ipywidgets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: IPython Widgets\n",
    "\n",
    "In this notebook, we will get an introduction to IPython widgets. These are tools that allow us to build interactivity into our notebooks often with a single line of code. These widgets are very useful for data exploration and analysis, for example, selecting certain data or updating charts. In effect, Widgets allow you to make Jupyter Notebooks into an interactive dashboard instead of a static document."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the below cell if needed. You can also do this from the command line. If in Jupyter lab, [check out the instructions for that environment](https://ipywidgets.readthedocs.io/en/stable/user_install.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:15.249992Z",
     "start_time": "2019-02-11T21:59:12.848653Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Enabling notebook extension jupyter-js-widgets/extension...\n",
      "      - Validating: ok\n"
     ]
    }
   ],
   "source": [
    "!pip install -U -q ipywidgets\n",
    "!jupyter nbextension enable --py widgetsnbextension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the other imports will use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:16.649010Z",
     "start_time": "2019-02-11T21:59:15.252863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standard Data Science Helpers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "import cufflinks as cf\n",
    "cf.go_offline(connected=True)\n",
    "cf.set_config_file(colorscale='plotly', world_readable=True)\n",
    "\n",
    "# Extra options\n",
    "pd.options.display.max_rows = 30\n",
    "pd.options.display.max_columns = 25\n",
    "\n",
    "# Show all code cells outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:16.677478Z",
     "start_time": "2019-02-11T21:59:16.650547Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.display import Image, display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "For this project, we'll work with my medium stats data. You can grab your own data or just use mine! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claps</th>\n",
       "      <th>days_since_publication</th>\n",
       "      <th>fans</th>\n",
       "      <th>link</th>\n",
       "      <th>num_responses</th>\n",
       "      <th>publication</th>\n",
       "      <th>published_date</th>\n",
       "      <th>read_ratio</th>\n",
       "      <th>read_time</th>\n",
       "      <th>reads</th>\n",
       "      <th>started_date</th>\n",
       "      <th>tags</th>\n",
       "      <th>text</th>\n",
       "      <th>title</th>\n",
       "      <th>title_word_count</th>\n",
       "      <th>type</th>\n",
       "      <th>views</th>\n",
       "      <th>word_count</th>\n",
       "      <th>claps_per_word</th>\n",
       "      <th>editing_days</th>\n",
       "      <th>&lt;tag&gt;Education</th>\n",
       "      <th>&lt;tag&gt;Data Science</th>\n",
       "      <th>&lt;tag&gt;Towards Data Science</th>\n",
       "      <th>&lt;tag&gt;Machine Learning</th>\n",
       "      <th>&lt;tag&gt;Python</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2</td>\n",
       "      <td>597.301123</td>\n",
       "      <td>2</td>\n",
       "      <td>https://medium.com/p/screw-the-environment-but...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-10 14:25:00</td>\n",
       "      <td>42.17</td>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>2017-06-10 14:24:00</td>\n",
       "      <td>[Climate Change, Economics]</td>\n",
       "      <td>Screw the Environment, but Consider Your Walle...</td>\n",
       "      <td>Screw the Environment, but Consider Your Wallet</td>\n",
       "      <td>8</td>\n",
       "      <td>published</td>\n",
       "      <td>166</td>\n",
       "      <td>1859</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>18</td>\n",
       "      <td>589.983168</td>\n",
       "      <td>3</td>\n",
       "      <td>https://medium.com/p/the-vanquishing-of-war-pl...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-17 22:02:00</td>\n",
       "      <td>30.34</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>2017-06-17 22:02:00</td>\n",
       "      <td>[Climate Change, Humanity, Optimism, History]</td>\n",
       "      <td>The Vanquishing of War, Plague and Famine Part...</td>\n",
       "      <td>The Vanquishing of War, Plague and Famine</td>\n",
       "      <td>8</td>\n",
       "      <td>published</td>\n",
       "      <td>178</td>\n",
       "      <td>3891</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>51</td>\n",
       "      <td>577.363292</td>\n",
       "      <td>20</td>\n",
       "      <td>https://medium.com/p/capstone-project-mercedes...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-30 12:55:00</td>\n",
       "      <td>20.02</td>\n",
       "      <td>42</td>\n",
       "      <td>222</td>\n",
       "      <td>2017-06-30 12:00:00</td>\n",
       "      <td>[Machine Learning, Python, Udacity, Kaggle]</td>\n",
       "      <td>Capstone Project: Mercedes-Benz Greener Manufa...</td>\n",
       "      <td>Capstone Project: Mercedes-Benz Greener Manufa...</td>\n",
       "      <td>7</td>\n",
       "      <td>published</td>\n",
       "      <td>1109</td>\n",
       "      <td>12025</td>\n",
       "      <td>0.004241</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0</td>\n",
       "      <td>576.520688</td>\n",
       "      <td>0</td>\n",
       "      <td>https://medium.com/p/home-of-the-scared-5af0fe...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-01 09:08:00</td>\n",
       "      <td>35.85</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-06-30 18:21:00</td>\n",
       "      <td>[Politics, Books, News, Media Criticism]</td>\n",
       "      <td>Home of the Scared A review of A Culture of Fe...</td>\n",
       "      <td>Home of the Scared</td>\n",
       "      <td>4</td>\n",
       "      <td>published</td>\n",
       "      <td>53</td>\n",
       "      <td>2533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0</td>\n",
       "      <td>572.533035</td>\n",
       "      <td>0</td>\n",
       "      <td>https://medium.com/p/the-triumph-of-peace-f485...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-05 08:51:00</td>\n",
       "      <td>8.47</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-07-03 20:18:00</td>\n",
       "      <td>[Books, Psychology, History, Humanism]</td>\n",
       "      <td>The Triumph of Peace A review of The Better An...</td>\n",
       "      <td>The Triumph of Peace</td>\n",
       "      <td>4</td>\n",
       "      <td>published</td>\n",
       "      <td>59</td>\n",
       "      <td>3892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     claps  days_since_publication  fans  \\\n",
       "129      2              597.301123     2   \n",
       "125     18              589.983168     3   \n",
       "132     51              577.363292    20   \n",
       "126      0              576.520688     0   \n",
       "121      0              572.533035     0   \n",
       "\n",
       "                                                  link  num_responses  \\\n",
       "129  https://medium.com/p/screw-the-environment-but...              0   \n",
       "125  https://medium.com/p/the-vanquishing-of-war-pl...              0   \n",
       "132  https://medium.com/p/capstone-project-mercedes...              0   \n",
       "126  https://medium.com/p/home-of-the-scared-5af0fe...              0   \n",
       "121  https://medium.com/p/the-triumph-of-peace-f485...              0   \n",
       "\n",
       "    publication      published_date  read_ratio  read_time  reads  \\\n",
       "129        None 2017-06-10 14:25:00       42.17          7     70   \n",
       "125        None 2017-06-17 22:02:00       30.34         14     54   \n",
       "132        None 2017-06-30 12:55:00       20.02         42    222   \n",
       "126        None 2017-07-01 09:08:00       35.85          9     19   \n",
       "121        None 2017-07-05 08:51:00        8.47         14      5   \n",
       "\n",
       "           started_date                                           tags  \\\n",
       "129 2017-06-10 14:24:00                    [Climate Change, Economics]   \n",
       "125 2017-06-17 22:02:00  [Climate Change, Humanity, Optimism, History]   \n",
       "132 2017-06-30 12:00:00    [Machine Learning, Python, Udacity, Kaggle]   \n",
       "126 2017-06-30 18:21:00       [Politics, Books, News, Media Criticism]   \n",
       "121 2017-07-03 20:18:00         [Books, Psychology, History, Humanism]   \n",
       "\n",
       "                                                  text  \\\n",
       "129  Screw the Environment, but Consider Your Walle...   \n",
       "125  The Vanquishing of War, Plague and Famine Part...   \n",
       "132  Capstone Project: Mercedes-Benz Greener Manufa...   \n",
       "126  Home of the Scared A review of A Culture of Fe...   \n",
       "121  The Triumph of Peace A review of The Better An...   \n",
       "\n",
       "                                                 title  title_word_count  \\\n",
       "129    Screw the Environment, but Consider Your Wallet                 8   \n",
       "125          The Vanquishing of War, Plague and Famine                 8   \n",
       "132  Capstone Project: Mercedes-Benz Greener Manufa...                 7   \n",
       "126                                 Home of the Scared                 4   \n",
       "121                               The Triumph of Peace                 4   \n",
       "\n",
       "          type  views  word_count  claps_per_word  editing_days  \\\n",
       "129  published    166        1859        0.001076             0   \n",
       "125  published    178        3891        0.004626             0   \n",
       "132  published   1109       12025        0.004241             0   \n",
       "126  published     53        2533        0.000000             0   \n",
       "121  published     59        3892        0.000000             1   \n",
       "\n",
       "     <tag>Education  <tag>Data Science  <tag>Towards Data Science  \\\n",
       "129               0                  0                          0   \n",
       "125               0                  0                          0   \n",
       "132               0                  0                          0   \n",
       "126               0                  0                          0   \n",
       "121               0                  0                          0   \n",
       "\n",
       "     <tag>Machine Learning  <tag>Python  \n",
       "129                      0            0  \n",
       "125                      0            0  \n",
       "132                      1            1  \n",
       "126                      0            0  \n",
       "121                      0            0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"https://github.com/Archana-blog/notebooks/blob/6963e561905bf15daa3a87f1ecae87cda8481699/data?raw=true\", engine='pyarrow')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:17.847235Z",
     "start_time": "2019-02-11T21:59:17.741021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claps</th>\n",
       "      <th>days_since_publication</th>\n",
       "      <th>fans</th>\n",
       "      <th>num_responses</th>\n",
       "      <th>read_ratio</th>\n",
       "      <th>read_time</th>\n",
       "      <th>reads</th>\n",
       "      <th>title_word_count</th>\n",
       "      <th>views</th>\n",
       "      <th>word_count</th>\n",
       "      <th>claps_per_word</th>\n",
       "      <th>editing_days</th>\n",
       "      <th>&lt;tag&gt;Education</th>\n",
       "      <th>&lt;tag&gt;Data Science</th>\n",
       "      <th>&lt;tag&gt;Towards Data Science</th>\n",
       "      <th>&lt;tag&gt;Machine Learning</th>\n",
       "      <th>&lt;tag&gt;Python</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1815.263158</td>\n",
       "      <td>248.407273</td>\n",
       "      <td>352.052632</td>\n",
       "      <td>7.045113</td>\n",
       "      <td>29.074662</td>\n",
       "      <td>12.917293</td>\n",
       "      <td>6336.300752</td>\n",
       "      <td>7.127820</td>\n",
       "      <td>23404.030075</td>\n",
       "      <td>3029.120301</td>\n",
       "      <td>0.957638</td>\n",
       "      <td>20.330827</td>\n",
       "      <td>0.729323</td>\n",
       "      <td>0.609023</td>\n",
       "      <td>0.436090</td>\n",
       "      <td>0.383459</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2449.074661</td>\n",
       "      <td>179.370879</td>\n",
       "      <td>479.060117</td>\n",
       "      <td>9.056108</td>\n",
       "      <td>12.417670</td>\n",
       "      <td>9.510795</td>\n",
       "      <td>9007.284726</td>\n",
       "      <td>3.158475</td>\n",
       "      <td>33995.636496</td>\n",
       "      <td>2393.414456</td>\n",
       "      <td>1.846756</td>\n",
       "      <td>74.111579</td>\n",
       "      <td>0.445989</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>0.497774</td>\n",
       "      <td>0.488067</td>\n",
       "      <td>0.466587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.218629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.110000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>74.543822</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1375.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>0.052115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>815.000000</td>\n",
       "      <td>245.416130</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>27.060000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2049.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7608.000000</td>\n",
       "      <td>2456.000000</td>\n",
       "      <td>0.421525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>376.080598</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>34.910000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7815.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>30141.000000</td>\n",
       "      <td>3553.000000</td>\n",
       "      <td>1.099366</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13600.000000</td>\n",
       "      <td>597.301123</td>\n",
       "      <td>2588.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>74.370000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>41978.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>173714.000000</td>\n",
       "      <td>15063.000000</td>\n",
       "      <td>17.891817</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              claps  days_since_publication         fans  num_responses  \\\n",
       "count    133.000000              133.000000   133.000000     133.000000   \n",
       "mean    1815.263158              248.407273   352.052632       7.045113   \n",
       "std     2449.074661              179.370879   479.060117       9.056108   \n",
       "min        0.000000                1.218629     0.000000       0.000000   \n",
       "25%      121.000000               74.543822    23.000000       0.000000   \n",
       "50%      815.000000              245.416130   136.000000       4.000000   \n",
       "75%     2700.000000              376.080598   528.000000      12.000000   \n",
       "max    13600.000000              597.301123  2588.000000      59.000000   \n",
       "\n",
       "       read_ratio   read_time         reads  title_word_count          views  \\\n",
       "count  133.000000  133.000000    133.000000        133.000000     133.000000   \n",
       "mean    29.074662   12.917293   6336.300752          7.127820   23404.030075   \n",
       "std     12.417670    9.510795   9007.284726          3.158475   33995.636496   \n",
       "min      8.110000    1.000000      1.000000          2.000000       3.000000   \n",
       "25%     20.020000    8.000000    363.000000          5.000000    1375.000000   \n",
       "50%     27.060000   10.000000   2049.000000          7.000000    7608.000000   \n",
       "75%     34.910000   14.000000   7815.000000          8.000000   30141.000000   \n",
       "max     74.370000   54.000000  41978.000000         16.000000  173714.000000   \n",
       "\n",
       "         word_count  claps_per_word  editing_days  <tag>Education  \\\n",
       "count    133.000000      133.000000    133.000000      133.000000   \n",
       "mean    3029.120301        0.957638     20.330827        0.729323   \n",
       "std     2393.414456        1.846756     74.111579        0.445989   \n",
       "min      163.000000        0.000000    -13.000000        0.000000   \n",
       "25%     1653.000000        0.052115      0.000000        0.000000   \n",
       "50%     2456.000000        0.421525      1.000000        1.000000   \n",
       "75%     3553.000000        1.099366      5.000000        1.000000   \n",
       "max    15063.000000       17.891817    349.000000        1.000000   \n",
       "\n",
       "       <tag>Data Science  <tag>Towards Data Science  <tag>Machine Learning  \\\n",
       "count         133.000000                 133.000000             133.000000   \n",
       "mean            0.609023                   0.436090               0.383459   \n",
       "std             0.489814                   0.497774               0.488067   \n",
       "min             0.000000                   0.000000               0.000000   \n",
       "25%             0.000000                   0.000000               0.000000   \n",
       "50%             1.000000                   0.000000               0.000000   \n",
       "75%             1.000000                   1.000000               1.000000   \n",
       "max             1.000000                   1.000000               1.000000   \n",
       "\n",
       "       <tag>Python  \n",
       "count   133.000000  \n",
       "mean      0.315789  \n",
       "std       0.466587  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Widgets\n",
    "\n",
    "Let's get started using some widgets! We'll start off pretty simple just to see how the interface works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:17.894781Z",
     "start_time": "2019-02-11T21:59:17.849710Z"
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make a function interactive, all we have to do is use the `interact` decorator. This will automatically infer the input types for us! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:18.144261Z",
     "start_time": "2019-02-11T21:59:17.897741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d99c26183744e5d83215cc268459af3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='claps', description='column'), IntSlider(value=5000, description='x', max=15…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def show_articles_more_than(column='claps', x=5000):\n",
    "    display(HTML(f'<h2>Showing articles with more than {x} {column}<h2>'))\n",
    "    display(df.loc[df[column] > x, ['title', 'published_date', 'read_time', 'tags', 'views', 'reads']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ac0bff2e6e5487da48bd7eea4b133ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='claps', description='column'), IntSlider(value=2000, description='x', max=60…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def show_articles_more_than(column='claps', x=2000):\n",
    "    display(HTML(f'<h2>Showing articles with more than {x} {column}<h2>'))\n",
    "    display(df.loc[df[column] > 2000, ['claps','title', 'published_date', 'read_time', 'tags', 'views', 'reads']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `interact` decorator automatically inferred we want a `text` box for the `column` and an `int` slider for `x`! This makes it incredibly simple to add interactivity. We can also set the options how we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:18.251039Z",
     "start_time": "2019-02-11T21:59:18.147133Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d3770ecf9464a3d93f1413ef8fb4943",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3000, description='x', max=5000, min=1000, step=100), Dropdown(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def show_titles_more_than(x=(1000, 5000, 100),\n",
    "                          column=list(df.select_dtypes('number').columns), \n",
    "                          ):\n",
    "    # display(HTML(f'<h2>Showing articles with more than {x} {column}<h2>'))\n",
    "    display(df.loc[df[column] > x, ['title', 'published_date', 'read_time', 'tags', 'views', 'reads']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This now gives us a `dropdown` for the `column` selection and still an `int` slider for `x`, but with limits. This can be useful when we need to enforce certains constraints on the interaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataframe Explorer\n",
    "\n",
    "Let's look at a few more examples of using widgets to explore data. Here we create a widget that quickly lets us find correlations between columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:18.682329Z",
     "start_time": "2019-02-11T21:59:18.610843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae0b5fcbbad347d98a62e943e0c4ca20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='column1', options=('claps', 'days_since_publication', 'fans', 'num…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def correlations(column1=list(df.select_dtypes('number').columns), \n",
    "                 column2=list(df.select_dtypes('number').columns)):\n",
    "    print(f\"Correlation: {df[column1].corr(df[column2])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's one to describe a specific column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:18.734969Z",
     "start_time": "2019-02-11T21:59:18.683939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fd98c464f9243478762d75d67987c11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='column', options=('claps', 'days_since_publication', 'fans', 'link…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def describe(column=list(df.columns)):\n",
    "    print(df[column].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive Widgets for Plots\n",
    "\n",
    "We can use the same basic approach to create interactive widgets for plots. This expands the capabilities of the already powerful plotly visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:19.742380Z",
     "start_time": "2019-02-11T21:59:18.736434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89d01b2bffad4dddba20f73baa421443",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=('claps', 'days_since_publication', 'fans', 'num_respo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def scatter_plot(x=list(df.select_dtypes('number').columns), \n",
    "                 y=list(df.select_dtypes('number').columns)[1:]):\n",
    "    df.iplot(kind='scatter', x=x, y=y, mode='markers', \n",
    "             xTitle=x.title(), yTitle=y.title(), title=f'{y.title()} vs {x.title()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add some options to control the column scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:20.056907Z",
     "start_time": "2019-02-11T21:59:19.745853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18e23d77cecc46b3bc6649081d740061",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=('claps', 'days_since_publication', 'fans', 'num_respo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def scatter_plot(x=list(df.select_dtypes('number').columns), \n",
    "                 y=list(df.select_dtypes('number').columns)[1:],\n",
    "                 theme=list(cf.themes.THEMES.keys()), \n",
    "                 colorscale=list(cf.colors._scales_names.keys())):\n",
    "    \n",
    "    df.iplot(kind='scatter', x=x, y=y, mode='markers', \n",
    "             xTitle=x.title(), yTitle=y.title(), \n",
    "             text='title',\n",
    "             title=f'{y.title()} vs {x.title()}',\n",
    "            theme=theme, colorscale=colorscale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next plot lets us choose the grouping category for the plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:20.694691Z",
     "start_time": "2019-02-11T21:59:20.059308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d61f12cb0cb3440294c33ff91d05ce15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=('claps', 'days_since_publication', 'fans', 'num_respo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['binned_read_time'] = pd.cut(df['read_time'], bins=range(0, 56, 5))\n",
    "df['binned_read_time'] = df['binned_read_time'].astype(str)\n",
    "\n",
    "df['binned_word_count'] = pd.cut(df['word_count'], bins=range(0, 100001, 1000))\n",
    "df['binned_word_count'] = df['binned_word_count'].astype(str)\n",
    "\n",
    "@interact\n",
    "def scatter_plot(x=list(df.select_dtypes('number').columns), \n",
    "                 y=list(df.select_dtypes('number').columns)[1:],\n",
    "                 categories=['binned_read_time', 'binned_word_count', 'publication', 'type'],\n",
    "                 theme=list(cf.themes.THEMES.keys()), \n",
    "                 colorscale=list(cf.colors._scales_names.keys())):\n",
    "    \n",
    "    df.iplot(kind='scatter', x=x, y=y, mode='markers', \n",
    "             categories=categories, \n",
    "             xTitle=x.title(), yTitle=y.title(), \n",
    "             text='title',\n",
    "             title=f'{y.title()} vs {x.title()}',\n",
    "             theme=theme, colorscale=colorscale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed this plot was a little slow to update. When that is the case, we can use `interact_manual` which only updates the function when the button is pressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:20.751182Z",
     "start_time": "2019-02-11T21:59:20.696239Z"
    }
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:20.859857Z",
     "start_time": "2019-02-11T21:59:20.753440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab449c236f0244b6abba2486484984f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=('claps', 'days_since_publication', 'fans', 'num_respo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact_manual\n",
    "def scatter_plot(x=list(df.select_dtypes('number').columns), \n",
    "                 y=list(df.select_dtypes('number').columns)[1:],\n",
    "                 categories=['binned_read_time', 'binned_word_count', 'publication', 'type'],\n",
    "                 theme=list(cf.themes.THEMES.keys()), \n",
    "                 colorscale=list(cf.colors._scales_names.keys())):\n",
    "    \n",
    "    df.iplot(kind='scatter', x=x, y=y, mode='markers', \n",
    "             categories=categories, \n",
    "             xTitle=x.title(), yTitle=y.title(), \n",
    "             text='title',\n",
    "             title=f'{y.title()} vs {x.title()}',\n",
    "             theme=theme, colorscale=colorscale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Our Own Widgets\n",
    "\n",
    "The decorator `interact` (or `interact_manual`) is not the only way to use widgets. We can also explicity create our own. One of the most useful I've found is the `DataPicker`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:20.916568Z",
     "start_time": "2019-02-11T21:59:20.861736Z"
    }
   },
   "outputs": [],
   "source": [
    "df.set_index('published_date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:21.047340Z",
     "start_time": "2019-02-11T21:59:20.920596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87771db350674cb4a899cab92137a9c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(DatePicker(value=Timestamp('2018-01-01 00:00:00'), description='start_date'), DatePicker…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Archana\\Anaconda3\\Anaconda3_\\lib\\site-packages\\traitlets\\traitlets.py:586: FutureWarning:\n",
      "\n",
      "Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior.  In a future version these will be considered non-comparable.Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_articles_published(start_date, end_date):\n",
    "    start_date = pd.Timestamp(start_date)\n",
    "    end_date = pd.Timestamp(end_date)\n",
    "    stat_df = df.loc[(df.index >= start_date) & (df.index <= end_date)].copy()\n",
    "    total_words = stat_df['word_count'].sum()\n",
    "    total_read_time = stat_df['read_time'].sum()\n",
    "    num_articles = len(stat_df)\n",
    "    print(f'You published {num_articles} articles between {start_date.date()} and {end_date.date()}.')\n",
    "    print(f'These articles totalled {total_words:,} words and {total_read_time/60:.2f} hours to read.')\n",
    "    \n",
    "_ = interact(print_articles_published,\n",
    "             start_date=widgets.DatePicker(value=pd.to_datetime('2018-01-01')),\n",
    "             end_date=widgets.DatePicker(value=pd.to_datetime('2019-01-01')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this function, we use a `Dropdown` and a `DatePicker` to plot one column cumulatively up to a certain time. Instead of having to write this ourselves, we can just let `ipywidgets` do all the work!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:21.284716Z",
     "start_time": "2019-02-11T21:59:21.049952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d18233439ed44cab8c9e3cc549a76bbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='column', options=('claps', 'days_since_publication', 'fans', 'num_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_up_to(column, date):\n",
    "    date = pd.Timestamp(date)\n",
    "    plot_df = df.loc[df.index <= date].copy()\n",
    "    plot_df[column].cumsum().iplot(mode='markers+lines', \n",
    "                                   xTitle='published date',\n",
    "                                   yTitle=column, \n",
    "                                  title=f'Cumulative {column.title()} Until {date.date()}')\n",
    "    \n",
    "_ = interact(plot_up_to, column=widgets.Dropdown(options=list(df.select_dtypes('number').columns)), \n",
    "             date = widgets.DatePicker(value=pd.to_datetime('2019-01-01')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linked Values\n",
    "\n",
    "We can link the value of two widgets to each other using the `jslink` function. This ties the values to be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:21.947232Z",
     "start_time": "2019-02-11T21:59:21.817802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97018c912a2e45f89cf32b764da79740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntText(value=100, description='column1_value'), IntSlider(value=100, description='colum…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_less_than(column1_value, column2_value):\n",
    "    display(df.loc[(df['views'] < column1_value) & \n",
    "                    (df['reads'] < column2_value), \n",
    "                   ['title', 'read_time', 'tags', 'views', 'reads']])\n",
    "        \n",
    "column1_value=widgets.IntText(value=100, label='First')\n",
    "column2_value=widgets.IntSlider(value=100, label='Second')\n",
    "\n",
    "linked = widgets.jslink((column1_value, 'value'),\n",
    "                        (column2_value, 'value'))\n",
    "\n",
    "less_than = interact(show_less_than, column1_value=column1_value,\n",
    "                 column2_value=column2_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm not exactly sure why you would want to link two widgets, but there you go! We can unlink them using the `unlink` command (sometimes syntax does make sense)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:22.001784Z",
     "start_time": "2019-02-11T21:59:21.948880Z"
    }
   },
   "outputs": [],
   "source": [
    "linked.unlink()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:22.072554Z",
     "start_time": "2019-02-11T21:59:22.003500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97018c912a2e45f89cf32b764da79740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntText(value=100, description='column1_value'), IntSlider(value=100, description='colum…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "less_than.widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "These widgets are not going to change your life, but they do make notebooks closer to interactive dashboards. I've only shown you some of the capabilities so be sure to look at the [documentation for the full details]. The Jupyter Notebook is useful by itself, but with additional tools, it can be an even better data exploration and analysis technology. Thanks to the efforts of many developers and contributors to open-source, we have these great technologies, so we might as well get the most from these libraries! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:22.128193Z",
     "start_time": "2019-02-11T21:59:22.074179Z"
    }
   },
   "outputs": [],
   "source": [
    "cscales = ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n",
    "            'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n",
    "            'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:22.211843Z",
     "start_time": "2019-02-11T21:59:22.130241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6803a1f21534c9bb36a98ea3fa56533",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='colorscale', options=('Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Blue…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.figure_factory as ff\n",
    "\n",
    "corrs = df.corr()\n",
    "\n",
    "@interact_manual\n",
    "def plot_corrs(colorscale=cscales):\n",
    "    figure = ff.create_annotated_heatmap(z = corrs.round(2).values, \n",
    "                                     x =list(corrs.columns), \n",
    "                                     y=list(corrs.index), \n",
    "                                     colorscale=colorscale,\n",
    "                                     annotation_text=corrs.round(2).values)\n",
    "    iplot(figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T21:59:22.438146Z",
     "start_time": "2019-02-11T21:59:22.213746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdcf56f465c641118a0b1d38dbcf62da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='column1', options=('claps', 'views', 'read', 'word_count'), value=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def plot_spread(column1=['claps', 'views', 'read', 'word_count'], \n",
    "                 column2=['views', 'claps', 'read', 'word_count']):\n",
    "    df.iplot(kind='ratio',\n",
    "                                                   y=column1,\n",
    "                                                   secondary_y=column2,\n",
    "                                                   title=f'{column1.title()} and {column2.title()} Spread Plot',\n",
    "                                 xTitle='Published Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# libraries & dataset\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "sns.set(style=\"darkgrid\")\n",
    "df = sns.load_dataset('iris')\n",
    " \n",
    "# plotting both distibutions on the same figure\n",
    "fig = sns.kdeplot(df['petal_length'], shade=True, color=\"b\")\n",
    "fig = sns.kdeplot(df['sepal_length'], shade=True, color=\"g\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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