ESG report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "\n",
    "* [Company Overview](#CompanyOverview)\n",
    "    * [D&I Index Ranking](#D&IIndexRanking)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this article, we will use Python version 3.7.7:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code runs on Python version 3.7.7\n"
     ]
    }
   ],
   "source": [
    "# The ' from ... import ' structure here allows us to only import the module ' python_version ' from the library ' platform ':\n",
    "from platform import python_version\n",
    "print(\"This code runs on Python version \" + python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COMPANY OVERVIEW <a class=\"anchor\" id=\"CompanyOverview\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import eikon as ek\n",
    "\n",
    "# The key is placed in a text file so that it may be used in this code without showing it itself:\n",
    "eikon_key = open(\"eikon.txt\",\"r\")\n",
    "ek.set_app_key(str(eikon_key.read()))\n",
    "# It is best to close the files we opened in order to make sure that we don't stop any other services/programs from accessing them if they need to:\n",
    "eikon_key.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "instrument_list = [\"VOD.L\", \"IBM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def function(instruments = instrument_list,\n",
    "             overshadowed_currency = None,\n",
    "             market_cap_currency = None,\n",
    "             revenue_currency = None,\n",
    "             currency_of_precedence = None):\n",
    "    \n",
    "    \n",
    "    ## Currency Preperation:\n",
    "    \n",
    "    # ' overshadowed_currency ' takes last priority over all other (more granular) currency specifications in our function's arguments.\n",
    "    if overshadowed_currency == None:\n",
    "        cur = \"Native\"\n",
    "    else:\n",
    "        cur = str(overshadowed_currency)\n",
    "        market_cap_cur = str(overshadowed_currency)\n",
    "        revenue_cur = str()\n",
    "    \n",
    "    # If the function's user wants market capitalisation at a specific currency, let it be:\n",
    "    if market_cap_currency == None:\n",
    "        market_cap_cur = \"Native\"\n",
    "    else:\n",
    "        market_cap_cur = str(market_cap_currency)\n",
    "    \n",
    "    # If the function's user wants revenues at a specific currency, let it be:\n",
    "    if market_cap_currency == None:\n",
    "        revenue_cur = \"Native\"\n",
    "    else:\n",
    "        revenue_cur = str(revenue_currency)\n",
    "    \n",
    "    # ' currency_of_precedence ' takes presidence over all other (more granular) currency specifications in our function's arguments.\n",
    "    if currency_of_precedence == None:\n",
    "        cur = \"Native\"\n",
    "    else:\n",
    "        cur = str(currency_of_precedence)\n",
    "        market_cap_cur = str(currency_of_precedence)\n",
    "    \n",
    "    \n",
    "    ## Data Collection:\n",
    "    \n",
    "    df = ek.get_data(\n",
    "        instruments = instruments,\n",
    "        fields = [\n",
    "            'TR.CommonName',\n",
    "            f'TR.CompanyMarketCap(Curn={market_cap_cur})',\n",
    "            'TR.TRBCIndustry',\n",
    "            'TR.TRBCIndustryGroup',\n",
    "            f'TR.InvtrRevenue(Curn={revenue_cur})',\n",
    "            'TR.CompanyNumEmploy',\n",
    "            'TR.TRDIRScore',\n",
    "            'TR.ExchangeCountry',\n",
    "            'TR.CompanyFYearEnd',\n",
    "            'TR.CSRReportingScope',\n",
    "            'TR.TRESGScore',\n",
    "            'TR.TRESGScoreGrade',\n",
    "            'TR.TRESGCControversiesScore',\n",
    "            'TR.TRESGCControversiesScoreGrade',\n",
    "            'TR.TRESGCScore',\n",
    "            'TR.TRESGCScoreGrade',\n",
    "            'TR.EnvironmentPillarScore',\n",
    "            'TR.EnvironmentPillarScoreGrade',\n",
    "            'TR.SocialPillarScore',\n",
    "            'TR.SocialPillarScoreGrade',\n",
    "            'TR.GovernancePillarScore',\n",
    "            'TR.GovernancePillarScoreGrade'\n",
    "        ]\n",
    "    )[0] # ' ek.get_data ' returns two objects, (i) the pandas data-frame with the data we're after, and (ii) any error messages that come with it if any do; thus the ' [0] '.\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\r-tutorial\\lib\\site-packages\\eikon\\data_grid.py:247: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "  data = pd.np.array([[get_data_value(value) for value in row] for row in data_dict['data']])\n"
     ]
    }
   ],
   "source": [
    "df = function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Instrument</th>\n",
       "      <td>VOD.L</td>\n",
       "      <td>IBM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Common Name</th>\n",
       "      <td>Vodafone Group PLC</td>\n",
       "      <td>International Business Machines Corp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Market Cap</th>\n",
       "      <td>3.58183e+10</td>\n",
       "      <td>1.14032e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRBC Industry Name</th>\n",
       "      <td>Wireless Telecommunications Services</td>\n",
       "      <td>IT Services &amp; Consulting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRBC Industry Group Name</th>\n",
       "      <td>Telecommunications Services</td>\n",
       "      <td>Software &amp; IT Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <td>2.6116e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Employees</th>\n",
       "      <td>95219</td>\n",
       "      <td>375300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIR Score</th>\n",
       "      <td>68.25</td>\n",
       "      <td>67.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country of Exchange</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United States of America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiscal Year End Date</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>2021-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Reporting Scope</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Score</th>\n",
       "      <td>81.065</td>\n",
       "      <td>70.7481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Score Grade</th>\n",
       "      <td>A-</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Controversies Score</th>\n",
       "      <td>5.35714</td>\n",
       "      <td>83.5443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Controversies Score Grade</th>\n",
       "      <td>D-</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Combined Score</th>\n",
       "      <td>43.2111</td>\n",
       "      <td>70.7481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESG Combined Score Grade</th>\n",
       "      <td>C+</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Environmental Pillar Score</th>\n",
       "      <td>71.0226</td>\n",
       "      <td>79.1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Environmental Pillar Score Grade</th>\n",
       "      <td>B+</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Pillar Score</th>\n",
       "      <td>80.2924</td>\n",
       "      <td>97.7823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Pillar Score Grade</th>\n",
       "      <td>A-</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Governance Pillar Score</th>\n",
       "      <td>90.3136</td>\n",
       "      <td>44.9498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Governance Pillar Score Grade</th>\n",
       "      <td>A</td>\n",
       "      <td>C+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     0  \\\n",
       "Instrument                                                       VOD.L   \n",
       "Company Common Name                                 Vodafone Group PLC   \n",
       "Company Market Cap                                         3.58183e+10   \n",
       "TRBC Industry Name                Wireless Telecommunications Services   \n",
       "TRBC Industry Group Name                   Telecommunications Services   \n",
       "Revenue                                                     2.6116e+09   \n",
       "Number of Employees                                              95219   \n",
       "DIR Score                                                        68.25   \n",
       "Country of Exchange                                     United Kingdom   \n",
       "Fiscal Year End Date                                        2021-03-31   \n",
       "ESG Reporting Scope                                                100   \n",
       "ESG Score                                                       81.065   \n",
       "ESG Score Grade                                                     A-   \n",
       "ESG Controversies Score                                        5.35714   \n",
       "ESG Controversies Score Grade                                       D-   \n",
       "ESG Combined Score                                             43.2111   \n",
       "ESG Combined Score Grade                                            C+   \n",
       "Environmental Pillar Score                                     71.0226   \n",
       "Environmental Pillar Score Grade                                    B+   \n",
       "Social Pillar Score                                            80.2924   \n",
       "Social Pillar Score Grade                                           A-   \n",
       "Governance Pillar Score                                        90.3136   \n",
       "Governance Pillar Score Grade                                        A   \n",
       "\n",
       "                                                                     1  \n",
       "Instrument                                                         IBM  \n",
       "Company Common Name               International Business Machines Corp  \n",
       "Company Market Cap                                         1.14032e+11  \n",
       "TRBC Industry Name                            IT Services & Consulting  \n",
       "TRBC Industry Group Name                        Software & IT Services  \n",
       "Revenue                                                            NaN  \n",
       "Number of Employees                                             375300  \n",
       "DIR Score                                                        67.25  \n",
       "Country of Exchange                           United States of America  \n",
       "Fiscal Year End Date                                        2021-12-31  \n",
       "ESG Reporting Scope                                                100  \n",
       "ESG Score                                                      70.7481  \n",
       "ESG Score Grade                                                     B+  \n",
       "ESG Controversies Score                                        83.5443  \n",
       "ESG Controversies Score Grade                                        A  \n",
       "ESG Combined Score                                             70.7481  \n",
       "ESG Combined Score Grade                                            B+  \n",
       "Environmental Pillar Score                                     79.1444  \n",
       "Environmental Pillar Score Grade                                    A-  \n",
       "Social Pillar Score                                            97.7823  \n",
       "Social Pillar Score Grade                                           A+  \n",
       "Governance Pillar Score                                        44.9498  \n",
       "Governance Pillar Score Grade                                       C+  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### D&I Index Ranking <a class=\"anchor\" id=\"D&IIndexRanking\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to look at data in our  ```df``` spcifically for a set of data points, let's store them in a list ```heatmap_headers``` for ease:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap_headers = [\"ESG Score\",\n",
    "                   \"ESG Controversies Score\",\n",
    "                   \"ESG Combined Score\",\n",
    "                   \"Environmental Pillar Score\",\n",
    "                   \"Social Pillar Score\",\n",
    "                   \"Governance Pillar Score\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Now let's specify what will be written on top of our heatmap](https://likegeeks.com/seaborn-heatmap-tutorial/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap_data = np.array(\n",
    "    [df[df[\"Instrument\"] == \"VOD.L\"][\n",
    "        heatmap_headers\n",
    "    ].values.tolist()[0]]).reshape(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[81.06499722,  5.35714286, 43.21107004],\n",
       "       [71.02259429, 80.29240792, 90.31364356]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap_text = np.array(\n",
    "    [f\"{i}\\n{j}\\n{k}\" for i,j,k in zip(\n",
    "        df[df[\"Instrument\"] == \"VOD.L\"][\n",
    "            [f\"{p} Grade\" for p in heatmap_headers]].values[0].tolist(),\n",
    "        [f\"{round(q, 2)}%\" for q in heatmap_data.flatten().tolist()],\n",
    "        [r.replace(\" \", \"\\n\") for r in heatmap_headers])]).reshape(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['A-\\n81.06%\\nESG\\nScore', 'D-\\n5.36%\\nESG\\nControversies\\nScore',\n",
       "        'C+\\n43.21%\\nESG\\nCombined\\nScore'],\n",
       "       ['B+\\n71.02%\\nEnvironmental\\nPillar\\nScore',\n",
       "        'A-\\n80.29%\\nSocial\\nPillar\\nScore',\n",
       "        'A\\n90.31%\\nGovernance\\nPillar\\nScore']], dtype='<U36')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Define the color scheme of our heatmap](https://stackoverflow.com/questions/38246559/how-to-create-a-heat-map-in-python-that-ranges-from-green-to-red) using [matplotlib.colors.LinearSegmentedColormap](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.LinearSegmentedColormap.html#matplotlib.colors.LinearSegmentedColormap):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dictionary defines the colormap\n",
    "cdict = {'green':  ((0.0, 0.0, 0.0),   # no red at 0\n",
    "                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white\n",
    "                  (1.0, 0.8, 0.8)),  # set to 0.8 so its not too bright at 1\n",
    "\n",
    "        'red': ((0.0, 0.8, 0.8),   # set to 0.8 so its not too bright at 0\n",
    "                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white\n",
    "                  (1.0, 0.0, 0.0)),  # no green at 1\n",
    "\n",
    "        'blue':  ((0.0, 0.0, 0.0),   # no blue at 0\n",
    "                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white\n",
    "                  (1.0, 0.0, 0.0))   # no blue at 1\n",
    "       }\n",
    "\n",
    "# Create the colormap using the dictionary\n",
    "GnRd = colors.LinearSegmentedColormap('GnRd', cdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's plot our heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(font_scale = 1.2)\n",
    "heat_map = sb.heatmap(heatmap_data,\n",
    "                      fmt = '', # this is to stop the ' Unknown format code 'g' for object of type 'numpy.str_' ' error.\n",
    "                      annot = heatmap_text,\n",
    "                      cbar = False,\n",
    "                      cmap = GnRd, # can also use format such as ' sb.diverging_palette(250, 30, l=65, center=\"dark\", as_cmap=True) '.\n",
    "                      xticklabels = False,\n",
    "                      yticklabels = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ESG SUMMARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dat(instrument, field):\n",
    "    \"\"\"We are going to request for data from the Pandas data-frame ' df ' a lot.\n",
    "    I thus thought is more concise to create this function.\n",
    "    The name ' Dat ' simply refers to Data, as in the data requested from df.\"\"\"\n",
    "    \n",
    "    return df[df[\"Instrument\"] == instrument][field][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\r-tutorial\\lib\\site-packages\\eikon\\data_grid.py:247: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "  data = pd.np.array([[get_data_value(value) for value in row] for row in data_dict['data']])\n"
     ]
    }
   ],
   "source": [
    "esg_score_timeseries = ek.get_data(\n",
    "    instruments = [\"VOD.L\"],\n",
    "    fields = [\"TR.TRESGScore(SDate=0,EDate=-4,Period=FY0,Frq=FY)\"])[0][\"ESG Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "esg_summary = (\n",
    "    Dat(\"VOD.L\", \"Company Common Name\") +\n",
    "    \" is a(n) \" + Dat(\"VOD.L\", \"TRBC Industry Group Name\") +\n",
    "    \" headquartered in (the) \" + Dat(\"VOD.L\", \"Country of Exchange\") +\n",
    "    \". For the fiscal year ended in \" +\n",
    "    datetime.datetime.strptime(Dat(\"VOD.L\", \"Fiscal Year End Date\"), \"%Y-%m-%d\").strftime(\"%B %Y\") +\n",
    "    \", \" + \"VOD.L\" +\n",
    "    \" received an ESG score of \" + str(round(Dat(\"VOD.L\", \"ESG Score\"), 2)) + \n",
    "    \" which is equivalent to Grade '\" + Dat(\"VOD.L\", \"ESG Score Grade\") + \"'.\\n\" +\n",
    "    \"Over the last 5 years, the company has an average ESG score of \" +\n",
    "    str(round(esg_score_timeseries.mean(), ndigits = 2)) + \n",
    "    \", and a median ESG score of \" + str(round(esg_score_timeseries.median(), ndigits = 2)) +\n",
    "    \". Refinitiv's ESG score is calculated as a sum of weighted individual pillar scores.\\n\" +\n",
    "    \"VOD.L\" + \"'s controversy score of \" +\n",
    "    str(round(Dat(\"VOD.L\", \"ESG Controversies Score\"), 2)) +\n",
    "    \" (Grade: '\" + Dat(\"VOD.L\", \"ESG Controversies Score Grade\") +\n",
    "    \"') is calculated based on total count of controversies related to the company, \" +\n",
    "    \"as collected by Refinitiv ESG team and normalized based on company’s market capitalization.\" +\n",
    "    \" Discounted by its controversies, \" + \"VOD.L\" + \" received an ESG Combined score of \" +\n",
    "    str(round(Dat(\"VOD.L\", \"ESG Combined Score\"), 2)) +\n",
    "    \" (Grade: '\" + Dat(\"VOD.L\", \"ESG Combined Score Grade\") + \"') for the year.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vodafone Group PLC is a(n) Telecommunications Services headquartered in (the) United Kingdom. For the fiscal year ended in March 2021, VOD.L received an ESG score of 81.06 which is equivalent to Grade 'A-'.\n",
      "Over the last 5 years, the company has an average ESG score of 81.28, and a median ESG score of 81.01. Refinitiv's ESG score is calculated as a sum of weighted individual pillar scores.\n",
      "VOD.L's controversy score of 5.36 (Grade: 'D-') is calculated based on total count of controversies related to the company, as collected by Refinitiv ESG team and normalized based on company’s market capitalization. Discounted by its controversies, VOD.L received an ESG Combined score of 43.21 (Grade: 'C+') for the year.\n"
     ]
    }
   ],
   "source": [
    "print(esg_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COMPANY RELATIVE PERFORMANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "peers,err = ek.get_data(['PEERS(VOD.L)','VOD.L'],['TR.CommonName','TR.CompanyMarketCap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Instrument</th>\n",
       "      <th>Company Common Name</th>\n",
       "      <th>Company Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BT.L</td>\n",
       "      <td>BT Group PLC</td>\n",
       "      <td>1.367792e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DTEGn.DE</td>\n",
       "      <td>Deutsche Telekom AG</td>\n",
       "      <td>7.849264e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TEF.MC</td>\n",
       "      <td>Telefonica SA</td>\n",
       "      <td>2.216480e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ORAN.PA</td>\n",
       "      <td>Orange SA</td>\n",
       "      <td>2.731177e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BOUY.PA</td>\n",
       "      <td>Bouygues SA</td>\n",
       "      <td>1.352637e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCMN.S</td>\n",
       "      <td>Swisscom AG</td>\n",
       "      <td>2.497122e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>O2Dn.DE</td>\n",
       "      <td>Telefonica Deutschland Holding AG</td>\n",
       "      <td>7.085390e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TLIT.MI</td>\n",
       "      <td>Telecom Italia SpA</td>\n",
       "      <td>9.902179e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UTDI.DE</td>\n",
       "      <td>United Internet AG</td>\n",
       "      <td>7.173840e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LBTYA.OQ</td>\n",
       "      <td>Liberty Global PLC</td>\n",
       "      <td>1.473490e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ILD.PA</td>\n",
       "      <td>Iliad SA</td>\n",
       "      <td>9.101736e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DRIG.DE</td>\n",
       "      <td>1&amp;1 Drillisch AG</td>\n",
       "      <td>4.576260e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CLNX.MC</td>\n",
       "      <td>Cellnex Telecom SA</td>\n",
       "      <td>2.117473e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PROX.BR</td>\n",
       "      <td>Proximus NV</td>\n",
       "      <td>5.841432e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KPN.AS</td>\n",
       "      <td>Koninklijke KPN NV</td>\n",
       "      <td>1.245681e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TELA.VI</td>\n",
       "      <td>Telekom Austria AG</td>\n",
       "      <td>4.370769e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TALK.L</td>\n",
       "      <td>Talktalk Telecom Group PLC</td>\n",
       "      <td>1.116158e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VIV.PA</td>\n",
       "      <td>Vivendi SE</td>\n",
       "      <td>3.289231e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TEL.OL</td>\n",
       "      <td>Telenor ASA</td>\n",
       "      <td>2.107834e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TELIA.ST</td>\n",
       "      <td>Telia Company AB</td>\n",
       "      <td>1.495996e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TEL2b.ST</td>\n",
       "      <td>Tele2 AB</td>\n",
       "      <td>8.025415e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ELISA.HE</td>\n",
       "      <td>Elisa Oyj</td>\n",
       "      <td>8.367301e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OBEL.BR</td>\n",
       "      <td>Orange Belgium SA</td>\n",
       "      <td>1.387029e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>INWT.MI</td>\n",
       "      <td>Infrastrutture Wireless Italiane SpA</td>\n",
       "      <td>8.404948e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TMUS.OQ</td>\n",
       "      <td>T-Mobile US Inc</td>\n",
       "      <td>1.579853e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>OTEr.AT</td>\n",
       "      <td>Hellenic Telecommunications Organization SA</td>\n",
       "      <td>6.044844e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>FNTGn.DE</td>\n",
       "      <td>freenet AG</td>\n",
       "      <td>2.606042e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TNET.BR</td>\n",
       "      <td>Telenet Group Holding NV</td>\n",
       "      <td>3.832111e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GAMA.L</td>\n",
       "      <td>Gamma Communications PLC</td>\n",
       "      <td>1.495633e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SGEF.PA</td>\n",
       "      <td>Vinci SA</td>\n",
       "      <td>5.405523e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RENA.PA</td>\n",
       "      <td>Renault SA</td>\n",
       "      <td>1.179824e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SGOB.PA</td>\n",
       "      <td>Compagnie de Saint Gobain SA</td>\n",
       "      <td>2.632082e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>AAF.L</td>\n",
       "      <td>Airtel Africa PLC</td>\n",
       "      <td>2.994765e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GEBN.S</td>\n",
       "      <td>Geberit AG</td>\n",
       "      <td>2.157462e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MS.MI</td>\n",
       "      <td>Mediaset SpA</td>\n",
       "      <td>3.162306e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>SESFd.PA</td>\n",
       "      <td>SES SA</td>\n",
       "      <td>3.175209e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EKTL.MC</td>\n",
       "      <td>Euskaltel SA</td>\n",
       "      <td>1.599566e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>STLA.MI</td>\n",
       "      <td>Stellantis NV</td>\n",
       "      <td>4.815931e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>RWAY.MI</td>\n",
       "      <td>Rai Way SpA</td>\n",
       "      <td>1.264089e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CRH.I</td>\n",
       "      <td>CRH PLC</td>\n",
       "      <td>3.182655e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>VZ.N</td>\n",
       "      <td>Verizon Communications Inc</td>\n",
       "      <td>2.302052e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>T.N</td>\n",
       "      <td>AT&amp;T Inc</td>\n",
       "      <td>2.125979e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>VON.AS</td>\n",
       "      <td>VEON Ltd</td>\n",
       "      <td>2.694982e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SSAq.L</td>\n",
       "      <td>AFK Sistema PAO</td>\n",
       "      <td>4.726170e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>TIGOsdb.ST</td>\n",
       "      <td>Millicom International Cellular SA</td>\n",
       "      <td>3.495156e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ICEG.OL</td>\n",
       "      <td>Ice Group ASA</td>\n",
       "      <td>3.785958e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>SNC.LS</td>\n",
       "      <td>Sonaecom SGPS SA</td>\n",
       "      <td>4.557712e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NETMb.ST</td>\n",
       "      <td>Netmore Group AB</td>\n",
       "      <td>1.693661e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ARCANE.ST</td>\n",
       "      <td>Arcane Crypto AB</td>\n",
       "      <td>2.975026e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>VOD.L</td>\n",
       "      <td>Vodafone Group PLC</td>\n",
       "      <td>3.581828e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Instrument                          Company Common Name  \\\n",
       "0         BT.L                                 BT Group PLC   \n",
       "1     DTEGn.DE                          Deutsche Telekom AG   \n",
       "2       TEF.MC                                Telefonica SA   \n",
       "3      ORAN.PA                                    Orange SA   \n",
       "4      BOUY.PA                                  Bouygues SA   \n",
       "5       SCMN.S                                  Swisscom AG   \n",
       "6      O2Dn.DE            Telefonica Deutschland Holding AG   \n",
       "7      TLIT.MI                           Telecom Italia SpA   \n",
       "8      UTDI.DE                           United Internet AG   \n",
       "9     LBTYA.OQ                           Liberty Global PLC   \n",
       "10      ILD.PA                                     Iliad SA   \n",
       "11     DRIG.DE                             1&1 Drillisch AG   \n",
       "12     CLNX.MC                           Cellnex Telecom SA   \n",
       "13     PROX.BR                                  Proximus NV   \n",
       "14      KPN.AS                           Koninklijke KPN NV   \n",
       "15     TELA.VI                           Telekom Austria AG   \n",
       "16      TALK.L                   Talktalk Telecom Group PLC   \n",
       "17      VIV.PA                                   Vivendi SE   \n",
       "18      TEL.OL                                  Telenor ASA   \n",
       "19    TELIA.ST                             Telia Company AB   \n",
       "20    TEL2b.ST                                     Tele2 AB   \n",
       "21    ELISA.HE                                    Elisa Oyj   \n",
       "22     OBEL.BR                            Orange Belgium SA   \n",
       "23     INWT.MI         Infrastrutture Wireless Italiane SpA   \n",
       "24     TMUS.OQ                              T-Mobile US Inc   \n",
       "25     OTEr.AT  Hellenic Telecommunications Organization SA   \n",
       "26    FNTGn.DE                                   freenet AG   \n",
       "27     TNET.BR                     Telenet Group Holding NV   \n",
       "28      GAMA.L                     Gamma Communications PLC   \n",
       "29     SGEF.PA                                     Vinci SA   \n",
       "30     RENA.PA                                   Renault SA   \n",
       "31     SGOB.PA                 Compagnie de Saint Gobain SA   \n",
       "32       AAF.L                            Airtel Africa PLC   \n",
       "33      GEBN.S                                   Geberit AG   \n",
       "34       MS.MI                                 Mediaset SpA   \n",
       "35    SESFd.PA                                       SES SA   \n",
       "36     EKTL.MC                                 Euskaltel SA   \n",
       "37     STLA.MI                                Stellantis NV   \n",
       "38     RWAY.MI                                  Rai Way SpA   \n",
       "39       CRH.I                                      CRH PLC   \n",
       "40        VZ.N                   Verizon Communications Inc   \n",
       "41         T.N                                     AT&T Inc   \n",
       "42      VON.AS                                     VEON Ltd   \n",
       "43      SSAq.L                              AFK Sistema PAO   \n",
       "44  TIGOsdb.ST           Millicom International Cellular SA   \n",
       "45     ICEG.OL                                Ice Group ASA   \n",
       "46      SNC.LS                             Sonaecom SGPS SA   \n",
       "47    NETMb.ST                             Netmore Group AB   \n",
       "48   ARCANE.ST                             Arcane Crypto AB   \n",
       "49       VOD.L                           Vodafone Group PLC   \n",
       "\n",
       "    Company Market Cap  \n",
       "0         1.367792e+10  \n",
       "1         7.849264e+10  \n",
       "2         2.216480e+10  \n",
       "3         2.731177e+10  \n",
       "4         1.352637e+10  \n",
       "5         2.497122e+10  \n",
       "6         7.085390e+09  \n",
       "7         9.902179e+09  \n",
       "8         7.173840e+09  \n",
       "9         1.473490e+10  \n",
       "10        9.101736e+09  \n",
       "11        4.576260e+09  \n",
       "12        2.117473e+10  \n",
       "13        5.841432e+09  \n",
       "14        1.245681e+10  \n",
       "15        4.370769e+09  \n",
       "16        1.116158e+09  \n",
       "17        3.289231e+10  \n",
       "18        2.107834e+11  \n",
       "19        1.495996e+11  \n",
       "20        8.025415e+10  \n",
       "21        8.367301e+09  \n",
       "22        1.387029e+09  \n",
       "23        8.404948e+09  \n",
       "24        1.579853e+11  \n",
       "25        6.044844e+09  \n",
       "26        2.606042e+09  \n",
       "27        3.832111e+09  \n",
       "28        1.495633e+09  \n",
       "29        5.405523e+10  \n",
       "30        1.179824e+10  \n",
       "31        2.632082e+10  \n",
       "32        2.994765e+09  \n",
       "33        2.157462e+10  \n",
       "34        3.162306e+09  \n",
       "35        3.175209e+09  \n",
       "36        1.599566e+09  \n",
       "37        4.815931e+10  \n",
       "38        1.264089e+09  \n",
       "39        3.182655e+10  \n",
       "40        2.302052e+11  \n",
       "41        2.125979e+11  \n",
       "42        2.694982e+09  \n",
       "43        4.726170e+09  \n",
       "44        3.495156e+10  \n",
       "45        3.785958e+09  \n",
       "46        4.557712e+08  \n",
       "47        1.693661e+08  \n",
       "48        2.975026e+09  \n",
       "49        3.581828e+10  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References:\n",
    "\n",
    "* [Sequential colormap](https://likegeeks.com/seaborn-heatmap-tutorial/)\n",
    "* [Sequential “cubehelix” palettes](https://seaborn.pydata.org/tutorial/color_palettes.html)\n",
    "* [seaborn.diverging_palette](https://seaborn.pydata.org/generated/seaborn.diverging_palette.html)\n",
    "* [seaborn.set_color_codes](https://seaborn.pydata.org/generated/seaborn.set_color_codes.html#seaborn.set_color_codes)\n",
    "* [seaborn.color_palette](https://seaborn.pydata.org/generated/seaborn.color_palette.html)\n",
    "* [seaborn.set_palette](https://seaborn.pydata.org/generated/seaborn.set_palette.html#seaborn.set_palette)\n",
    "* [matplotlib.colors.Colormap](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.Colormap.html)\n",
    "* [seaborn.choose_diverging_palette](https://seaborn.pydata.org/generated/seaborn.choose_diverging_palette.html#seaborn.choose_diverging_palette)\n",
    "* [Sequential color palettes](https://seaborn.pydata.org/tutorial/color_palettes.html)\n",
    "* [Choosing color palettes](https://seaborn.pydata.org/tutorial/color_palettes.html)\n",
    "* [Using circular color systems](https://seaborn.pydata.org/tutorial/color_palettes.html)\n",
    "* [Seaborn heatmap tutorial (Python Data Visualization): Add text over heatmap](https://likegeeks.com/seaborn-heatmap-tutorial/)\n",
    "* [Graph point on straight line (number line) in Python](https://stackoverflow.com/questions/23186804/graph-point-on-straight-line-number-line-in-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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