Create_Interactive_plots_using_Python.ipynb

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  "metadata": {
    "colab": {
      "name": "Create Interactive plots using Python.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMhG3S/e6AGedmI9oDY5zse",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/natnew/200-Projects-For-Beginners-Using-Python-Series/blob/main/Create_Interactive_plots_using_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Install libraries"
      ],
      "metadata": {
        "id": "Xn58mkFhFPg6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hc3LUAFm-iR0",
        "outputId": "e24bc115-f505-4a41-e6a9-dc634e4e328e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install plotly"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly"
      ],
      "metadata": {
        "id": "vugGbat7DVzw"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "tOzbKaGODTvH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Project"
      ],
      "metadata": {
        "id": "U8feW8WcFUE9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(dict(a=[1,2,3], b =[4,5,6]))"
      ],
      "metadata": {
        "id": "iH-9xmGGDYqj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "T6H8PRcpDruU",
        "outputId": "84ed86fd-8c08-4762-de41-47a088d53886"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a  b\n",
              "0  1  4\n",
              "1  2  5\n",
              "2  3  6"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b287e6c0-7d97-49b9-b55e-d06be3eaf5a5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b287e6c0-7d97-49b9-b55e-d06be3eaf5a5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b287e6c0-7d97-49b9-b55e-d06be3eaf5a5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
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    {
      "cell_type": "markdown",
      "source": [
        "Static Plot"
      ],
      "metadata": {
        "id": "2IDIcfX1D0lG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.plotting.backend = 'matplotlib'"
      ],
      "metadata": {
        "id": "csTqIaF9Ekkk"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "PmRnCoGQDtlx",
        "outputId": "b90770bb-6e00-44c9-cebe-39a8ef44aebd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f199b78e790>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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M2Xm146y1u4HdAPX19XbKlcyaDV9phuJlmjInIpIgmblqTcAnjTEdwE+Au4wx/zst1SxYrpAWEbnCpEFtrf2WtbbcWlsNfAb4lbX2kbRXJiIiQHJX1CIikkVTuuHFWvsi8GJaKhERkQnpilpExOcU1CIiPqegFhHxOQW1iIjPKahFRHxOQS0i4nMKahERn1NQi4j4nIJaRMTnFNQiIj6noBYR8TkFtYiIzymoRUR8TkEtIuJzCmoREZ9TUIuI+JyCWkTE5xTUIiI+p6AWEfE5BbWIiM8pqEVEfE5BLSLicwpqERGfU1CLiPicglpExOcU1CIiPqegFhHxOQW1iIjPKahFRHxOQS0i4nMKahERn5s0qI0xhcaYA8aYN40xbxtj/jwThYmIiJOXxDHDwF3W2gvGmHzgFWPMC9bafWmuTURkWhmPWGbPMin/uZMGtbXWAhe8b/O9L5vySkREppmRsQhvdPayp6Wb5tYw/UOj/Oxr21P+OslcUWOMmQ0cAuqA71pr909wzC5gF0BlZWUqaxQR8YXxiOWdk/3sae1mb2uYV9t7uDQ6ziwD61eUsHP14rRcVScV1NbaceBWY8xC4HljzM3W2iNXHLMb2A1QX1+vK24RmfastbSeu8CeljB7W7vZ19ZD36VRAFYtmc+DmytoDAXYWhugZG5+2upIKqijrLW9xphfA/cARyY7XkRkuuk6P8heL5j3toY5OzAMQEXZXO5Zt5TGugANoQCLiwszVtOkQW2MWQSMeiE9F/gt4L+mvTIRkQzovjDM3tYwza3d7GkJc7xnEIDg/AIaQwGa6gI0hoJUlBVlrcZkrqiXAX/rjVPPAv7eWvsv6S1LRCQ9+odG2d/W466YW8K8d2YAgOLCPLbVBnisqZrGuiArF8/HmNTP4Lgeycz6eAvYmIFaRERSbmh0nIMd59nb2s2e1jCHu3qJWCjMn8Xm6jLu37iCxlCAm1eUpGVqXSpMaYxaRMTvRscjvNXVy96WMHtau3ntWC8j4xHyZhlurVjIV+9aSWMowMbKhRTkzc52uUlRUIvItBaJWN493U9za5g9Ld0caO/h4sg4xsDaZQt4tKmahlCALdVlzCuYnpE3PasWkZxlraW9+yJ7W93MjObWMOcH3ZS52kXz+J1N5TSGAmyrDVA6b06Wq00NBbWI+N6pvkuxoYzm1jCn+oYAWF5SyN1rltAYcjMzlpZkbspcJimoRcR3ei6OsK8tHLs1u637IgBl8+bQEAq4aXOhIFWBIt/MzEgnBbWIZN2F4TFebe9hT4u7yeSdU/0AzJszm621AT67tZLGUJCblhYzy6czM9JJQS0iGTc0Os7rx3tjd/+92dnLWMQyJ28Wt1WW8vSHV9EQCnJLeQn5s7VsvoJaRNJubDzCkZP9saGMVzt6GB6LMMvALeUL+dKOWhpDQW6rKqUwf3pMmcskBbWIpJy1lvfPXIgNZexvCzMwPAbATUuLeXhrFY2hAFtqy1hQmL7FjGYKBbWI3DBrLZ09l2LLfza3dtN9YQSAqkAR925YTmPILWYUnF+Q5WqnHwW1iFyXs/1DsbnMe1rCnOi9BMDi4gLuWLkoNjujvDR7ixnNFApqEUlK3+AozW3eKnOtYVrOusZPJXPzaagNxMaZQ4vm5cSUuUxSUIvIhAZHxnjVW8xob0uYIyf7sBbm5s9mS00Zn76tnKa6IGuWLfDtYkYzhYJaRIB4/79oML/eeZ7RcUv+bMPGylKeunslTXVBNpQvZE6epsxlkoJaJEdF+/9Fl/+M9v8zBm5eXsJjt9fQFApSX11K0RxFRTbp7IvkiGj/v73eKnOJ/f9WLp7Pv6svp7EuyLaaACVFmjLnJwpqkRms6/ygm5nRcnn/vxUL5/KRdUtoqgvSUBtg8YKZuZjRTKGgFplBui8M09wab8x6LBzt/zeHhlAwtphRRdlczcyYRhTUItNY/9AoB9p6Yst/Hj3t9f8ryGNrbYDPN1TTVBdk1RL/9P+TqVNQi0wjQ6PjHDp2PnZr9uETfYxHLAV5rv/fNz6ynKa6IDcvX0CeFjOaMRTUIj7m+v/1xcaYDx0/z8hYhNle/7+v7AzRGAqysXKhFjOawRTUIj4SiViOnh6IjTEfaO/hgreY0dplC/jdbVU01QXZXFPG/Gna/0+mTr9pkSyy1tIRHowt/9ncFqbnolvMqDY4j/tudUMZ22oDlM2Q/n8ydQpqkQw73TcUG2Nubu3mpNf/b+mCQnauXkRTKEhDKMDyhXOzXKn4hYJaJM3OXxyhuS0+Za7tnOv/V1qUT0MowFe8aXM1QS1mJBNTUIuk2MXhMQ6098SW/3z3dD/Wuv5/W2rK+OyWShpCAdYsXZCT/f9k6hTUIjdoeGyc14710uxdMb8R7f83exabqhbyhx9aRVNdgFvKF6r/n1wXBbXIFI1HLIdP9MVWmUvs/7e+fCG7tsf7/82doylzcuMU1CKTsNbywdl4/799bWEGhtyUudVLinloSyVNdUG2qv+fpImCWmQCnT2DsWDe2xqm+4JbzKiyrIiPr19Go7eY0aJi9f+T9Js0qI0xFcBzwBLAAruttd9Jd2EimXR2YMgtZtQSZk9rN13nXf+/RcUFNNUFYlPmKsrU/08yL5kr6jHg31trXzPGFAOHjDG/sNa+k+baRNKm79Io+9rCNHtrM3/g9f9bUJjHttoAX7yjlsZQgLrFWsxIsm/SoLbWngJOeY8HjDHvAisABbVMG5dGxnm1oyfWNfvIiT4iFgrz3WJGD9xWTmMowLrlJer/J74zpTFqY0w1sBHYP8G+XcAugMrKyhSUJnL9RsYivNnVGxvKeP246/+XN8uwsXIhT961ksZQgFsrF1KQp5kZ4m9JB7UxZj7wD8DXrLX9V+631u4GdgPU19fblFUokoRIxPLOqf7YTSavdvQwOOL6/61bvoDHmmpoCAXYXF3GPC1mJNNMUu9YY0w+LqR/aK39x/SWJDI51//vIs1eMO9rD9M76Pr/hRbN41PeUMa22gALi7SYkUxvycz6MMAPgHettf8t/SWJTOxE76XYusx7W7s50x/v//dba5bQWBegMRRkifr/yQyTzBV1E/A54LAx5g1v2x9ba/81fWWJQPjCMM1tYfa0uFXmOrz+f4F5c2gIuVBuqgtQWVakmRkyoyUz6+MVQH8KJO0GhkY50N7DnhZ3xRzt/ze/II9ttWV8rqGaproAqxYXazEjySn6VEWyZmh0nNeOnWePt5jRW12u/9+cvFlsri7lGx9ZTWMowPoVJer/JzlNQS0ZMzYe4a0T8f5/B4/F+/9tKC/hiR0hGusCbKosVf8/kQQKakmbSMTy3pmBWJup/Qn9/9YsW8DntlXRVOemzBVrMSORq1JQS8pYazkWHowNZexrDRP2+v/VBOfxyVuX0xQKsq22jMB8LWYkkiwFtdyQ031DsRZTe1vi/f+WLChgx6pFbpW5UIAV6v8nct0U1DIlvYMjbpW5VndrdrT/38KifBpqAzxxp+v/V6v+fyIpo6CWa7o4PMaBjp7YKnPvnHL9/4q8/n8PbXb9/9YuU/8/kXRRUMtlhsfGef14b2woI7H/38ZK1/+vMRRgQ4X6/4lkioI6x41HLEdO9MVuy361o4ehUa//34oSvrjdrctcX1Wm/n8iWaKgzjHR/n97W7rZc0X/v1VL5vOZzZU0hgJsrQ1QMldT5kT8QEGdAzp7BmPLfyb2/6som8vH1y+jIRSgIRRgcbEWMxLxIwX1DJTY/29vWzedPa7/X3B+AY2hAE3eKnPq/ycyPSioZ4C+S6PsbwvHxpnfP+P6/xV7/f8eb6qhsS7ISvX/E5mWFNTT0KWRcQ4e64nNzDh8Rf+/397oFs2/eYX6/4nMBArqaWB0PMKbnb2x5T9fP97LyHgk1v/vq17/v43q/ycyIymofSja/6/Zu/vvQHu8/9/aZQt4tKmahlCALer/J5IT9KfcB6y1tHVfjC3/2dx2ef+/BzbF+/+VzlP/P5Fco6DOkpO9l2JjzHtbw5zud4sZLS8p5ENrltDotZpaWqIpcyK5TkGdIeELw+xr63FLgLbE+/+Vxfr/BWgKBakKqP+fiFxOQZ0mA0OjvNrRE7vJ5N1T/YDr/7e1poxHtlXRVBdk9RL1/xORa1NQp8jQ6DivHT/vbjJp7ebNhP5/9VWlPP3hVTTWBblF/f9EZIoU1NdpbDzC4YTFjA52nGfY6/93S3kJX95RS1MoyKYq9f8TkRujoE5SJGJ5/+wAe1rCNLd2s7+thwGv/99NS4t5eKvr/7elRv3/RCS1FNRXYa3leM9g7CaT5oT+f9WBIu7dsJymOjdlLqj+fyKSRgrqBGf6vf5/3geAJ3rdYkaLiwvYvmqRmzJXF1T/PxHJqJwO6t7BEfZ5ixntaemm1ev/VzLX9f/78o5aGkJBQovU/09EsiengnpwZIwD7T2xW7PfPun6/83Nd/3/HtxcQWMoqP5/IuIrMzqoR8YivH78fGxmxhudvYyOW/JnGzZWlvK1u1fRWBdgQ/lC5uRpypyI+NOMCurxiOXtk32xoYyDHee5NOoWM1q/ooTHb3f9/zZXq/+fiEwf0zqorbW0nL0QC+Z9bWH6vf5/KxfP58HNFTSEAmyrCVBSpClzIjI9Tbug7uwZjI0x720Nc27A9f8rL53LR29eRmOd+v+JyMwyaVAbY54F7gXOWmtvTn9Jlzs3MExzW3yVueM9bjGjaP8/1wNQ/f9EZOZK5or6fwF/DTyX3lKc/qFR9rf1sKfF3WTy3pkBIN7/7wtN1TSp/5+I5JBJg9pa+7IxpjrdhQyNjvPg7n0c7uq9rP/ffRuX0xQKsm75Ai1mJCI5KWVj1MaYXcAugMrKyik/vzB/NjWBInasDNJYF1T/PxERj7HWTn6Qu6L+l2THqOvr6+3BgwdvrDIRkRxijDlkra2faJ/GEkREfE5BLSLic5MGtTHmx0AzsNoY02WMeTz9ZYmISFQysz4eykQhIiIyMQ19iIj4nIJaRMTnFNQiIj6noBYR8bmkbniZ8g815hxw7DqfHgS6U1hOqqiuqVFdU6O6pmYm1lVlrV000Y60BPWNMMYcvNrdOdmkuqZGdU2N6pqaXKtLQx8iIj6noBYR8Tk/BvXubBdwFapralTX1Kiuqcmpunw3Ri0iIpfz4xW1iIgkUFCLiPhcxoLaGHOPMeY9Y0yLMeabE+wvMMb8nbd/f2L7L2PMt7zt7xljPpLhur5ujHnHGPOWMeb/GWOqEvaNG2Pe8L7+KcN1PWqMOZfw+r+XsO/zxpgPvK/PZ7iu/55Q0/vGmN6Efek8X88aY84aY45cZb8xxjzj1f2WMWZTwr50nq/J6nrYq+ewMWavMWZDwr4Ob/sbxpiUduJIoq6dxpi+hN/Xnybsu+Z7IM11fSOhpiPee6rM25fO81VhjPm1lwVvG2OemuCY9L3HrLVp/wJmA61ALTAHeBNYe8UxXwG+7z3+DPB33uO13vEFQI33c2ZnsK47gSLv8RPRurzvL2TxfD0K/PUEzy0D2rz/lnqPSzNV1xXHPwk8m+7z5f3s7cAm4MhV9n8MeAEwwDZgf7rPV5J1NUZfD/hotC7v+w4gmKXztRPX1emG3gOpruuKYz8B/CpD52sZsMl7XAy8P8GfybS9xzJ1Rb0FaLHWtllrR4CfAPddccx9wN96j38K3G2MMd72n1hrh6217UCL9/MyUpe19tfW2kHv231AeYpe+4bquoaPAL+w1vZYa88DvwDuyVJdDwE/TtFrX5O19mWg5xqH3Ac8Z519wEJjzDLSe74mrctau9d7Xcjc+yuZ83U1N/LeTHVdmXx/nbLWvuY9HgDeBVZccVja3mOZCuoVQGfC91382//J2DHW2jGgDwgk+dx01pXocdzfmFGFxpiDxph9xpj7U1TTVOp6wPsn1k+NMRVTfG4668IbIqoBfpWwOV3nKxlXqz2d52uqrnx/WeDnxphDxjWPzrQGY8ybxpgXjDHrvG2+OF/GmCJc2P1DwuaMnC/jhmU3Avuv2JW291jKupDPdMaYR4B6YEfC5ipr7QljTC3wK2PMYWtta4ZK+mfgx9baYWPMl3D/GrkrQ6+djM8AP7XWjidsy+b58jVjzJ24oL49YU95Y0sAAAJcSURBVPPt3vlaDPzCGHPUu+LMhNdwv68LxpiPAf8XWJmh107GJ4A91trEq++0ny9jzHzcXw5fs9b2p/JnX0umrqhPABUJ35d72yY8xhiTB5QA4SSfm866MMZ8CPg28Elr7XB0u7X2hPffNuBF3N+yGanLWhtOqOVvgNuSfW4660rwGa74Z2kaz1cyrlZ7Os9XUowxt+B+h/dZa8PR7Qnn6yzwPKkb8puUtbbfWnvBe/yvQL4xJogPzpfnWu+vtJwvY0w+LqR/aK39xwkOSd97LB0D7xMMxOfhBtBriH8Ase6KY36fyz9M/Hvv8Tou/zCxjdR9mJhMXRtxH56svGJ7KVDgPQ4CH5CiD1WSrGtZwuPfBvbZ+AcX7V59pd7jskzV5R13E+6DHZOJ85XwGtVc/cOxj3P5Bz0H0n2+kqyrEve5S+MV2+cBxQmP9wL3ZLCupdHfHy7wjnvnLqn3QLrq8vaX4Max52XqfHn/788Bf3WNY9L2HkvZyU3if/RjuE9KW4Fve9v+AneVClAI/B/vTXsAqE147re9570HfDTDdf0SOAO84X39k7e9ETjsvVEPA49nuK7/Arztvf6vgZsSnvuYdx5bgC9ksi7v+/8E/OUVz0v3+foxcAoYxY0BPg58Gfiyt98A3/XqPgzUZ+h8TVbX3wDnE95fB73ttd65etP7PX87w3V9NeH9tY+Ev0gmeg9kqi7vmEdxEwwSn5fu83U7bgz8rYTf1ccy9R7TLeQiIj6nOxNFRHxOQS0i4nMKahERn1NQi4j4nIJaRMTnFNQiIj6noBYR8bn/D6qYyj9srcG5AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interactive plot"
      ],
      "metadata": {
        "id": "9eqy0zMoD2X_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.plotting.backend = 'plotly'"
      ],
      "metadata": {
        "id": "gpAWy8UJD5ww"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ApGk-9G_EPrt",
        "outputId": "6141dfd2-741f-4f3a-a07b-7ed568d15bef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "df =px.data.tips()\n",
        "fig = px.histogram(df, y='day', x='total_bill', color='sex', title='Histogram')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yfUAi4qmGE7O",
        "outputId": "ecd8fefe-4812-4a7c-eb67-53b67a88ca6d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "#Scatter plot\n",
        "fig = px.scatter(x =[1,2,3, 4, 5], y =[4,5,6, 7, 8])\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "lRmZmiCyGvQn",
        "outputId": "5d094104-d5d9-4de0-d1ee-2dbb09620431"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "#Basic Sunburst Plot\n",
        "data = dict(\n",
        "    character=[\"Eve\", \"Cain\", \"Seth\", \"Enos\", \"Noam\", \"Abel\", \"Awan\", \"Enoch\", \"Azura\"],\n",
        "    parent=[\"\", \"Eve\", \"Eve\", \"Seth\", \"Seth\", \"Eve\", \"Eve\", \"Awan\", \"Eve\" ],\n",
        "    value=[10, 14, 12, 10, 2, 6, 6, 4, 4])\n",
        "\n",
        "fig = px.sunburst(\n",
        "    data,\n",
        "    names='character',\n",
        "    parents='parent',\n",
        "    values='value',\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "i8BFNm0yHAZH",
        "outputId": "d91392de-99e3-4de7-8fef-e5d2cabfef4f"
      },
      "execution_count": 19,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "#Geographical Scatter Plot\n",
        "df = px.data.gapminder().query(\"year == 2007\")\n",
        "fig = px.scatter_geo(df, locations=\"iso_alpha\",\n",
        "                     size=\"pop\", # size of markers, \"pop\" is one of the columns of gapminder\n",
        "                     )\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "71OcltCkHNHV",
        "outputId": "b89021cb-473e-4300-d3e3-372cc2f77b78"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Polar chart \n",
        "import plotly.express as px\n",
        "df = px.data.wind()\n",
        "fig = px.scatter_polar(df, r=\"frequency\", theta=\"direction\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "_wyqOyebHxB8",
        "outputId": "338738e5-bbad-4a62-b2df-d3412df14a5b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "#Parallel Coordinates plot\n",
        "\n",
        "df = px.data.iris()\n",
        "fig = px.parallel_coordinates(df, color=\"species_id\",\n",
        "                              dimensions=['sepal_width', 'sepal_length', 'petal_width',\n",
        "                                          'petal_length'],\n",
        "                              color_continuous_scale=px.colors.diverging.Tealrose,\n",
        "                              color_continuous_midpoint=2)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "zA6h9P33H9RE",
        "outputId": "69f2745d-5b80-492f-9752-0417ba638458"
      },
      "execution_count": 24,
      "outputs": [
        {
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          "data": {
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