04-18T19:00:00.000Z\n",
      "                   ...           \n",
      "18236    2000-12-31T23:00:00.000Z\n",
      "18237    2000-12-31T23:00:00.000Z\n",
      "18238    2000-12-31T23:45:00.000Z\n",
      "18239    2000-12-31T23:45:00.000Z\n",
      "18240    2000-12-31T23:59:00.000Z\n",
      "Name: Time, Length: 18241, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "ufo2 = pd.read_json('ufoepoch.json')\n",
    "print(ufo2['Time'])\n",
    "type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>None</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>None</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>None</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>None</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>None</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>None</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>2000-12-31 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>None</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>2000-12-31 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>None</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>2000-12-31 23:59:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City Colors Reported Shape Reported State  \\\n",
       "0                    Ithaca            None       TRIANGLE    NY   \n",
       "1               Willingboro            None          OTHER    NJ   \n",
       "2                   Holyoke            None           OVAL    CO   \n",
       "3                   Abilene            None           DISK    KS   \n",
       "4      New York Worlds Fair            None          LIGHT    NY   \n",
       "...                     ...             ...            ...   ...   \n",
       "18236            Grant Park            None       TRIANGLE    IL   \n",
       "18237           Spirit Lake            None           DISK    IA   \n",
       "18238           Eagle River            None           None    WI   \n",
       "18239           Eagle River             RED          LIGHT    WI   \n",
       "18240                  Ybor            None           OVAL    FL   \n",
       "\n",
       "                           Time  \n",
       "0     1930-06-01 22:00:00+00:00  \n",
       "1     1930-06-30 20:00:00+00:00  \n",
       "2     1931-02-15 14:00:00+00:00  \n",
       "3     1931-06-01 13:00:00+00:00  \n",
       "4     1933-04-18 19:00:00+00:00  \n",
       "...                         ...  \n",
       "18236 2000-12-31 23:00:00+00:00  \n",
       "18237 2000-12-31 23:00:00+00:00  \n",
       "18238 2000-12-31 23:45:00+00:00  \n",
       "18239 2000-12-31 23:45:00+00:00  \n",
       "18240 2000-12-31 23:59:00+00:00  \n",
       "\n",
       "[18241 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo2['Time'] = pd.to_datetime(ufo2['Time'])  # change ISO time to a datetime object\n",
    "ufo2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series and value_counts() for items in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>None</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>1930-06-01 22:00:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>None</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00+00:00</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>None</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>1931-02-15 14:00:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>None</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>1931-06-01 13:00:00+00:00</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>None</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>1933-04-18 19:00:00+00:00</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>None</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>2000-12-31 23:00:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>None</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>2000-12-31 23:00:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>2000-12-31 23:45:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>None</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>2000-12-31 23:59:00+00:00</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City Colors Reported Shape Reported State  \\\n",
       "0                    Ithaca            None       TRIANGLE    NY   \n",
       "1               Willingboro            None          OTHER    NJ   \n",
       "2                   Holyoke            None           OVAL    CO   \n",
       "3                   Abilene            None           DISK    KS   \n",
       "4      New York Worlds Fair            None          LIGHT    NY   \n",
       "...                     ...             ...            ...   ...   \n",
       "18236            Grant Park            None       TRIANGLE    IL   \n",
       "18237           Spirit Lake            None           DISK    IA   \n",
       "18238           Eagle River            None           None    WI   \n",
       "18239           Eagle River             RED          LIGHT    WI   \n",
       "18240                  Ybor            None           OVAL    FL   \n",
       "\n",
       "                           Time Day  \n",
       "0     1930-06-01 22:00:00+00:00  SA  \n",
       "1     1930-06-30 20:00:00+00:00   S  \n",
       "2     1931-02-15 14:00:00+00:00  SA  \n",
       "3     1931-06-01 13:00:00+00:00   S  \n",
       "4     1933-04-18 19:00:00+00:00   M  \n",
       "...                         ...  ..  \n",
       "18236 2000-12-31 23:00:00+00:00  SA  \n",
       "18237 2000-12-31 23:00:00+00:00  SA  \n",
       "18238 2000-12-31 23:45:00+00:00  SA  \n",
       "18239 2000-12-31 23:45:00+00:00  SA  \n",
       "18240 2000-12-31 23:59:00+00:00  SA  \n",
       "\n",
       "[18241 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days = ['S','M','T','W','T','F','SA']\n",
    "ufo2['Day']= ufo2['Time'].dt.dayofweek.values\n",
    "# change integer day to letter value\n",
    "ufo2['Day'] = pd.Series(list(map(lambda x: days[x], ufo2['Time'].dt.dayofweek.values)))\n",
    "ufo2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T     5145\n",
       "M     2822\n",
       "SA    2689\n",
       "F     2687\n",
       "W     2598\n",
       "S     2300\n",
       "Name: Day, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=ufo2['Day'].value_counts()\n",
    "y.plot(kind='bar')\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyJSwM1xds1T39Icz390PhOmDwPwwvQjIvRvw3tA2UvurmNmtZrbVzLa2thbvHeVFREays6WTmZVlzK+tjLuUV5nwgVyP7vo7aXf+dfe17r7a3Vc3NDRM1mZFRApmZ2snKxtqMLO4S3mV8Yb+K6HbhvDYEtr3AafnLLc4tI3ULiIy5RTryB0Yf+ivB7IjcNYAv8pp/3AYxXMxcCR0A90PXG1mdeEA7tWhTURkSunsTXHgSHHdIjFX2akWMLOfAFcBc81sL9EonC8D95nZLUAz8IGw+AbgWqAJ6AZuBnD3djP7IrAlLPd37j704LCISMnbPXCLxBINfXe/cYRZbx1mWQduG2E764B1Y6pORKTENLUeA+CMecV1zZ0snZErIjKJdrZ0UZYwls5R6IuITHlNLZ0smTOD8mRxxmtxViUiUqJ2tnZyRpH254NCX0Rk0mRvkVisI3dAoS8iMmn+cPAY/WnntfNnxV3KiBT6IiKT5OGmQwBcunJOzJWMTKEvIjJJNje28rrXzGJebVXcpYxIoS8iMgl6+tJs2d3BFavmxl3KSSn0RUQmwWO72+hLZ7hiVXFfKFKhLyIyCTa/eIjKsgQXLq+Pu5STUuiLiEyCzY2tXLi8nqryZNylnJRCX0Rkgg4c6aGxpZM3F3nXDij0RUQmbHNjNFTzijOL+yAuKPRFRCZs04utNMyqLOqTsrIU+iIiE5DOOA83HeKKVXOL8vaIQyn0RUQmYPv+Ixzu7ufKM4u/Px8U+iIiE5Ltz7/sjOLvzweFvojIhGx6sZWzF9Yyd2Zl3KWMikJfRGScOntTPNncUfRn4eZS6IuIjNNjO9tIZZw3F/n1dnIp9EVExmlzYyvV5UkuWFYXdymjptAXERmnzY2HuHhFPZVlxX3phVwKfRGRcXi5vZtdh7pKqj8fFPoiIuOSHar55hK49EIuhb6IyDhsbmxlwWlVrGwo3pugD0ehLyIyRql0hkdK6NILuSYU+mb2kpk9a2ZPm9nW0FZvZhvNrDE81oV2M7NvmVmTmW0zs/Mn4w2IiBTatn1HOHo8xZtL5NILuSZjT/8t7n6uu68Oz+8AHnT3VcCD4TnAO4FV4e9W4M5JeG0RkYLb/OIhzOCylaXVnw/56d65Drg7TN8NXJ/Tfo9HHgNmm9mCPLy+iEhebW5s5Y2LTqOupiLuUsZsoqHvwK/N7EkzuzW0zXf3A2H6IDA/TC8CXs5Zd29oO4GZ3WpmW81sa2tr6wTLExGZXEeP9/P7lw+X3FDNrLIJrn+5u+8zs3nARjP7Q+5Md3cz87Fs0N3XAmsBVq9ePaZ1RUTy7dGmNtIZ54oSuvRCrgnt6bv7vvDYAvwSuBB4JdttEx5bwuL7gNNzVl8c2kRESsbmxlZqKpKct6R0Lr2Qa9yhb2Y1ZjYrOw1cDTwHrAfWhMXWAL8K0+uBD4dRPBcDR3K6gURESsLmxkNcsnIuFWWlOeJ9It0784FfhjGqZcA/u/u/m9kW4D4zuwVoBj4Qlt8AXAs0Ad3AzRN4bRGRgmtu62JPezf/+YrlcZcybuMOfXffBZwzTHsb8NZh2h24bbyvJyISt00vRoNLSvUgLkz8QK6IyJTRl8rQ0d1HW2cf7V19tHX10t6Vne7jkaZDLK6rZtmcGXGXOm4KfRGZso73p2nr6qO989UBHrX10R7a27r6OHY8Nex2zKBuRgX1NRXccvnSkrv0Qi6FvoiUBHenqy99QoC3hRBv78runZ/Y3t2XHnZbZQmjviYK8TkzK3hD3WzmhOf1NRUD03NmVlBfU8lp1eUkE6Ub9LkU+iISi0zGOXq8fyCg2zr76Og+McBPCPWuPvpSmWG3VVmWiII6hPSKhpkjBnh9TQW1VWUlvbc+EQp9EZkU6YwPCe0TgzvbpZKd7ujuI50Z/vzLmorkQIDPr63i9QtqT9wTD/OybTMqktM2xMdKoS8iwzrVQc32Ie2He/rxEc6hr60qY87MaC97yZwZnLdk9ogBXl9TQVV56dx+sNQo9EWmgb5UhsPdfXR099Me9rI7uvvo6Oqjvaufw919tIf5HV1R+7HeUx/UrK+p4Mz5s07oRqmfORjgc2oqqKupoDxZmicyTUUKfZEpbstL7bz/u7+b0Daqy5PMr61kXm0Vc2dWUJFMUJZMUJ40kgmjLJGgpz/NwaNRd86uhFGeTFCWiOaXJxOUJY2ysGw0HT1G20hQnjDKkomwfO5y0frJhEXLJxIkw2N2vrp2Rk+hLzLFrZo3k9VL62hq7WRWVRmzKsujx6roMWFGKpMhlfacx2i6P+2kM04qHU0f6e7nUGcvqdDen86Qyjj9qQy96cyIB1rzLfrhCX9Dfoyy09kfnsEfmCE/QNkflZwfntqqcv7LVSuZPaP0LqE8EoW+yBRXW1XOP9xwLo0tnTS90kljyzF2tXZx4EgPezucjDsZj0bTpD0Kc/fowGzanUwmWiadiZZLZ7LrDLbFLZ2JaumNno15/WTCSBgkzMJ09HxWVTkfeNPpCn0RKV7NbV1sePYgjS3HaGrppKml81Xj1SuSCWqry6hIJqgoS1CeTFBdkaSiLDHQVpGM2stz2irLBrtsEiEokxZNZx+j4CQE58mWHRqy0XQyAWbRMrkBPLidwfWHrpsb3MmEYeE1k6ELKDn0tUM904lCX2SK+eaDjfziqZNftbwvneFQZ9+4XyN7otJIQy6HkzAoDz8e0Q9HgmQihHLoYkkY0WMI/2TodknmBHkidOMkLHpMJk+cn93eYNgP/hAM/RHI/SFKmPH2s+ZxxrxZ4/5cSoH5SGOsisDq1at969atcZchUlL6Uhn2H+4hFbo8cvvr+9NOKh31xfelM1GffHjeH5bpS0X99Kmctv60s3bTrrjfWkG89OU/ibuECTOzJ3PuW34C7emLFMjuQ11877c7McvufYIxuCcKDExbzjRhuYSdOB8zcOdb/9EU59sqae89bxELZ1cPHLt41xsXxl1S3in0RSZBKp1h/TP7qShLUB9ulu0e/WXccWDNuifiLVJe5czXzOITV66Mu4yCUuiLjMPXf/2C9rBj9tBnrmJ2dfnA89yh+uHfQjjRSCQnumBbxqM2Qq/2vNqqAlZcHBT6IuPQ2NIZdwnTyrI5M/jNZ98SdxlTgkJfZBzu/NAFed1+fzpDd2+aY739dPWm6epL0dWb/UvTm8rQm0rTl8oMTPf250ynMuF5NH3CckPm9aYyYxqFky9zaipwou6wTDhXINs19umrXxt3eVOGQl8kJtv2Hubd334k7jLGZLgzX8sSg5dIyA6ZtJyD1JZzYNoMbn/LGayaP4uEGcf705xWXc7C2dVxv7VpQ6EvEpMiHi09oome+Qrwv+9/gQc/fdUkViVjodAXick5p88+YUx4V2+Kd35zM3vau09YbtHsamZWlg2esJR7olLuSUk5Z8UOPUEpeyJT7jYGTnLK2TtPmJ2wZ549G9aGDClNDNmTj54zMBzVhiyTO/22188v4KcsQyn0RUYpk3GOp9L09KXp7ktzvD9NT3/0/ITHMH28P1qupz8sG6aHrnu8P0N3Xyos9+oLll199nw+/6dnx/COZSpS6IsMI51xtu8/wiNNbTy68xBPNXfQNcL9Vk8mmTBmlCeprgh/5UmqyqPH+bPKqQpt1eVJZlSEeTltVRVJLl05Jw/vUKYrhb6UjI6uPv7+356nL/3qveFsd8RoZC8xkEo7/TnTqUy4THDa2X+4hyM9/QCcOX8m7z1/8cBt+aorBoN7RsVgOJ8Q3uG5bh4ixUahLyXjOw818fOn9uZt+5efMXfg2urnLD6NS1bO4ZKVc5g3a/qdwCNTl0JfJt2Rnn4+dvdWnj9wNGo44UzJyNHjw9+KLy6//os3c+b8qX11RRFQ6E9rfakMb/36b3i5vSfuUgpuy9+8jYZZlXGXIVJwBQ99M7sG+CaQBP7J3b9c6BokknEvusC/6eKllCVt4EqS2eucD9xEY+CGGdlhgYM37IhuvBGtM3gTjsEbcmRvnLFq3iwFvkxbBQ19M0sC3wHeDuwFtpjZenffUcg6ion74Onmmexp5050qVd3PMPArekyPnjRqOyt6qLrpUcHIe9+tJlNL7YO3Le0vWv8N8kYj5svWxauLDlY42DNPnh993D99nQm50Bqxnn3OQv54EVLdJNrkTwq9J7+hUCTu+8CMLN7geuASQ/9ZXf822Rvclr51NtWDYxEqSxLRgc4k4O3yss+ZtxpOdrLu85ZyMxK9RaKFLtC/1+6CHg55/le4KLcBczsVuBWgCVLloz7hepmlNPR3T/u9YvRLZcvZ0VDzQlnSZYnbeCWck0tnTS3dUfXQ0ka7rD/cA+zqsqpqUxSlkhw0Yp6FpxWBdmzKRm8NkoidIGctaBWe9siU1TR7Zq5+1pgLUS3Sxzvdn7/36+etJpERKaKQp85sg84Pef54tAmIiIFUOjQ3wKsMrPlZlYB3ACsL3ANIiLTVkG7d9w9ZWa3A/cTDdlc5+7bC1mDiMh0VvA+fXffAGwo9OuKiEjhu3dERCRGCn0RkWlEoS8iMo0o9EVEphHzIr47s5m1As0T2MRc4NAklZNvpVQrlFa9pVQrlFa9pVQrlFa9E6l1qbs3DDejqEN/osxsq7uvjruO0SilWqG06i2lWqG06i2lWqG06s1XrereERGZRhT6IiLTyFQP/bVxFzAGpVQrlFa9pVQrlFa9pVQrlFa9eal1Svfpi4jIiab6nr6IiORQ6IuITCMlFfpmts7MWszsuZy2c8zsd2b2rJn9i5nVhvYLzezp8PeMmb0nZ51rzOwFM2syszuKod6c+UvMrNPMPlPIesf42S4zs56cz/e7OetcEJZvMrNvWZ5uwTXWz9bM3hjmbQ/zqwpV7xg/2w/mfK5Pm1nGzM4tVK3jqLfczO4O7c+b2edy1im2722Fmf0gtD9jZlflrFOI78HpZvaQme0I38NPhvZ6M9toZo3hsS60W6ilycy2mdn5OdtaE5ZvNLM1YyokujF3afwBbwbOB57LadsCXBmmPwp8MUzPAMrC9AKgheiqoklgJ7ACqACeAc6Ku96c+T8Dfgp8JjwvSL1j/GyX5S43ZDtPABcT3Ynx/wHvjPuzDf/dtwHnhOdzgGSh6h3P9yC0vwHYWeSf7Z8D94bpGcBL4ftRjN/b24AfhOl5wJNAooDfgwXA+WF6FvAicBbwFeCO0H4H8L/C9LWhFgu1PR7a64Fd4bEuTNeNto6S2tN3901A+5DmM4FNYXoj8L6wbLe7p0J7FZA9Yj1wc3Z37wOyN2ePtV4AM7se2A3k3mOgIPWOtdbhmNkCoNbdH/Po23kPcP0klwqMud6rgW3u/kxYt83d04WqdwKf7Y1E/72L+bN1oMbMyoBqoA84SnF+b88C/iOs1wIcBlYX8HtwwN2fCtPHgOeJ7ht+HXB3WOzunNe+DrjHI48Bs0Ot7wA2unu7u3eE93jNaOsoqdAfwXYGv0zvJ+d2jGZ2kZltB54FPhF+BIa7OfuiAtUKI9RrZjOBvwL+x5Dl46x3xM8WWG5mvzez35rZFaFtUagvqyg+W6IQcDO738yeMrP/FtrjrPdkn23WnwE/CdPF+tn+DOgCDgB7gK+6ezvF+b19Bni3mZWZ2XLggjCv4J+tmS0DzgMeB+a7+4Ew6yAwP0yP9BlO6LOdCqH/UeC/mtmTRP9k6svOcPfH3f1s4E3A57L9uDEbqd4vAN9w9864ChvGSLUeAJa4+3nAXwL/bEOOTcRkpHrLgMuBD4bH95jZW+MpccCI31uIdliAbnd/briVYzBSvRcCaWAhsBz4tJmtiKfEASPVuo4oILcC/wA8SlR7QYUdvJ8Dn3L3o7nzwr808jqOvuB3zpps7v4Hon++Y2ZnAn8yzDLPm1kn8EfEfHP2k9R7EfCfzOwrwGwgY2bHifodY6l3pFrdvRfoDdNPmtlOor3pfaG+gtd6snqJ/kff5O6HwrwNRP3AP4qr3lF8b29gcC8fivez/XPg3929H2gxs0eA1UR7osX2vU0Bf5FdzsweJepX76BAn62ZlRMF/o/d/Reh+RUzW+DuB0L3TUtoHymr9gFXDWn/zWhrKPk9fTObFx4TwN8C3w3Pl4d+RsxsKfA6ooNMsd6cfaR63f0Kd1/m7suI9kL+p7t/O856T/LZNphZMkyvAFYBu8I/UY+a2cVh9MOHgV8VotaT1Ut0T+Y3mNmM8J24EtgRZ70nqTXb9gFCfz5E/cFx1XqKevcAfxzm1RAdcPwDxfm9nRFqxMzeDqTcvWDfg7Dtu4Dn3f3rObPWA9kROGtyXns98OEwiudi4Eio9X7gajOrCyN9rg5tozPZR6jz+Ue053MA6Cfae7sF+CTRr/WLwJcZPMv4JqK+vaeBp4Drc7ZzbVh+J/A3xVDvkPW+QBi9U6h6x/jZvm/IZ/unOdtZDTwXav32cO8vjs8W+FCo+TngK4Wsdxy1XgU8Nsx2iu6zBWYSjTbbDuwAPlvE39tlwAtEB1AfILr8cCG/B5cTdd1sC//vPB0+oznAg0BjqKs+LG/Ad0JNzwKrc7b1UaAp/N08ljp0GQYRkWmk5Lt3RERk9BT6IiLTiEJfRGQaUeiLiEwjCn0RkWlEoS8iMo0o9EVEppH/DzMLU3Vh32NWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = pd.DatetimeIndex(ufo2['Time']).year.value_counts()\n",
    "z.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}