DigitalBusinessTransformation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The purpose of this notebook is to visualize how increase of internet usage increase the ecommerce sales and how this process will affect businesses which will not undertake digital transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individuals using the Internet (% of population)\n",
    "http://api.worldbank.org/v2/en/indicator/IT.NET.USER.ZS?downloadformat=csv\n",
    "\n",
    "Checking the growth of title variable over 2 decades.\n",
    "Selected countries Poland, Germany and European Union."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-09-06 16:07:19--  http://api.worldbank.org/v2/en/indicator/IT.NET.USER.ZS?downloadformat=csv\n",
      "Resolving api.worldbank.org (api.worldbank.org)... 52.188.136.184\n",
      "Connecting to api.worldbank.org (api.worldbank.org)|52.188.136.184|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 51376 (50K) [application/zip]\n",
      "Saving to: ‘data.zip’\n",
      "\n",
      "data.zip            100%[===================>]  50,17K   166KB/s    in 0,3s    \n",
      "\n",
      "2020-09-06 16:07:20 (166 KB/s) - ‘data.zip’ saved [51376/51376]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget \"http://api.worldbank.org/v2/en/indicator/IT.NET.USER.ZS?downloadformat=csv\" -O data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data.zip\r\n",
      "  inflating: Metadata_Indicator_API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv  \r\n",
      "  inflating: API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv  \r\n",
      "  inflating: Metadata_Country_API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv  \r\n"
     ]
    }
   ],
   "source": [
    "!unzip -o data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meta Individuals using the Internet (% of population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Individuals using the Internet (% of population)'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./Metadata_Indicator_API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv\")[\"INDICATOR_NAME\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./Metadata_Indicator_API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv\")[\"SOURCE_NOTE\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_df = pd.read_csv(\"./API_IT.NET.USER.ZS_DS2_en_csv_v2_1307380.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "YEARS = [str(x) for x in range(2000, 2019, 1)]\n",
    "COLUMNS = [\"Country Name\", \"Country Code\", *YEARS]\n",
    "df = source_df[COLUMNS]\n",
    "# IMPORTANT not data for 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
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       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
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       "      <td>17.100000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>...</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
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       "      <td>0.004561</td>\n",
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       "      <td>1.224148</td>\n",
       "      <td>2.107124</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>...</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>5.9</td>\n",
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       "      <td>8.260000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>0.105046</td>\n",
       "      <td>0.136014</td>\n",
       "      <td>0.270377</td>\n",
       "      <td>0.370682</td>\n",
       "      <td>0.464815</td>\n",
       "      <td>1.143367</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8.9</td>\n",
       "      <td>21.40</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.339079</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>0.114097</td>\n",
       "      <td>0.325798</td>\n",
       "      <td>0.390081</td>\n",
       "      <td>0.971900</td>\n",
       "      <td>2.420388</td>\n",
       "      <td>6.043891</td>\n",
       "      <td>9.609991</td>\n",
       "      <td>15.036115</td>\n",
       "      <td>...</td>\n",
       "      <td>41.20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>54.655959</td>\n",
       "      <td>57.2</td>\n",
       "      <td>60.10</td>\n",
       "      <td>63.252933</td>\n",
       "      <td>66.363445</td>\n",
       "      <td>71.847041</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>10.538836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.260469</td>\n",
       "      <td>13.546413</td>\n",
       "      <td>26.837954</td>\n",
       "      <td>37.605766</td>\n",
       "      <td>48.936847</td>\n",
       "      <td>70.870000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.53</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.434425</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.90</td>\n",
       "      <td>96.910000</td>\n",
       "      <td>97.930637</td>\n",
       "      <td>91.567467</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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       "  Country Name Country Code       2000       2001       2002       2003  \\\n",
       "0        Aruba          ABW  15.442823  17.100000  18.800000  20.800000   \n",
       "1  Afghanistan          AFG        NaN   0.004723   0.004561   0.087891   \n",
       "2       Angola          AGO   0.105046   0.136014   0.270377   0.370682   \n",
       "3      Albania          ALB   0.114097   0.325798   0.390081   0.971900   \n",
       "4      Andorra          AND  10.538836        NaN  11.260469  13.546413   \n",
       "\n",
       "        2004       2005       2006       2007  ...   2009  2010  2011  \\\n",
       "0  23.000000  25.400000  28.000000  30.900000  ...  58.00  62.0  69.0   \n",
       "1   0.105809   1.224148   2.107124   1.900000  ...   3.55   4.0   5.0   \n",
       "2   0.464815   1.143367   1.500000   1.700000  ...   2.30   2.8   3.1   \n",
       "3   2.420388   6.043891   9.609991  15.036115  ...  41.20  45.0  49.0   \n",
       "4  26.837954  37.605766  48.936847  70.870000  ...  78.53  81.0  81.0   \n",
       "\n",
       "        2012  2013   2014       2015       2016       2017  2018  \n",
       "0  74.000000  78.9  83.78  88.661227  93.542454  97.170000   NaN  \n",
       "1   5.454545   5.9   7.00   8.260000  11.200000  13.500000   NaN  \n",
       "2   6.500000   8.9  21.40  12.400000  13.000000  14.339079   NaN  \n",
       "3  54.655959  57.2  60.10  63.252933  66.363445  71.847041   NaN  \n",
       "4  86.434425  94.0  95.90  96.910000  97.930637  91.567467   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
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     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "POLAND = \"POL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "poland_df = df[df[\"Country Code\"] == POLAND]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "         2000     2001   2002   2003   2004   2005   2006  2007   2008   2009  \\\n",
       "188  7.285429  9.90067  21.15  24.87  32.53  38.81  44.58  48.6  53.13  58.97   \n",
       "\n",
       "      2010       2011       2012     2013  2014    2015       2016       2017  \\\n",
       "188  62.32  61.949999  62.309997  62.8492  66.6  67.997  73.300704  75.985366   \n",
       "\n",
       "          2018  \n",
       "188  77.541735  "
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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    "poland_df[YEARS]"
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\"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Constat increase of internet users in Poland over 2 last decades.\"}, \"xaxis\": {\"gridwidth\": 1, \"showticklabels\": true, \"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"overlaying\": \"y\", \"side\": \"left\", \"tickfont\": {\"color\": \"#1f77b4\"}, \"title\": {\"font\": {\"color\": \"#1f77b4\"}, \"text\": \"Individuals using the Internet (% of population)\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('701e7338-c5d4-4727-822e-7e123214d32f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    " \n",
    "\n",
    "fig = go.Figure(data=[\n",
    "\n",
    "    \n",
    "    go.Scatter(mode=\"lines+text\", line={\"color\": \"royalblue\"}, name='average 2000s %',\n",
    "               text=['{0}%'.format(int(x)) for x in poland_df[YEARS].values[0]],\n",
    "               textposition=\"top left\",\n",
    "               x=YEARS, \n",
    "               y=poland_df[YEARS].values[0])\n",
    "])\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Constat increase of internet users in Poland over 2 last decades.\",\n",
    "    yaxis=dict(\n",
    "        title=\"Individuals using the Internet (% of population)\",\n",
    "        titlefont=dict(\n",
    "            color=\"#1f77b4\"\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            color=\"#1f77b4\"\n",
    "        ),\n",
    "        side=\"left\",\n",
    "        overlaying=\"y\"\n",
    "    ),\n",
    "    xaxis = dict(\n",
    "      title = 'Years',\n",
    "      showticklabels = True,\n",
    "      gridwidth = 1\n",
    "   ),\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EU E-commerce sales data\n",
    "#### https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ec_eseln2&lang=en\n",
    "You have to download data set manually through application interface. There is not direct url to trigger.\n",
    "\"DATASET: E-commerce sales [isoc_ec_eseln2]\"\n",
    "\"LAST UPDATE: 24.02.20 14:13:04\"\n",
    "\"EXTRACTION DATE: 08.09.20 11:03:09\"\n",
    "\"SOURCE OF DATA: Eurostat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  isoc_ec_eseln2.zip\n",
      "  inflating: isoc_ec_eseln2_Label.csv  \n",
      "  inflating: isoc_ec_eseln2_1_Data.csv  \n"
     ]
    }
   ],
   "source": [
    "!unzip -o isoc_ec_eseln2.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecommerce_sales_df = pd.read_csv(\"isoc_ec_eseln2_1_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>INDIC_IS</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SIZEN_R2</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag and Footnotes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>:</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
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       "      <td>Percentage of enterprises</td>\n",
       "      <td>Medium enterprises (50-249 persons employed), ...</td>\n",
       "      <td>:</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
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       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>Small enterprises (10-49 persons employed), wi...</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <th>74410</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Enterprises with web sales via e-commerce mark...</td>\n",
       "      <td>Percentage of enterprises receiving e-commerce...</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>:</td>\n",
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       "    <tr>\n",
       "      <th>74411</th>\n",
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       "      <td>Bosnia and Herzegovina</td>\n",
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       "      <td>Small enterprises (10-49 persons employed), wi...</td>\n",
       "      <td>:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74412</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Enterprises with web sales via e-commerce mark...</td>\n",
       "      <td>Percentage of enterprises receiving e-commerce...</td>\n",
       "      <td>Medium enterprises (50-249 persons employed), ...</td>\n",
       "      <td>:</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>74413</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Enterprises with web sales via e-commerce mark...</td>\n",
       "      <td>Percentage of enterprises receiving e-commerce...</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>74414</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Enterprises with web sales via e-commerce mark...</td>\n",
       "      <td>Percentage of enterprises receiving e-commerce...</td>\n",
       "      <td>Small enterprises (10-49 persons employed), wi...</td>\n",
       "      <td>:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74415 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TIME                                        GEO  \\\n",
       "0      2009  European Union - 27 countries (from 2020)   \n",
       "1      2009  European Union - 27 countries (from 2020)   \n",
       "2      2009  European Union - 27 countries (from 2020)   \n",
       "3      2009  European Union - 27 countries (from 2020)   \n",
       "4      2009  European Union - 27 countries (from 2020)   \n",
       "...     ...                                        ...   \n",
       "74410  2019                     Bosnia and Herzegovina   \n",
       "74411  2019                     Bosnia and Herzegovina   \n",
       "74412  2019                     Bosnia and Herzegovina   \n",
       "74413  2019                     Bosnia and Herzegovina   \n",
       "74414  2019                     Bosnia and Herzegovina   \n",
       "\n",
       "                                                INDIC_IS  \\\n",
       "0                      Enterprises with e-commerce sales   \n",
       "1                      Enterprises with e-commerce sales   \n",
       "2                      Enterprises with e-commerce sales   \n",
       "3                      Enterprises with e-commerce sales   \n",
       "4                      Enterprises with e-commerce sales   \n",
       "...                                                  ...   \n",
       "74410  Enterprises with web sales via e-commerce mark...   \n",
       "74411  Enterprises with web sales via e-commerce mark...   \n",
       "74412  Enterprises with web sales via e-commerce mark...   \n",
       "74413  Enterprises with web sales via e-commerce mark...   \n",
       "74414  Enterprises with web sales via e-commerce mark...   \n",
       "\n",
       "                                                    UNIT  \\\n",
       "0                              Percentage of enterprises   \n",
       "1                              Percentage of enterprises   \n",
       "2                              Percentage of enterprises   \n",
       "3                              Percentage of enterprises   \n",
       "4                              Percentage of enterprises   \n",
       "...                                                  ...   \n",
       "74410  Percentage of enterprises receiving e-commerce...   \n",
       "74411  Percentage of enterprises receiving e-commerce...   \n",
       "74412  Percentage of enterprises receiving e-commerce...   \n",
       "74413  Percentage of enterprises receiving e-commerce...   \n",
       "74414  Percentage of enterprises receiving e-commerce...   \n",
       "\n",
       "                                                SIZEN_R2 Value  \\\n",
       "0      All enterprises, without financial sector (10 ...     :   \n",
       "1      Small enterprises (10-49 persons employed), wi...     :   \n",
       "2      Medium enterprises (50-249 persons employed), ...     :   \n",
       "3      All enterprises, without financial sector (10 ...    12   \n",
       "4      Small enterprises (10-49 persons employed), wi...    11   \n",
       "...                                                  ...   ...   \n",
       "74410  All enterprises, without financial sector (10 ...     :   \n",
       "74411  Small enterprises (10-49 persons employed), wi...     :   \n",
       "74412  Medium enterprises (50-249 persons employed), ...     :   \n",
       "74413  All enterprises, without financial sector (10 ...     :   \n",
       "74414  Small enterprises (10-49 persons employed), wi...     :   \n",
       "\n",
       "      Flag and Footnotes  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  \n",
       "...                  ...  \n",
       "74410                NaN  \n",
       "74411                NaN  \n",
       "74412                NaN  \n",
       "74413                NaN  \n",
       "74414                NaN  \n",
       "\n",
       "[74415 rows x 7 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecommerce_sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask1 = ecommerce_sales_df[\"GEO\"] == \"Poland\"\n",
    "mask2 = ecommerce_sales_df[\"INDIC_IS\"] == \"Enterprises with e-commerce sales\"\n",
    "mask3 = ecommerce_sales_df[\"UNIT\"] == \"Percentage of enterprises\"\n",
    "mask4 = ecommerce_sales_df[\"SIZEN_R2\"] == \"All enterprises, without financial sector (10 persons employed or more)\"\n",
    "mask5 = ecommerce_sales_df[\"TIME\"] != 2009\n",
    "# excluded 2009 not enough data\n",
    "ecommerce_sales_df_poland = ecommerce_sales_df[mask1 & mask2 & mask3 & mask4 & mask5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "      <th>11055</th>\n",
       "      <td>2010</td>\n",
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       "      <th>17820</th>\n",
       "      <td>2011</td>\n",
       "      <td>Poland</td>\n",
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       "      <td>Percentage of enterprises</td>\n",
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       "      <th>24585</th>\n",
       "      <td>2012</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>31350</th>\n",
       "      <td>2013</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38115</th>\n",
       "      <td>2014</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44880</th>\n",
       "      <td>2015</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51645</th>\n",
       "      <td>2016</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58410</th>\n",
       "      <td>2017</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65175</th>\n",
       "      <td>2018</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71940</th>\n",
       "      <td>2019</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Enterprises with e-commerce sales</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TIME     GEO                           INDIC_IS  \\\n",
       "11055  2010  Poland  Enterprises with e-commerce sales   \n",
       "17820  2011  Poland  Enterprises with e-commerce sales   \n",
       "24585  2012  Poland  Enterprises with e-commerce sales   \n",
       "31350  2013  Poland  Enterprises with e-commerce sales   \n",
       "38115  2014  Poland  Enterprises with e-commerce sales   \n",
       "44880  2015  Poland  Enterprises with e-commerce sales   \n",
       "51645  2016  Poland  Enterprises with e-commerce sales   \n",
       "58410  2017  Poland  Enterprises with e-commerce sales   \n",
       "65175  2018  Poland  Enterprises with e-commerce sales   \n",
       "71940  2019  Poland  Enterprises with e-commerce sales   \n",
       "\n",
       "                            UNIT  \\\n",
       "11055  Percentage of enterprises   \n",
       "17820  Percentage of enterprises   \n",
       "24585  Percentage of enterprises   \n",
       "31350  Percentage of enterprises   \n",
       "38115  Percentage of enterprises   \n",
       "44880  Percentage of enterprises   \n",
       "51645  Percentage of enterprises   \n",
       "58410  Percentage of enterprises   \n",
       "65175  Percentage of enterprises   \n",
       "71940  Percentage of enterprises   \n",
       "\n",
       "                                                SIZEN_R2 Value  \\\n",
       "11055  All enterprises, without financial sector (10 ...     9   \n",
       "17820  All enterprises, without financial sector (10 ...    10   \n",
       "24585  All enterprises, without financial sector (10 ...    11   \n",
       "31350  All enterprises, without financial sector (10 ...    11   \n",
       "38115  All enterprises, without financial sector (10 ...    12   \n",
       "44880  All enterprises, without financial sector (10 ...    12   \n",
       "51645  All enterprises, without financial sector (10 ...    12   \n",
       "58410  All enterprises, without financial sector (10 ...    12   \n",
       "65175  All enterprises, without financial sector (10 ...    14   \n",
       "71940  All enterprises, without financial sector (10 ...    16   \n",
       "\n",
       "      Flag and Footnotes  \n",
       "11055                NaN  \n",
       "17820                NaN  \n",
       "24585                NaN  \n",
       "31350                NaN  \n",
       "38115                NaN  \n",
       "44880                NaN  \n",
       "51645                NaN  \n",
       "58410                NaN  \n",
       "65175                NaN  \n",
       "71940                NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecommerce_sales_df_poland"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Social Media Used By Purpose\n",
    "#### https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_cismp&lang=en\n",
    "You have to download data set manually through application interface.\n",
    "\"DATASET: Social media use by purpose   [isoc_cismp]\"\n",
    "\"LAST UPDATE: 24.02.20 14:13:07\"\n",
    "\"EXTRACTION DATE: 08.09.20 12:21:29\"\n",
    "\"SOURCE OF DATA: Eurostat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  isoc_cismp.zip\r\n",
      "  inflating: isoc_cismp_Label.csv    \r\n",
      "  inflating: isoc_cismp_1_Data.csv   \r\n"
     ]
    }
   ],
   "source": [
    "!unzip -o isoc_cismp.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_media_used_by_purpose_df = pd.read_csv(\"isoc_cismp_1_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
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       "      <th>SIZEN_R2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Website has online ordering, reservation or bo...</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Website has online ordering, reservation or bo...</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>Small enterprises (10-49 persons employed), wi...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Website has online ordering, reservation or bo...</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>Medium enterprises (50-249 persons employed), ...</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Website has online ordering, reservation or bo...</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>SMEs (10-249 persons employed), without financ...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>Website has online ordering, reservation or bo...</td>\n",
       "      <td>Percentage of enterprises</td>\n",
       "      <td>Large enterprises (250 persons employed or mor...</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15995</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Collaborate with business partners (e.g. suppl...</td>\n",
       "      <td>Percentage of the enterprises with internet ac...</td>\n",
       "      <td>All enterprises, without financial sector (10 ...</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15996</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Collaborate with business partners (e.g. suppl...</td>\n",
       "      <td>Percentage of the enterprises with internet ac...</td>\n",
       "      <td>Small enterprises (10-49 persons employed), wi...</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15997</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Collaborate with business partners (e.g. suppl...</td>\n",
       "      <td>Percentage of the enterprises with internet ac...</td>\n",
       "      <td>Medium enterprises (50-249 persons employed), ...</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15998</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Collaborate with business partners (e.g. suppl...</td>\n",
       "      <td>Percentage of the enterprises with internet ac...</td>\n",
       "      <td>SMEs (10-249 persons employed), without financ...</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>2019</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Collaborate with business partners (e.g. suppl...</td>\n",
       "      <td>Percentage of the enterprises with internet ac...</td>\n",
       "      <td>Large enterprises (250 persons employed or mor...</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "       TIME                                        GEO  \\\n",
       "0      2013  European Union - 27 countries (from 2020)   \n",
       "1      2013  European Union - 27 countries (from 2020)   \n",
       "2      2013  European Union - 27 countries (from 2020)   \n",
       "3      2013  European Union - 27 countries (from 2020)   \n",
       "4      2013  European Union - 27 countries (from 2020)   \n",
       "...     ...                                        ...   \n",
       "15995  2019                     Bosnia and Herzegovina   \n",
       "15996  2019                     Bosnia and Herzegovina   \n",
       "15997  2019                     Bosnia and Herzegovina   \n",
       "15998  2019                     Bosnia and Herzegovina   \n",
       "15999  2019                     Bosnia and Herzegovina   \n",
       "\n",
       "                                                INDIC_IS  \\\n",
       "0      Website has online ordering, reservation or bo...   \n",
       "1      Website has online ordering, reservation or bo...   \n",
       "2      Website has online ordering, reservation or bo...   \n",
       "3      Website has online ordering, reservation or bo...   \n",
       "4      Website has online ordering, reservation or bo...   \n",
       "...                                                  ...   \n",
       "15995  Collaborate with business partners (e.g. suppl...   \n",
       "15996  Collaborate with business partners (e.g. suppl...   \n",
       "15997  Collaborate with business partners (e.g. suppl...   \n",
       "15998  Collaborate with business partners (e.g. suppl...   \n",
       "15999  Collaborate with business partners (e.g. suppl...   \n",
       "\n",
       "                                                    UNIT  \\\n",
       "0                              Percentage of enterprises   \n",
       "1                              Percentage of enterprises   \n",
       "2                              Percentage of enterprises   \n",
       "3                              Percentage of enterprises   \n",
       "4                              Percentage of enterprises   \n",
       "...                                                  ...   \n",
       "15995  Percentage of the enterprises with internet ac...   \n",
       "15996  Percentage of the enterprises with internet ac...   \n",
       "15997  Percentage of the enterprises with internet ac...   \n",
       "15998  Percentage of the enterprises with internet ac...   \n",
       "15999  Percentage of the enterprises with internet ac...   \n",
       "\n",
       "                                                SIZEN_R2 Value  \\\n",
       "0      All enterprises, without financial sector (10 ...     5   \n",
       "1      Small enterprises (10-49 persons employed), wi...     5   \n",
       "2      Medium enterprises (50-249 persons employed), ...     8   \n",
       "3      SMEs (10-249 persons employed), without financ...     5   \n",
       "4      Large enterprises (250 persons employed or mor...    13   \n",
       "...                                                  ...   ...   \n",
       "15995  All enterprises, without financial sector (10 ...    24   \n",
       "15996  Small enterprises (10-49 persons employed), wi...    23   \n",
       "15997  Medium enterprises (50-249 persons employed), ...    28   \n",
       "15998  SMEs (10-249 persons employed), without financ...    24   \n",
       "15999  Large enterprises (250 persons employed or mor...    33   \n",
       "\n",
       "      Flag and Footnotes  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  \n",
       "...                  ...  \n",
       "15995                NaN  \n",
       "15996                NaN  \n",
       "15997                NaN  \n",
       "15998                NaN  \n",
       "15999                NaN  \n",
       "\n",
       "[16000 rows x 7 columns]"
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     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_media_used_by_purpose_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask1 = social_media_used_by_purpose_df[\"GEO\"] == \"Poland\"\n",
    "mask2 = social_media_used_by_purpose_df[\"INDIC_IS\"] == \"Develop the enterprise's image or market products\"\n",
    "mask3 = social_media_used_by_purpose_df[\"UNIT\"] == \"Percentage of enterprises\"\n",
    "mask4 = social_media_used_by_purpose_df[\"SIZEN_R2\"] == \"All enterprises, without financial sector (10 persons employed or more)\"\n",
    "social_media_used_by_purpose_df_poland = social_media_used_by_purpose_df[mask1 & mask2 & mask3 & mask4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "       TIME     GEO                                           INDIC_IS  \\\n",
       "2520   2013  Poland  Develop the enterprise's image or market products   \n",
       "6520   2015  Poland  Develop the enterprise's image or market products   \n",
       "10520  2017  Poland  Develop the enterprise's image or market products   \n",
       "14520  2019  Poland  Develop the enterprise's image or market products   \n",
       "\n",
       "                            UNIT  \\\n",
       "2520   Percentage of enterprises   \n",
       "6520   Percentage of enterprises   \n",
       "10520  Percentage of enterprises   \n",
       "14520  Percentage of enterprises   \n",
       "\n",
       "                                                SIZEN_R2 Value  \\\n",
       "2520   All enterprises, without financial sector (10 ...    15   \n",
       "6520   All enterprises, without financial sector (10 ...    21   \n",
       "10520  All enterprises, without financial sector (10 ...    25   \n",
       "14520  All enterprises, without financial sector (10 ...    32   \n",
       "\n",
       "      Flag and Footnotes  \n",
       "2520                 NaN  \n",
       "6520                 NaN  \n",
       "10520                NaN  \n",
       "14520                NaN  "
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     "execution_count": 47,
     "metadata": {},
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   "source": [
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   ]
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   "execution_count": 48,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3d4e7f9f-d36f-44e9-aab3-b00663f8dc90');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig2 = go.Figure()\n",
    "\n",
    "fig2 = go.Figure(data=[\n",
    "    go.Scatter(mode=\"lines+text\", \n",
    "               line={\"color\": 'royalblue'},\n",
    "               textposition=\"top left\",\n",
    "               text=['{0}%'.format(int(x)) for x in social_media_used_by_purpose_df_poland['Value'].values],\n",
    "               name=\"Social Media Used to Develop the enterprise's image or market products\", \n",
    "               x=social_media_used_by_purpose_df_poland['TIME'].values, \n",
    "               y=social_media_used_by_purpose_df_poland['Value'].values),\n",
    "    \n",
    "    go.Scatter(mode=\"lines+text\",\n",
    "               textposition=\"top left\",\n",
    "               text=['{0}%'.format(int(x)) for x in ecommerce_sales_df_poland['Value'].values],\n",
    "               line={\"color\": 'red'}, name=\"Enterprises with e-commerce sales\", \n",
    "               x=ecommerce_sales_df_poland['TIME'].values, \n",
    "               y=ecommerce_sales_df_poland['Value'].values),\n",
    "])\n",
    "\n",
    "fig2.update_layout(\n",
    "    title=\"Usage of e-commerce and social media to increase sales (Poland)\",\n",
    "    yaxis=dict(\n",
    "        title=\"% of enterprises\",\n",
    "        titlefont=dict(\n",
    "            color=\"#1f77b4\"\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            color=\"#1f77b4\"\n",
    "        ),\n",
    "        side=\"left\",\n",
    "        overlaying=\"y\"\n",
    "    ),\n",
    "    xaxis = dict(\n",
    "      title = 'Years',\n",
    "      showgrid = True,\n",
    "      zeroline = True,\n",
    "      showline = True,\n",
    "      showticklabels = True,\n",
    "      gridwidth = 1\n",
    "   ),\n",
    ")\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "fig3 = go.Figure()\n",
    "\n",
    "#  \"Enterprises with e-commerce sales\"\n",
    "\n",
    "fig3 = go.Figure(data=[\n",
    "\n",
    "    go.Scatter(mode=\"lines+text\", line={\"color\": \"royalblue\"}, name='Internet Users',\n",
    "               text=['{0}%'.format(int(x)) for x in poland_df[YEARS].values[0]],\n",
    "               textposition=\"top left\",\n",
    "               yaxis=\"y1\",\n",
    "               x=YEARS, \n",
    "               y=poland_df[YEARS].values[0]),\n",
    "    go.Scatter(mode='markers',\n",
    "               name='Enterprises',\n",
    "               yaxis=\"y2\",\n",
    "               x=social_media_used_by_purpose_df_poland['TIME'].values, \n",
    "               y=social_media_used_by_purpose_df_poland['Value'].values, \n",
    "               marker=dict(size=[int(x) for x in social_media_used_by_purpose_df_poland['Value'].values],\n",
    "                          color=\"red\")\n",
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    "fig3.update_layout(\n",
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    "    yaxis=dict(\n",
    "        title=\"Individuals using the Internet (% of population)\",\n",
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    "      title = 'Years',\n",
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