widgets_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install -U -q ipywidgets\n",
    "!pip3 install -U -q pyarrow\n",
    "!jupyter nbextension enable --py widgetsnbextension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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-latest.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-latest.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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.display import Image, display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('https://github.com/WillKoehrsen/Data-Analysis/blob/master/medium/data/medium_data_2019_01_06?raw=true')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>126.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1741.777778</td>\n",
       "      <td>239.016273</td>\n",
       "      <td>337.380952</td>\n",
       "      <td>6.571429</td>\n",
       "      <td>29.400397</td>\n",
       "      <td>13.023810</td>\n",
       "      <td>5997.500000</td>\n",
       "      <td>7.095238</td>\n",
       "      <td>22240.547619</td>\n",
       "      <td>3049.603175</td>\n",
       "      <td>0.918837</td>\n",
       "      <td>21.253968</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.325397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2407.860909</td>\n",
       "      <td>175.078653</td>\n",
       "      <td>463.520785</td>\n",
       "      <td>8.665729</td>\n",
       "      <td>12.731898</td>\n",
       "      <td>9.736089</td>\n",
       "      <td>8493.803505</td>\n",
       "      <td>3.175981</td>\n",
       "      <td>32305.397567</td>\n",
       "      <td>2448.060885</td>\n",
       "      <td>1.841372</td>\n",
       "      <td>76.040824</td>\n",
       "      <td>0.453557</td>\n",
       "      <td>0.489444</td>\n",
       "      <td>0.496847</td>\n",
       "      <td>0.492805</td>\n",
       "      <td>0.470393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.465462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.060000</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>104.000000</td>\n",
       "      <td>67.033577</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.417500</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>308.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1270.750000</td>\n",
       "      <td>1661.000000</td>\n",
       "      <td>0.042908</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>768.500000</td>\n",
       "      <td>234.382589</td>\n",
       "      <td>128.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1878.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7120.000000</td>\n",
       "      <td>2422.500000</td>\n",
       "      <td>0.392453</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>2675.000000</td>\n",
       "      <td>356.874854</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>35.732500</td>\n",
       "      <td>14.750000</td>\n",
       "      <td>7195.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27843.500000</td>\n",
       "      <td>3565.000000</td>\n",
       "      <td>1.085298</td>\n",
       "      <td>4.750000</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>13500.000000</td>\n",
       "      <td>574.742788</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>74.530000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>39505.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>161596.000000</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    126.000000              126.000000   126.000000     126.000000   \n",
       "mean    1741.777778              239.016273   337.380952       6.571429   \n",
       "std     2407.860909              175.078653   463.520785       8.665729   \n",
       "min        0.000000                0.465462     0.000000       0.000000   \n",
       "25%      104.000000               67.033577    19.250000       0.000000   \n",
       "50%      768.500000              234.382589   128.500000       3.500000   \n",
       "75%     2675.000000              356.874854   525.000000      11.000000   \n",
       "max    13500.000000              574.742788  2531.000000      57.000000   \n",
       "\n",
       "       read_ratio   read_time         reads  title_word_count          views  \\\n",
       "count  126.000000  126.000000    126.000000        126.000000     126.000000   \n",
       "mean    29.400397   13.023810   5997.500000          7.095238   22240.547619   \n",
       "std     12.731898    9.736089   8493.803505          3.175981   32305.397567   \n",
       "min      6.060000    1.000000      1.000000          2.000000       3.000000   \n",
       "25%     20.417500    7.250000    308.250000          5.000000    1270.750000   \n",
       "50%     27.150000   10.000000   1878.500000          7.000000    7120.000000   \n",
       "75%     35.732500   14.750000   7195.750000          8.000000   27843.500000   \n",
       "max     74.530000   54.000000  39505.000000         16.000000  161596.000000   \n",
       "\n",
       "         word_count  claps_per_word  editing_days  <tag>Education  \\\n",
       "count    126.000000      126.000000    126.000000      126.000000   \n",
       "mean    3049.603175        0.918837     21.253968        0.714286   \n",
       "std     2448.060885        1.841372     76.040824        0.453557   \n",
       "min      163.000000        0.000000    -13.000000        0.000000   \n",
       "25%     1661.000000        0.042908      0.000000        0.000000   \n",
       "50%     2422.500000        0.392453      1.000000        1.000000   \n",
       "75%     3565.000000        1.085298      4.750000        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         126.000000                 126.000000             126.000000   \n",
       "mean            0.611111                   0.428571               0.404762   \n",
       "std             0.489444                   0.496847               0.492805   \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   126.000000  \n",
       "mean      0.325397  \n",
       "std       0.470393  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efc670b2cc1447cbad7f2b875220abce",
       "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebea08c889694b39b656fd17a23c49f3",
       "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": [
    "@interact\n",
    "def show_titles_more_than(column=list(df.select_dtypes('number').columns), \n",
    "                          x=(1000, 5000, 100)):\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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51f9b7d794c0446d8a049f12b931b60a",
       "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()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdir = 'nature/'\n",
    "\n",
    "@interact\n",
    "def show_images(file=os.listdir(fdir)):\n",
    "    display(Image(fdir+file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls -a -t -r -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import pprint\n",
    "\n",
    "root_dir = '/Users/mmandelbrod'\n",
    "dirs = [d for d in os.listdir(root_dir) if not '.' in d]\n",
    "\n",
    "@interact\n",
    "def show_dir(dir=dirs):\n",
    "    x = subprocess.check_output(f\"cd {root_dir}{dir} && ls -a -t -r -l -h\", shell=True).decode()\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d01a098858a4aa3a0cad4b02cbac5b0",
       "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": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63b7b771ff8449a48d80307aea65456c",
       "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": [
    "@interact\n",
    "def describe(column=list(df.columns)):\n",
    "    print(df[column].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b538fd34b9c4b8fa7a69d24223f7717",
       "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afc7ac61ee40413e86c73c315b561d7f",
       "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"
    }
   ],
   "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')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7ca6d8e42324e13aa3afd01f2c0ef79",
       "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')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'images'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-af1e4ec9c6e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdirectory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDropdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'images'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'nature'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'assorted'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDropdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'images'"
     ]
    }
   ],
   "source": [
    "directory = widgets.Dropdown(options=['images', 'nature', 'assorted'])\n",
    "images = widgets.Dropdown(options=os.listdir(directory.value))\n",
    "\n",
    "def update_images(*args):\n",
    "    images.options = os.listdir(directory.value)\n",
    "\n",
    "directory.observe(update_images, 'value')\n",
    "\n",
    "def show_images(fdir, file):\n",
    "    display(Image(f'{fdir}/{file}'))\n",
    "\n",
    "_ = interact(show_images, fdir=directory, file=images)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2938ae5835b47d382ab5a2555398633",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='tag', options=('Towards Data Science', 'Education', 'Machine Learn…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_stats_by_tag(tag):\n",
    "    display(df.groupby(f'<tag>{tag}').describe()[['views', 'reads', 'claps', 'read_ratio']])\n",
    "    \n",
    "stats = interact(show_stats_by_tag,\n",
    "                tag=widgets.Dropdown(options=['Towards Data Science', 'Education', 'Machine Learning', 'Python', 'Data Science']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "country=Australia<br>year=%{x}<br>lifeExp=%{y}<extra></extra>",
         "legendgroup": "Australia",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "Australia",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1952,
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          1962,
          1967,
          1972,
          1977,
          1982,
          1987,
          1992,
          1997,
          2002,
          2007
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         "xaxis": "x",
         "y": [
          69.12,
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          71.1,
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          73.49,
          74.74,
          76.32,
          77.56,
          78.83,
          80.37,
          81.235
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         "yaxis": "y"
        },
        {
         "hovertemplate": "country=New Zealand<br>year=%{x}<br>lifeExp=%{y}<extra></extra>",
         "legendgroup": "New Zealand",
         "line": {
          "color": "#EF553B",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "New Zealand",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1952,
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         "xaxis": "x",
         "y": [
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         "yaxis": "y"
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       "layout": {
        "legend": {
         "title": {
          "text": "country"
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         "tracegroupgap": 0
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
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       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "                        })\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#toggle lines\n",
    "import plotly.express as px\n",
    "\n",
    "df = px.data.gapminder().query(\"continent=='Oceania'\")\n",
    "fig = px.line(df, x=\"year\", y=\"lifeExp\", color='country')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'PIL'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-0bf1c9d2502a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-1-0bf1c9d2502a>\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'PIL'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_scatter(x=np.random.rand(100), y=np.random.rand(100), mode='markers',\n",
    "                marker={'size': 30, 'color': np.random.rand(100), 'opacity': 0.6, \n",
    "                        'colorscale': 'Viridis'});\n",
    "\n",
    "def show(fig):\n",
    "    import io\n",
    "    import plotly.io as pio\n",
    "    from PIL import Image\n",
    "    buf = io.BytesIO()\n",
    "    pio.write_image(fig, buf)\n",
    "    img = Image.open(buf)\n",
    "    img.show() \n",
    "\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SystemExit",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3327: UserWarning:\n",
      "\n",
      "To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "# fig = go.Figure()\n",
    "# fig.add_scatter(x=np.random.rand(100), y=np.random.rand(100), mode='markers',\n",
    "#                 marker={'size': 30, 'color': np.random.rand(100), 'opacity': 0.6, \n",
    "#                         'colorscale': 'Viridis'});\n",
    "df = px.data.gapminder().query(\"continent=='Oceania'\")\n",
    "fig = px.line(df, x=\"year\", y=\"lifeExp\", color='country')\n",
    "\n",
    "def show_in_window(fig):\n",
    "    import sys, os\n",
    "    import plotly.offline\n",
    "    from PyQt5.QtCore import QUrl\n",
    "    from PyQt5.QtWebEngineWidgets import QWebEngineView\n",
    "    from PyQt5.QtWidgets import QApplication\n",
    "\n",
    "    plotly.offline.plot(fig, filename='name.html', auto_open=False)\n",
    "\n",
    "    app = QApplication(sys.argv)\n",
    "    web = QWebEngineView()\n",
    "    file_path = os.path.abspath(os.path.join(os.path.dirname(\"__file__\"), \"name.html\"))\n",
    "    web.load(QUrl.fromLocalFile(file_path))\n",
    "    web.show()\n",
    "    sys.exit(app.exec_())\n",
    "\n",
    "\n",
    "show_in_window(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-d9d1658c42d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprobs_fig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprobs_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"one\"\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "probs_fig = go.Figure()\n",
    "probs_fig.add_trace(go.scatter( x= [1,2,3], y=[4,5,6], label=\"one\" ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "add_trace() got an unexpected keyword argument 'label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-c1c4c3765baa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'markers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: add_trace() got an unexpected keyword argument 'label'"
     ]
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "\n",
    "N = 1000\n",
    "t = np.linspace(0, 10, 100)\n",
    "y = np.sin(t)\n",
    "\n",
    "# fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=t, y=y, mode='markers'), label=\"a\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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