economic_impact.ipynb

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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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  },
  "orig_nbformat": 2,
  "file_extension": ".py",
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  "name": "python",
  "npconvert_exporter": "python",
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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/Users/kamilestankeviciute/anaconda3/envs/tensorflow/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n  interactivity=interactivity, compiler=compiler, result=result)\n"
    }
   ],
   "source": [
    "lse_historical_data = pd.read_csv('Datasets/lse_historical_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>year</th>\n      <th>month</th>\n      <th>ticker</th>\n      <th>list_date</th>\n      <th>company_name</th>\n      <th>icb_industry</th>\n      <th>icb_supersector</th>\n      <th>country_of_incorporation</th>\n      <th>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>OPM</td>\n      <td>2006-08-02</td>\n      <td>1PM PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>40.036935</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>SPA</td>\n      <td>2009-02-02</td>\n      <td>1SPATIAL PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>29.467662</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>C21</td>\n      <td>2005-04-15</td>\n      <td>21ST CENTURY TECHNOLOGY PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>2.564093</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>III</td>\n      <td>1994-07-18</td>\n      <td>3I GROUP PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>8765.510388</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>3IN</td>\n      <td>2007-03-13</td>\n      <td>3I INFRASTRUCTURE PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>Jersey</td>\n      <td>2025.382649</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>159395</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOX</td>\n      <td>2001-12-10</td>\n      <td>ZINCOX RESOURCES PLC</td>\n      <td>Mining</td>\n      <td>General Mining</td>\n      <td>United Kingdom</td>\n      <td>19.141342</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159396</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOL</td>\n      <td>2004-05-18</td>\n      <td>ZOLTAV RESOURCES INC</td>\n      <td>General Financial</td>\n      <td>Specialty Finance</td>\n      <td>Cayman Islands</td>\n      <td>28.190878</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159397</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOO</td>\n      <td>2001-05-14</td>\n      <td>ZOO DIGITAL GROUP PLC</td>\n      <td>Software &amp; Computer Services</td>\n      <td>Software</td>\n      <td>United Kingdom</td>\n      <td>2.250767</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159398</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZTF</td>\n      <td>1995-02-28</td>\n      <td>ZOTEFOAMS</td>\n      <td>Chemicals</td>\n      <td>Specialty Chemicals</td>\n      <td>United Kingdom</td>\n      <td>81.457315</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159399</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZYT</td>\n      <td>2000-07-06</td>\n      <td>ZYTRONIC</td>\n      <td>Electronic &amp; Electrical Equipment</td>\n      <td>Electronic Equipment</td>\n      <td>United Kingdom</td>\n      <td>22.067038</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>159400 rows × 12 columns</p>\n</div>",
      "text/plain": "        year month ticker   list_date                         company_name  \\\n0       2017    11    OPM  2006-08-02                              1PM PLC   \n1       2017    11    SPA  2009-02-02                         1SPATIAL PLC   \n2       2017    11    C21  2005-04-15          21ST CENTURY TECHNOLOGY PLC   \n3       2017    11    III  1994-07-18                         3I GROUP PLC   \n4       2017    11    3IN  2007-03-13                3I INFRASTRUCTURE PLC   \n...      ...   ...    ...         ...                                  ...   \n159395  2013     6    ZOX  2001-12-10  ZINCOX RESOURCES PLC                  \n159396  2013     6    ZOL  2004-05-18  ZOLTAV RESOURCES INC                  \n159397  2013     6    ZOO  2001-05-14  ZOO DIGITAL GROUP PLC                 \n159398  2013     6    ZTF  1995-02-28  ZOTEFOAMS                             \n159399  2013     6    ZYT  2000-07-06  ZYTRONIC                              \n\n                             icb_industry       icb_supersector  \\\n0                      Financial Services            Financials   \n1             Industrial Goods & Services           Industrials   \n2             Industrial Goods & Services           Industrials   \n3                      Financial Services            Financials   \n4                      Financial Services            Financials   \n...                                   ...                   ...   \n159395                             Mining        General Mining   \n159396                  General Financial     Specialty Finance   \n159397       Software & Computer Services              Software   \n159398                          Chemicals   Specialty Chemicals   \n159399  Electronic & Electrical Equipment  Electronic Equipment   \n\n       country_of_incorporation   market_cap  eps  p/e  p/s  \n0                United Kingdom    40.036935  NaN  NaN  NaN  \n1                United Kingdom    29.467662  NaN  NaN  NaN  \n2                United Kingdom     2.564093  NaN  NaN  NaN  \n3                United Kingdom  8765.510388  NaN  NaN  NaN  \n4                        Jersey  2025.382649  NaN  NaN  NaN  \n...                         ...          ...  ...  ...  ...  \n159395           United Kingdom    19.141342  NaN  NaN  NaN  \n159396           Cayman Islands    28.190878  NaN  NaN  NaN  \n159397           United Kingdom     2.250767  NaN  NaN  NaN  \n159398           United Kingdom    81.457315  NaN  NaN  NaN  \n159399           United Kingdom    22.067038  NaN  NaN  NaN  \n\n[159400 rows x 12 columns]"
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lse_historical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "2014    25362\n2013    24940\n2016    24197\n2018    24125\n2017    23790\n2015    22934\n2019    14052\nName: year, dtype: int64"
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lse_historical_data['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>year</th>\n      <th>month</th>\n      <th>ticker</th>\n      <th>list_date</th>\n      <th>company_name</th>\n      <th>icb_industry</th>\n      <th>icb_supersector</th>\n      <th>country_of_incorporation</th>\n      <th>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>OPM</td>\n      <td>2006-08-02</td>\n      <td>1PM PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>40.036935</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>SPA</td>\n      <td>2009-02-02</td>\n      <td>1SPATIAL PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>29.467662</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>C21</td>\n      <td>2005-04-15</td>\n      <td>21ST CENTURY TECHNOLOGY PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>2.564093</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>III</td>\n      <td>1994-07-18</td>\n      <td>3I GROUP PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>8765.510388</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>3IN</td>\n      <td>2007-03-13</td>\n      <td>3I INFRASTRUCTURE PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>Jersey</td>\n      <td>2025.382649</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>159395</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOX</td>\n      <td>2001-12-10</td>\n      <td>ZINCOX RESOURCES PLC</td>\n      <td>Mining</td>\n      <td>General Mining</td>\n      <td>United Kingdom</td>\n      <td>19.141342</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159396</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOL</td>\n      <td>2004-05-18</td>\n      <td>ZOLTAV RESOURCES INC</td>\n      <td>General Financial</td>\n      <td>Specialty Finance</td>\n      <td>Cayman Islands</td>\n      <td>28.190878</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159397</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOO</td>\n      <td>2001-05-14</td>\n      <td>ZOO DIGITAL GROUP PLC</td>\n      <td>Software &amp; Computer Services</td>\n      <td>Software</td>\n      <td>United Kingdom</td>\n      <td>2.250767</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159398</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZTF</td>\n      <td>1995-02-28</td>\n      <td>ZOTEFOAMS</td>\n      <td>Chemicals</td>\n      <td>Specialty Chemicals</td>\n      <td>United Kingdom</td>\n      <td>81.457315</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>159399</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZYT</td>\n      <td>2000-07-06</td>\n      <td>ZYTRONIC</td>\n      <td>Electronic &amp; Electrical Equipment</td>\n      <td>Electronic Equipment</td>\n      <td>United Kingdom</td>\n      <td>22.067038</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>159400 rows × 12 columns</p>\n</div>",
      "text/plain": "        year month ticker   list_date                         company_name  \\\n0       2017    11    OPM  2006-08-02                              1PM PLC   \n1       2017    11    SPA  2009-02-02                         1SPATIAL PLC   \n2       2017    11    C21  2005-04-15          21ST CENTURY TECHNOLOGY PLC   \n3       2017    11    III  1994-07-18                         3I GROUP PLC   \n4       2017    11    3IN  2007-03-13                3I INFRASTRUCTURE PLC   \n...      ...   ...    ...         ...                                  ...   \n159395  2013     6    ZOX  2001-12-10  ZINCOX RESOURCES PLC                  \n159396  2013     6    ZOL  2004-05-18  ZOLTAV RESOURCES INC                  \n159397  2013     6    ZOO  2001-05-14  ZOO DIGITAL GROUP PLC                 \n159398  2013     6    ZTF  1995-02-28  ZOTEFOAMS                             \n159399  2013     6    ZYT  2000-07-06  ZYTRONIC                              \n\n                             icb_industry       icb_supersector  \\\n0                      Financial Services            Financials   \n1             Industrial Goods & Services           Industrials   \n2             Industrial Goods & Services           Industrials   \n3                      Financial Services            Financials   \n4                      Financial Services            Financials   \n...                                   ...                   ...   \n159395                             Mining        General Mining   \n159396                  General Financial     Specialty Finance   \n159397       Software & Computer Services              Software   \n159398                          Chemicals   Specialty Chemicals   \n159399  Electronic & Electrical Equipment  Electronic Equipment   \n\n       country_of_incorporation   market_cap  eps  p/e  p/s  \n0                United Kingdom    40.036935  NaN  NaN  NaN  \n1                United Kingdom    29.467662  NaN  NaN  NaN  \n2                United Kingdom     2.564093  NaN  NaN  NaN  \n3                United Kingdom  8765.510388  NaN  NaN  NaN  \n4                        Jersey  2025.382649  NaN  NaN  NaN  \n...                         ...          ...  ...  ...  ...  \n159395           United Kingdom    19.141342  NaN  NaN  NaN  \n159396           Cayman Islands    28.190878  NaN  NaN  NaN  \n159397           United Kingdom     2.250767  NaN  NaN  NaN  \n159398           United Kingdom    81.457315  NaN  NaN  NaN  \n159399           United Kingdom    22.067038  NaN  NaN  NaN  \n\n[159400 rows x 12 columns]"
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lse_historical_data.replace({'month': {'Feb': '2'}}, inplace=True)\n",
    "lse_historical_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Equity Investment Instruments             18455\nFinancial Services                        16038\nIndustrial Goods & Services                7733\nMining                                     7515\nSupport Services                           7510\n                                          ...  \nDebentures & Loans                           49\nFood; Beverage and Tobacco                   49\nConstruction and Materials                   43\nPersonal Care; Drug and Grocery Stores       21\nAutomobiles and Parts                         9\nName: icb_industry, Length: 68, dtype: int64"
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lse_historical_data['icb_industry'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Financials                       20936\nEquity Investment Instruments    18455\nIndustrials                       9166\nConsumer Services                 6563\nBasic Materials                   5553\n                                 ...  \nRailroads                           49\nForestry                            47\nBrewers                             45\nMortgage Finance                    25\nReal Estate Investment Trusts       19\nName: icb_supersector, Length: 127, dtype: int64"
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lse_historical_data['icb_supersector'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0          True\n1         False\n2         False\n3          True\n4          True\n          ...  \n159395    False\n159396     True\n159397    False\n159398    False\n159399    False\nName: icb_industry, Length: 159400, dtype: object"
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finance_sector = lse_historical_data['icb_industry'].str.lower().str.contains('financ')\n",
    "# finance_sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>year</th>\n      <th>month</th>\n      <th>ticker</th>\n      <th>list_date</th>\n      <th>company_name</th>\n      <th>icb_industry</th>\n      <th>icb_supersector</th>\n      <th>country_of_incorporation</th>\n      <th>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>OPM</td>\n      <td>2006-08-02</td>\n      <td>1PM PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>40.036935</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>SPA</td>\n      <td>2009-02-02</td>\n      <td>1SPATIAL PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>29.467662</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>C21</td>\n      <td>2005-04-15</td>\n      <td>21ST CENTURY TECHNOLOGY PLC</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>Industrials</td>\n      <td>United Kingdom</td>\n      <td>2.564093</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>III</td>\n      <td>1994-07-18</td>\n      <td>3I GROUP PLC</td>\n      <td>Financial Services</td>\n      <td>Financials</td>\n      <td>United Kingdom</td>\n      <td>8765.510388</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>DDDD</td>\n      <td>2014-02-18</td>\n      <td>4D PHARMA PLC</td>\n      <td>Healthcare</td>\n      <td>Health Care</td>\n      <td>United Kingdom</td>\n      <td>221.041717</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>159391</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZATT</td>\n      <td>2012-04-16</td>\n      <td>ZATTIKKA PLC</td>\n      <td>Leisure Goods</td>\n      <td>Toys</td>\n      <td>United Kingdom</td>\n      <td>6.554384</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>159395</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOX</td>\n      <td>2001-12-10</td>\n      <td>ZINCOX RESOURCES PLC</td>\n      <td>Mining</td>\n      <td>General Mining</td>\n      <td>United Kingdom</td>\n      <td>19.141342</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>159397</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZOO</td>\n      <td>2001-05-14</td>\n      <td>ZOO DIGITAL GROUP PLC</td>\n      <td>Software &amp; Computer Services</td>\n      <td>Software</td>\n      <td>United Kingdom</td>\n      <td>2.250767</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>159398</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZTF</td>\n      <td>1995-02-28</td>\n      <td>ZOTEFOAMS</td>\n      <td>Chemicals</td>\n      <td>Specialty Chemicals</td>\n      <td>United Kingdom</td>\n      <td>81.457315</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>159399</td>\n      <td>2013</td>\n      <td>6</td>\n      <td>ZYT</td>\n      <td>2000-07-06</td>\n      <td>ZYTRONIC</td>\n      <td>Electronic &amp; Electrical Equipment</td>\n      <td>Electronic Equipment</td>\n      <td>United Kingdom</td>\n      <td>22.067038</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n  </tbody>\n</table>\n<p>116949 rows × 12 columns</p>\n</div>",
      "text/plain": "        year month ticker   list_date                         company_name  \\\n0       2017    11    OPM  2006-08-02                              1PM PLC   \n1       2017    11    SPA  2009-02-02                         1SPATIAL PLC   \n2       2017    11    C21  2005-04-15          21ST CENTURY TECHNOLOGY PLC   \n3       2017    11    III  1994-07-18                         3I GROUP PLC   \n5       2017    11   DDDD  2014-02-18                        4D PHARMA PLC   \n...      ...   ...    ...         ...                                  ...   \n159391  2013     6   ZATT  2012-04-16  ZATTIKKA PLC                          \n159395  2013     6    ZOX  2001-12-10  ZINCOX RESOURCES PLC                  \n159397  2013     6    ZOO  2001-05-14  ZOO DIGITAL GROUP PLC                 \n159398  2013     6    ZTF  1995-02-28  ZOTEFOAMS                             \n159399  2013     6    ZYT  2000-07-06  ZYTRONIC                              \n\n                             icb_industry       icb_supersector  \\\n0                      Financial Services            Financials   \n1             Industrial Goods & Services           Industrials   \n2             Industrial Goods & Services           Industrials   \n3                      Financial Services            Financials   \n5                              Healthcare           Health Care   \n...                                   ...                   ...   \n159391                      Leisure Goods                  Toys   \n159395                             Mining        General Mining   \n159397       Software & Computer Services              Software   \n159398                          Chemicals   Specialty Chemicals   \n159399  Electronic & Electrical Equipment  Electronic Equipment   \n\n       country_of_incorporation   market_cap   eps       p/e       p/s  \n0                United Kingdom    40.036935  0.07  9.797308  0.501623  \n1                United Kingdom    29.467662  0.07  9.797308  0.501623  \n2                United Kingdom     2.564093  0.07  9.797308  0.501623  \n3                United Kingdom  8765.510388  0.07  9.797308  0.501623  \n5                United Kingdom   221.041717  0.07  9.797308  0.501623  \n...                         ...          ...   ...       ...       ...  \n159391           United Kingdom     6.554384  0.07  9.797308  0.501623  \n159395           United Kingdom    19.141342  0.07  9.797308  0.501623  \n159397           United Kingdom     2.250767  0.07  9.797308  0.501623  \n159398           United Kingdom    81.457315  0.07  9.797308  0.501623  \n159399           United Kingdom    22.067038  0.07  9.797308  0.501623  \n\n[116949 rows x 12 columns]"
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finance_sector_df = lse_historical_data[finance_sector == True]\n",
    "finance_sector_df = lse_historical_data[lse_historical_data['country_of_incorporation'] == 'United Kingdom']\n",
    "finance_sector_df['eps'].fillna(finance_sector_df['eps'].median(), inplace=True)\n",
    "finance_sector_df['p/e'].fillna(finance_sector_df['p/e'].median(), inplace=True)\n",
    "finance_sector_df['p/s'].fillna(finance_sector_df['p/s'].median(), inplace=True)\n",
    "finance_sector_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>2013</td>\n      <td>2.486565e+07</td>\n      <td>1296.16</td>\n      <td>181386.165411</td>\n      <td>9288.551387</td>\n    </tr>\n    <tr>\n      <td>2014</td>\n      <td>2.537089e+07</td>\n      <td>1386.99</td>\n      <td>178654.347022</td>\n      <td>10629.944488</td>\n    </tr>\n    <tr>\n      <td>2015</td>\n      <td>2.559171e+07</td>\n      <td>1463.01</td>\n      <td>164093.109428</td>\n      <td>10414.699240</td>\n    </tr>\n    <tr>\n      <td>2016</td>\n      <td>2.526892e+07</td>\n      <td>1483.31</td>\n      <td>173689.948205</td>\n      <td>11139.257528</td>\n    </tr>\n    <tr>\n      <td>2017</td>\n      <td>2.838882e+07</td>\n      <td>1732.30</td>\n      <td>172895.554887</td>\n      <td>11670.321709</td>\n    </tr>\n    <tr>\n      <td>2018</td>\n      <td>2.867107e+07</td>\n      <td>1614.60</td>\n      <td>167710.987630</td>\n      <td>11634.884106</td>\n    </tr>\n    <tr>\n      <td>2019</td>\n      <td>1.646377e+07</td>\n      <td>979.70</td>\n      <td>99101.929175</td>\n      <td>6053.027473</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "        market_cap      eps            p/e           p/s\nyear                                                    \n2013  2.486565e+07  1296.16  181386.165411   9288.551387\n2014  2.537089e+07  1386.99  178654.347022  10629.944488\n2015  2.559171e+07  1463.01  164093.109428  10414.699240\n2016  2.526892e+07  1483.31  173689.948205  11139.257528\n2017  2.838882e+07  1732.30  172895.554887  11670.321709\n2018  2.867107e+07  1614.60  167710.987630  11634.884106\n2019  1.646377e+07   979.70   99101.929175   6053.027473"
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_sector_df_by_year = finance_sector_df.groupby('year').sum()\n",
    "finance_sector_df_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>year</th>\n      <th>month</th>\n      <th>icb_industry</th>\n      <th>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>Financial Services</td>\n      <td>40.036935</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>29.467662</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>2.564093</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>Financial Services</td>\n      <td>8765.510388</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>Healthcare</td>\n      <td>221.041717</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>153228</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>Basic Resources</td>\n      <td>0.760000</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>153229</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>Media</td>\n      <td>6.250000</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>153231</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>Technology</td>\n      <td>91.100000</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>153232</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>Chemicals</td>\n      <td>255.030000</td>\n      <td>0.07</td>\n      <td>9.797308</td>\n      <td>0.501623</td>\n    </tr>\n    <tr>\n      <td>153233</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>Industrial Goods &amp; Services</td>\n      <td>74.200000</td>\n      <td>0.23</td>\n      <td>20.163043</td>\n      <td>1.375000</td>\n    </tr>\n  </tbody>\n</table>\n<p>53148 rows × 7 columns</p>\n</div>",
      "text/plain": "        year month                 icb_industry   market_cap   eps        p/e  \\\n0       2017    11           Financial Services    40.036935  0.07   9.797308   \n1       2017    11  Industrial Goods & Services    29.467662  0.07   9.797308   \n2       2017    11  Industrial Goods & Services     2.564093  0.07   9.797308   \n3       2017    11           Financial Services  8765.510388  0.07   9.797308   \n5       2017    11                   Healthcare   221.041717  0.07   9.797308   \n...      ...   ...                          ...          ...   ...        ...   \n153228  2018     9              Basic Resources     0.760000  0.07   9.797308   \n153229  2018     9                        Media     6.250000  0.07   9.797308   \n153231  2018     9                   Technology    91.100000  0.07   9.797308   \n153232  2018     9                    Chemicals   255.030000  0.07   9.797308   \n153233  2018     9  Industrial Goods & Services    74.200000  0.23  20.163043   \n\n             p/s  \n0       0.501623  \n1       0.501623  \n2       0.501623  \n3       0.501623  \n5       0.501623  \n...          ...  \n153228  0.501623  \n153229  0.501623  \n153231  0.501623  \n153232  0.501623  \n153233  1.375000  \n\n[53148 rows x 7 columns]"
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_sector_condensed = finance_sector_df[['year', 'month', 'icb_industry', 'market_cap', 'eps', 'p/e', 'p/s']]\n",
    "finance_sector_2016_2018 = finance_sector_condensed[(finance_sector_df['year'] >= 2016) & (finance_sector_df['year'] <= 2018)]\n",
    "finance_sector_2016_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>year</th>\n      <th>month</th>\n      <th>market_cap</th>\n      <th>eps</th>\n      <th>p/e</th>\n      <th>p/s</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>2.014890e+06</td>\n      <td>116.52</td>\n      <td>14660.247854</td>\n      <td>839.231484</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>2016</td>\n      <td>2</td>\n      <td>2.005124e+06</td>\n      <td>103.81</td>\n      <td>14529.408448</td>\n      <td>743.906766</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>2016</td>\n      <td>3</td>\n      <td>2.033259e+06</td>\n      <td>146.78</td>\n      <td>14595.328733</td>\n      <td>1180.460814</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2016</td>\n      <td>4</td>\n      <td>2.051037e+06</td>\n      <td>132.45</td>\n      <td>14554.936651</td>\n      <td>844.829413</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2016</td>\n      <td>5</td>\n      <td>2.057435e+06</td>\n      <td>102.98</td>\n      <td>14549.254697</td>\n      <td>769.275637</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2016</td>\n      <td>6</td>\n      <td>2.092092e+06</td>\n      <td>112.19</td>\n      <td>14255.187485</td>\n      <td>935.136201</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2016</td>\n      <td>7</td>\n      <td>2.170603e+06</td>\n      <td>111.50</td>\n      <td>14494.124943</td>\n      <td>788.918717</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>2016</td>\n      <td>8</td>\n      <td>2.196752e+06</td>\n      <td>109.82</td>\n      <td>14431.894159</td>\n      <td>784.064941</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>2016</td>\n      <td>9</td>\n      <td>2.203278e+06</td>\n      <td>139.49</td>\n      <td>14566.826317</td>\n      <td>890.673414</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>2016</td>\n      <td>10</td>\n      <td>2.141623e+06</td>\n      <td>109.12</td>\n      <td>14543.808637</td>\n      <td>759.701333</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>2016</td>\n      <td>11</td>\n      <td>2.096922e+06</td>\n      <td>107.56</td>\n      <td>14252.468768</td>\n      <td>779.661984</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>2016</td>\n      <td>12</td>\n      <td>2.205906e+06</td>\n      <td>191.09</td>\n      <td>14256.461512</td>\n      <td>1823.396822</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2.196911e+06</td>\n      <td>117.73</td>\n      <td>14259.329663</td>\n      <td>829.516736</td>\n    </tr>\n    <tr>\n      <td>20</td>\n      <td>2017</td>\n      <td>2</td>\n      <td>2.284388e+06</td>\n      <td>102.83</td>\n      <td>14392.246130</td>\n      <td>736.884046</td>\n    </tr>\n    <tr>\n      <td>21</td>\n      <td>2017</td>\n      <td>3</td>\n      <td>2.313033e+06</td>\n      <td>305.86</td>\n      <td>14834.663649</td>\n      <td>1368.350492</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2017</td>\n      <td>4</td>\n      <td>6.693572e+05</td>\n      <td>25.03</td>\n      <td>3336.002987</td>\n      <td>191.739658</td>\n    </tr>\n    <tr>\n      <td>22</td>\n      <td>2017</td>\n      <td>4</td>\n      <td>1.639614e+06</td>\n      <td>134.13</td>\n      <td>10903.437595</td>\n      <td>690.125371</td>\n    </tr>\n    <tr>\n      <td>23</td>\n      <td>2017</td>\n      <td>5</td>\n      <td>2.401992e+06</td>\n      <td>112.43</td>\n      <td>14303.955089</td>\n      <td>778.047050</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2017</td>\n      <td>6</td>\n      <td>2.336707e+06</td>\n      <td>126.77</td>\n      <td>14574.436934</td>\n      <td>964.695425</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>2017</td>\n      <td>7</td>\n      <td>2.390591e+06</td>\n      <td>117.63</td>\n      <td>14610.309273</td>\n      <td>823.283136</td>\n    </tr>\n    <tr>\n      <td>24</td>\n      <td>2017</td>\n      <td>8</td>\n      <td>2.412059e+06</td>\n      <td>112.93</td>\n      <td>14571.292803</td>\n      <td>797.864462</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>2017</td>\n      <td>9</td>\n      <td>2.402152e+06</td>\n      <td>137.58</td>\n      <td>14736.965210</td>\n      <td>908.829143</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>2017</td>\n      <td>10</td>\n      <td>2.443611e+06</td>\n      <td>111.72</td>\n      <td>14422.800335</td>\n      <td>773.210633</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>2017</td>\n      <td>11</td>\n      <td>2.395169e+06</td>\n      <td>113.48</td>\n      <td>14471.751556</td>\n      <td>802.635714</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>2017</td>\n      <td>12</td>\n      <td>2.503234e+06</td>\n      <td>214.18</td>\n      <td>13478.363664</td>\n      <td>2005.139844</td>\n    </tr>\n    <tr>\n      <td>31</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>2.453333e+06</td>\n      <td>122.04</td>\n      <td>14470.858597</td>\n      <td>808.030560</td>\n    </tr>\n    <tr>\n      <td>25</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>2.360731e+06</td>\n      <td>103.25</td>\n      <td>14451.029981</td>\n      <td>739.893783</td>\n    </tr>\n    <tr>\n      <td>26</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>2.303684e+06</td>\n      <td>315.23</td>\n      <td>14066.345259</td>\n      <td>1385.655792</td>\n    </tr>\n    <tr>\n      <td>33</td>\n      <td>2018</td>\n      <td>4</td>\n      <td>2.450368e+06</td>\n      <td>113.39</td>\n      <td>10758.786500</td>\n      <td>849.565596</td>\n    </tr>\n    <tr>\n      <td>27</td>\n      <td>2018</td>\n      <td>5</td>\n      <td>2.496458e+06</td>\n      <td>108.55</td>\n      <td>14725.705770</td>\n      <td>781.461029</td>\n    </tr>\n    <tr>\n      <td>34</td>\n      <td>2018</td>\n      <td>6</td>\n      <td>2.481240e+06</td>\n      <td>120.85</td>\n      <td>14788.368343</td>\n      <td>961.718673</td>\n    </tr>\n    <tr>\n      <td>35</td>\n      <td>2018</td>\n      <td>7</td>\n      <td>2.520783e+06</td>\n      <td>113.60</td>\n      <td>14678.095993</td>\n      <td>825.391858</td>\n    </tr>\n    <tr>\n      <td>36</td>\n      <td>2018</td>\n      <td>8</td>\n      <td>2.428325e+06</td>\n      <td>108.38</td>\n      <td>14649.275632</td>\n      <td>799.733008</td>\n    </tr>\n    <tr>\n      <td>28</td>\n      <td>2018</td>\n      <td>9</td>\n      <td>2.460615e+06</td>\n      <td>115.05</td>\n      <td>14302.558358</td>\n      <td>889.100357</td>\n    </tr>\n    <tr>\n      <td>29</td>\n      <td>2018</td>\n      <td>10</td>\n      <td>2.322706e+06</td>\n      <td>105.41</td>\n      <td>14819.308925</td>\n      <td>775.038729</td>\n    </tr>\n    <tr>\n      <td>32</td>\n      <td>2018</td>\n      <td>11</td>\n      <td>2.241889e+06</td>\n      <td>106.54</td>\n      <td>13559.039688</td>\n      <td>796.246135</td>\n    </tr>\n    <tr>\n      <td>30</td>\n      <td>2018</td>\n      <td>12</td>\n      <td>2.150939e+06</td>\n      <td>182.31</td>\n      <td>12441.614586</td>\n      <td>2023.048587</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "    year  month    market_cap     eps           p/e          p/s\n0   2016      1  2.014890e+06  116.52  14660.247854   839.231484\n9   2016      2  2.005124e+06  103.81  14529.408448   743.906766\n10  2016      3  2.033259e+06  146.78  14595.328733  1180.460814\n1   2016      4  2.051037e+06  132.45  14554.936651   844.829413\n2   2016      5  2.057435e+06  102.98  14549.254697   769.275637\n3   2016      6  2.092092e+06  112.19  14255.187485   935.136201\n4   2016      7  2.170603e+06  111.50  14494.124943   788.918717\n5   2016      8  2.196752e+06  109.82  14431.894159   784.064941\n11  2016      9  2.203278e+06  139.49  14566.826317   890.673414\n7   2016     10  2.141623e+06  109.12  14543.808637   759.701333\n6   2016     11  2.096922e+06  107.56  14252.468768   779.661984\n8   2016     12  2.205906e+06  191.09  14256.461512  1823.396822\n12  2017      1  2.196911e+06  117.73  14259.329663   829.516736\n20  2017      2  2.284388e+06  102.83  14392.246130   736.884046\n21  2017      3  2.313033e+06  305.86  14834.663649  1368.350492\n13  2017      4  6.693572e+05   25.03   3336.002987   191.739658\n22  2017      4  1.639614e+06  134.13  10903.437595   690.125371\n23  2017      5  2.401992e+06  112.43  14303.955089   778.047050\n14  2017      6  2.336707e+06  126.77  14574.436934   964.695425\n15  2017      7  2.390591e+06  117.63  14610.309273   823.283136\n24  2017      8  2.412059e+06  112.93  14571.292803   797.864462\n16  2017      9  2.402152e+06  137.58  14736.965210   908.829143\n17  2017     10  2.443611e+06  111.72  14422.800335   773.210633\n18  2017     11  2.395169e+06  113.48  14471.751556   802.635714\n19  2017     12  2.503234e+06  214.18  13478.363664  2005.139844\n31  2018      1  2.453333e+06  122.04  14470.858597   808.030560\n25  2018      2  2.360731e+06  103.25  14451.029981   739.893783\n26  2018      3  2.303684e+06  315.23  14066.345259  1385.655792\n33  2018      4  2.450368e+06  113.39  10758.786500   849.565596\n27  2018      5  2.496458e+06  108.55  14725.705770   781.461029\n34  2018      6  2.481240e+06  120.85  14788.368343   961.718673\n35  2018      7  2.520783e+06  113.60  14678.095993   825.391858\n36  2018      8  2.428325e+06  108.38  14649.275632   799.733008\n28  2018      9  2.460615e+06  115.05  14302.558358   889.100357\n29  2018     10  2.322706e+06  105.41  14819.308925   775.038729\n32  2018     11  2.241889e+06  106.54  13559.039688   796.246135\n30  2018     12  2.150939e+06  182.31  12441.614586  2023.048587"
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_sector_2016_2018_by_month = finance_sector_2016_2018.groupby(['year', 'month']).sum().reset_index()\n",
    "finance_sector_2016_2018_by_month[['year', 'month']] = finance_sector_2016_2018_by_month[['year', 'month']].astype('int64')\n",
    "finance_sector_2016_2018_by_month = finance_sector_2016_2018_by_month.sort_values(['year', 'month'])\n",
    "finance_sector_2016_2018_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/Users/kamilestankeviciute/anaconda3/envs/tensorflow/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \n/Users/kamilestankeviciute/anaconda3/envs/tensorflow/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  errors=errors,\n"
    }
   ],
   "source": [
    "finance_sector_2016_2018_by_month_market_cap = finance_sector_2016_2018_by_month[['year', 'month', 'market_cap']]\n",
    "finance_sector_2016_2018_by_month_market_cap['date'] = pd.to_datetime(finance_sector_2016_2018_by_month_market_cap[['year', 'month']].assign(Day=1))\n",
    "finance_sector_2016_2018_by_month_market_cap.drop(['year', 'month'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1398f6590>"
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "finance_sector_2016_2018_by_month_market_cap.plot(x='date', y='market_cap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}