Data_visualization_in_python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data_visualization_in_python.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/beingshivam/Data_Analysis/blob/master/Data_visualization_in_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCSRFDufKuoZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9c5eeab7-27fd-46d2-d42c-83bed544b614"
      },
      "source": [
        "\n",
        "\"\"\"\n",
        "Visualization in python\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nVisualization in python\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7znT8k2K2La",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "718d5577-1522-4ec2-dd16-5d43e2e09c61"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# line chart\n",
        "\n",
        "'''\n",
        "A line chart or line graph is a type of chart\n",
        "which displays information as a series of data points called ‘markers’ \n",
        "connected by straight line segments.\n",
        "\n",
        "'''\n",
        "\n",
        "x = [1,2,3,4,5,6]\n",
        "y = [10,12,30,21,45,50]\n",
        "\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f63ccac98d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hx6mNOyv5+SurGJHbmS8N7xV0HBFpAir0OPSxcXJXa5ycSKJQocehR+et470N\nu7jvylPornFyIglDhR5nVm/XODmRRKVCjyPVtQ3j5NI1Tk4kIelM0Tjy0OwCVmzdyx9vOEPj5EQS\nkFbocWLppt384Y21fG5oDy4edFLQcUQkAOGMoHvMzErMbPlhj/3EzLaY2ZLQ16XRjSmf5uA4ua7p\nrblP4+REElY4K/QngIuP8Pj97j4k9PXPyMaSY3FwnNyvrz5N4+REElijhe7ubwFlTZBFjsM7a3fw\n2Nvr+fLZGicnkuhOZB/6rWa2LLRLpuPRNjKzCWa2yMwWlZaWnsDbyX8qr6rhe88tI6dzW+65ROPk\nRBLd8Rb6w0BfYAiwDZh0tA3dfYq757t7fkaGVpCR9D8zVrJtz34mXTNE4+RE5PgK3d2L3b3O3euB\nPwPDIhtLGvP6ymKeXbQ5NE7uqD8giUgCOa5CN7Nuh929Clh+tG0l8soqq7nnRY2TE5GPa/TndDN7\nGhgFdDGzzcB9wCgzGwI4sAG4OYoZ5TDuzg//8aHGyYnIJzRa6O5+3REefjQKWSQMLy3dyj8/3M73\nPtNf4+RE5GN0pmgzsn1PFT/6x3KNkxORI1KhNxPuzt0vLKO6rp7J1wzRODkR+QS1QjNx+Di53l1S\ng44jIjFIhd4MaJyciIRDhR7j6uqdu55dSlILjZMTkU+n0wtj3CNz17Fo4y4mjR+scXIi8qm0Qo9h\nq7eXM+m1NVw0MJPPaZyciDRChR6jDh8n9wuNkxORMGiXS4yoqatn4859FJZUUFhSzoL1ZRonJyLH\nRIXexKpq6li/o5KCkgoKi8spLK2goLiCDTsrqanzQ9v16NCG74zO1Tg5EQmbCj1KKg7UsrakoqG4\nQ6vuwpIKisr2UR/q7RYG2Z3akts1nQsHZpKbkUZeZhp9M9JIba0/GhE5NmqNE7R7X/Wh0i4orqCw\ntGHlvXVP1aFtkpOM3l1SOaV7e64c0oO8rmnkdk2jd5dUUpJ1cS0RiQwVehjcndLyAw2lfbC8Qyvu\nHRXVh7ZLSW5Bbtc0hvXuRF5mOn1DK+7sTm1J1qn6IhJlKvTD1Nc7W/fsp6CkomF3SWjFXVBczt6q\n2kPbpae0JLdrGqMHdCW3axp5XdPJ7ZpGjw5tdOKPiAQmIQu9tq6eorJ9h1bch+/r3l9Td2i7zqmt\nyO2axhWDu5PXNY28zIbi7preWocRikjMCWfAxWPA5UCJuw8KPdYJeAbIoWHAxTXuvit6MY/Pgdo6\nNuzYd2j3SMORJRWs31FJdV39oe26tU8ht2sa1w7reWi1nds1jU6prQJMLyJybMJZoT8BPAQ8ddhj\n9wCz3P2XZnZP6P7dkY8Xnn0Blk+LAAAEa0lEQVTVtawtqaSwtJyC4v9bdW8s20dd6JASO3hESUYa\nowZkhI4oSadvRirpKclBRRcRiZhwJha9ZWY5//HwZ2kYSwfwJPAGTVDoe/bXHDoE8P/2b1ewZff+\nQ9u0bGHkdEml/0npXHZat0P7uPtk6IgSEYlvx7sPPdPdt4VubwcyI5TniB6cVcBf52+kpPzAocda\nt2xB34w0zujVkWvP7NlQ3Jlp9OqcqiNKRCQhnfCHou7uZuZHe97MJgATALKzs4/rPTLbtWZkv4xD\nx2/ndU2nR8c2JOmIEhGRQ4630IvNrJu7bzOzbkDJ0TZ09ynAFID8/PyjFv+n+cKZ2XzhzOP7x0BE\nJFEc776Jl4AbQ7dvBKZHJo6IiByvRgvdzJ4G3gX6m9lmM7sJ+CUw1swKgAtD90VEJEDhHOVy3VGe\nGhPhLCIicgJ0OIiISJxQoYuIxAkVuohInFChi4jECRW6iEicMPfjOtfn+N7MrBTYeJwv7wLsiGCc\n5kDfc2LQ95wYTuR77uXuGY1t1KSFfiLMbJG75wedoynpe04M+p4TQ1N8z9rlIiISJ1ToIiJxojkV\n+pSgAwRA33Ni0PecGKL+PTebfegiIvLpmtMKXUREPkXMF7qZPWZmJWa2POgsTcXMeprZHDNbaWYr\nzGxi0JmizcxSzGyhmS0Nfc8/DTpTUzCzJDP7wMxmBJ2lKZjZBjP70MyWmNmioPM0BTPrYGbPm9lH\nZrbKzM6O2nvF+i4XMxsJVABPufugoPM0hdDQkG7u/r6ZpQOLgXHuvjLgaFFjZgakunuFmSUD84CJ\n7j4/4GhRZWZ3AvlAO3e/POg80WZmG4B8d0+YY9DN7Elgrrs/YmatgLbuvjsa7xXzK3R3fwsoCzpH\nU3L3be7+fuh2ObAK6BFsqujyBhWhu8mhr9hebZwgM8sCLgMeCTqLRIeZtQdGAo8CuHt1tMocmkGh\nJzozywGGAguCTRJ9od0PS2gYaTjT3eP9e/4d8H2gPuggTciB18xscWjecLzrDZQCj4d2rT1iZqnR\nejMVegwzszTgBeB2d98bdJ5oc/c6dx8CZAHDzCxud7GZ2eVAibsvDjpLEzvX3U8HLgG+HdqlGs9a\nAqcDD7v7UKASuCdab6ZCj1Gh/cgvAFPd/cWg8zSl0I+kc4CLg84SRSOAK0P7lKcBo83sr8FGij53\n3xL6tQT4OzAs2ERRtxnYfNhPm8/TUPBRoUKPQaEPCB8FVrn75KDzNAUzyzCzDqHbbYCxwEfBpooe\nd7/X3bPcPQe4Fpjt7jcEHCuqzCw19CE/od0OFwFxffSau28HNplZ/9BDY4CoHdzQ6EzRoIWGVI8C\nupjZZuA+d3802FRRNwL4EvBhaJ8ywA/c/Z8BZoq2bsCTZpZEw0LjWXdPiEP5Ekgm8PeG9Qotgb+5\n+7+DjdQkbgOmho5wWQd8NVpvFPOHLYqISHi0y0VEJE6o0EVE4oQKXUQkTqjQRUTihApdRCROqNBF\nROKECl1EJE6o0EVE4sT/BzILelo/7+RoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JHk1q3uK_z4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "4ab7f5d0-b1a7-43e2-d8a6-f3a77d52b74a"
      },
      "source": [
        "# sin graph using pyplot\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import sin\n",
        "\n",
        "x = [x*0.1 for x in range(100)]\n",
        "y = sin(x)\n",
        "plt.xlabel('Value of x')\n",
        "plt.ylabel('Value of y')\n",
        "plt.title('Sin graph')\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f63cf4b5eb8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XdehLkvhf3K2A/SJyj4hM9tK5NwMTRWSsiEQDNwMfGa0lIjOBP+JOJpWdtg8T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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rq2X_xChLI1A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "b5f1dfb6-cf94-4531-a0f2-f88eabfd2b62"
      },
      "source": [
        "#cos graph using pyplot\n",
        "\n",
        "from numpy import cos\n",
        "\n",
        "x = [x*0.1 for x in range(100)]\n",
        "y= cos(x)\n",
        "plt.xlabel('Value of x')\n",
        "plt.ylabel('Value of y')\n",
        "plt.title('Cosine graph')\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f63ccca4b70>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2yqp+/ag9CSVNVZ9V1Xpnew6wsnqA6929C0MyEuzDFGCWbi4lQjwz4xrfiomK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\nKMYYY7zCEooxxhiv+P+oL+ZX15NsVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRg-yeIxLLwS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "28582db7-33a9-418b-dc43-e1399d5e80fa"
      },
      "source": [
        "#real life example- with colored graph\n",
        "\n",
        "c = [1,2,3,4,5,6]\n",
        "number_of_students = [20,30,60,50,70,40]\n",
        "\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('No. of students')\n",
        "plt.title('Class vs No. of students')\n",
        "\n",
        "plt.plot(c,number_of_students, color='g')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f63ccc55400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3EFE7wk5qxqFKniqsfnk13Wt257uN31Hsi2JM3DTxf53JpU2WlgmNJoTUFVeB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\nMcaEuP8DkQcyaplOIFQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbJbC2jfLOjY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "97604e46-70f8-4734-da9a-3c8b759be398"
      },
      "source": [
        "#multi-line graph\n",
        "\n",
        "c = [1,2,3,4,5,6]\n",
        "students = [20,30,60,50,70,40]\n",
        "avg_marks = [34,54,23,47,78,16]\n",
        "teachers = [10,12,10,13,10,14]\n",
        "\n",
        "plt.xlabel('Class')\n",
        "plt.title('Class vs No. of students')\n",
        "plt.plot(c,students, color='g', label='students')\n",
        "plt.plot(c,avg_marks, color='r',label='average marks')\n",
        "plt.plot(c,teachers, color='y', label='no. of teachers')\n",
        "\n",
        "plt.legend(loc='upper left')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f63ccb52b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g9mwwk95iJFEYdGe6fQyOwfxmyYSAABg9Grp2herVoy3y87afaTe/HTUK1eCf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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KL5KpPO3LREb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "669935ab-6ee4-42f0-ec27-a7c33b62b6b5"
      },
      "source": [
        "#Bar chart/graph\n",
        "\n",
        "'''\n",
        "A bar chart or bar graph is a chart or graph that presents categorical data \n",
        "with rectangular bars with heights or lengths proportional to the values that they represent. \n",
        "The bars can be plotted vertically or horizontally.”\n",
        "'''\n",
        "\n",
        "#simple bar chart\n",
        "\n",
        "x = ['delhi','pune','mumbai']\n",
        "y = [20,30,40]\n",
        "\n",
        "plt.bar(x,y)\n",
        "\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L+Rbgr5P8EvA4c587tQz4zlj/dUvMQh/NU4BHu6O2QfZ0z/vw6344HXhdbgF7+cn/To8/\nYP6eecP791WAdVX13okkfHKZ//V9fN7448x9refvG/jp/TNo3f0Ots/fDEwBZ1fV/yXZzhP3/VHP\nUy793QFclOSEJM8Afh34EfDNJG8AyJyzljKknmBlkpd2w28C7gS2M3fEDfCbPV5jPfD6JM8CSHJy\nkl4flqRF2w68CCDJi4DThniNC5Mcn+SZwLnMfUzJzwG7ujJ/JT0/7OpoY6H3VFV3A58E7gE+x9w3\nCcz95r80yT3AVvxM+CPN14G3JdkGnAR8FPgA8HdJZpk7Cj+kqroP+DPgC0k2A7cByycX+Unt08DJ\n3SmVy4BvDPEam4ENwH8Cf1FV/w1cD8x0p2d+F7h/THmPKL5TVM1KMg3cXFUvWOIo0mHhEbokNcIj\ndElqhEfoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqRH/D6qRiSipErGfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTYbFgCqMa1H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "9872bf43-cb92-418d-9f98-581569c92e52"
      },
      "source": [
        "#simple bar graph with random values\n",
        "\n",
        "import random\n",
        "\n",
        "random.seed(123)\n",
        "x = ['Delhi','Pune','Mumbai']\n",
        "y = [random.randint(0,100),random.randint(0,100),random.randint(0,100)]\n",
        "\n",
        "plt.bar(x,y)\n",
        "\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7zyZ51lrWqBU9CDwvyTFJngn8ao99HN/puwr44L4z7iV2AWcAJDkDGGdq69wkRyQ5huEv\njK8w/FS1pwvzV9Pzy64ONZ4h9rfv4/sGhmcRfwN8uNv2Vww/8t3RzcstAuetRZF6et0b+g+A2xhe\nXfG1Hrs5vlNWVfcDf77Mpk8Dv9lNqdzKU6ct+9gB3MLw2xj/sKr+K8k1wD920zML9Bv3Q453ikpS\nI5xykaRGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXi/wFwAFo0mAQpgQAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-0bnE-JOzKb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "8f700715-22bc-40ef-8a99-8e232dad07b6"
      },
      "source": [
        "#bar chart with random values and color, labels\n",
        "\n",
        "import random\n",
        "\n",
        "random.seed(123)\n",
        "x = ['Delhi','Pune','Mumbai']\n",
        "y = [random.randint(0,100),random.randint(0,100),random.randint(0,100)]\n",
        "\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Population')\n",
        "plt.title('City vs Population')\n",
        "\n",
        "plt.bar(x,y, color = 'red')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9d8JHxEvPwk8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "5493649b-8f67-40e7-93a3-690ede21961a"
      },
      "source": [
        "#stacked 100% bar chart with sub components\n",
        "\n",
        "'''\n",
        "when you have to show components of components\n",
        "'''\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "x = [\"a\",\"b\",\"c\",\"d\"]\n",
        "y1 = np.array([3,8,6,4])\n",
        "y2 = np.array([10,2,4,3])\n",
        "y3 = np.array([5,6,2,5])\n",
        "\n",
        "sum1 = y1+y2+y3\n",
        "\n",
        "#normalization\n",
        "\n",
        "y1=y1/sum1*100\n",
        "y2=y2/sum1*100\n",
        "y3=y3/sum1*100\n",
        "plt.figure(figsize=(4,3))\n",
        "\n",
        "#stack bars\n",
        "\n",
        "plt.bar(x,y1,label='y1')\n",
        "plt.bar(x,y2,bottom=y1,label='y2')\n",
        "plt.bar(x,y3,bottom=y1+y2,label='y3')\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 4 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAADFCAYAAACsArwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 288x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6BupRyVRhec",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "f82c3270-bb9e-4b40-efd7-c76a266229df"
      },
      "source": [
        "#Histograms\n",
        "\n",
        "'''\n",
        "Histograms are density estimates. A density estimate gives a good impression of the distribution of the data. \n",
        "The idea is to locally represent the data density by counting the number of \n",
        "observations in a sequence of consecutive intervals (bins).\n",
        "'''\n",
        "\n",
        "x = [1,2,6,5,3,7,8,4,56,7,8,98,67,76]\n",
        "\n",
        "plt.hist(x, bins=3, color ='green')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([10.,  1.,  3.]),\n",
              " array([ 1.        , 33.33333333, 65.66666667, 98.        ]),\n",
              " <a list of 3 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOI0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdQKOVvdSRSM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "144313d7-deb5-4020-dcd1-9db31d5d2406"
      },
      "source": [
        "#histogram with random values\n",
        "\n",
        "import random\n",
        "\n",
        "x = random.sample(range(10,100),10)\n",
        "print(x)\n",
        "\n",
        "plt.hist(x,bins = 4, color='purple')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[62, 44, 23, 14, 58, 78, 81, 52, 53, 16]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([3., 1., 4., 2.]),\n",
              " array([14.  , 30.75, 47.5 , 64.25, 81.  ]),\n",
              " <a list of 4 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLhR5vv8Sldh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "3e40cfb9-c879-4ac1-aa2c-26fdc23a6b57"
      },
      "source": [
        "#histogram with more than 1 data point - parallel\n",
        "\n",
        "my_rand = random.sample(range(1,100),20)\n",
        "my_rand2 = random.sample(range(1,100),20)\n",
        "\n",
        "plt.hist([my_rand,my_rand2], bins=4, color = ['green','red'])\n",
        "legend=['Rand1','Rand2']\n",
        "plt.legend(legend)\n",
        "plt.xlabel('Bins')\n",
        "plt.ylabel('Random numbers')\n",
        "plt.title(\"Random Variable Distribution\")\n",
        "\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Random Variable Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxkUj96sUHSbEmPSlos6YCU\n9ZmZWfdSP2z9CuDOiDhR0ruALRPXZ2Zm3UiW+CVtCxwMTAGIiLXA2lT1mZlZMSmbesYALcC1kv4o\n6RpJQ9ovJGmqpLmS5ra0tCQMx/o0qb4vsxJJmfg3ByYC34uICcCrwBfaLxQR0yKiKSKahg0bljAc\nMzODtIm/GWiOiAfz8dlkHwRmZlZHyRJ/RDwPPCtpj3zSJOCRVPWZmVkxPbq4K2kzYKuIWFVwlXOB\nmfkdPU8BZ/QwPjMzq7Juz/gl/VjSNvmF2YXAI5I+W6TwiFiQt9/vGxHHR8TLGxuwmZltnCJNPXvn\nZ/jHA3eQ3a1zatKozMwsmSKJf6CkgWSJ/7aIWAdE2rDMzCyVIon/+8ASYAhwv6RdgaJt/GZm1sd0\neXE3v5i7PCKGV0x7Bvjb1IGZmVkaXZ7xR8RbwOfaTYuIWJ80KjMzS6ZIU8//SPp3SSMlbdf2Sh6Z\nmZklUeQ+/n/O/55dMS2A3aofjpmZpdZt4o+IMbUIxMzMaqPID7i2lPRFSdPy8fdKOjp9aGZmlkKR\nNv5ryfrR/2A+vgz4/8kiMjOzpIok/t0j4uvAOoCIeA1wB+ZmZpuoIol/raTB5L/WlbQ78EbSqMzM\nLJkid/VcDNwJjJQ0EziQ/HGKZma26SlyV8/dkuYD+5M18ZwXES8mj8zMzJIo2h//h4GDyJp7BgI3\nJ4vIzMySKnI753eBTwIPk/XH/wlJV6cOzMzM0ihyxv8RYK+IaLu4Ox1YlDQqMzNLpkjifwIYBSzN\nx0fm07olaQnwCvAmsD4imnoRo5mZVVGniV/Sz8na9LcGFkv6Qz7+AeAPPajjb30x2Mys7+jqjP8b\nNYvCzMxqptPEHxG/qhyXtE1Xy3dWDHCXpAB+EBHT2i8gaSowFWDUqFE9LN6qRZfW98fYfpanWe10\nm8jzxHwZ8DrwFtm9/EW7ZT4oIpZJ2gG4W9KjEXF/5QL5h8E0gKamJv//m5klVuQM/rPA2N6000fE\nsvzvC5JuBt4P3N/1WmZmllKRvnqeBF7racGShkjaum0YmEz2OwAzM6ujImf8FwC/lfQgFZ2zRcSn\nullvR+BmSW31/Dgi7uxtoGZmVh1FEv8PgHvJfrn7VtGCI+IpYHwv4zIzs0SKJP6BEfGZ5JGYmVlN\nFGnjv0PSVEk7S9qu7ZU8MjMzS6LIGf/J+d8LKqYVvZ3TzMz6mCL98Y+pRSBmZlYbRX7AdVpH0yNi\nRvXDMTOz1Io09fxNxfAgYBIwH3DiNzPbBBVp6jm3clxSA/CTZBGZmVlSRe7qae9VwO3+ZmabqCJt\n/G398kP2QbE38LOUQZmZWTpF2vgr++VfDyyNiOZE8ZiZWWJF2vh/1d0yZma26ei2jV/S30t6XNJK\nSaskvSJpVS2CMzOz6ivS1PN14JiIWJw6GDMzS6/IXT3LnfTNzPqPImf8cyX9FLiFDfvjvylZVGZm\nlkyRxL8N2RO4JldMC8CJ38xsE1Tkrp4zahGImZnVRm9+udsjkgZI+qOk21PXZWZm3Uue+IHzAF8c\nNjPrI5ImfkkjgKOAa1LWY2ZmxRXpq6cBOA0YXbl8RHyqQPmXA58Dtu6i/KnAVIBRo0YVKLKPkupb\nf0T3y5j1Ubq0vv8/cXG5/n+KnPH/gizpPwzMq3h1SdLRwAsR0eWyETEtIpoiomnYsGEFwjEzs41R\n5HbOQRHxmV6UfSBwrKQjyR7gso2kGyLio70oy8zMqqTIGf/1kj4uaWdJ27W9ulspIi6IiBERMRo4\nCbjXSd/MrP6KnPGvBf4DuIi/9ssfwG6pgjIzs3SKJP7zgfdExIu9rSQi7gPu6+36ZmZWPUWaep4g\n67LBzMz6gSJn/K8CCyTNYcNO2orczmlmZn1MkcR/S/4yM7N+oEgnbdMlvQt4Xz7psYhYlzYsMzNL\npcgvdw8BpgNLAAEjJZ0eEfenDc3MzFIo0tTzTWByRDwGIOl9wCxgv5SBmZlZGkXu6hnYlvQBIuIv\nwMB0IZmZWUpFH714DXBDPn4KMDddSGZmllKRxP8vwNlA2+2bvwa+mywiMzNLqshdPW8A38pfZma2\nies08Ut6mL/2zfMOEbFvkojMzCyprs74j87/np3/vT7/+1G6+EAwM7O+rdPEHxFLASQdFhETKmZ9\nXtJ84AupgzMzs+orcjunJB1YMfLBguuZmVkfVOSunrOAH0naluyXuy8DZyaNyszMkilyV888YHye\n+ImIlcmjMjOzZIr01bMF8A9kD1zfXBIAEXFZ0sjMzCyJIk09twIrgXlU9MffHUmDgPuBLfJ6ZkfE\nxb0J0szMqqdI4h8REYf3ouw3gI9ExGpJA4EHJN0REb/vRVlmZlYlRe7O+a2kcT0tODKr89GB+cv3\n/5uZ1VmRM/6DgCmSniY7ixdZXu/2l7uSBpA1Eb0HuDoiHuxgmanAVIBRo0b1IHSzvkOXqq71+4xq\nI6m+7x9R23ewSOI/oreFR8SbQKOkBuBmSWMjYmG7ZaYB0wCampp8/JqZJdZtU09ELM1/xbuG7MSi\n7VVYRLQCc4DeXCswM7Mq6jbxSzpW0uPA08CvyB7BeEeB9YblZ/pIGgwcBjy6UdGamdlGK3Jx98vA\n/sBfImIMMAkocmfOzsAcSX8GHgLujojbex2pmZlVRZE2/nURsULSZpI2i4g5ki7vbqWI+DMwobvl\nzMystook/lZJW5H9GGumpBeAV9OGZWZmqRRp6jkOeA34NHAn8CRwTMqgzMwsnSKdtLWd3b8FTJe0\nGXAyMDNlYGZmlkanZ/yStpF0gaSrJE1W5hzgKeCfaheimZlVU1dn/NeT9b3/O+BjwIVkv9o9PiIW\n1CA2MzNLoKvEv1tEjAOQdA3wHDAqIl6vSWRmZpZEVxd317UN5F0vNDvpm5lt+ro64x8vaVU+LGBw\nPt7WSds2yaMzM7Oq6zTxR8SAWgZiZma1UeQ+fjMz60ec+M3MSsaJ38ysZJz4zcxKxonfzKxknPjN\nzErGid/MrGSc+M3MSiZZ4pc0UtIcSY9IWiTpvFR1mZlZcUWewNVb64HzI2K+pK2BeZLujohHEtZp\nZmbdSHbGHxHPRcT8fPgVYDEwPFV9ZmZWTE3a+CWNJnvw+oMdzJsqaa6kuS0tLbUIx8ys1JIn/vxB\n7TcC/xYRq9rPj4hpEdEUEU3Dhg1LHY6ZWeklTfySBpIl/ZkRcVPKuszMrJiUd/UI+CGwOCK+laoe\nMzPrmZRn/AcCpwIfkbQgfx2ZsD4zMysg2e2cEfEA2dO6zMysD/Evd83MSsaJ38ysZJz4zcxKxonf\nzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSsaJ38ys\nZJz4zcxKxonfzKxknPjNzEom5TN3fyTpBUkLU9VhZmY9l/KM/zrg8ITlm5lZLyRL/BFxP/BSqvLN\nzKx36t7GL2mqpLmS5ra0tNQ7HDOzfq/uiT8ipkVEU0Q0DRs2rN7hmJn1e3VP/GZmVltO/GZmJZPy\nds5ZwO+APSQ1SzorVV1mZlbc5qkKjoiTU5VtZma956YeM7OSceI3MysZJ34zs5Jx4jczKxknfjOz\nknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx\n4jczKxknfjOzkkma+CUdLukxSU9I+kLKuszMrJiUz9wdAFwNHAHsDZwsae9U9ZmZWTEpz/jfDzwR\nEU9FxFrgJ8BxCeszM7MCkj1sHRgOPFsx3gx8oP1CkqYCU/PR1ZIeq5g9FHgxWYRVpHRFF9sHShhB\nDXQTffrjYNPYf53uh7pHX7v9l+RY2MT2X/t9sGtPq0uZ+AuJiGnAtI7mSZobEU01DqlP8T7wPmjj\n/eB9ANXZBymbepYBIyvGR+TTzMysjlIm/oeA90oaI+ldwEnAbQnrMzOzApI19UTEeknnAL8EBgA/\niohFPSymwyagkvE+8D5o4/3gfQBV2AeKiGoEYmZmmwj/ctfMrGSc+M3MSqZPJv4ydvUgaaSkOZIe\nkbRI0nn59O0k3S3p8fzvu+sda2qSBkj6o6Tb8/Exkh7Mj4ef5jcL9GuSGiTNlvSopMWSDijbsSDp\n0/n/wkJJsyQNKsOxIOlHkl6QtLBiWofvvTLfyffHnyVNLFJHn0v8Je7qYT1wfkTsDewPnJ1v9xeA\neyLivcA9+Xh/dx6wuGL8a8C3I+I9wMvAWXWJqrauAO6MiD2B8WT7ozTHgqThwKeApogYS3aDyEmU\n41i4Dji83bTO3vsjgPfmr6nA94pU0OcSPyXt6iEinouI+fnwK2T/6MPJtn16vth04Pj6RFgbkkYA\nRwHX5OMCPgLMzhcpwz7YFjgY+CFARKyNiFZKdiyQ3XU4WNLmwJbAc5TgWIiI+4GX2k3u7L0/DpgR\nmd8DDZJ27q6Ovpj4O+rqYXidYqkLSaOBCcCDwI4R8Vw+63lgxzqFVSuXA58D3srHtwdaI2J9Pl6G\n42EM0AJcmzd5XSNpCCU6FiJiGfAN4BmyhL8SmEf5joU2nb33vcqXfTHxl5qkrYAbgX+LiFWV8yK7\n97bf3n8r6WjghYiYV+9Y6mxzYCLwvYiYALxKu2adEhwL7yY7mx0D7AIM4Z3NH6VUjfe+Lyb+0nb1\nIGkgWdKfGRE35ZOXt311y/++UK/4auBA4FhJS8ia+D5C1tbdkH/dh3IcD81Ac0Q8mI/PJvsgKNOx\ncCjwdES0RMQ64Cay46Nsx0Kbzt77XuXLvpj4S9nVQ96W/UNgcUR8q2LWbcDp+fDpwK21jq1WIuKC\niBgREaPJ3vd7I+IUYA5wYr5Yv94HABHxPPCspD3ySZOARyjRsUDWxLO/pC3z/422fVCqY6FCZ+/9\nbcBp+d09+wMrK5qEOhcRfe4FHAn8BXgSuKje8dRomw8i+/r2Z2BB/jqSrI37HuBx4H+A7eoda432\nxyHA7fnwbsAfgCeA/wK2qHd8Ndj+RmBufjzcAry7bMcCcCnwKLAQuB7YogzHAjCL7LrGOrJvf2d1\n9t6T9Sh9dZ4rHya7C6rbOtxlg5lZyfTFph4zM0vIid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfSk3S\nm5IWSPqTpPmSPphP30XS7O7WN9sU+XZOKzVJqyNiq3z474ALI+LDdQ7LLCmf8Zv91TZkXf0iaXRb\nf+iSpki6SdKdeX/oX8+nD5B0Xd5f/MOSPl3H2M0KS/awdbNNxGBJC4BBwM5k/QN1pJGsx9Q3gMck\nXQnsAAyPrL94JDXUIF6zjeYzfiu7NRHRGNkDTw4HZuR9w7R3T0SsjIjXyfqM2RV4CthN0pWSDgdW\ndbCeWZ/jxG+Wi4jfAUOBYR3MfqNi+E1g84h4mezpWPcBnyR/eIxZX+emHrOcpD3JHvG3guyJT90t\nPxRYGxE3SnoMuCFxiGZV4cRvZdfWxg9ZT4enR8SbHbf2vMNwsqdktX1zviBFgGbV5ts5zcxKxm38\nZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYl838T+HsGPCH6bgAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9JYuPYIoN2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "b6c91eed-67f5-49c4-a855-397f6e92cb21"
      },
      "source": [
        "#Horizontal histogram\n",
        "\n",
        "x = ['Dravid','Sachin','Virat','Gambhir']\n",
        "runs = [200,300,400,500]\n",
        "\n",
        "plt.barh(x,runs, color='orange')\n",
        "plt.xlabel('Runs')\n",
        "plt.ylabel('Player')\n",
        "plt.title('Run scored by cricketer')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Run scored by cricketer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fskR8dkNovja",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "56b17c41-a6b2-41be-8e21-c29db78f5617"
      },
      "source": [
        "#Area chart\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# Data\n",
        "x=range(1,6)\n",
        "y=[ [1,4,6,8,9], [2,2,7,10,12], [2,8,5,10,6] ]\n",
        "\n",
        "# Plot\n",
        "plt.stackplot(x,y, labels=['A','B','C'])\n",
        "plt.legend(loc='upper left')\n",
        "plt.xlabel('value of X')\n",
        "plt.ylabel('value of Y')\n",
        "plt.title('Area chart of X vs Y')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j+FEwgpjubTy+kftW3MfJppNuRwk7sRLLtwZO5K5tS4hva3Y7jjEhpy+3qjRB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\nEBhjTJSzQmCMMVHu/wPpMTz2iCkcWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iC8zpGIFG1jd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "outputId": "f183abe2-ebae-45a9-c67f-cd609ee0d304"
      },
      "source": [
        "#Box & whisker/ Boxplot \n",
        "\n",
        "'''\n",
        "A box and whisker plot, or boxplot for short, is generally used to summarize \n",
        "the distribution of a data sample.The x-axis is used to represent the data sample, \n",
        "where multiple boxplots can be drawn side by side on the x-axis if desired\n",
        "'''\n",
        "\n",
        "import random\n",
        "\n",
        "random.seed(123)\n",
        "\n",
        "a=random.sample(range(1,100),20)\n",
        "b=random.sample(range(1,100),20)\n",
        "c=random.sample(range(1,100),20)\n",
        "d=random.sample(range(1,100),20)\n",
        "\n",
        "list_ex = [a,b,c,d]\n",
        "plt.xlabel('Data sample')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Boxplot')\n",
        "plt.boxplot(list_ex)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f63bf1a2128>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf1bb668>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf09dda0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0b1518>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f63bf1a2940>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf1a2c88>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf09d0b8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf09d400>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0a47f0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0a4b38>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0b1f28>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0ba2b0>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f63bf1bb358>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf09da90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0b1208>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0ba940>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f63bf1a2fd0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf09d748>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0a4e80>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0ba5f8>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f63bf1a2278>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf1a25f8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf1bb9e8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf1bbd30>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0a4160>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0a44a8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0b1898>,\n",
              "  <matplotlib.lines.Line2D at 0x7f63bf0b1be0>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1ZOQBUVV3VtX1zfMHgM3AocDpwCVNs0uAM0ZdmyTpUZ1eg0iyCngmcC1wUFXd2bx1F3BQ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\nZUBIkloZEJKkVv8ft4vfsYL5RJIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfhluz8BKUag",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "eb617db1-1850-4bc5-8a6d-d64710b463b0"
      },
      "source": [
        "#boxplot with fancy colors\n",
        "\n",
        "bp=plt.boxplot(list_ex,patch_artist = True)\n",
        "for box in bp['boxes']:\n",
        "  \n",
        "  box.set(color='orange',linewidth=2)\n",
        "for whisker in bp['whiskers']:\n",
        "  whisker.set(color = 'red',linewidth=2)\n",
        "for cap in bp['caps']:\n",
        "  cap.set(color='green',linewidth=2)\n",
        "for median in bp['medians']:\n",
        "  median.set(color='blue',linewidth=2)\n",
        "for flier in bp['fliers']:\n",
        "  flier.set(marker='o',color = 'black', alpha=0.5)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x/w9mJxaGfPu0XwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdiYNDteLLa1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "42b0b46b-999d-43c7-b54b-5695672249e2"
      },
      "source": [
        "#scatter plot\n",
        "\n",
        "'''\n",
        "Scatter plot is an easy to make but interesting visualization \n",
        "which gives a clear picture of how the data is distributed\n",
        "'''\n",
        "\n",
        "dravid = [89, 90, 70, 89, 100, 80, 90, 100, 80, 34]\n",
        "sachin = [90,89,67,79,100,50,100,80,80,99]\n",
        "kohli = [80,90,70,99,70,98,97,90,56,60]\n",
        "\n",
        "runs=[10,20,30,40,50,60,70,80,90,100]\n",
        "\n",
        "plt.scatter(runs,dravid, color='green')\n",
        "plt.scatter(runs,sachin,color='red')\n",
        "plt.scatter(runs,kohli, color='blue')\n",
        "plt.xlabel('Score Range')\n",
        "plt.ylabel('Runs Scored')\n",
        "plt.title('Score Card')\n",
        "plt.legend(['dravid','sachin','kohli'])\n",
        "plt.show()\n"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BJJ0LzANeBlYBnyTpc7gSmJyWnRQRLwy0n5F0UluZKZNOSbNKMNRO6vGDbVAMEfFV4Kvb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\nZpmcIMzMLJMThJmZZfr/+1HOsJHJFu0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFLvLV-NNYMG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fa93f20b-6657-45ca-c940-abf16d0216df"
      },
      "source": [
        "#scatter plot with area bubble\n",
        "\n",
        "\n",
        "labels = ['sachin','dravid','pant','kohli']\n",
        "size = [100,25,70,50]\n",
        "colors=['blue','red','orange','green']\n",
        "explode = (0.1,0,0,0)\n",
        "plt.pie(size,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%',shadow=True,startangle=140)\n",
        "plt.axis('equal')\n",
        "plt.title('Performances of Cricketers')\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HUmRKvDZE2Y2OsBKLIcAhwGZK2cR4RlHJWq+NahEp2LxkfwKuAmYAZcDXwH7An537r5s5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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KW8C317qO9o8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}