MatplotLib (1).ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMciVd5PfsO1"
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      "source": [
        "#STATISTICAL FOUNDATIONS FOR DATA SCIENCE"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Welcome to SFDS LAb***"
      ],
      "metadata": {
        "id": "YQ_StATrGeiT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "_BPOFOCaq3PG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Tm3O511a5NH"
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      "source": [
        "#### Using Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvFHjxdiViZj"
      },
      "source": [
        "**What is Google Colab?**\n",
        "\n",
        "Colab is Google's implementation of [Jupyter Notebook](https://jupyter.org/). Like all Jupyter Notebooks, a Colab consists of two kinds of components:\n",
        "\n",
        "  * **Text cells**, which contain explanations. You are currently reading a text cell.\n",
        "  * **Code cells**, which contain Python code for you to run. Code cells have a light gray background.\n",
        "\n",
        "You *read* the text cells and *run* the code cells.\n",
        "\n",
        "**Running code cells**\n",
        "\n",
        "You must run code cells in order. In other words, you may only run a code cell once all the code cells preceding it have already been run.\n",
        "\n",
        "To run a code cell press Shift + Enter or place the cursor anywhere inside the [ ] area at the top left of a code cell. The area inside the [ ] will display an arrow. Click it to run a cell. You can also run all cells by clicking Runtime -> Run all\n",
        "\n",
        "**If you see errors...**\n",
        "\n",
        "The most common reasons for seeing code cell errors are as follows:\n",
        "\n",
        "  * You didn't run *all* of the code cells preceding the current code cell.\n",
        "  * If the code cell is labeled as a **Task**, then:\n",
        "    *  You haven't yet written the code that implements the task.\n",
        "    *  You did write the code, but the code contained errors.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-z8Ste2Mf_SA"
      },
      "source": [
        "##Basic plotting in matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "k44YHjqZBvDE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9ByC-oSdPDZ"
      },
      "source": [
        "!pip install matplotlib==3.4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "600+700"
      ],
      "metadata": {
        "id": "Jv3QuRBtBvGQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXUEp4BYxNNl"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5h007uKI4Y0"
      },
      "source": [
        "For all Matplotlib plots, we start by creating a figure and an axes. In their simplest form, a figure and axes can be created as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWvhdFOLI6nO"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "# ax.grid()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2iILsX9JFd9"
      },
      "source": [
        "In Matplotlib, the figure (an instance of the class plt.Figure) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels. The axes (an instance of the class plt.Axes) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvbOnqeOJdQ-"
      },
      "source": [
        "###Line Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJMrKFIiJLv6"
      },
      "source": [
        "We'll commonly use the variable name `fig` to refer to a figure instance, and `ax` to refer to an axes instance or group of axes instances.\n",
        "\n",
        "Once we have created an axes, we can use the a`x.plot` function to plot some data. Let's start with a simple sinusoid:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyAbGxiOGu1-"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x));"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJOv1BEzFk_Y"
      },
      "source": [
        "# Lets add a title and labels to the plot\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x))\n",
        "ax.set_title(' First Simple Plot graph')   # Add a title\n",
        "ax.set_xlabel('x label values')          # Add x label\n",
        "ax.set_ylabel('y label values');         # Add y label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1dU7jlpHIg_"
      },
      "source": [
        "Multiple lines on same plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So_xW71nGg-N"
      },
      "source": [
        "# Lets add a title to the plot above\n",
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x))\n",
        "ax.plot(x, np.cos(x))\n",
        "#ax.plot(x, np.tan(x))\n",
        "#ax.plot(x, np.cot(x))\n",
        "ax.set_title('Multiple Lines  graph');\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywU3eWMFH19a"
      },
      "source": [
        "Adding a legend"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3dVf8_3Hwcd"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x), label = 'sin')\n",
        "ax.plot(x, np.cos(x), label = 'cos')\n",
        "ax.set_title('Multiple Lines');\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "ax.legend()\n",
        "# ax.legend(loc=1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g114HFn9K5jr"
      },
      "source": [
        "Line colors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zt4_CjuYK7qK"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x), label = 'sin', color = 'm')   # specify color by name\n",
        "ax.plot(x, np.cos(x), label = 'cos', color = 'c')    # short color code (rgbcmyk)\n",
        "#ax.plot(x, np.tan(x), label = 'tan', color = 'y')\n",
        "ax.set_title('Multiple Lines');\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "ax.legend();\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDu4lD3HMiv1"
      },
      "source": [
        "Line Styles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QhNvKo8Ln8c"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "# ax.grid(linestyle = '--')\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x), label = 'sin', linestyle = 'dashed', color = 'c')\n",
        "ax.plot(x, np.cos(x), label = 'cos', linestyle = 'dotted', color = 'b')\n",
        "ax.plot(x, np.sin(x+1), label = 'sin(x+1)', linestyle ='dashdot',color = 'r')\n",
        "ax.set_title('Multiple Lines')\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "ax.legend()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yzo3-DTlOLFT"
      },
      "source": [
        "Axes Limits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kKnHjIpOayZ"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "\n",
        "x = np.linspace(0, 10, 1000)\n",
        "ax.plot(x, np.sin(x), label = 'sin', linestyle = 'dashed', color = 'r')\n",
        "\n",
        "ax.set_xlim(1, 5)\n",
        "ax.set_ylim(-1,2 );\n",
        "ax.set_title('Multiple Lines');\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "ax.legend();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaTST-IBBqmp"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkZDQU2EQVcW"
      },
      "source": [
        "Subplots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdYQ65NnE8c2"
      },
      "source": [
        "fig, ax = plt.subplots()        # a figure with a single Axes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKJqrm44ReeW"
      },
      "source": [
        "Multiple subplots"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.pyplot import *"
      ],
      "metadata": {
        "id": "2-R-L5DZCxYa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClssL6i2RbYS"
      },
      "source": [
        "\n",
        "fig, axs = plt.subplots(2, 2, figsize=(10,10)) # a figure with a 2x2 grid of Axes\n",
        "x = np.linspace(0, 10, 1000)\n",
        "axs[0,0].plot(x, np.sin(x), label = 'sin')\n",
        "#axs[0,0].legend(loc='upper left')\n",
        "axs[0,1].plot(x, np.cos(x), label = 'cos', color = 'maroon', linestyle = 'dashed')\n",
        "axs[1,0].plot(x, np.sin(x+2), label = 'sin(x+2)', color = 'blue', linestyle = 'dotted')\n",
        "axs[1,1].plot(x, np.sin(x+4),label = 'sin(x+4)', color = 'green', linestyle = 'dashdot')\n",
        "#ax.set_title('Multiple subplots');\n",
        "fig.suptitle('Multiple subplots\\\n",
        "from a all TM Fuctions', fontsize=25)\n",
        "labels = [\"sin\", \"cos\", \"sin(x+2)\", \"sin(x+4)\" ]\n",
        "ax.set_xlabel('x label')\n",
        "ax.set_ylabel('y label')\n",
        "fig.legend(axs[0,0], axs[0,1], axs[1,0], axs[1,1], labels=labels, loc=\"upper right\")\n",
        "#ax.legend();\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuZTe9wJ_eQW"
      },
      "source": [
        "###Bar Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cb563KdsUZ04"
      },
      "source": [
        "### Simple bar chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bx1kJdn0TnpW"
      },
      "source": [
        "data = {'Apple': 10, 'Orange': 15, 'Lemon': 5, 'Lime': 20}\n",
        "names = list(data.keys())\n",
        "values = list(data.values())\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "# ax.grid(linestyle='--', color='0.85')  # Color: grayscale between 0 and 1\n",
        "\n",
        "ax.bar(names, values);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGmdlQk0Vlao"
      },
      "source": [
        "data = {'Apple': 10, 'Orange': 15, 'Lemon': 5, 'Lime': 20,\n",
        "        'Bannan': 30, 'WM': 18, 'mango': 30}\n",
        "names = list(data.keys())\n",
        "values = list(data.values())\n",
        "fig = plt.figure(figsize=(10,7))\n",
        "ax = plt.axes()\n",
        "ax.bar(names, values, color='r', width = 0.40)\n",
        "ax.set_xlabel('Fruit')\n",
        "ax.set_ylabel('Quantity')\n",
        "ax.set_title('Fruit Quantities')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iME4d9PwYvdG"
      },
      "source": [
        "Horizontal Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHM9ZE8TWnjA"
      },
      "source": [
        "data = {'Apple': 10,\n",
        "        'Orange': 15,\n",
        "        'Banana': 13,\n",
        "        'Pear': 9,\n",
        "        'Pomegrante': 11,\n",
        "        'Lemon': 5,\n",
        "        'Lime': 20}\n",
        "names = list(data.keys())\n",
        "values = list(data.values())\n",
        "ax = plt.axes()\n",
        "ax.barh(names, values, color='c', height=0.80)            # Horizontal bar-chart\n",
        "ax.set_xlabel('Quantity')\n",
        "ax.set_ylabel('Fruit')\n",
        "ax.set_title('Fruit Quantities');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njKY0BEdZuDI"
      },
      "source": [
        "Grouped Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dRCDzzjakDL"
      },
      "source": [
        "labels = ['G1', 'G2', 'G3', 'G4', 'G5']\n",
        "men_means = [20, 34, 30, 35, 27]\n",
        "women_means = [25, 32, 34, 20, 25]\n",
        "x = np.arange(len(labels))  # the label locations\n",
        "width = 0.40  # the width of the bars\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(x - width/2, men_means, width, label='Men')\n",
        "rects2 = ax.bar(x + width/2, women_means, width, label='Women')\n",
        "# # Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_xlabel('Groups')\n",
        "ax.set_title('Scores by group and gender')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgjPN5A7eDWp"
      },
      "source": [
        "Adding Bar Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11xd6sQgeC34"
      },
      "source": [
        "#fig, ax = plt.subplots()\n",
        "fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "rects1 = ax.bar(x - width/2, men_means, width, label='Men')\n",
        "rects2 = ax.bar(x + width/2, women_means, width, label='Women')\n",
        "\n",
        "# # Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_xlabel('Groups')\n",
        "ax.set_title('Scores by group and gender')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "# Add bar labels\n",
        "ax.bar_label(rects1, padding=3) # bar_label exists in matplotlib versions > 3.4\n",
        "ax.bar_label(rects2, padding=3)\n",
        "ax.set_ylim(0,40);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAhZYxxrbptE"
      },
      "source": [
        "Stacked bar chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8S_04sebotM"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.grid(linestyle='--', color='0.95', axis = 'x');\n",
        "\n",
        "ax.set_axisbelow(True)    # Grid behind bars\n",
        "\n",
        "p1 = ax.bar(labels, men_means, width, label='Men')\n",
        "p2 = ax.bar(labels, women_means, width, bottom=men_means,\n",
        "       label='Women')\n",
        "\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_xlabel('Groups')\n",
        "ax.set_title('Scores by group and gender')\n",
        "ax.legend()\n",
        "\n",
        "# Label with label_type 'center' instead of the default 'edge'\n",
        "ax.bar_label(p1, label_type='center', color='y')\n",
        "ax.bar_label(p2, label_type='center', color='k')\n",
        "ax.bar_label(p2)\n",
        "ax.set_ylim(0,100);\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_azf05zkJb4"
      },
      "source": [
        "###Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN7F5QNVEl-m"
      },
      "source": [
        "x = np.random.randn(20)\n",
        "y = np.random.randn(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "ImNhI4vqA3-t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "3saaqGFFA74B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qs6j_hxo6Pp"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x, y);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jn41qi-cJoPH"
      },
      "source": [
        "Markers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewdm2yYEEkYa"
      },
      "source": [
        "fig, ax = plt.subplots()        # a figure with a single Axes\n",
        "ax.scatter(x, y, marker = \"v\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpZf0b2NFROZ"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(16,12));\n",
        "\n",
        "# marker symbol\n",
        "axs[0, 0].scatter(x, y, s=80, marker=\">\")\n",
        "axs[0, 0].set_title(\"Right Triangle \")\n",
        "# marker from TeX\n",
        "axs[0, 1].scatter(x, y, s=80, marker=r'$\\alpha$')\n",
        "axs[0, 1].set_title(r\"marker=r'\\$\\alpha\\$'\")\n",
        "\n",
        "# marker from path\n",
        "verts = [[-1, -1], [1, -1], [1, 1], [-1, -1]]\n",
        "axs[0, 2].scatter(x, y, s=80, marker=verts)\n",
        "axs[0, 2].set_title(\"marker=verts\")\n",
        "\n",
        "# regular polygon marker\n",
        "axs[1, 0].scatter(x, y, s=80, marker=(5, 0))\n",
        "axs[1, 0].set_title(\"polygon\")\n",
        "\n",
        "# regular star marker\n",
        "axs[1, 1].scatter(x, y, s=80, marker=(5, 1))\n",
        "axs[1, 1].set_title(\"star\")\n",
        "\n",
        "# regular asterisk marker\n",
        "axs[1, 2].scatter(x, y, s=80, marker=(5, 2))\n",
        "axs[1, 2].set_title(\"asterisk\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75TQ_5aSFRiS"
      },
      "source": [
        "https://matplotlib.org/stable/api/markers_api.html#module-matplotlib.markers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "c_PloxsKF4s_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "j7aCCzA0F7VJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7-vuO_jJsUf"
      },
      "source": [
        "Colors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQvN8paDJG92"
      },
      "source": [
        "plt.style.use('seaborn-darkgrid')\n",
        "\n",
        "z1 = np.sqrt(x**2 + y**2)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "pos = ax.scatter(x, y, c=z1, cmap='hot', marker='3')\n",
        "\n",
        "fig.colorbar(pos);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLRcDX9tJ2fO"
      },
      "source": [
        "https://matplotlib.org/stable/gallery/color/colormap_reference.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqn657WkNvX1"
      },
      "source": [
        "x = np.random.randn(100)\n",
        "y = np.random.randn(100)\n",
        "\n",
        "z1 = np.sqrt(x**2 + y**2)\n",
        "z2 = np.random.randint(100, 200, size=len(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "QJlDYnNHHpur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "Y8mKnlbRHuyG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z1"
      ],
      "metadata": {
        "id": "_Ht3CXjaHyhz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z2"
      ],
      "metadata": {
        "id": "c9uVXICGH8-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0llqKCKzQgi"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "pos = ax.scatter(x, y, c=z1, s=z2, alpha = 0.55, cmap='viridis')\n",
        "fig.colorbar(pos);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnOtbgS7OloE"
      },
      "source": [
        "x = np.linspace(0, 10, 45)\n",
        "y = np.cos(x)\n",
        "\n",
        "plt.plot(x, y, '>', color='r');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmsbbkvCOzez"
      },
      "source": [
        "plt.plot(x, y, 'o-', color='maroon');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtVrPwzuQa95"
      },
      "source": [
        " Note: `plt.plot` should be preferred over `plt.scatter` for large datasets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IETtcFCXzSIX"
      },
      "source": [
        "###Box Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-ovEARDRkFz"
      },
      "source": [
        "A box plot displays a summary of a set of data containing the minimum, first quartile, median, third quartile, and maximum. In a box plot, we draw a box from the **first quartile** to the **third quartile**. A vertical line goes through the box at the **median**. The whiskers go from each quartile to the **minimum** or **maximum**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Mu5lD3jT_Da"
      },
      "source": [
        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Eg6h2vh69mya2KYNbf7sM/1z4cyZIv02uzG2VSsx5vQ1xitwQ2HHx7FwYGGmpfXTp4t977dooe3C7wgkAOEgxykQDua4s2joWbMm9atbl04cPqw3du7ECfFFMaZKFTqVmSn2701Lo/n9+9OZrCwx4zzk0Ucp5pZbLP9S48RrO8/SRN9wA3WoUEFnfPjHH8WXb/6iyF8+ZaSwCYdzJ09S7G23Udc77iCeavdOS0aNEjc2z2/YXIcFBt8EwyUcQrHBU2q9atWi+f360a4NG4idWFxihZjx7bc+VVho8EkdKCV16SLG/Ov334sqobLj0Akzm/Hmm0VMdK5cWQi3S+fPF9mPjcghAOEgxxcQDua48zdevubwddE7/bZnD0WVK0cjmzQRRfxF0TOljB5N/K05mJDFdytX0ry+fT0PLzYf7P1DlWs7D5ZnvDtVqlRk3B/Hxgr+afPnF9lv10bYhMPWJUvEwHgqy1/K3LdPlHMYwzMF63jPY0LNB2tjx/Lloo/8B8IfVsqHfvghoLlZPXpQx4oVKWPbNr0O37Tfe/JJert69YAK21s4hMrOn3DI/v13MS3I3wqC+QPVO4yMrQQgHGzFrRuDcNBRhJzh6+fr11wjZg0CHdz/gQfENdN7TdvlCxeo9z33EF9bg0mrPvxQzGoEU5frOO3azmPyJxz4vsn3JH9fVINlZaZe2ITDwiFDxMC+GDPGb//4hsoD73zTTUXKg3V8kYNC3AjFxrGMDPp6zhwhGri/HMO7eO6cX4v5ubkU//zzYkwH09OJ/0hGNG5MXW6+WSwK8nsQEXkLh1DZacJhanQ0HT90SKwT4T/OdldfTVs+/zyQWeyPAAIQDnKcAOFQeu58beNr4cB69QI2ktC8uajjOSORNm+e+ALF4etg14mFSzhwx1W4tnM/WTjwF1IO9fDszaS2bQVb/i0rhU048LdvPrl4UYy/xAtZeDqL6+RdvqxXCfamnpOdTTylH+jDN/FAKVgbnsdzezNiYkR/N86Y4VlUJH85J0fMMHCM772nnhIOP7B9e5E63hvewiFUdppw4Lghh4eYafwLL0hdPOM9Rmz7JwDh4J9LuPdCOJSeMK9Z4GvMsAYNAjbC68K4zjfJyXqdLYsW0edDh4ppdy4LNOvA6ye06zqHtHvXrq1v8343XdsZHgsHXlTap04dwZSv87vWr9e5ysiETTiwUuSTY9G77/odF6snLucVtp4p2Js6Lxbh4wN9+OmMQClYG97HXzx7ltqVL08zu3f3LiqyzQtWtCcleHFlSclbOITKThMOvMaBQyntr71WzHrwmgukyCYA4SDHPxAOped+8sgRcd3lWc1AaXijRqLOvs2bfarwWi6+bnOc3l/ib9eBruu8303XduajhSpYMPETh8zgq08+8YfOtn1hEw4/rFsnBsiP6fhL+//zH1HOj2d6pmBv6qy4vv/yy4Cf4labBmvDs19anh93TB44UNv0+c0zKRwyYIHBj53yGgNeb1Bc8hYOobLzFA5sZ21iomDLJ1lx6ry4PqHMHgIQDvZw9rYC4eBNJPhtvsbxYj3+cuRv4TuX85ouXgfBMwT+Ej9C7vkUmGcdvv5p13ae5eXHOLVt/u2maztz0YQD5zm8wsKK2XPISFYKm3Dgk2dw/fri5PFeIMOD5ZsrKyc+STyTmZu6ZzvF5Utrg8MjvG6AT95AaU6vXmJaiR/fzPrlF/GMMz9/G+gPiNvxFg6hsuNQCLP0fKpi3EsviX0lzY4EGgf220MAwsEezt5WIBy8iYS2vSwhQVxfNiQl+RzIT0Lw9Yiva/4SX9/43Q/BvMshnGscPPsWqdd27iM/geL5VAU/ScF8+Yk/ngWXkcImHHgwfEPjlxGNadaMPB8J5FgXfyP/bMAAnzGf+eMPAYVvwOFKxdnghZC8NoHjbByPW/XRR3rfV3zwgXiBVaB+aX9Mno+YZu7dS7zegZ/I8FzL4dlG3LPPijFvS0nRd4fC7tulS8XxvK5BSyxUWPXzCeYZZ9TK8TsyCEA4yPEDhIM57vxiOQ5HcKiZX1SnJX6DJN/Q+EujNjPwzYIF4sV2vKicRcOmmTPFlLv3CwC1Njx/hyocnHZtZ0Y8c8NrHPiepCXti/eHr75a4msCtGOs/B1W4cAd5dkGftKg2//8j3ijGD9GwieW581VG9Ce1FSxqI9vdrzIb/GIEUVeHKXVM/O7JBufvPWWcNLRn3+mCa+9Jm68/FQEv0iJ3+fAT0oESjtXr6aV48f7FHNMj4/lPxrPxC9LmfL66+LE0BTkmsmT9SrBsONHNzkcwsfz2obEN97Q38j509dfiwWoLNL4BSxIkUcAwkGOTyAczHPnx7yXjBwpps75DYd8E+MvKzzT4PnkmfaUGK9LG/r44zS7Z8+gvymHIhycdm3nJ+TimjYV13a+vvO9k8PSnJgv30d5/8inny4xHG7e20VbCLtw0Mzx7AIPkj88jR+pif8YtNel8o2eFxie/O03n5u+nf1XhZ2dTJxiC8JBjichHKzjzjdsfoshX9s5ROrvRXkcCuCPzIRru3X0bRMO3OVNn34qFnXwN2BWoZ6q1LohObMlsHOmXyEc5PgVwsFa7vzPq/ixSRYPo597TvzDK2stOLc1Fa/ttgoHdj3HoHjV//hXXhFxf/7HKHhsMLg/CrALjpNKtSAc5HgLwsF67vwE144VK2h6p07iHQ+8ts1z3Zb1Fp3TomrXdtuFg3NcjZGAgHkCEA7mGZamBQiH0lDDMSDwJwEIB5wJICCRAISDHPgQDnK4w6ozCEA4OMOPGIWiBCAc5DgOwkEOd1h1BgEIB2f4EaNQlACEgxzHQTjI4Q6rziAA4eAMP2IUihKAcJDjOAgHOdxh1RkEIByc4UeMQlECEA5yHAfhIIc7rDqDAISDhX7kF58UePyLcAubRlMOJQDhIMexEA5yuKtsNf/SJZW7b2nfIRwsxJm5bRvNfOxJC1tEU04nAOEgx8MQDnK4q2z1/RtupLycHJWHYFnfIRwsQ0kE4WAhTJc0BeEgx9EQDnK4q2wVwsHwHoSDwcJ0DsLBNELXNQDhIMflEA5yuKtsFcLB8B6Eg8HCdA7CwTRC1zUA4SDH5RAOcrirbBXCwfAehIPBwnQOwsE0Qtc1AOEgx+UQDnK4q2wVwsHwHoSDwcJ0DsLBNELXNQDhIMflEA5yuKtsFcLB8B6Eg8HCdA7CwTRC1zUA4SDH5RAOcrirbBXCwfAehIPBwnQOwsE0Qtc1AOEgx+UQDnK4q2wVwsHwHoSDwcJ0DsLBNELXNQDhIMflEA5yuKtsFcLB8B6Eg8HCdI6Fw6f1H6NL2dn4gEFQ58DmuHjaOGCg6XPP7gYWtmhFcWXK65+M1Wvs7oIpewubt6Kf/rU4KB/h7xnXMz4HIByMPzkIB4OF6RwLh3E330bjKlfBBwyCOgcS/lKR1vfua/rcs7sB1YXDnAaNaWyFSkH5CH/PuJ7xORB/9XV4c+R/LzQQDhZecRGqsBCmS5pCqEKOoxGqkMNdZauYcTC8B+FgsDCdg3AwjdB1DUA4yHE5hIMc7ipbhXAwvAfhYLAwnYNwMI3QdQ1AOMhxOYSDHO4qW4VwMLwH4WCwMJ2DcDCN0HUNQDjIcTmEgxzuKluFcDC8B+FgsDCdg3AwjdB1DUA4yHE5hIMc7ipbhXAwvAfhYLAwnYNwMI3QdQ1AOMhxOYSDHO4qW4VwMLwH4WCwMJ2DcDCN0HUNQDjIcTmEgxzuKluFcDC8B+FgsDCdg3AwjdB1DUA4yHE5hIMc7ipbhXAwvAfhYLAwnYNwMI3QdQ1AOMhxOYSDHO4qW4VwMLwH4WCwMJ07lp5O/EY9JBAIlgCEQ7CkrK0H4WAtTze0lvi32pR34YIbhlriGCEcSkSECiAQPgIQDuFjW1zLEA7F0UEZCBRPAMKheD4oBYGwEoBwCCvegI1DOAREgwIQKJEAhEOJiFABBMJHYGnf/jSvfXT4DISpZdX/yRWEQ5hODDTrCgIQDha6OSMjg4YMGWJhi2jK6QQw4yDHw6Pr3E/rpiTKMQ6rShKIjo6my5cvK9l3qzsN4WAh0VOnTtHGjRstbBFNOZ3A8ncG0oIOnZQbpuozDtOeaEDbPktWjjs6LI9ASkoK5efny+tABFmGcIggZ6Ar7iMws3MMTWn5onIDV104IFSh3CmHDkcQAQgHC52BUIWFMF3SFEIVchyNUIUc7ipbRajC8B6Eg8HCdA6hCtMIXdfAlKi2NPaZZ5Ubt+ozDghVKHfKSe8wQhWGCyAcDBbIgYDtBDaMGEXLu/Ww3a5Zg6oLB4QqzJ4BON7NBCAcLPQ+QhUWwnRJU6mjx9Cqnr2UG63qwgGhCuVOOekdRqjCcAGEg8HCdA6hCtMIXdfAhP99jeIbNVFu3KoLB4QqlDvlpHcYoQrDBRAOBgvkQMB2AlgcaTtyYRChCjncYdUZBCAcLPQjQhUWwnRJU3Nju9L0l19VbrSqzzggVKHcKSe9wwhVGC6AcDBYmM4hVGEaoesaSOnbn+bjldO2+x2hCtuRK28QoQrDhRAOBgvkQMB2AghV2I5cGESoQg53WHUGAQgHC/2IUIWFMF3SVELLF2nYY08oN1qEKpRzGTpskgBCFQZACAeDhekcQhWmEbquAcw4yHE5QhVyuKtsFaEKw3sQDgYL5EDAdgJf9BtAn73R0Xa7Zg2qPuOAUIXZMwDHu5kAhIOF3keowkKYLmlqdkwsTX3xFeVGq7pwwFMVyp1y0juMUIXhAggHg4XpHEIVphG6rgGEKuS4HKEKOdxVtopQheE9CAeDBXIgYDuBSa3bUMLTz9hu16xB1WccEKowewbgeDcTgHCw0PsIVVgI0yVNfTVqNK3o/k/lRqu6cECoQrlTTnqHEaowXADhYLAwnUOowjRC1zXw1ag4WtG9p3LjVl04IFSh3CknvcMIVRgugHAwWCAHArYTmPRaFL3fpKntds0aVF04IFRh9gzA8W4mAOFgofcRqrAQpkuawuJIOY5GqEIOd5WtIlRheA/CwWBhOodQhWmErmtgdsyblNjqZeXGrfqMA0IVyp1y0juMUIXhAggHgwVyIGA7gWUD3qHkaLwAym7wCFXYTRz2nEQAwsFCbyJUYSFMlzSFUIUcRyNUIYe7ylYRqjC8B+FgsDCdQ6jCNELXNYB/ciXH5QhVyOGuslWEKgzvQTgYLJADAdsJYMbBduTCIEIVcrjDqjMIQDhY6EeEKiyE6ZKmlvTpR1MffYKOfve9Up85DRpTXJny+mfb5ClK9X90ldtp3ZREl5xlGKYVBBCqMChCOBgsTOcQqjCN0HUNrJw7j8ZUuIk+rf+YUp+kug/StDp19c/H9eor1f8P76xJ6StWuu58w4BLTwChCoMdhIPBAjkQAAEQAAEQAIESCEA4lAAolGKEKkKhhbpMICkpiRISEgDDZgLMfOfOnTZbhTmVCSBUYXgPwsFgYTqHUIVphK5r4OzZs3T69GnXjVv2gDdv3kxHjx6V3Q3YV4gAQhWGsyAcDBbIgYDtBLKysigzM9N2uzAIAiAAAqUlAOFQWnJ+jkOowg8U7CqWwNy5c2ny5MnF1kGh9QQQqrCeqdNbRKjC8DCEg8HCdA6hCtMI0QAI2EIAoQpbMDvKCEIVhjshHAwWyIGA7QRWrFhBycnJttuFQRAAARAoLQEIh9KS83McQhV+oGBXsQT2799Pu3btKrYOCq0ngFCF9Uyd3iJCFYaHIRwMFqZzCFWYRogGQMAWAghV2ILZUUYQqjDcCeFgsEAOBGwn8MEHH9DAgQNttwuDIAACIFBaAhAOpSXn5ziEKvxAwS4QiEACCFVEoFMivEsIVRgOgnAwWJjOIVRhGqHrGti+fTvxtDmSvQQQqrCXtxOsIVRheBHCwWCBHAjYTiA1NZVWrVplu93SGPxtzx7K+e9bLvNzc2lvWhpdKSgoTVM4BgRAQGECEA4WOg+hCgthoqmgCCwePpzerl6d2pQpQ2NbtQp4zPhXXhF1OleuTMkhrqk4/OOP9N5TT0biGlAAAAXrSURBVInjD6anCxtp8+eL7fQ1awLajOQChCoi2TuR2TeEKgy/QDgYLEznEKowjdB1DSQmJtKoUaNMjfvLCRPETTyqbFnK3LvXp60/fv1VlLO4mNWjh095MDu2LFok2uBZB04Xzpyh/yxcSLkXLwZzeMTVQagi4lwS8R1CqMJwEYSDwQI5ELCdwIULF+j8+fOm7H6TnEwxVaqIG/uMmBiftub17UtDn3hClC8ZOdKnvKQdhYWFtC0lRRzvT5h4H89hDCQQAAHnEoBwsNC3CFVYCNMlTfE/uDp06JCp0bJwmNu7N715663U/tpr6fSxY3p7l86fp67VqtGmmTN9hEPe5cs0s3t36nf//RRbtaoIYRTk54tjWSx8PnQojWjcWHzeeeihP4XDvn1itmFDUhINffxx4pkITnzc/H79aHrnzjSwXj0a+fTTdO7kSVG2dckSer9FC5oUFUUpo0eL/nS/8046sH27KJfxA6EKGdTVtolQheE/CAeDhekcQhWmEbquAX7d9LRp00yNm4XDwsGDidc7cDhi0bvv6u2tTUykT99+m7Z98YWPcJjUti2t+ugjscAxJT5elPM2p38NG0YsFlhccEpo3lyU/75/P3HoY8moUWKbwxWc1k+fLrYv5+QQi46ON94o2uayfZs3E6+t4FDK0rg42rF8udjmNmUlhCpkkVfXLkIVhu8gHAwWyIGAkgQ04XAmK0vMOPBNWruB965dW6x78BYOmfv2iRv9inHjaP20abTs/ffFNs8U5GRnU/QNN9DiESN0HqmzZolyFg6ceMEkixRNOOzetIkmtG5NhVeuEIcqetx9N8148039eJ6FGN6okb79wYsvUq9atfRtZEAABNQhAOFgoa8QqrAQpkua4m8xc+bMMTVaTThwI9M7dRI39DWTJ9P3X35J8S+8INr2Fg58TNurrhIzEduXLSPtwwLgp6+/Fm2kr12r90tb43D055/FviO7dxcRDrzz4tmzYiaCn+7gGYcp7dvrxw96+GER8tB2TO3QgThcISshVCGLvLp2EaowfAfhYLAwnUOowjRC1zXAYnOvnychQgHhKRwO79olbuj8jX/UM8/QztWrRVPewkELLXiuh9BsfrNggWhj06efarv0xZGBhAO3061GDT080efeeyNaOCBUobsWmSAJIFRhgIJwMFggBwK2E+D1APwxk/49dy7N799fbyLu2WfFjb9nzZp6298uXSr2aU9V7Fq/XmzzYkXPxIslf9ywQZRNjY7Wi0qacfjwH/8gtqelPnXqRLRw0PqJ3yAAAqETgHAInVnAIxCqCIgGBQEIjB8/ngYNGhSgNLjdvLCRb9xa4hAFrz/ghZFa4kWPvE97XLMgL0+ECtpdfTV9MWYM8YucOHywfOxYsViSb/xv/OUvxAKD10tMbtdOHL964kTx7oaf/v3vIjZ4bURUuXLE+1nIsC3ep62J4PUM7z72mNYdGtuypXiEVN9hcwahCpuBO8AcQhWGEyEcDBamcwhVmEaIBkIkwE9M8A2+Xfny4nFHfgU0z2DwTZofxeS0cvx4euv228XNvGPFiuKxSd6fsW2bvp9v9CwcWFBw4sWP//zb38QxnSpVorimTSn2tttobp8+QkwMb9hQlPW97z7ak5pK/Mglr2vgujz7MbNbN9GnBYMGifAFt8995Mc4ORTSoUIFcbznAkxh2KYfCFXYBNpBZhCqMJwJ4WCwQA4EbCewdetW2rRpk+12NYMsMo5lZOj/g0Lbr/3O/v138Y4GnnXQ3vGglXn/5rdIasKDyy5fuOBdBdsgAAIOIADhYKETEaqwEKZLmuJvvuvWrXPJaCNnmAhVRI4vVOkJQhWGpyAcDBamcwhVmEaIBkDAFgIIVdiC2VFGEKow3AnhYLBADgRsJzBx4kQaNmyY7XZhEARAAARKSwDCobTk/ByHUIUfKNhVLIHc3Fy6/N/XOhdbEYWWEkCowlKcrmgMoQrDzRAOBgvTOYQqTCN0XQP8D65YcCLZSwChCnt5O8EaQhWGF/8fDy5ZGIBY3HoAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWjCzKEoRznr"
      },
      "source": [
        "# Create some data\n",
        "import numpy as np\n",
        "spread = np.random.rand(50) * 100\n",
        "center = np.ones(25) * 50\n",
        "flier_high = np.random.rand(10) * 100 + 100\n",
        "flier_low = np.random.rand(10) * -100\n",
        "data = np.concatenate((spread, center, flier_high, flier_low))\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdAzyl1LSRXG"
      },
      "source": [
        "# Basic boxplot\n",
        "from matplotlib import pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data)\n",
        "ax.set_title('Basic Box Plot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic boxplot\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data,1, '')\n",
        "ax.set_title('Basic Box Plot');"
      ],
      "metadata": {
        "id": "3G60pvn4NgMZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeo9kyqSzUXI"
      },
      "source": [
        "# Notched boxplot\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data, 1)\n",
        "ax.set_title('Notched Box Plot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FUfvW1ETMhc"
      },
      "source": [
        "# Notched boxplot without outliers\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data, 1, '')\n",
        "ax.set_title('Notched Box Plot without Outliers');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xKoY0_yUOmq"
      },
      "source": [
        "# Notched boxplot with outliers\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data, 0, 'r^')\n",
        "ax.set_title('Boxplot with different symbol for outliers');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9BLfT54Umey"
      },
      "source": [
        "# Horizontal box plot\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data, 1, 'bs', 0)\n",
        "ax.set_title('Horizontal box plot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8Tm0BolUvYj"
      },
      "source": [
        "collectn_1 = np.random.normal(100, 10, 200)\n",
        "collectn_2 = np.random.normal(80, 30, 200)\n",
        "collectn_3 = np.random.normal(90, 20, 200)\n",
        "collectn_4 = np.random.normal(100, 10, 200)\n",
        "collectn_5 = np.random.normal(50, 50, 200)\n",
        "\n",
        "## combine these different collections into a list\n",
        "data_to_plot = [collectn_1, collectn_2, collectn_3, collectn_4, collectn_5]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_to_plot"
      ],
      "metadata": {
        "id": "4uTD_qYR_l1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E0QKaMkVFK3"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data_to_plot);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Even=[2,4,6,8,10]\n",
        "Odd=[1,3,5,7,9]\n",
        "primenum=[2,3,5,7,11,13,17,19,23,31,37]\n",
        "square=[4,9,16,25,36]\n",
        "cube=[1,8,27,64]\n",
        "\n",
        "## combine these different collections into a list\n",
        "dataEO = [Even, Odd, primenum, square, cube]\n",
        "\n"
      ],
      "metadata": {
        "id": "VHHmoG8YQzQZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataEO"
      ],
      "metadata": {
        "id": "PXyk1CLvRdRY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(dataEO);"
      ],
      "metadata": {
        "id": "tL_HD5iARlAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Violin plot**"
      ],
      "metadata": {
        "id": "LRmNFvXeDNg7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ndpe2Ne8VqMH"
      },
      "source": [
        "\n",
        "Violin plots are similar to box plots, except that they also show the probability density of the data at different values. These plots include a marker for the median of the data and a box indicating the interquartile range, as in the standard box plots. Overlaid on this box plot is a kernel density estimation. Like box plots, violin plots are used to represent comparison of a variable distribution (or sample distribution) across different \"categories\"."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Even=[2,4,6,8,10]\n",
        "Odd=[1,3,5,7,9]\n",
        "primenum=[2,3,5,7,11,13,17,19,23,31,37]\n",
        "square=[4,9,16,25,36]\n",
        "cube=[1,8,27,64]\n",
        "\n",
        "## combine these different collections into a list\n",
        "dataEO = [Even, Odd, primenum, square, cube]"
      ],
      "metadata": {
        "id": "_i8X-cZ2EN-5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataEO"
      ],
      "metadata": {
        "id": "-Q9WeqDBIG-V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFbC55rTVXlR"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.violinplot(dataEO)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.violinplot(dataEO,vert=False);"
      ],
      "metadata": {
        "id": "vPZpRNbCymGN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(nrows=1, ncols=5)\n",
        "\n",
        "# Plot violin plot on axes 1\n",
        "ax1.violinplot(Even)\n",
        "ax1.set_title('Even')\n",
        "\n",
        "# Plot violin plot on axes 2\n",
        "ax2.violinplot(Odd)\n",
        "ax2.set_title('Odd')\n",
        "\n",
        "# Plot violin plot on axes 3\n",
        "ax3.violinplot( primenum)\n",
        "ax3.set_title('primenum')\n",
        "\n",
        "# Plot violin plot on axes 4\n",
        "ax4.violinplot( square)\n",
        "ax4.set_title('square')\n",
        "\n",
        "# Plot violin plot on axes 5\n",
        "ax5.violinplot( cube)\n",
        "ax5.set_title('cube')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZZiC1WMA1W35"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqgMK4u7zg2y"
      },
      "source": [
        "###Histograms"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A histogram is an accurate representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable. It is a kind of bar graph.\n",
        "\n",
        "To construct a histogram, follow these steps −\n",
        "\n",
        "\n",
        "*  Bin the range of values.\n",
        "*   Count how many values fall into each interval.\n",
        "*  Divide the entire range of values into a series of intervals.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XXYpS2f_4yKN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following example plots a histogram of marks obtained by students in a class. Four bins, 0-25, 26-50, 51-75, and 76-100 are defined. The Histogram shows number of students falling in this range."
      ],
      "metadata": {
        "id": "51FKw_We327s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1,1)\n",
        "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])\n",
        "ax.hist(a, bins = [0,25,50,75,100])\n",
        "ax.set_title(\"histogram of result\")\n",
        "ax.set_xticks([0,25,50,75,100])\n",
        "ax.set_xlabel('marks')\n",
        "ax.set_ylabel('no. of students')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "aOa6v57j3gpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig,ax = plt.subplots(1,1)\n",
        "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])\n",
        "ax.hist(a, bins = [0,20,40,60,80,100])\n",
        "ax.set_title(\"histogram of result\")\n",
        "ax.set_xticks([0,20,40,60,80,100])\n",
        "ax.set_xlabel('marks')\n",
        "ax.set_ylabel('no. of students')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3UGA_0yJ4e1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.random.randn(1000)\n"
      ],
      "metadata": {
        "id": "5O_vCtTz_-_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "uZ1yA7Pu__I6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.hist(data);"
      ],
      "metadata": {
        "id": "tHRjvoRDALxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "o-1GqhU9AtBv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "An0gAVRuXkD2"
      },
      "source": [
        "The hist() function has many options to tune both the calculation and the display."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3wVdVbIXmxA"
      },
      "source": [
        "plt.hist(data, bins=90,\n",
        "         alpha=1,\n",
        "         histtype='stepfilled',\n",
        "         color='green',\n",
        "         edgecolor='red');\n",
        "\n",
        "         #'bar', 'barstacked', 'step', 'stepfilled'\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eugk5tezYQ3y"
      },
      "source": [
        "Comparing histograms of several distributions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9K8JvsiYQLP"
      },
      "source": [
        "x1 = np.random.normal(0, 0.8, 1000)\n",
        "x2 = np.random.normal(-2, 1, 1000)\n",
        "x3 = np.random.normal(3, 2, 1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftE7QmSwYqrr"
      },
      "source": [
        "kwargs = dict(histtype='stepfilled',\n",
        "              alpha=0.3,\n",
        "              density=True,\n",
        "              bins=40)\n",
        "\n",
        "plt.hist(x1, **kwargs)\n",
        "plt.hist(x2, **kwargs)\n",
        "plt.hist(x3, **kwargs);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pie Chart**"
      ],
      "metadata": {
        "id": "6ZmbAnQr7YIR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A Pie Chart can only display one series of data. Pie charts show the size of items (called wedge) in one data series, proportional to the sum of the items. The data points in a pie chart are shown as a percentage of the whole pie.\n",
        "\n",
        "Matplotlib API has a pie() function that generates a pie diagram representing data in an array. The fractional area of each wedge is given by x/sum(x). If sum(x)< 1, then the values of x give the fractional area directly and the array will not be normalized. Theresulting pie will have an empty wedge of size 1 - sum(x).\n",
        "\n",
        "The pie chart looks best if the figure and axes are square, or the Axes aspect is equal."
      ],
      "metadata": {
        "id": "DxeeQtFd7YdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.axis('equal')\n",
        "langs = ['C', 'C++', 'Java', 'Python', 'PHP']\n",
        "students = [23,17,35,29,12]\n",
        "ax.pie(students, labels = langs,autopct='%1.2f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Wz52ECFA5u2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#alternative way of writing pie chart program\n",
        "\n",
        "langs = ['C', 'C++', 'Java', 'Python', 'PHP']\n",
        "students = [23,17,35,29,12]\n",
        "plt.pie(students, labels = langs,autopct='%1.2f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "EKvrfGC8JDG4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels='Apple','Bannana','Mango','lemen'\n",
        "Sizes= [6, 10, 15, 9]\n",
        "fig,ax=plt.subplots()\n",
        "fig,ax1=plt.subplots()\n",
        "ax.pie (Sizes, labels=labels, autopct='%.1f %%')\n",
        "ax1.pie(students, labels = langs,autopct='%1.2f%%')\n",
        "plt.show ()"
      ],
      "metadata": {
        "id": "fsg5zQtf8XsM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTsaP62ca_9y"
      },
      "source": [
        "## Visualizing some datasets with seaborn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IEfdsc_bqkg"
      },
      "source": [
        "####Iris dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49nifogZbHdX"
      },
      "source": [
        "import sklearn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn import datasets\n",
        "\n",
        "# Load the iris dataset\n",
        "#iris = datasets.load_iris()\n",
        "iris=pd.read_csv(\"/content/Iris.csv\")\n",
        "\n",
        "# Load it to a pandas dataframe:\n",
        "iris_df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
        "                     columns= iris['feature_names'] + ['label'])\n",
        "\n",
        "iris_df['label_names'] = iris_df['label'].apply(lambda x: iris.target_names[int(x)])\n",
        "\n",
        "# Let's have a look at how the data looks like\n",
        "display(iris_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from array import array\n",
        "data = load_iris()\n",
        "data.target[[10, 25, 50]]\n",
        "a = np.array([0, 0, 1])  #array([0, 0, 1])\n",
        "list(data.target_names)\n",
        "['setosa', 'versicolor', 'virginica']"
      ],
      "metadata": {
        "id": "kwz_ZpCrF_Mg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eASPNiL9bU-r"
      },
      "source": [
        "# Visualize the data using Matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "colours = ['orange', 'blue', 'green']\n",
        "species = iris.target_names\n",
        "\n",
        "f = plt.figure(figsize=(15,10))\n",
        "\n",
        "for i in range(0, 3):\n",
        "    species_df = iris_df[iris_df['label'] == i]\n",
        "    plt.scatter(\n",
        "        species_df['petal length (cm)'],\n",
        "        species_df['sepal length (cm)'],\n",
        "        color=colours[i],\n",
        "        alpha=0.5,\n",
        "        label=species[i]\n",
        "    )\n",
        "plt.xlabel('petal length (cm)')\n",
        "plt.ylabel('sepal length (cm)')\n",
        "plt.title('Iris dataset: petal length vs sepal length')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnhOPF2Lbkf4"
      },
      "source": [
        "# Visualize the data using Seaborn\n",
        "# Matrix scatter plot.\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.pairplot(iris_df[iris.feature_names + ['label_names']], hue=\"label_names\",height=3);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmC_nmM7b-No"
      },
      "source": [
        "#### Cars dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayuGSz07cAf4"
      },
      "source": [
        "from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "\n",
        "# Loaad the csv file into a pandas dataframe\n",
        "\n",
        "cars_df = pd.read_csv('/content/drive/My Drive/data/cars.csv', index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpKWVdPzcGSg"
      },
      "source": [
        "# Let's explore some rows.\n",
        "cars_df.head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4hBssXicuYs"
      },
      "source": [
        "Let's look at the distribution of cars by the state."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwFPpqWhcwOs"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,10))\n",
        "\n",
        "graph = sns.countplot(x='state',\n",
        "              data=cars_df,\n",
        "              ax=ax,\n",
        "              order=cars_df.state.value_counts().index)\n",
        "graph.set_xticklabels(graph.get_xticklabels(),rotation=90)\n",
        "\n",
        "for p in graph.patches:\n",
        "    height = p.get_height()\n",
        "    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha=\"center\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU0e5Fw0eggb"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,8))\n",
        "sns.violinplot(x ='title_status', y='price', data=cars_df)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCDgYFuneo2-",
        "collapsed": true
      },
      "source": [
        "cars_df.loc[cars_df.groupby('color')['color'].transform('size') < 40, 'color'] = 'other'\n",
        "palette = ['white', 'gray', 'darkgray', 'silver', 'indianred',  'khaki', 'royalblue','orange']\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(14,8))\n",
        "sns.boxplot(x ='price', y='color', data=cars_df, order = cars_df.color.value_counts().index, palette=palette)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}