dash.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Companies and Regional Economic Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from matplotlib import font_manager\n",
    "font_manager.findSystemFonts(fontpaths=None, fontext=\"ttf\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_dirs = ['assets/']\n",
    "font_files = font_manager.findSystemFonts(fontpaths=font_dirs)\n",
    "\n",
    "for font_file in font_files:\n",
    "    font_manager.fontManager.addfont(font_file)\n",
    "\n",
    "mpl.rcParams.update({\n",
    "    'font.family' : 'DM Sans',\n",
    "    'axes.labelweight' : 'bold',\n",
    "    'axes.labelcolor' : '#6E6C83',\n",
    "    'axes.edgecolor' : '#DEDEE9',\n",
    "    'ytick.minor.size' : 0,\n",
    "    'ytick.major.size' : 0,\n",
    "    'ytick.color' : '#6E6C83',\n",
    "    'xtick.minor.size' : 0,\n",
    "    'xtick.major.size' : 0,\n",
    "    'xtick.color' : '#6E6C83',\n",
    "    'lines.color' : '#DEDEE9',\n",
    "    'savefig.dpi' : 300,\n",
    "    'figure.figsize' : [12,7],\n",
    "    'figure.facecolor' : '#FFFFFF'\n",
    "})\n",
    "\n",
    "colors = ['#4A3AFF', '#0B0086', '#766AFF', '#E1DEFF']\n",
    "colors2 = ['#0B0086', '#4A3AFF', '#766AFF', '#E1DEFF']\n",
    "\n",
    "bgc = ['#FFFFFF']\n",
    "textc = ['#1E1B39', '#6E6C83', '#4A3AFF', '#FFFFFF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20139, 75) (189064, 6) (18, 6) (91661, 6)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/refined.csv') # company id, financials, location\n",
    "fin = pd.read_csv('data/financials.csv') # company id, years, financials\n",
    "gdp = pd.read_csv('data/region_gdp.csv') # region gdp per year\n",
    "\n",
    "# company id, location (better than in the other file), name, and  code id for whether its still active or not\n",
    "states = pd.read_csv('data/states.csv')\n",
    "\n",
    "print(df.shape, fin.shape, gdp.shape, states.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of **null** values within the **'refined.csv'** dataframe.\n",
    "\n",
    "That is because not all companies were active for all the years from **2005 to 2022**, and not all companies reported their financial data for some years. The file containing all the necessary financials is **'financials.csv'** where each companies financials are **grouped by year**.\n",
    "\n",
    "The only cleaning that has to be done for this is for the **'location'** column where some locations are the same, but written in different ways. So we will take the locations from the **'states.csv'** file, that was provided for by the **government** data website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF - REFINED.CSV EDA\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20139 entries, 0 to 20138\n",
      "Data columns (total 75 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   id              20139 non-null  int64  \n",
      " 1   founded         20139 non-null  int64  \n",
      " 2   location        20139 non-null  object \n",
      " 3   turnover_2022   19339 non-null  float64\n",
      " 4   profit_2022     19339 non-null  float64\n",
      " 5   debts_2022      19339 non-null  float64\n",
      " 6   employees_2022  19339 non-null  float64\n",
      " 7   turnover_2021   17928 non-null  float64\n",
      " 8   profit_2021     17928 non-null  float64\n",
      " 9   debts_2021      17928 non-null  float64\n",
      " 10  employees_2021  17928 non-null  float64\n",
      " 11  turnover_2020   16554 non-null  float64\n",
      " 12  profit_2020     16554 non-null  float64\n",
      " 13  debts_2020      16554 non-null  float64\n",
      " 14  employees_2020  16554 non-null  float64\n",
      " 15  turnover_2019   15409 non-null  float64\n",
      " 16  profit_2019     15409 non-null  float64\n",
      " 17  debts_2019      15409 non-null  float64\n",
      " 18  employees_2019  15409 non-null  float64\n",
      " 19  turnover_2018   13784 non-null  float64\n",
      " 20  profit_2018     13784 non-null  float64\n",
      " 21  debts_2018      13784 non-null  float64\n",
      " 22  employees_2018  13784 non-null  float64\n",
      " 23  turnover_2017   12636 non-null  float64\n",
      " 24  profit_2017     12636 non-null  float64\n",
      " 25  debts_2017      12636 non-null  float64\n",
      " 26  employees_2017  12636 non-null  float64\n",
      " 27  turnover_2016   11342 non-null  float64\n",
      " 28  profit_2016     11342 non-null  float64\n",
      " 29  debts_2016      11342 non-null  float64\n",
      " 30  employees_2016  11342 non-null  float64\n",
      " 31  turnover_2015   10264 non-null  float64\n",
      " 32  profit_2015     10264 non-null  float64\n",
      " 33  debts_2015      10264 non-null  float64\n",
      " 34  employees_2015  10264 non-null  float64\n",
      " 35  turnover_2014   9521 non-null   float64\n",
      " 36  profit_2014     9521 non-null   float64\n",
      " 37  debts_2014      9521 non-null   float64\n",
      " 38  employees_2014  9521 non-null   float64\n",
      " 39  turnover_2013   8933 non-null   float64\n",
      " 40  profit_2013     8933 non-null   float64\n",
      " 41  debts_2013      8933 non-null   float64\n",
      " 42  employees_2013  8933 non-null   float64\n",
      " 43  turnover_2012   8324 non-null   float64\n",
      " 44  profit_2012     8324 non-null   float64\n",
      " 45  debts_2012      8324 non-null   float64\n",
      " 46  employees_2012  8324 non-null   float64\n",
      " 47  turnover_2011   7788 non-null   float64\n",
      " 48  profit_2011     7788 non-null   float64\n",
      " 49  debts_2011      7788 non-null   float64\n",
      " 50  employees_2011  7788 non-null   float64\n",
      " 51  turnover_2010   7283 non-null   float64\n",
      " 52  profit_2010     7283 non-null   float64\n",
      " 53  debts_2010      7283 non-null   float64\n",
      " 54  employees_2010  7283 non-null   float64\n",
      " 55  turnover_2009   6934 non-null   float64\n",
      " 56  profit_2009     6934 non-null   float64\n",
      " 57  debts_2009      6934 non-null   float64\n",
      " 58  employees_2009  6934 non-null   float64\n",
      " 59  turnover_2008   6674 non-null   float64\n",
      " 60  profit_2008     6674 non-null   float64\n",
      " 61  debts_2008      6674 non-null   float64\n",
      " 62  employees_2008  6674 non-null   float64\n",
      " 63  turnover_2007   6071 non-null   float64\n",
      " 64  profit_2007     6071 non-null   float64\n",
      " 65  debts_2007      6071 non-null   float64\n",
      " 66  employees_2007  6071 non-null   float64\n",
      " 67  turnover_2006   5425 non-null   float64\n",
      " 68  profit_2006     5425 non-null   float64\n",
      " 69  debts_2006      5425 non-null   float64\n",
      " 70  employees_2006  5425 non-null   float64\n",
      " 71  turnover_2005   4859 non-null   float64\n",
      " 72  profit_2005     4859 non-null   float64\n",
      " 73  debts_2005      4859 non-null   float64\n",
      " 74  employees_2005  4859 non-null   float64\n",
      "dtypes: float64(72), int64(2), object(1)\n",
      "memory usage: 11.5+ MB\n",
      "None\n",
      "_____________________________________________\n",
      "                 id       founded  turnover_2022   profit_2022    debts_2022  \\\n",
      "count  2.013900e+04  20139.000000   1.933900e+04  1.933900e+04  1.933900e+04   \n",
      "mean   2.990184e+07   2011.678733   5.703808e+05  2.949844e+04  2.635100e+05   \n",
      "std    1.335456e+07      8.891578   2.076620e+07  6.536063e+05  7.672877e+06   \n",
      "min    6.133400e+04   1991.000000  -2.587000e+03 -7.254303e+07 -4.367400e+04   \n",
      "25%    1.814043e+07   2005.000000   3.053500e+03 -5.000000e+00  1.569000e+03   \n",
      "50%    3.441545e+07   2015.000000   2.707400e+04  3.055000e+03  1.037500e+04   \n",
      "75%    4.099241e+07   2019.000000   9.915300e+04  1.952050e+04  4.949350e+04   \n",
      "max    4.738927e+07   2022.000000   2.774259e+09  1.799923e+07  8.434641e+08   \n",
      "\n",
      "       employees_2022  turnover_2021   profit_2021    debts_2021  \\\n",
      "count    19339.000000   1.792800e+04  1.792800e+04  1.792800e+04   \n",
      "mean         4.737835   4.666023e+05  3.101671e+04  2.175438e+05   \n",
      "std         47.094422   1.186339e+07  2.935262e+05  4.927557e+06   \n",
      "min          0.000000  -2.688000e+03 -1.336605e+07 -1.052310e+05   \n",
      "25%          0.000000   3.109000e+03  0.000000e+00  1.434750e+03   \n",
      "50%          1.000000   2.529350e+04  2.821500e+03  9.952000e+03   \n",
      "75%          2.000000   9.038150e+04  1.748150e+04  4.682725e+04   \n",
      "max       5384.000000   1.378702e+09  1.787672e+07  4.753313e+08   \n",
      "\n",
      "       employees_2021  ...    debts_2007  employees_2007  turnover_2006  \\\n",
      "count    17928.000000  ...  6.071000e+03     6071.000000   5.425000e+03   \n",
      "mean         4.943552  ...  2.320260e+05       10.890133   4.790753e+05   \n",
      "std         48.493492  ...  2.813726e+06      113.174818   1.050049e+07   \n",
      "min          0.000000  ...  0.000000e+00        0.000000   0.000000e+00   \n",
      "25%          0.000000  ...  5.439000e+03        1.000000   9.243000e+03   \n",
      "50%          1.000000  ...  2.049500e+04        2.000000   3.647400e+04   \n",
      "75%          3.000000  ...  7.366550e+04        6.000000   1.316050e+05   \n",
      "max       5314.000000  ...  1.596989e+08     7846.000000   7.355279e+08   \n",
      "\n",
      "        profit_2006    debts_2006  employees_2006  turnover_2005  \\\n",
      "count  5.425000e+03  5.425000e+03     5425.000000   4.859000e+03   \n",
      "mean   2.665815e+04  1.932981e+05       11.778986   4.065722e+05   \n",
      "std    3.702212e+05  2.797973e+06      128.155818   8.692775e+06   \n",
      "min   -1.219004e+06  0.000000e+00        0.000000   0.000000e+00   \n",
      "25%   -1.970000e+02  4.830000e+03        1.000000   8.185000e+03   \n",
      "50%    1.395000e+03  1.788400e+04        2.000000   3.007300e+04   \n",
      "75%    1.025800e+04  6.577500e+04        6.000000   1.077820e+05   \n",
      "max    2.563031e+07  1.804905e+08     8563.000000   5.697520e+08   \n",
      "\n",
      "        profit_2005    debts_2005  employees_2005  \n",
      "count  4.859000e+03  4.859000e+03     4859.000000  \n",
      "mean   9.022430e+03  1.380330e+05       12.996913  \n",
      "std    4.934833e+05  1.547225e+06      147.254623  \n",
      "min   -3.315123e+07 -1.012000e+03        0.000000  \n",
      "25%   -1.000000e+02  3.432500e+03        1.000000  \n",
      "50%    1.344000e+03  1.370800e+04        2.000000  \n",
      "75%    8.205000e+03  4.986900e+04        6.000000  \n",
      "max    4.670442e+06  7.690248e+07     9377.000000  \n",
      "\n",
      "[8 rows x 74 columns]\n"
     ]
    },
    {
     "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",
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       "    }\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>id</th>\n",
       "      <th>founded</th>\n",
       "      <th>location</th>\n",
       "      <th>turnover_2022</th>\n",
       "      <th>profit_2022</th>\n",
       "      <th>debts_2022</th>\n",
       "      <th>employees_2022</th>\n",
       "      <th>turnover_2021</th>\n",
       "      <th>profit_2021</th>\n",
       "      <th>debts_2021</th>\n",
       "      <th>...</th>\n",
       "      <th>debts_2007</th>\n",
       "      <th>employees_2007</th>\n",
       "      <th>turnover_2006</th>\n",
       "      <th>profit_2006</th>\n",
       "      <th>debts_2006</th>\n",
       "      <th>employees_2006</th>\n",
       "      <th>turnover_2005</th>\n",
       "      <th>profit_2005</th>\n",
       "      <th>debts_2005</th>\n",
       "      <th>employees_2005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12056960</td>\n",
       "      <td>1999</td>\n",
       "      <td>Loc. Targu Mures</td>\n",
       "      <td>20994.0</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>8204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65014.0</td>\n",
       "      <td>11451.0</td>\n",
       "      <td>19414.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12021.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>-979.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38791.0</td>\n",
       "      <td>24547.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10666988</td>\n",
       "      <td>1999</td>\n",
       "      <td>Loc. Sighisoara</td>\n",
       "      <td>147322811.0</td>\n",
       "      <td>9439869.0</td>\n",
       "      <td>22280864.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>112422027.0</td>\n",
       "      <td>6752691.0</td>\n",
       "      <td>13066302.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6646370.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>46807963.0</td>\n",
       "      <td>2219596.0</td>\n",
       "      <td>3628111.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>37239539.0</td>\n",
       "      <td>3846087.0</td>\n",
       "      <td>2963655.0</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10023982</td>\n",
       "      <td>1997</td>\n",
       "      <td>Loc. Targu Mures</td>\n",
       "      <td>37162.0</td>\n",
       "      <td>-7077.0</td>\n",
       "      <td>2726.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74470.0</td>\n",
       "      <td>-7729.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>146285.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>54983.0</td>\n",
       "      <td>126498.0</td>\n",
       "      <td>86423.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>81442.0</td>\n",
       "      <td>-20777.0</td>\n",
       "      <td>139619.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  founded          location  turnover_2022  profit_2022  \\\n",
       "0  12056960     1999  Loc. Targu Mures        20994.0       1461.0   \n",
       "1  10666988     1999   Loc. Sighisoara    147322811.0    9439869.0   \n",
       "2  10023982     1997  Loc. Targu Mures        37162.0      -7077.0   \n",
       "\n",
       "   debts_2022  employees_2022  turnover_2021  profit_2021  debts_2021  ...  \\\n",
       "0      8204.0             0.0        65014.0      11451.0     19414.0  ...   \n",
       "1  22280864.0           423.0    112422027.0    6752691.0  13066302.0  ...   \n",
       "2      2726.0             4.0        74470.0      -7729.0      5009.0  ...   \n",
       "\n",
       "   debts_2007  employees_2007  turnover_2006  profit_2006  debts_2006  \\\n",
       "0     12021.0             0.0          444.0       -979.0        13.0   \n",
       "1   6646370.0           416.0     46807963.0    2219596.0   3628111.0   \n",
       "2    146285.0            16.0        54983.0     126498.0     86423.0   \n",
       "\n",
       "   employees_2006  turnover_2005  profit_2005  debts_2005  employees_2005  \n",
       "0             0.0        38791.0      24547.0      2158.0             0.0  \n",
       "1           424.0     37239539.0    3846087.0   2963655.0           418.0  \n",
       "2            17.0        81442.0     -20777.0    139619.0            17.0  \n",
       "\n",
       "[3 rows x 75 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA for the REFINED CSV\n",
    "print('DF - REFINED.CSV EDA')\n",
    "print(df.info())\n",
    "print('_________'*5)\n",
    "print(df.describe())\n",
    "display(df.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIN - FINANCIALS.CSV EDA\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 189064 entries, 0 to 189063\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count   Dtype\n",
      "---  ------     --------------   -----\n",
      " 0   year       189064 non-null  int64\n",
      " 1   id         189064 non-null  int64\n",
      " 2   employees  189064 non-null  int64\n",
      " 3   turnover   189064 non-null  int64\n",
      " 4   profit     189064 non-null  int64\n",
      " 5   debts      189064 non-null  int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 8.7 MB\n",
      "None\n",
      "_____________________________________________\n",
      "                year            id      employees      turnover        profit  \\\n",
      "count  189064.000000  1.890640e+05  189064.000000  1.890640e+05  1.890640e+05   \n",
      "mean     2015.587404  2.136939e+07       7.056293  4.704307e+05  2.534963e+04   \n",
      "std         4.981324  1.238239e+07      62.933255  1.187683e+07  7.374805e+05   \n",
      "min      2005.000000  6.133400e+04       0.000000 -2.724400e+04 -7.254303e+07   \n",
      "25%      2012.000000  1.330003e+07       0.000000  5.073000e+03 -4.040000e+02   \n",
      "50%      2017.000000  1.875940e+07       1.000000  2.761750e+04  1.356000e+03   \n",
      "75%      2020.000000  3.214651e+07       4.000000  1.028642e+05  1.143525e+04   \n",
      "max      2022.000000  4.738927e+07    9377.000000  2.774259e+09  1.191976e+08   \n",
      "\n",
      "              debts  \n",
      "count  1.890640e+05  \n",
      "mean   2.259006e+05  \n",
      "std    4.476999e+06  \n",
      "min   -1.052310e+05  \n",
      "25%    2.702000e+03  \n",
      "50%    1.500600e+04  \n",
      "75%    6.032625e+04  \n",
      "max    8.434641e+08  \n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>employees</th>\n",
       "      <th>turnover</th>\n",
       "      <th>profit</th>\n",
       "      <th>debts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>61334</td>\n",
       "      <td>1</td>\n",
       "      <td>13477</td>\n",
       "      <td>1563</td>\n",
       "      <td>2162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005</td>\n",
       "      <td>200106</td>\n",
       "      <td>243</td>\n",
       "      <td>31400665</td>\n",
       "      <td>217953</td>\n",
       "      <td>16318096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005</td>\n",
       "      <td>236061</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-18</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      id  employees  turnover  profit     debts\n",
       "0  2005   61334          1     13477    1563      2162\n",
       "1  2005  200106        243  31400665  217953  16318096\n",
       "2  2005  236061          0         0     -18       136"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA for the FINANCIALS CSV\n",
    "print('FIN - FINANCIALS.CSV EDA')\n",
    "print(fin.info())\n",
    "print('_________'*5)\n",
    "print(fin.describe())\n",
    "display(fin.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need the **Years** as **year** and **Value_EUR** as **gdp**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP - REGION_GDP.CSV EDA\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18 entries, 0 to 17\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Years      18 non-null     int64  \n",
      " 1   LEI        18 non-null     object \n",
      " 2   Value_LEI  18 non-null     float64\n",
      " 3   EUR        18 non-null     object \n",
      " 4   Value_EUR  18 non-null     float64\n",
      " 5   Rate       18 non-null     float64\n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 996.0+ bytes\n",
      "None\n",
      "_____________________________________________\n",
      "             Years     Value_LEI    Value_EUR       Rate\n",
      "count    18.000000     18.000000    18.000000  18.000000\n",
      "mean   2013.500000  16036.316667  3569.905556   4.378333\n",
      "std       5.338539   6538.895980  1140.695128   0.456679\n",
      "min    2005.000000   6815.900000  1853.600000   3.380000\n",
      "25%    2009.250000  11564.975000  2852.357500   4.242500\n",
      "50%    2013.500000  14186.800000  3183.675000   4.480000\n",
      "75%    2017.750000  20219.375000  4336.157500   4.660000\n",
      "max    2022.000000  29605.500000  5984.060000   4.950000\n"
     ]
    },
    {
     "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",
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       "    }\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>Years</th>\n",
       "      <th>LEI</th>\n",
       "      <th>Value_LEI</th>\n",
       "      <th>EUR</th>\n",
       "      <th>Value_EUR</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>Millions lei</td>\n",
       "      <td>6815.9</td>\n",
       "      <td>Millions eur</td>\n",
       "      <td>1853.60</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>Millions lei</td>\n",
       "      <td>8103.6</td>\n",
       "      <td>Millions eur</td>\n",
       "      <td>2396.31</td>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>Millions lei</td>\n",
       "      <td>10288.1</td>\n",
       "      <td>Millions eur</td>\n",
       "      <td>2849.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Years           LEI  Value_LEI           EUR  Value_EUR  Rate\n",
       "0   2005  Millions lei     6815.9  Millions eur    1853.60  3.68\n",
       "1   2006  Millions lei     8103.6  Millions eur    2396.31  3.38\n",
       "2   2007  Millions lei    10288.1  Millions eur    2849.75  3.61"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA for the REGION GDP CSV\n",
    "print('GDP - REGION_GDP.CSV EDA')\n",
    "print(gdp.info())\n",
    "print('_________'*5)\n",
    "print(gdp.describe())\n",
    "display(gdp.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see many missing values. We'll cross check either the **financials** or the **refined** datasets for the company id to see if all of them are found within the dataframe. If they are, then we take the locations of those that do appear. If all of our companies also show up with a code, then we might be able to use it in some way to show whether a company is still active or not. However we must be careful since we only get a code, and not also a year of when a company stopped being active, or when they re-started their activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STATES - STATES.CSV EDA\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 91661 entries, 0 to 91660\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   name          91661 non-null  object \n",
      " 1   id            86976 non-null  float64\n",
      " 2   registration  91661 non-null  object \n",
      " 3   euid          91661 non-null  object \n",
      " 4   state         75737 non-null  float64\n",
      " 5   location      91660 non-null  object \n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 4.2+ MB\n",
      "None\n",
      "_____________________________________________\n",
      "                 id         state\n",
      "count  8.697600e+04  75737.000000\n",
      "mean   2.554579e+07   1070.573894\n",
      "std    1.467381e+07     17.345674\n",
      "min    0.000000e+00   1048.000000\n",
      "25%    1.482521e+07   1048.000000\n",
      "50%    2.569016e+07   1084.000000\n",
      "75%    3.780218e+07   1084.000000\n",
      "max    1.216209e+09   1145.000000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>registration</th>\n",
       "      <th>euid</th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DARABANT HORVATH ISTVAN ÎNTREPRINDERE INDIVIDUALĂ</td>\n",
       "      <td>33462634.0</td>\n",
       "      <td>F26/869/2014</td>\n",
       "      <td>ROONRC.F26/869/2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NAGY I  ILONA IBOLYA ÎNTREPRINDERE INDIVIDUALĂ</td>\n",
       "      <td>33462618.0</td>\n",
       "      <td>F26/870/2014</td>\n",
       "      <td>ROONRC.F26/870/2014</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POP MIREL OVIDIU PERSOANĂ FIZICĂ AUTORIZATĂ</td>\n",
       "      <td>33462600.0</td>\n",
       "      <td>F26/871/2014</td>\n",
       "      <td>ROONRC.F26/871/2014</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name          id  \\\n",
       "0  DARABANT HORVATH ISTVAN ÎNTREPRINDERE INDIVIDUALĂ  33462634.0   \n",
       "1     NAGY I  ILONA IBOLYA ÎNTREPRINDERE INDIVIDUALĂ  33462618.0   \n",
       "2        POP MIREL OVIDIU PERSOANĂ FIZICĂ AUTORIZATĂ  33462600.0   \n",
       "\n",
       "   registration                 euid   state                location  \n",
       "0  F26/869/2014  ROONRC.F26/869/2014     NaN  Municipiul Târgu Mureş  \n",
       "1  F26/870/2014  ROONRC.F26/870/2014  1048.0  Municipiul Târgu Mureş  \n",
       "2  F26/871/2014  ROONRC.F26/871/2014  1048.0  Municipiul Târgu Mureş  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA for the STATES CSV\n",
    "print('STATES - STATES.CSV EDA')\n",
    "print(states.info())\n",
    "print('_________'*5)\n",
    "print(states.describe())\n",
    "display(states.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "name                0\n",
      "id                  0\n",
      "registration        0\n",
      "euid                0\n",
      "state           15924\n",
      "location            1\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33462634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33462618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33462600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id\n",
       "0  33462634\n",
       "1  33462618\n",
       "2  33462600"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping the 'id' missing values, duplicates, and converting the id to\n",
    "# the same datatype as the id in df\n",
    "states = states.dropna(subset=['id'])\n",
    "states['id'] = states['id'].astype(np.int64)\n",
    "states = states[~states['id'].duplicated()]\n",
    "print(states['id'].duplicated().sum())\n",
    "print(states.isna().sum())\n",
    "display(states[['id']].head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a new dataframe with the id, and the location from 'df' and 'states'\n",
    "# we'll be using the 'states' locations as they are consistent, and don't have\n",
    "# different spellings for the same location. we might anonimize the locations later\n",
    "loc_df = df[['id','location']].copy()\n",
    "loc_df = pd.merge(loc_df, states[['id','location']], on='id', how='left')\n",
    "loc_df = loc_df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>id</th>\n",
       "      <th>location_x</th>\n",
       "      <th>location_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, location_x, location_y]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 459 unique locations\n"
     ]
    }
   ],
   "source": [
    "# we have a few NaNs, so we fill the values for the location_y with the correct ones\n",
    "loc_df.loc[(loc_df['location_x'] == 'Loc. Ludus') & loc_df['location_y'].isnull(), 'location_y'] = 'Loc. Luduş, Oraş Luduş'\n",
    "loc_df.loc[(loc_df['location_x'].isin(['Loc. Targu Mures','Targu Mures'])) & loc_df['location_y'].isnull(), 'location_y'] = 'Municipiul Târgu Mureş'\n",
    "loc_df.loc[(loc_df['location_x'] == 'Sat Seleus, Com. Zagar') & loc_df['location_y'].isnull(), 'location_y'] = 'Sat Seleuş, Comuna Zagăr'\n",
    "loc_df.loc[(loc_df['location_x'] == 'Loc. Sighisoara') & loc_df['location_y'].isnull(), 'location_y'] = 'Municipiul Sighişoara'\n",
    "\n",
    "display(loc_df[loc_df['location_y'].isnull()])\n",
    "print('There are', len(loc_df['location_y'].unique()), 'unique locations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>gdp</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>1853.60</td>\n",
       "      <td>Millions eur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>2396.31</td>\n",
       "      <td>Millions eur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>2849.75</td>\n",
       "      <td>Millions eur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      gdp          unit\n",
       "0  2005  1853.60  Millions eur\n",
       "1  2006  2396.31  Millions eur\n",
       "2  2007  2849.75  Millions eur"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the columns for the GDP dataframe, and dropping the other columns as they're not needed\n",
    "gdp.rename(columns={'Years':'year', 'Value_EUR':'gdp', 'EUR':'unit'}, inplace=True)\n",
    "gdp = gdp[['year','gdp','unit']]\n",
    "gdp.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most of these visuals will have no titles or axis titles as they're going to be used for the presentation\n",
    "\n",
    "### Number of Companies Founded per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "companies_founded = df.groupby(['founded'])['id'].count().reset_index()\n",
    "companies_founded.rename(columns={'founded':'year','id':'count'}, inplace=True)\n",
    "sns.barplot(data=companies_founded.tail(18), x='year', y='count', color=colors[0])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Region GDP Growth over Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=gdp, x='year', y='gdp', color=colors[0]).yaxis.set_major_formatter('€{x:1.0f} mn')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### % GDP Growth + % Number of Newly Registered Companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdp_comp = pd.merge(gdp, companies_founded.tail(18), on='year', how='left')\n",
    "\n",
    "gdp_comp['gdp_pct'] = round((gdp_comp['gdp'] / gdp_comp['gdp'].max() * 100),2)\n",
    "gdp_comp['count_pct'] = round((gdp_comp['count'] / gdp_comp['count'].max() * 100),2)\n",
    "\n",
    "gdp_fig = gdp_comp.plot.bar(rot=0, color=colors, x='year', y=['gdp_pct','count_pct'])\n",
    "gdp_fig.get_legend().remove()\n",
    "gdp_fig.yaxis.set_major_formatter('{x:1.0f}%')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year on Year Change\n",
    "\n",
    "Change - Year on Year - in Numbers - How many companies that were active last year, aren't active this year.\n",
    "\n",
    "Difference between: Total Active Companies in Current Year, Newly Registered Companies in Current Year, Total Active Companies in Previous Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_on_year = fin.groupby(['year'])['id'].count().reset_index()\n",
    "year_on_year = pd.merge(year_on_year, companies_founded[['year','count']], on='year', how='left')\n",
    "year_on_year.rename(columns={'id':'active','count':'founded'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>active</th>\n",
       "      <th>founded</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020</td>\n",
       "      <td>16553</td>\n",
       "      <td>1192</td>\n",
       "      <td>-48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021</td>\n",
       "      <td>17927</td>\n",
       "      <td>1424</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022</td>\n",
       "      <td>19337</td>\n",
       "      <td>1469</td>\n",
       "      <td>-59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  active  founded  diff\n",
       "15  2020   16553     1192   -48\n",
       "16  2021   17927     1424   -50\n",
       "17  2022   19337     1469   -59"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "founded = year_on_year['founded'].values\n",
    "act = year_on_year['active'].values\n",
    "diff = []\n",
    "\n",
    "for i in range(len(act)):\n",
    "    if i == 0:\n",
    "        diff.append(0)\n",
    "    else:\n",
    "        diff.append(act[i] - founded[i] - act[i-1])\n",
    "\n",
    "year_on_year['diff'] = diff\n",
    "year_on_year.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# difference between number of newly registered companies and the ones that either paused their activity or retook it (if 'diff' > 0)\n",
    "year_on_year.tail(4).plot.bar(rot=0, color=colors, x='year', y=['founded','diff']).get_legend().remove()\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debt per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_total = fin.groupby('year').sum().reset_index()\n",
    "yearly_total['debts'] = yearly_total['debts'] / 1000000000\n",
    "yearly_total['profit'] = yearly_total['profit'] / 1000000\n",
    "yearly_total['turnover'] = yearly_total['turnover'] / 1000000000\n",
    "\n",
    "sns.barplot(data=yearly_total, x='year', y='debts', color=colors[0], ci=None).yaxis.set_major_formatter('€{x:1.2f} bn')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover vs Debts per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7tk/Tpu8cRxhyw/CMHTM+SbJ06bJUNGlSZ/uKJk2yaNGStfa7a7/eef31aZk1a26d8TFjns0uu+6Y8vKyetWsy7S33s7Spctrv168aEmmz5iVHbpus/4fAAAAAGymNijwJ++sxh9z7CEZfOXQjBnzbKqq5mf69FkZOmR43pz6dgYMPChJstfeu2TChFfyxNjnMm/egrz66tTcPeKhfGyffkmSV1+dmt9ecV1qamqy3Xads9/+u+eqwcMyadIbmTt3fobf9WCeG/9yjj7mk0lSr5p1qaioyDV/uTVvz5id6dNn5S9/viU9e+6Qbt2229CPAgAAADYbG/xYviQZeET/VFa2y4OjxubmG+9Js62apWfPHXLx185Np04dkiQ77tgt//LZU3L33Q9nyJDhadO6ZfY/YI/acL5k8bLMnDk3q1fXpLy8PKefcVzuHvFQrrvm9ixdujxdu26Tr1x4drbbrnPt+9an5r322HPnbLtt5/zudzdk2bLl2XWX3vn06cc0xMcAAAAAm42yUun9bn/Hh6mpqcmUKXPSvXunlJdv8MkSAAAAW7TjDl7/bYaPbvh5NJTNsZ/1yaFSKgAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABRQxaaeAAAAwEfVcQev/zbDRzf8PCgmK/wAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABVWzqCQAAAKyP4w5e/22Gj274ecDmzgo/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAX0kQr8kya9kW9c8tNNPQ0AAABodBUNtaNnn3kxo0aOyYwZs1NR0STbbd8lA4/on759e9XWLFu2PLfden+ef+7lVK9enb59e+XU045M+/Zt17nPp8dNzL33PJo5c+alY8f2OeLIg7Pvfruvdw0AAAB81DRI4B/5wJjcd+/onHTKgPTp0yOrV9fk+edeylWDh+W8z52Wfrv1SZL8+U83p2XLFrn4a/+SsvLyPDhqTP74h5tyyTc+t9Y+n3zy+dw47O4MGnRMeu3YLa+9OjXDht6dmpqa7H/AnvWuAQAAgI+iDQ788+YtyPC7RuUrF52TXr261o4PGNg/LVu2yLRpM9Nvtz6ZNOmNzJk9L9/5jzPSpEmTJMlpnzoqU6ZMT6lUSllZWe22NTU1ueO2B3LqqUfWrtZXVu6eVdXVufOOUdl3vz2SlD60prz8H/t8t+fGv5w7bn8g8+cvTNeu2+a0Tx2VHbpukyS5/to70qGyXRYsWJSnx01M8+ZbZcAR/XPooftv6EcFAAAAG80GBf6rBg/L88+/kiQZfOWQ2vEvnD8ovft0z0H9964de2Hi5PTbrU+eenJCHnn4qSxavCR7771rjj/+0DphP0nenDojS5cuy8f26VdnfJ99dsvNN96TN9+ckZRKH1rTvft2a825unp1ht81KmeceVw6VLbLg6PG5re/uS6XfedLadu2dZLk4YeeyBFHfjyXfedLeeGFyblp2N3ZsVfXdO227YZ8XAAAsEkcd/D6bzN8dMPPA9i4NijwDzrj2NSUSlmxYmXOOvuE2vE1wfndZs+qyvz5i/L223Ny+pnHZsXylfnr9Xdm6dJldbZNkqp5C9KufZs0a9a0znizZk3Tvn3bVFXNT5IPrVlX4K+pqcnJpx6R3n26J0lOPe3IvPbqmxk7ZnyOOPKdn4Q77LBNBgw8KEny8Y/vk7GPj8+kSVMEfgAAALYYGxT4hw0ZkYkTJiVJfv7TP9SOr1nhf7dVq6pTVTU/37/4omy1VbMkybmfPSW//uXVOenkgWnVqsU/ikvv/56llFKWspRK71+0pub99On9j7mVlZWlz049Mm3azNqxzl061alv1651VqxY+f6TAgAAgM3MBq/wV3Zsn3HjJubrl5xXO75mhb+qan4WLlySHj22T5u2rbLNNp1qw36S7LB9l9TU1GTBgkV1An+HynaZP39RVq5cVWcFf+XKVZk/b2E6dGibUvKhNevj3ZcVlJet62DBBxyFAAAAgM1M+YZs3LZt6xx62AFZvmx5Fi5cnI4d26djx/Zp2vSd4whDbhiesWPGJ0l23LFbZs2qSnX16trtp8+YlSYVTVLZoV2d/Xbrtm1atmiep8dNrDM+btyEtGjRPF27bVuvmvczefKUOl9PmvRGuv79pn0AAABQBBsU+JOksrJdjjn2kAy+cmjGjHk2VVXzM336rAwdMjxvTn279lr4ffbdLeXl5bn6z7dk1qyqvDl1Rq69+rb0P2jvNG+xVZYuXZZf/fIvmTlzbsrLy3PiSYfnllvuzVNPPp+qqgV56qkJufXm+3L8CYelvLy8XjXr0qSiSW679f5Mnjwlc+fOz8033ZN5VQs8xg8AAIBC2eDH8iXJwCP6p/Lvd7y/+cZ70myrZunZc4dc/LVz06lThyTv3EzvKxedk1tuuic//+kf0rRpRfbdb/ecdPKAJO9c4z/z7blZvnxFkmT/A/ZMWXl57rvvscy9YXgqO7bPpz59VJ1gXp+a92rfrk1OOe3I3HTjPZkzuyo77LBN/vXCs9O6dcuG+CgAAABgs1BW+qC73/GBampqMmXKnHTv3ul9zygAAIBNrWiP5StSP0XqJdFP0vj9rE8OlVIBAACggAR+AAAAKCCBHwAAAApI4AcAAIACEvgBAACggAR+AAAAKKCKTT0BAADY3GyOj+ICWF9W+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAooIpNPQEAAIrhuIPXf5vhoxt+HgC8wwo/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQBWbegIAAB9Vxx28/tsMH93w8wCgmKzwAwAAQAEJ/AAAAFBAAj8AAAAUkMAPAAAABeSmfQAAANBAWpf9ZL23WVy6rBFmYoUfAAAACkngBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACqhiU08AAKC+jjt4/bcZPrrh5wEAWwIr/AAAAFBAAj8AAAAUkMAPAAAABSTwAwAAQAEJ/AAAAFBAAj8AAAAUkMAPAAAABSTwAwAAQAEJ/AAAAFBAAj8AAAAUkMAPAAAABSTwAwAAQAEJ/AAAAFBAAj8AAAAUkMAPAAAABVSxqScAAJub4w5e/22Gj274eQAAbAgr/AAAAFBAVvgBoOCcsQAAH01W+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCAPlKBf+yY8fnJj3+/qacBAAAAja7BHsv37DMvZtTIMZkxY3YqKppku+27ZOAR/dO3b6911t/zt0cyYvhDufCr56RPnx7rrHn4oSfz0ENPZMH8Rdlm261z3PGHZpdddlzvGgAAAPioaZDAP/KBMbnv3tE56ZQB6dOnR1avrsnzz72UqwYPy3mfOy39dutTp37GjNl5+OGn0qJF8/fd5733PJpHHn4qp595XLbbbutMeH5S/viHG3P+Badn55171rsGAAAAPoo2OPDPm7cgw+8ala9cdE569epaOz5gYP+0bNki06bNrBP4a2pqcsP1d+b4Ew7NvX97dJ37XLJkae6959F88YLTs9Pfg/snD9kvixYtyV13jMrO3+hZr5r3M/rRcbn33tFZumRZduzdLZ/+9NHp2KlDkuSKX1+TvfbeNS+//HpeevHVtGnTKieceHg+tk+/Df2oAAAAYKPZoMB/1eBhef75V5Ikg68cUjv+hfMHpXef7jmo/95rbTNq5NhstVWzHHTQ3u8b+F95+Y20at2yNsivceCBe+beex7NokVLMnnSlA+tadOm1Vr7rpo7P2MeH5/Pf+FTad68eUbc9WB++9vr863LLkizZk2TJH+7++GcdPLAnHraERnz+LO57ro7smPvbmnXrs36fUAAAACwiWxQ4B90xrGpKZWyYsXKnHX2CbXjbdu2Xmf9rFlVeeCBx3PJJed94H6rqhZk660r1xrv2KlDmlQ0SVXVgnrVrCvwr1y5Kuece1K6dOmYJDn7Myfmh9+/IhOef6V2Fb/fbn2y/wF7JEmOOfaQjB79TF5//a3stdcuHzhvAAAA2FxsUOAfNmREJk6YlCT5+U//UDu+ZoX/3UqlUob89c4cddTHa0+ff3+lD3iplLKy+tasrW3b1rVhP0maNq1Irx275q23ZtYG/ne/XlZWlnZtW2flipUfMmcAAADYfGzwCn9lx/YZN25ivv6uVfs1K/xVVfOzcOGS9OixfR55+KnU1JTyiU/u96H77dChXWbPrlprfO6ceVm9uibt27etV836ePcBgrJ1HC0ofcDxBQAAANjcbFDgb9u2dQ497ICMfnRcFi5cnJ49d6jz+pAbhqdjxw7p0WP7PPjg2Myfvyjf/MbPal9ftWpVfv+7Idlv/z1y+hnH1o7v3LdnFl+3JK+8/Hqda/THjBmfrl23Tdu2retVsy4LFy7OrFlz07nzO6v41auq8/prb+VjH3NTPoB/1nEHr/82w0c3/DwAAPiHDb5Lf2Vluxxz7CEZfOXQnHTKgOy0U48sX74yjzz8ZN6c+nYGnf5OkL/wonOyenVNnW1//rOrMmjQMdm5b6/MmlWVv15/R87/4ulp1apljjjy4Fx7ze1/f+Re50x4/pWMfODxnH/B6UlSr5p1ad58q1x3ze351KCj37lp3/AH07x5s+z2nkcHAgAAwJZsgwN/kgw8on8qK9vlwVFjc/ON96TZVs3Ss+cOufhr56bT36/Xr6xsv9Z25WVlade+Tdq0aZV58xZk5ttzsmrlqqRVixx9zCez1VZb5Zab78nCBYvTZZtO+dznP5W+fXvVbl+fmvfq2nWbHNR/71z9l1uzcMHi9Nqxa778lbNS0bRBPgoAAADWQ+uyn6z3NotLlzXCTIqnwVLux/bpt97Pqv/vn32j9r+7ddsuP/npv9d5/bDDD8hhhx/wgfuoT80aBxy4Zw44cM8kyT777rbOmosuPnetsUu/dX699g8AAACbi/JNPQEAAACg4Qn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABRQxaaeANB4jjt4/bcZPrrh5wEAAGx8VvgBAACggAR+AAAAKCCn9AMAAGxBWpf9ZL23WVy6rBFmwubOCj8AAAAUkBV+AACg8KyK81FkhR8AAAAKSOAHAACAAhL4AQAAoIBcww+wiRx38PpvM3x0w88DAIBissIPAAAABSTwAwAAQAEJ/AAAAFBAAj8AAAAUkMAPAAAABSTwAwAAQAEJ/AAAAFBAAj8AAAAUUMWmngAAALD5aV32k/XeZnHpskaYCfDPssIPAAAABWSFH9hiHHfw+m8zfHTDzwMAALYEAj+8h1AJAAAUgVP6AQAAoIAEfgAAACgggR8AAAAKSOAHAACAAhL4AQAAoIAEfgAAACgggR8AAAAKqGJTTwAAgI+u1mU/We9tFpcua4SZABSPFX4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCA3LQPAAAagBsQApsbgZ8NdtzB67/N8NENPw8AAAD+wSn9AAAAUEACPwAAABSQU/oBALYgrhMHoL6s8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFFDFpp4AAEBj8tx6AD6qrPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEAVm3oCAMDmp3XZT9Z7m8WlyxphJgDAP+sjt8J//bV35KZhf9vU0wAAAIBG1aAr/M8+82JGjRyTGTNmp6KiSbbbvksGHtE/ffv2qq2ZMGFSht85KjNnzU37dm1y2OEH5hOf3Hed+1uxYmXuvGNknn3mxaxaVZ1eO3bNKacekc6dO65XDQA0NiviAMDmpsEC/8gHxuS+e0fnpFMGpE+fHlm9uibPP/dSrho8LOd97rT0261PZsyYnT9ddWNOPGlA9tp7l7z+2lu59prb0rFj++zar/da+/zjVTdm5YpVOf+Lg9KyVYvcf99jufxX1+Sbl30xbdq0qncNAAAAfNQ0yCn98+YtyPC7RuX8CwblwAP3SseO7dO5c2UGDOyf0z51VKZNm5kkeW78S+nRY/scetgBad++bfb+2K7ZY4++eeGFyWvtc+LEyZk6ZUbOv+D0dO+xfbbeujJnnnV8OneuzMgHxtS7Zl1W16zOTcP+lm9d+otc9s1fZNjQu7Ny5ara1y++6L/y4guv5hc/+2Mu+bef5Bc/+2NtDwAAALAl2ODAf9XgYfnB965IdfXqDL5ySL516c/zrUt/nsmTpiRJDuq/d4486uNJkk8esl++8MVBdSfQpCxNm659osHECZOy22590qpVizrj+x+4Z16YOKneNesy7qmJWb5iRS75xudywZfPzGuvvZkb/npXnZrbb38gJ586MN+87IK0adsq115zez0/EQAAANj0NviU/kFnHJuaUikrVqzMWWefUDvetm3rtWpbtGhe5+vFi5Zk4oTJ+devnLVW7byqBenRc/u1xrt07piqqgX1rlmXFi2b58yzjk+TJk2y9dbJOZ85Mb/42VU5+ZSBadeuTZLk8MMPTO/e3ZMkJ500ID/58e+zZMnStGrV8n33CwAAAJuLDQ78w4aMyMQJ76ym//ynf6gd/8L5g9K7T/f33a5UKuWGG4Znz736pnuPtUN7KaV1b7eeNevSq+cOadKkSe3XO+ywTVq2bJHp02bVBv4uXf5x07+2fx9bsWJlgwT+4w5e/22Gj97gtwUAAOAjpEFW+Cs7ts+4cRPz9UvOqx1fs8JfVTU/CxcuSY93hfqamlKG3HBXlixZms+ed+o691tZ2S6zZ1WtNT5r1tx0qGxX75p1Kiv70OGy8rVrSh92JAEAAAA2Exsc+Nu2bZ1DDzsgox8dl4ULF6dnzx3qvD7khuHp2LFDbeBftao61193RxYtWpIvffnMdV6/nyS79uuT6665PUuWLKtzjf4TY8Zn111717tmXV5/7c2sXl2TJk3euYXBtGkzs3Tp8my3fZd/7kMAYL15jB0AQONqkLv0V1a2yzHHHpLBVw7NmDHPpqpqfqZPn5WhQ4bnzalvZ8DAg5Ik8+cvzOW/uiarVq7KZ849KdXVq7N48dIsXrw0SfLM0y/k6j/fkiTZbbc+2aHrNvnD74dmypTpmT27KkP+eldmzpybwwccWO+adVm+fGWG/PWuzJ5dlSlvTMu119ye/fbbfZ33HQAAAIAt0Qav8K8x8Ij+qaxslwdHjc3NN96TZls1S8+eO+Tir52bTp06JElGDH8oU6dOT5JMmFD3Lvq/vuK7WbhwcWbOmls79oXzP507bh+Zwb8fmlWrVqVXr6756sXn1gnm9al5r0MO3S+rVq3Or395daqrV+dj++yaU049sqE+CgAAANjkGizwJ8nH9umXj+3T731fP+vsE+rcyf+9Djl0/xxy6P61XzdvvlUGnX5MBp1+zPtuU5+adzv7MyfW/veJJx2+zppfX/HdOl+3bNl8rTEAAADYnDXIKf0AAADA5kXgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAJq0MfyAdC4Wpf9ZL23WVy6rBFmAgDA5s4KPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAVVs6gkAm5fWZT9Z720Wly5rhJk0jKL1AwAA9WWFHwAAAApI4AcAAIACcko/NACnjQMAAJsbK/wAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAVUsaknwEdT67KfrPc2i0uXNcJMAAAAiskKPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABSQwA8AAAAFJPADAABAAQn8AAAAUEACPwAAABRQxaaeAPXTuuwn673N4tJljTATAAAAtgRW+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCABH4AAAAoIIEfAAAACkjgBwAAgAIS+AEAAKCAPnKB/4ffvyLPPvPipp4GAAAANKqKhtrRs8+8mFEjx2TGjNmpqGiS7bbvkoFH9E/fvr1qaxYtWpLbbr0vL77wakqlpO8uvXLKqUekbdvW69zn7NlVue3W+zN50pRUNK3Innv2zUknD8hWWzVbrxoAAAD4qGmQFf6RD4zJ0CEjctDBe+ebl30xX/v6edlll165avCwTJwwKUlSvao6v7niuixfvjIXfvUz+erXzs3KFSvzm8uvzapV1Wvtc/Hipbn8V9ekTeuWueQbn88FXzo906bNzB9+P3S9agAAAOCjaINX+OfNW5Dhd43KVy46J716da0dHzCwf1q2bJFp02am32598vjjz6Z6VXU+9/nT0qRJkyTJeZ//VH78o99lzOPP5hOf3LfOfh+4//Fs3bkyZ5x1fO3YFy8YlP/84W8zceLk9OvXu14167JgwaJc+bsbMnnSlLRu3TKHHX5gDjl0/yTJpElvZNiQETn6mE/mrjtHZdGiJdlllx1z5tknpGXL5hv6cQEAAMBGsUGB/6rBw/L8868kSQZfOaR2/AvnD0rvPt1zUP+9a8cmTpyUffbtVxv2k6Siokn23Xf3vDBx8lqBf+LESTn88APrjLVq1TK77b5TXpg4Kf369a5XzbrcPeKhHHX0J3P6Gcdm6tQZueH6O9O0aUX6H/yxJMn8+Yvy5BPP54Ivn5nly1fk2qtvy90jHsppnzqqzn5KpVKSpKampl6f1xrNW6xX+Tt9tW663tus77z+WfrZfPspUi+JfpLNt58i9ZLoJ9l8+ylSL4l+ks23nyL1kugn2Xz7KVIviX6Sxu9nTe2aPPpBykr1qXofCxcuzpAbhmfFipU56+wTasfbtm2dpk3rHkv4yY+uzMAj+me//feoM/7UUxNy3z2P5rLvfKnO+Dcu+Wm+9OUzs2PvbnXG773n0bz+2lu54Mtn1KvmvX74/SvSo8f2+ZfzTq0de3DU2Ix+9Ol85z++nEmT3shvLr8u3//hRamsbJckeeThpzL60XH51rcvqLOv6urqvPlm1Yd9TAAAANCgunatTEXFB6/hb9AK/7AhI2qv0f/5T/9QO75mhf/d3veoQqmUlJWt3xvXp/wDat47t5127plbb7mv9l4CTZtW1Ib9JGnXrk1WrFi51n7Ky8vTtWtlysrKUra+PQAAAMB6KpVKKZVKKS//8FvybVDgH3TGsans2D7jxk3M1y85r3Z8zV33q6rmZ+HCJenRY/tUdmiX2bPXXg2fNasqHTq0XWu8Q4e2mT27aq3V+1mz5qZDh3b1rlmX9wvnZR/y+nuVl5fX60MGAACAjW2D0mrbtq1z6GEHZPmy5Vm4cHE6dmyfjh3b157OP+SG4Rk7ZnySZNd+vTPuqYlZvXp17fbV1asz7qkJ2XXXta+137VfnzzxxHN1xpYsWZYJz7+SXf9+bX59atZl0qQpdb9+5Y1ss+3WqWjaYE8pBAAAgE1qg5enKyvb5ZhjD8ngK4dmzJhnU1U1P9Onz8rQIcPz5tS3M2DgQUmSg/rvnSYVTfKnP96c6dNnZcaM2fnzn25OeXlZ7c39br/t/owaOSZJMmDgQZk1c26G/PWuzJpVlalTp2fw74dmu+271N6Mrz416/LSi69l1MixmTdvQcaPfyl3j3horZv/AQAAwJasQZa0Bx7RP5WV7fLgqLG5+cZ70myrZunZc4dc/LVz06lThyTvXBd/4UXn5NZb7ssVv74mSbJz31658KufqT0jYM6c+Vm9+p07DrZp0ypf/dq5ufWW+/KLn1+VioqK7LnnzjnppIG1p9zXp2ZdzjjzuIwbNyF/u/vhNG+xVY497tAccOCeDfFRAAAAwGZhg+7SDwAAAGyeXLS+ES1ftiK33npfnhv/UqqrV2ennXrktE8fXftEgDenzsjtt92fqVNnpHmLrXLAAXvk6GMOSZMm/7jy4sNqSqVSLv33n2XlylV13vuss09o0LMYNkYvSfL223Nyy8335PXX3spWWzXLPvvulhNOPDwVFU0arJeN1dPYMePz1+vvXOd7f/+HF6aysv0W08saD9z/eB595KksWLg4XTp3zPEnHJZ+u/VpsD42Zi+PPjouIx94PAvmL8rWnTvm6GM+kb322qVBe2nIvl566bU8OGpsXnvtzfzs55fW7vfpcRNz7z2PZs6ceenYsX2OOPLg7Lvf7o3WR2P3Vb2qOk+Nm5AHRz2RDh3a5oIvrf241S2hj/rsc0vqZcmSZbntlvsyYcIrWbWqOj177pDTPn10ttmmU6P20lj9vNuSJUvz4x/9Ptt06ZiLLj53i+znjTem5Zf/8+e13ut7P7gwHTu236J6SZIJEyZl+J2jMmvW3FRWtsvhAw/KQQft3Wh9NGZP1197x1r3n0reuUT2+z+8aIvqJUmWL1+R22+7P88+81JWrVqVXjt2zWmfOjpdunTc4npZtao6d94xMk89NSGrVq5K9x7b59TTjsx223XebPtIkvvuHZ3Rj47LokVLsu22nXPyKQPrPJFs0aIlue3W+/LiC6+mVEr67tIrp5x6RO1N1rfEvt5+e04eHDU2Tz35fL76tXPTrdt2W2QfyT9+vs2cNTft27XJYYcfmE98ct8G7UPg34iGDh2RmW/PyYVf/UyaNWuam278W/7yp5vz9X//XGa+PSdXXHFtDjlkv5x51vGZN29hhg0dkfnzF+Xsc05MknrVLFiwKE2alOdHP/l6nfduvlWzLa6XJUuW5v9+c30+/ol9csaZx2fJ4qW55eZ7M/yuB3PSyQMatJ+N0dM++/RbKwy//vpbuXHY3Wnffu0nVWzOvSTJE2Ofy8gHHs+5nz0lnTtX5sUXXs2f/nhTvvXtC7L11pVbXC933j4yZ559fLp23TavvPJ6rr/2jrRu3TK9e3f/oOltkr5efXVq7v3bI+ncpVNKNf84SevJJ5/PjcPuzqBBx6TXjt3y2qtTM2zo3ampqcn+B2ycy5Yauq+HH34yr78+Le3bt9ko82+sPj5sn1taL9dfd0eWLlmWr1x0Tpo33yr33P1I/viHYfnOf/xro/bSWP2828033ZNmG/Emvo3Rz5zZVdl11945+zMn1nmvli1bbHG9TJ48JX+9/s4MOv2Y9Oi+fWbMmJ2/Xn9n2rZp3eAHmDdGT58adHROOmVgnfcY+cDjmVe1YIvrJUluufnezJw5NxdedE6aN2+W++9/PIOvHJLv/Me/pry88R5Z3Ri93Djs7kx5Y1ouuOD0NG/RPGMefza/ueK6fPc//jUtWzbf7PpIksdGP53773ssn/3cqdl2m63zyCNPZfDvh+Z73/9KWrdplepV1fnNFdelU6cOufCrn0lZWVnuumNkfnP5tfnGN8+vvax6S+vrz3+8KXvs2bf2keqNpbH7mDFjdv501Y058aQB2WvvXfL6a2/l2mtuS8eO7T/wBvTrrcRGsXr16tK/Xfyj0ssvvVY7NmPG7NJXL/zP0uLFS0p/+uNNpWuvvq3ONm+/Paf0ta/+qDR9+qxSqVSqV82kV94o/fJ//lyIXkYMf7B0zdW31qlZtGhJ6a233t5ie3qva6++rXTXnSO3yF6GDhlRuu6a2+vU/PAHV5SefnriFtfL//7iT6VRI8fUqbl7xEOlwb8f2mC9vFtD9FUqlUqvvPJ66d+//t+1+/zut39ZevyxZ+ps99hjT5e+++1fllavrmmUXt6tMfpaY8TwB0tX/u6Gxm3g7xrr3+eD9rkl9VIqlUrXX3dHadq0mbVfT5s2s3TxRf9ZWrlyVaP1Uio17vdYqVQqPf/8K6X//OFvS488/FTp8l9d3XiN/F1j9TNi+EOlO+9o2N8tH6axern8V1eXHnvs6TrbzZ41tzR37rzGaeRdGvv77Z33qCn94HuXl1568dXGaaL2fRqnlx/91+9KYx5/tvbrqqr5pa9e+J+lRQsXb1G9LF++ovS1r/6o9OabM+psd8WvrymNfKDu3webUx9XXH5tafhdo+rUfPuy/y0988wLpVKpVHr4oSdL/+8HvylVV1fXvr5qVXXphz+4ovTwQ09usX2t8dUL/7M0Zcq0LbaPv939cOnXv/xLndf//MebSzcOu7tBe/EQ+Y2krKws3/v+hXVO4SgvL09ZWVmalDfJiy+8mv0P2KPONl26dEzPXjvkhYmTU1NT+tCaJJk9pyqt27TK1X+5Nf/xnV/lx//1u9pHI25pvUycODm77No7d94xMj/6z//Lj390ZZ5/7uVsv32XBu1nY/b0bkuXLs/48S/lgAP32iJ76devdyZNeiNzZlclSV584dWsWL4yO+7YbYvrZcWKlWnWrGmdmqZNm2byex7hubn0tS5vTp2RpUuX5WP79Kszvs8+u2XZsuV5880ZDd/IezRGX5tCY/TxYftsLI31b3LW2SfUnua6YsXKPPLwk9m5b69GWy1aozG/x5YtW54bh47IGWcd1+h9rNFY/cyZU5XVq1fn8l9dne98+5f55f/8OVPemNZofSSN08vyZSvy2mtvpnv37XPN1bfm+9+7PL/65V+yYOHiBr0M7v1sjJ9pL7/0WpJkp517NtzE16GxeunXr0+eeeaFLFu2PDU1NRnz+LPp0XOHtG7Taovqpbq6OjU1NWnWrO4ZsU2bNs3kyZvv3wL/8tlTMvCIg+vUNCkvT9OKd36GTZw4Kfvs2y9Nmvzj90xFRZPsu+/ujfZ7d2P0tTFsjD4+ech++cIXB9V5vbxJWYP/DnJK/0ZSVlaW9h3qnrY9dsyz2WXXHVNdXZ0VK1Zm685rnwbduXPHVFXNz9IlSz+0JknmzJ6Xl158NSefekSOPvoTmTZtZv56/Z1p2rRirSCw+fdSlQdHjs2Ovbvl8+cPyptvzsiQv96Vioom2W//Pdbadkvo6d2efOK5dO+xfe2TLBrKxuql3259MmXKtPzn//u/VFQ0SU1NKV+84PQGvSZsY/Wya78+ue/e0enabdt07bptXp08NePHv5Tly1dkxYqV2aqBL4nZ0L7WpWregrRr32atAxfNmjVN+/ZtU1U1P927N841bms0Rl+bQmP08UH7bN5iqwab+/q8b0P8m/zge5dn/vyF2W67LvnSv57ZUNN+X43Zz6233Jfddt8pvXt3z9w5H1zbUBqrnzmz5+W1V9/Mpz59dDp16pCnnpqQKy6/Npd9+4J0bODfOWs0Ri9z5s5LUpYbrr8rB3/iYznqqE9k7Njn8rvf/jWXfuv8dO7cuNeJb4yfaY+NfjoHHrTXBz5dqiE0Vi/Hn3BYfvd/f81l3/xFysvL07p1q/zbJZ9t4NnX1Ri9tGrVMt17bJ+bb/pbzjr7hLRp0zqPPfZ0Zs+uSvPmjfMzuiH6aPOeAysvvvhqampqakPqvKoF2Wcdf/937tIx4599sYE6qWtj9LUxbIw+WrSoe6nI4kVLMnHC5PzrV85qwE4SK/ybyOuvv5XRjz6dk08emA98TEKplLKU1asmST7+iX3y7e9+OR//+D7psk2nfGyffjn2uEPy4INPNODs62qsXlauqk7r1i1zyqlHZJttOmW//XbPMcc2bi9rNFZP7/b4Y8/kwIP22rCJ1kNj9fLc+Jfz+GPP5rzPn5ZvXPqFfOrTR+Waq2/LrFlzG3D2dTVWL8ce+8n07t09//uLP+eSf/tJ7rxzZI486uNJkoqNcDR5ffta92sfsFk+YLtG1CB9bQYao49373Njauhevvq1c3PhRZ9JZWW7XDV4WKNfT/leDdXPSy+9lkmvvJETTjy8wee4PhqqnzPPPiH/fukX0m+3PumyTaccd/yh6bNTj4we/UyDz/n9NEQvq1a9s+q65159c+CBe6XLNp1y4kmHp3fv7hn96NONMu8P0tD//1mwYFFeeGHyWquEG0ND9XL7bfdn1cpVueir5+bfL/1C9tyrb35/5dCN+rOgoXr57HmnZOWKVfned3+db37jZ5k6ZXoO6r/3RjvjZ0P7WLJkaYYNGZETTxpQu1DxvvsplZJGPsi0RmP0tSk0dh+lUik33DA8e+7VN917bN9wE4/Av0lMnTo9Vw0elrPPOSFdtumUVq1aplmzppk9q2qt2lmzq9Khsm29apKkQ4d2a93xebvtOqdq7vwtrpe2bVqlW/dt67y+/Q5dGv3GNo3Z0xqvvfZmFixYlD337NtofSSN28tDDz2RAUf0z1577ZJttt06B398n+yx58559JFxW1wvFU0rcvZnTsx//+zf818//rd87d8+m+pV1amsbFfnTqubS1/r0qGyXebPX7TWEzpWrlyV+fMWpkOHdW/XWBqqr02tMfp47z43lobqpbp6debPX5gkqaxsn959uudzX/h05s1bmJf+fnryxtBQ/axYsTJDbxieQWcc22grefXRkN9r22zTKa1a1b1B33bbdd5oZ9I0VC9t/7461q3bOv4WmNf4N7l7t8b4WTB2zPj06dMjHTo07pM63qshfxaMfnRcBp1+bHbs3S3bbdc5p33qqKxatSovvvBqY7eRpGH/XSor2+fif/uX/Pi/L8mP//uSnHX2CZlXtSCdtm6cs2LebUP7WLJkaf7vt3/NXnvvUucAUmWHdpk9ex37mFW1Uf4uaKy+NrbG7qOmppQb/npXlixZmk8POqbB5y/wb2STJr2R3/9uSD59+jHZfY+dkyTl5WXZZdcd88TYuo9pmTlzbl5/7a3ssmvvetUkyT1/eySPP1b3CP60abMa/LTxjdFLrx27Zcb02XVqZkyf1aB3gN/YPa3x2Oins8++uzXqUePG7mXJkmWpaFL32uMmTZpk0aIlW1wvD9z/eJ4eNzFbbdWs9vSqp5+e2LB3SG3AvtalW7dt07JF8zw9bmKd8XHjJqRFi+bp+p4/mBtTQ/a1KTVGH+va58bQkL3MmjU33/+Py2tD/xrV1RtvRa8h+3lu/EupqlqQP111U75xyU/zjUt+mmFDR+TVV9/MNy75aao2wt3TG/p77U9/vClTpkyvMza9kf4WeK+G7KVDZft06NA2M2bU/VtgeiP/LfBejfGzoFQqZczjz+bA/ns11rTXqSF7WbFiZaqrV6dJxdp/Cyxe3PB/C7xXQ/+7XHfN7Xl7xuy0atUiTZtWpHpVdZ57/uXN/m+BefMW5IpfX5udduqRk95z1tiu/Xpn3FMTs3r16tqx6urVGffUhOzayL93G7Ovjamx+1i1qjrXXH1r5s6dny99+cxGyQYC/0b0yMNP5U9X3ZxBZxyb3r27Z/HipVm8eGlWrlyVY489JOPHv5Thdz2YuXPnZ/KkKfnjH4Zln3361d4YqT41227bObfecl/GPTUh8+ctzPjxL+XuEQ/l0MMP2OJ6OeywAzJhwqTc87dHMn/ewjz/3Mu5e8TDOezwAxu0l43ZU/LOzfqefebFRj2df2P0ss8+/fLA/Y/llZdfz7x5C/LM0y/kySeea7B7RWzMXjpt3SG33HxvXnzx1VRVLcg9f3skr7z8eg4fcFCD9tKQfb1XeXl5Tjzp8Nxyy7156snnU1W1IE89NSG33nxfjj/hsJSXb5wf9w3d16bSGH180D63pF623XbrbLvt1rn2mtszY8bsvP32nAwbOiJNmjTJTjv1aNReGqOf3ffYOd/93r/m0m+dX/u/wwccmG7dts2l3zo/7do17iMhG+N7rX37trn+2tszedKUzJ+3MPf87ZFMnjwl/Q/+2BbVS3l5WQ49/MDcdeeojH/2pX/0MmlKDv544/bSWD2t8dJLr2XFipXZffedNkofScP30qpVi/Tt2ys333RPpk2bmblz5uXuEQ9l/vyFjf7IxMb4d2nRsnmG3DA8b735dmbOnJurr74tbdq0atSzMTe0j1dfnZr//cWfs+deu2TAwP612y9btjxJclD/vdOkokn+9MebM336rMyYMTt//tPNKS8vy0H9995i+9pYGruP+fMX5vJfXZNVK1flM+eelOrq1bU1DamsVCp94GUINIyqqgX54fevWOdrRx/ziRxz7CGZ8sa03HH7A5k6dUZatGiefffbPccdf0idO2vWp+aJsc/lgfsfy+w589K+XZscc9wh2W+/3bfIXiZMmJQRwx/MzLfnpF27NjniqINz0EEN/wNqY/b08ENPZuzY8fnGpV9o8D42Zi+lUin33/dYHhv9dBYsXJzOW1dm4BH9s+8W+r32yMNPZeQDj2fRoiXZoes2OeXUIxvtJncN1dekSW9k8JVD8/P/+Wbt2JNPPp/773ssc+fMS2XH9hk48KDsf8CejdLHezVmX3ePeChTp87IBV86Y4vsoz773FJ6SZL58xbmtlvvz0svvZrVq2vSvft2OfVTRzX6gZvG/B57twdHjc3zz72ciy4+t1H6WKOx+lm9enVGDH8oTz7xfBYvWZodtu+STw06Ot26Nd6NOxurl1KplJEPPJ5HHxmXhQsXZ9vtOue0045Mz15dG62Xxu4pSf541U2prGyXU049otH7SBqvl6VLl+X22x7I88+9nJUrV6V7j+1z0skDtsjvtVWrqnP7rfdn3LiJKZVq0rfvjjn1U0c26M2IG7qP//eD32TuOi7b7d27W+3Pr4ULF+fWW+7LSy++c5nFzn175ZRTj2i0g5kbq68kufii/8ol3/hco3y/bYw+/nr9ne/7NLVfX/HdButF4AcAAIACcko/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEABCfwAAABQQAI/AAAAFJDADwAAAAUk8AMAAEAB/X+e8yVDykIanAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fins_fig = yearly_total.plot.bar(x='year', y=['turnover', 'debts'], color=colors)\n",
    "fins_fig.get_legend().remove()\n",
    "fins_fig.yaxis.set_major_formatter('€{x:1.2f} bn')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profit Total per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit_fig = yearly_total.plot.bar(x='year', y='profit', color=colors[0])\n",
    "profit_fig.get_legend().remove()\n",
    "profit_fig.yaxis.set_major_formatter('€{x:1.0f} mn')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2019 (Pre-Pandemic) and 2020-2022 (Pandemic) - Profit of Companies total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years_profits = yearly_total.tail(4).copy()\n",
    "years_profits = years_profits.iloc[::-1]\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.barplot(data=years_profits, x='profit', y='year', palette=colors2, ci=None, orient='h', order=years_profits['year']).xaxis.set_major_formatter('')\n",
    "plt.tick_params(axis='y', which='major', pad=25)\n",
    "plt.yticks(fontsize=25, fontweight='bold', color='#1E1B39')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Companies per location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locations = loc_df.groupby('location_y')['id'].count().reset_index()\n",
    "locations.sort_values(by='id', ascending=False, inplace=True)\n",
    "locations\n",
    "\n",
    "sns.barplot(locations.head(5), x='id', y='location_y', orient='h', color=colors[0])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 contain 59.01% of all companies. The rest contain 40.99% of the total combined\n",
      "First 3 contain 52.93% of all companies. The rest contain 47.07% of the total combined\n",
      "Tg. Mures has 39.83%, Reghin has 7.65%, and Sighisoara has 5.46%\n"
     ]
    }
   ],
   "source": [
    "total_comp = locations.id.sum()\n",
    "total_top5 = locations.id.head(5).sum()\n",
    "total_rest = locations.id.iloc[5::].sum()\n",
    "\n",
    "pct_top5 = round((total_top5 / total_comp * 100),2)\n",
    "pct_top3 = round((locations.id.head(3).sum() / total_comp * 100),2)\n",
    "pct_rest5 = round((total_rest / total_comp * 100),2)\n",
    "pct_rest3 = round((locations.id.iloc[3::].sum() / total_comp * 100),2)\n",
    "\n",
    "pct_mures = round((locations.id.iloc[0] / total_comp * 100),2)\n",
    "pct_reghin = round((locations.id.iloc[1] / total_comp * 100),2)\n",
    "pct_sigh = round((locations.id.iloc[2] / total_comp * 100),2)\n",
    "\n",
    "print(f'First 5 contain {pct_top5}% of all companies. The rest contain {pct_rest5}% of the total combined')\n",
    "print(f'First 3 contain {pct_top3}% of all companies. The rest contain {pct_rest3}% of the total combined')\n",
    "print(f'Tg. Mures has {pct_mures}%, Reghin has {pct_reghin}%, and Sighisoara has {pct_sigh}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contribution of Companies based on size to population, employment, turnover"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of Company - By Number of Employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of companies with NO employees:  7185 and with employees 12152\n",
      "Number of companies 0-249 employees:  19308 and 0-49:  19098\n",
      "Number LARGE companies 2022 29 and ALL companies: 19337\n"
     ]
    }
   ],
   "source": [
    "size_empl = fin.copy()\n",
    "size_empl.loc[size_empl['employees'].between(0,49), 'size'] = 'Small'\n",
    "size_empl.loc[size_empl['employees'].between(50,249), 'size'] = 'Medium'\n",
    "size_empl.loc[size_empl['employees'] >= 250, 'size'] = 'Large'\n",
    "\n",
    "size_empl.loc[size_empl['employees'] < 1, 'employer'] = 'No'\n",
    "size_empl.loc[size_empl['employees'] > 0, 'employer'] = 'Yes'\n",
    "\n",
    "print('Number of companies with NO employees: ',len(size_empl[(size_empl['employer'] == 'No') & (size_empl['year'] == 2022)]), 'and with employees', len(size_empl[(size_empl['employer'] == 'Yes') & (size_empl['year'] == 2022)]))\n",
    "print('Number of companies 0-249 employees: ',len(size_empl[(size_empl['size'].isin(['Small', 'Medium'])) & (size_empl['year'] == 2022)]), 'and 0-49: ', len(size_empl[(size_empl['size'].isin(['Small'])) & (size_empl['year'] == 2022)]))\n",
    "print('Number LARGE companies 2022', len(size_empl[(size_empl['size'].isin(['Large'])) & (size_empl['year'] == 2022)]), 'and ALL companies:', len(size_empl[(size_empl['year'] == 2022)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
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       "      <td>2022</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>2022</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>2022</td>\n",
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       "      <td>0</td>\n",
       "      <td>134282690</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2022</td>\n",
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      "text/plain": [
       "    year    size employer  employees    turnover     profit       debts\n",
       "68  2022   Large      Yes      20471  5236683771  -60767816  2385203993\n",
       "69  2022  Medium      Yes      21758  2011995921  117487620   768295524\n",
       "70  2022   Small       No          0   134282690   41056462   229179253\n",
       "71  2022   Small      Yes      49396  3647632004  472693983  1713341740"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>size</th>\n",
       "      <th>employer</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2022</td>\n",
       "      <td>Large</td>\n",
       "      <td>Yes</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2022</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2022</td>\n",
       "      <td>Small</td>\n",
       "      <td>No</td>\n",
       "      <td>7185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2022</td>\n",
       "      <td>Small</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year    size employer     id\n",
       "68  2022   Large      Yes     29\n",
       "69  2022  Medium      Yes    210\n",
       "70  2022   Small       No   7185\n",
       "71  2022   Small      Yes  11913"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_sum = size_empl.groupby(['year','size','employer']).sum().reset_index()\n",
    "display(size_sum.drop(columns='id').tail(4))\n",
    "\n",
    "size_sum2 = size_empl.groupby(['year','size','employer'])[['id']].count().reset_index()\n",
    "display(size_sum2.tail(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>size</th>\n",
       "      <th>id</th>\n",
       "      <th>employees</th>\n",
       "      <th>turnover</th>\n",
       "      <th>pct_pop</th>\n",
       "      <th>pct_emp</th>\n",
       "      <th>pct_to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Large</td>\n",
       "      <td>29</td>\n",
       "      <td>20471</td>\n",
       "      <td>5236683771</td>\n",
       "      <td>0.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "      <td>210</td>\n",
       "      <td>21758</td>\n",
       "      <td>2011995921</td>\n",
       "      <td>1.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Small</td>\n",
       "      <td>19098</td>\n",
       "      <td>49396</td>\n",
       "      <td>3781914694</td>\n",
       "      <td>98.8</td>\n",
       "      <td>53.9</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size     id  employees    turnover  pct_pop  pct_emp  pct_to\n",
       "0   Large     29      20471  5236683771      0.1     22.3    47.5\n",
       "1  Medium    210      21758  2011995921      1.1     23.7    18.2\n",
       "2   Small  19098      49396  3781914694     98.8     53.9    34.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_sum3 = size_empl[size_empl['year'] == 2022].groupby(['size'])[['id']].count().reset_index()\n",
    "size_sum3 = pd.merge(size_sum3,size_empl[size_empl['year'] == 2022].groupby(['size'])[['employees','turnover']].sum().reset_index(), on='size', how='left')\n",
    "size_sum3['pct_pop'] = round((size_sum3['id'] / size_sum3['id'].sum() * 100),1)\n",
    "size_sum3['pct_emp'] = round((size_sum3['employees'] / size_sum3['employees'].sum() * 100),1)\n",
    "size_sum3['pct_to'] = round((size_sum3['turnover'] / size_sum3['turnover'].sum() * 100),1)\n",
    "display(size_sum3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Companies and their characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_fig = size_sum3[::-1].plot.bar(x='size', y=['pct_pop', 'pct_emp','pct_to'], color=colors)\n",
    "size_fig.get_legend().remove()\n",
    "size_fig.yaxis.set_major_formatter('{x:1.0f}%')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profitability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "profitable = size_empl.copy()\n",
    "profitable.loc[profitable['profit'] == 0, 'prof'] = 'Break-even'\n",
    "profitable.loc[profitable['profit'] > 0, 'prof'] = 'Profitable'\n",
    "profitable.loc[profitable['profit'] < 0, 'prof'] = 'Not Profitable'\n",
    "\n",
    "profitable['prof_margin'] = round(((profitable['profit'] / profitable['turnover']) * 100),2)\n",
    "\n",
    "profitable.loc[profitable['prof_margin'].isnull(), 'prof_margin'] = 0\n",
    "profitable.loc[profitable['prof_margin'] == -np.inf, 'prof_margin'] = -100\n",
    "profitable.loc[profitable['prof_margin'] == np.inf, 'prof_margin'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profitability % Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average number of companies based on profitability profile \n",
      "\n",
      " prof\n",
      "Break-even         4.901667\n",
      "Not Profitable    32.415556\n",
      "Profitable        62.683889\n",
      "Name: avg, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "prof_count = profitable.groupby(['year','prof'])[['id']].count().reset_index()\n",
    "prof_avg = pd.merge(prof_count,year_on_year[['year','active']], on='year', how='left' )\n",
    "prof_avg['avg'] = round(((prof_avg['id']/prof_avg['active'] * 100)),2)\n",
    "\n",
    "print('average number of companies based on profitability profile \\n\\n', prof_avg.groupby('prof')['avg'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### % Profitable Companies in 2022 - Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pz42C806HyZNSd7R0HTrCr86AYVul7kQpGV6q2K3/gCsuSt3Fsg0ZBsedAl26uP1Zai+NJZgxI9xd9nzGD6WsrYUTTocttkndiVIxvFSpu/8F/3tO6i6WrliE/Q+FfQ9wtEWKobExfJ/dcDVcc0W27zErFuG4U2Hrb6TuRCkYXqrQQ/fDuadle6qoT7/wk9Wg9aHG0CJFVW6CV8fAWSfDZ5+k7mbJikU49jew7Y6pO1Fshpcq89SjYVdRY4Z/otp86/BAqqtzUa6USqkUdh+ee3p4bmRVoQg//7U3Ulcbw0sVeeHZcFfR3LmpO1m82g7heP999nWaSMqC+d+H/7whXC+Q1R2JhUI4PmHH3VN3olgML1Xi1ZfhpGNg1qzUnSxej57hDId11je0SFnT1ARvjIXTT8jubqRiEU46C4ZtmboTxWB4qQLvvAkn/AymZ/Tk3IGDwumZXbs7TSRlVWMpXOx46nHw5mupu1m8uk7wPxeGm+VV2QwvFW78ODjupzAloz8tbbMD/OKkMG/thYpStjU2hh1I550Bj4xM3c3ide0OI/4YbqVW5TK8VLBPPobjjsjm0d81NXDAobDfQa5vkfJk/vfr9VfB1ZdDFt8g/VaBcy8L09GqTIaXCjV5UgguH76fupNF1XcOh84N3TKEGEn5Uy7DqMfg96fCrJmpu1nU2oPgrIugc5fUnag9GF4q0PRpcPxP4d23UneyqJX6w6kjYOX+ThNJedfYCB+Nh9/+Ej4en7qbRQ0ZCqeMcC1dJXKwvsI0NoaDpbIYXAauB+dfASutYnCRKkGxGL6fz78iLLzPmvm3ZvsjeuUxvFSYyy8M37BZs8lQOOfiMITrT0FS5aithc6d4ZxLYJPNUnezqH/fA3/9Y+ou1NYMLxXkntvg9ptSd7GobXaA00aEQ+gccZEqT7E2fH+fdm74fs+am67J5rNRrWd4qRAvPw+Xnpu6i0XtNRyOPy3cT2RwkSpXsRi+z48/Dfb8TupuFvWnC+DRB1N3obbigt0K8PGH8PND4fMpqTv5ooN+Em6EllR9brgarsrYdE2HjnD2xTBog9SdaHkZXnJu5gz45U/gvbdTd7JAsQhHHg877ZG6E0kp3X8nXHh2ONguK/r0gwuuhG7dU3ei5eG0UY41NcGI07IVXGpr4Vdnwg67pe5EUmo77Aa/PjNbi/Q/nQBn/zbszFR+GV5y7G+XZeuq+toO8Ov/gWFbeWKupPAcGLZVeC7UdkjdzQIvPANX/yl1F1oevmJy6sF74cZrUnexQIeO8JuzYLMtDC6SFigUwnPh5LPCcyIrbvo7PPGf1F2otXzN5NBrY+DCs1J3sUCHjvCbs2HIMIOLpEUVCrDpsPCcyEqAKZfhD2fA+AxeoaJl81WTMxM/hTNOgLlzU3cS1HaAk38XDqcyuEhakkIhPCdOytAU0ozpcOavYPbs1J2opXzd5EhTE5x7OkyamLqToLY2zGU74iKpOQoF2HTzbC3ife9tuChDI9lqHl85OXLT3+GFZ1N3ERSLcOIZsNnXDC6Smm/+GpgTTodCRg6ufOh+uO3G1F2oJXzt5MRrY+Cay1N3scCRx7urSFLrFAqw+dZw1PGpO1ngz/8LY15M3YWay1dPDsycAb8/JTvnEhz0k3AAncFFUmsVCuE5cuBhqTsJSiU462SYPCl1J2oOXz85cOl58NH41F0Eew33yH9JbWe/A7NzF9LEz+Ds34T1hco2w0vGPXRfuNI9C7bZAQ47JnUXkirNYcdk5zbql0bDrf9I3YWWxfCSYR9/CBePSN1FsMlQ+KU/kUhqB+VyeL5sslnqToJrLodx76buQktjeMmoxlJY5zJzRupOYOB64fTcmhrXuUhqe4UCUBMOsRs4KHU34RytP5wRnsPKJl9FGfX3K+HVV1J3ASv1hzP+EA6Vysq2RkmVp1gMz5kzzg/PndReHws3XJ26Cy2J4SWDXhoNN2bgm6a+M5w6InwuGlwktbNi8YvPndT+8Vd46/XUXWhxDC8ZM+1zGHFa+rUlNTVw3Cmwcv/snIQpqfLV1obnznGnhOdQSqUSnHcGNDSk7UOLMrxkzKXnwWefpO4CDjgUhm7piIuk+IrF8PzZ/0epO4F334Jr/5y6C32Z4SVDnnkSHr4/dRew7Y6w30Hpf+qRVL1qauC7B8PXM7CF+qZrs7EGUQsYXjJizhy45NzUXYSV/j//dfppK0lqaoJfnJR+B1JTY9h9NGdO2j60gOElI669EiZ8mLaHHj3hlN+HXUVuiZaUWqEQppB+e054PqX0wTi46tK0PWgBX1EZ8O5bcOt1aXuo7QAnnwVdu7nORVJ2FIvQrXt4PtV2SNvLbTfBi8+l7UGB4SWxchkuOif9pYuHHAHrrA9FdxZJyphibXg+HXJ42j7KZTj/d04fZYHhJbG7/wmvvpy2h69tDfvs61SRpOwqFGCf/WDzrdP2MeFDuOFvaXuQ4SWpyZPgr5el7aFPP/iFdxZJyoGmJjj25PDcSunma+GjD9L2UO0MLwn96QKYMS1d/WIRTjgd6uocdZGUfYUC1HWC409Le11Jw1z44/np6svwksyzT8EjD6Tt4fs/gkHre4KupPyorYX1NoD9f5i2j2eegCceSdtDNTO8JDBnDlwyIm0PQ4bBfgdCjV8BknKmpgD7HgibDE3bx58ucPFuKr66ErjuSvg44ZkuPXqFe0Nc5yIpr5qa4PhT057/8snHLt5NxfAS2fvvwa3/SNvDsSdDly6uc5GUX8VieI794uS0fdxybQgxisvXV2RXXhxuKk1l5z1hyFDPc5GUf8Va2HRYeK6lMncu/OWSdPWrleElopdGw6jH0tXv0w8OO9rpIkmVo6kpPNdSbp9+ZCSMeTFd/WpkeImkXA6jLikdc2I4XtvpIkmVolCADh3gqBPS9vGnC8JzXnH4GovkkZHw+th09XfeM6zMd1u0pEqThemjN16FkXenq19tDC8RNDTAVQlP0nW6SFKly8L00d8ug9mz0tWvJoaXCO68JdyHkYrTRZIqXRamjyZ+Bnfckq5+NfF11s5mzoDrE54D4HSRpGqRhemjW6519CUGw0s7++f18PmUNLW7dYcfH+V0kaTq0dQEhx4JXbunqT91iqMvMRhe2tG0z9MeSHfIEdDRSxclVZH5lzcecni6Hhx9aX++1trRjdeEaaMU1tsQdtojnEIpSdWkWISdvwmDNkxT39GX9md4aSeTJsIdN6epXSjAz46DxoQn+UpSSo2N8LP/Tjfy7OhL+zK8tJPrr4I5s9PU3m1vWHOgVwBIql7FIqy1Duy6V5r6U6eEnaZqH4aXdjBpItx7e5raXbvDwYe7SFeSmprg4COga7c09W++FmYn+iG20hle2sHtN0LD3DS1D/5JWKzmIl1J1a5QgE6dwnMxhalT4M5Eywcqna+4NjZ7Ftz1zzS1B20Au+zpIl1Jmq9YhF32Cs/HFBx9aR+GlzZ23x0wfVqa2oce7SJdSfqyxhIcelSa2o6+tA/DSxtqbIR/3pCm9tAtYf2NXKQrSV9WrIX1B8PQLdLUd/Sl7Rle2tDjD6e5w6hQgB/8NIQnSdKiGhvDczLFesCpU+CuW+PXrWSGlzZ0y3Vp6m67EwxYw7UukrQkxSIMWBO23TFN/TtudhdoWzK8tJGXn4fXx8SvW9shrKT3m0KSlq6pCQ76SXhuxjbhI3jmifh1K5XhpY2kGnXZbW/o1cet0ZK0LIUC9O4bnpsp3O7C3TbjK68NjB8Hox6LX7e+M3z/h/HrSlJelcvhuVnfOX7t0aNg/Pvx61Yiw0sbuOUf4Rsitm99F7qsADU18WtLUh4VCuG5uc9+8WuXyy7cbSuGl+U0dTL8++74dTt3gW9/z+kiSWqpQgH+63tpRl/uv8tt023BV99yuv1mmJvgKoA9vgV1dfHrSlIlqOsUnqOxzZgGD90Xv26lMbwsh8YS3P2v+HU7dgyjLk4XSVLr1NSE52jHjvFr3+HC3eVmeFkOzzwJUybFr7vTHrBiV8OLJLVWTU24bXrH3ePXfudNeOWF+HUrieFlOTxwV/yahSIMPyDNAmFJqiTlMgw/MDxXY7vjlvg1K4nhpZU+nwqjHo9fd9sdoE8/F+pK0vIqFKBvP9hmh/i1H38IJk2MX7dS+ApspYfug1JD3Jo1NbDfwd5hJEltpakJvntQ/Gn4UgnuSbBmslIYXlrpgQTbo4dtBaut7h1GktRWCgVYbQ0YumX82vf8yx9GW8vw0grvvgVvvRa/7ne+7xe6JLW1xkYYvn/8uhM/gxeeiV+3EhheWiHFQt3V14INNnbURZLaWrEYnq+rrxm/9iMj49esBIaXFmoswYMJDhja41thjlSS1PZKJdg9waF1jz8CDZHXT1YCw0sLpTjbpb5zOIugtjZuXUmqFrW14Qyt2FcGzJgGzz4Zt2YlMLy0UIopo+13SXMKpCRVk44d4Rs7x6/r1FHLGV5aINXZLnsOB8+kk6T2VQb2Gh6/7lOPelljSxleWiDF2S4bfiVsj/ZQOklqX/O3TW/4lbh1Z8+Cpx+LWzPvfCW2wIP3xq/5zf9yoa4kxVIqwR7fjl/34Qfi18wzw0szffYpvD42bs0ePWGr7VyoK0mx1NbC1ttB9x5x6z77JMycEbdmnhleminFkN62O3lztCTFVlMIz9+Y5s6FJx6JWzPPDC/N9OSj8WvusFv8mpKkcDxFbI84ddRshpdmmD0bXnw2bs3VVoe11nGhriTFViiE5++qA+LWff6ZsKtVy+arsRlGPx2G9GLabmfvMZKkVBobYbtd4tYsleCxh+LWzCvDSzOMSjFltLv3GElSKsUi7Jhg6t51L81jeFmGchmefiJuzfUHQ5++cWtKkr6oTz9Yf6O4NV9+Hhoij/TnkeFlGV4fC5Mnxq35jV0820WSUiuVwvM4pjmzYcxLcWvmkeFlGZ6KPGVUWxvWu3i2iySllep5/NyouPXyyPCyDKMin+8yZBh06RK3piRp8bqsAEOGxq05+um49fLI8LIUn3wM77wZt+bXtnbKSJKyolSCzbeOW/Pt12HqlLg188bwshSxR11qamCLbZ0ykqSsqK0Nz+WYp52Xy+HMFy2Z4WUpnoocXtZZD7p1j1tTkrR03XvAwEFxa4523ctSGV6WoGEuvDw6bs3Nt4ZGp4wkKVMaG2Hzr8et6bqXpTO8LMEbr8U/VXfL7aDgwXSSlCmFAmy5bdyan30C496NWzNPDC9LMPbFuPX6rQwD1vAWaUnKmpoaWH1N6LtS3LrPO/qyRIaXJYh9SNCwraCpKW5NSVLzNDXF33XkeS9LZnhZgrGRw8sWkedTJUnNVy7D1yI/p18e7dEZS2J4WYzx4+Lusa/rBBttEuZVJUnZUyzC4E3C8zqWWbPi/yCdF74uFyP2lNH6g6Ho2S6SlGnF2vgXNY6JvP4yLwwvixH7i2XjTR0alKSsK5XC8zqmN16NWy8vDC+LEXvkZZPNnDKSpKwrFOArm8Wt+eZrcevlha/ML/l8aljzEkun+nByo+FFkrKtUAgnocdc9/LpBJg6OV69vPCV+SVjXwqrymPZYHBYCCZJyr5iETbYOG7NNxx9WYTh5UtiTxkNdr2LJOVGqQSDh8St+abrXhZhePmS2CfrDtnMkRdJyotCAYYMjVvTRbuLMrwspKEh7hdJfWdYe12vBJCkvCgUYOC6Yb1iLC7aXZThZSHj3ol7GeO663sRoyTlTaEYnt+xfPYJTJ4Ur14eGF4W8v67cesNHBSuWpck5UdjIwxcL25N1718keFlIbGvH4/9xS9JahsDB8Wt57qXLzK8LCT2yMt6G7lYV5LypliMf02A4eWLDC8LiTnyssKK0LdfvHqSpLbTdyXoskK8em+5aPcLDC/zlErw0Qfx6q29brxakqS2F/M5PvEzmPRZvHpZZ3iZ58P34y6edbGuJOVXikW7778Xt16WGV7mib7TyMW6kpRf5fiLdj8eH7delhle5om908jFupKUX8Xa+It2PzK8/B/Dyzwxh+M61btYV5Lyru9KcU/a/fjDeLWyzvAyT8xpo1VWjVdLktR+Yj7PDS8LGF6ApiYYPy5evf4D4tWSJLWfVVaLV8vwsoDhBZjwEcyZE69e/9XC1mxJUn6VSuF5Hsu0z2HG9Hj1sszwQvydRv1XAy+SlqR8qyFueAEX7c5neCH+UNyANcNKdUlSfhVrw/M8JqeOAsML8U8tjDlHKklqP7FHXgwvgeEFmDwpXq1u3aFz53j1JEntp3MX6No9Xj0PqgsML8QNL+40kqTKEnP0xZGXwPACTJ4Yr9bK/ePVkiS1v5jPdRfsBoYX4o689OztNmlJqhSNJejVO169Tyd4qS8YXmhshKmT49Xr2QvK5Xj1JEntp6kMPXrFqxf7nZVVVR9epk4JJ+zG0rO3FzJKUqUoFuOOvABMnxa3XhZVfXiJud4FoE9fKFT9n7okVYZCAXpHvmjX8GJ4ibreBaBXn7j1JEntq3fk57rhxfASfeSlW4+49SRJ7atb97j1DC+Gl6jhZYUVoUOHePUkSe2vQ0foskK8eoYXw0v0bdKSpMoT8/lueDG8RB156dEzXi1JUjwxn+/TP49XK6uqPrx8PjVerc5d4tWSJMUT8/nuyIvhhblz49Wq90JGSapIMZ/v0wwvhpeGyOEl5oF4kqT219QEnSOGlxmGF8NLQ0O8WvX1hhdJqjRNTdCpPl49p40ML3HDS2fAe40kqbKUod41L1EZXiJPG5ldJKmylIm75sXwYniJG17qoaYmXj1JUvurqQnP91jmzIHGUrx6WWR4iTxt5KWMklRZCoX4u0mrff1k1b9Ko468dDG8SFKlKRTin+PVaHipbjFHXrzXSJIqU4fauPWaGuPWy5qqDi8NDVCOuILWURdJqkw1kZ/vThtVsZhTRmB4kaRKFfv5bnipYjGnjMDwIkmVyvASV+RZumyJHV5iDytKkuJYfS343UXx6tXVxauVRVUdXkqRw0u5ypOyJFWqLivAxpum7qJ6VPVYQLEYt161D/NJktQWqjq8dOgYt57hRZKk5Vfd4SXyuSuGF0mSlp/hJaLYC4QlSapE1R1eIk8bzZrh6IskScurqsNLTQ3URtxvNWum4UWSpOVV1eEF4k4dzZoV9zoCSZIqUdWHl7r6eLVmzYSaeOUkSapIVR9eOneOV2vWTEwvkiQtp6oPL51ijrzM8n4jSZKWV9W/Susjj7wYXiRJWj5V/yqNHV4kSdLyMbxEnDaaOSNeLUmSKpXhJeLIy+RJ8WpJklSpqj68dOser9akz+LVkiSpUlV9eOnVJ16t6dO830iSpOVleIkYXgCmTo5bT5KkSmN4iRxeJn4at54kSZXG8BI5vHz6iZczSpK0PKo+vPToCYVivHqTPoPGxnj1JEmqNFUfXopF6NEjXr1JE6HG+40kSWq1qg8vEHfqaNJnUFsbr54kSZXG8ELc8PLR+Hi1JEmqRIYXoFfveLXGj4tXS5KkSmR4Ie7Iy9QpMNMLGiVJajXDC/G3S3/4ftx6kiRVEsML8cPLuHegsRS3piRJlcLwQvzwMv59KMctKUlSxTC8AH1XgkLEP4nx77tdWpKk1jK8AHV10G/lePXccSRJUusZXuZZfa14tT78IF4tSZIqjeFlnpjhZfYs+GRCvHqSJFUSw8s8McMLwKsve0GjJEmtYXiZZ43I4eXNV+PWkySpUhhe5uk/AGo7xKv35mvhRmtJktQyhpd5amuh/2rx6r31erxakiRVEsPLQmKue5k+zUW7kiS1huFlIbHXvbhoV5KkljO8LCT2jiMX7UqS1HKGl4VEDy8u2pUkqcUMLwtZaRXoVB+v3utjoclpI0mSWsTwspCaGhiwRrx6s2aGXUdlr5iWJKnZDC9fsva6ceuNfsZFu5IktYTh5Us2/Ercei89F86YkSRJzWN4+ZLBQ+LWG/OSIy+SJLWE4eVLeveFfivHqzd7Vth11NQUr6YkSXlmeFmMjSJPHT3/jOFFkqTmMrwsxoabxK33outeJElqNsPLYmy0Sdx6Y1+CxlLcmpIk5ZXhZTH6rwY9e8WrN2c2vPy8U0eSJDWH4WUJYm+ZfuI/cetJkpRXhpcliD11NOoxKPi3IUnSMvm6XILY4WXCRzDuXa8KkCRpWQwvS7D6WrBi17g1H3/IixolSVoWw8sS1NTABhvHrfnUo1B0y7QkSUtleFmK2It233gVpk6JW1OSpLwxvCzFVzePW69chicehpJnvkiStESGl6VYY21YaZW4NZ981NN2JUlaGsPLMnzt63HrjR4FM6bHrSlJUp4YXpZhi23i1iuV4KH7nTqSJGlJDC/LsMHG0K173JoP3uvUkSRJS2J4WYZCAYZtHbfm2Jfg00/i1pQkKS8ML82wReR1LwAj74JGD6yTJGkRhpdmGDIUOtXHrfnQfVAsxq0pSVIeGF6aoWMdbDosbs3334O334Cmprh1JUnKOsNLM22xbfyaI++OX1OSpKwzvDTTsC3jT+M8fL+3TEuS9GWGl2ZaYUXYaEjcmpMnwWMPeeaLJEkLM7y0QIpdR3fc7JkvkiQtzPDSAltsAzU1cWu+8kJYvOvCXUmSAsNLC/TuCxt/NX7d22+EyJlJkqTMMry00M7fjF/z3/fC3Lnx60qSlEWGlxbacpuweDemWTPhgbtcuCtJEhheWqxjHWy3c/y6d97qwl1JksDw0iq77Bm/5ntvw5gXve9IkiTDSyustQ4MHBS/7k1/974jSZIML620c4LRl1GPhW3Tjr5IkqqZ4aWVttsJ6uri1iyX4fq/OvoiSapuhpdW6rICbLld/LoPj4RPJ3honSSpehlelkOKM1+aGuHGq+Of9CtJUlYYXpbD4CGwcv/4de+/E6Z97o3TkqTqZHhZDjU1sNMe8evOnQu3XGt4kSRVJ8PLctpxdygkWEB7560wZ078upIkpWZ4WU69+sBW28avO3NGGH1x4a4kqdoYXtrAd/ZPU/fWf8CM6U4fSZKqi+GlDQwcBJtsFr/urJnw9z/HrytJUkqGlzaSavTl7n/BZ584fSRJqh6GlzYyZGia+45KDXDVZVDwb1KSVCV85bWhVKMvD98P4971ziNJUnUwvLShrbaDVVaNX7epCa682DuPJEnVwfDShgoF+Pb/S1P76cdh7EvQWEpTX5KkWAwvbWyH3aB7zzS1L78QirVpakuSFIvhpY11rIO9h6ep/doYuPd2175Ikiqb4aUd7PFt6NwlTe2//hHmzHbrtCSpchle2kGXFWC3vdPU/nwK/PVSt05LkiqXr7h2sve+UNshTe27/wXvvOniXUlSZTK8tJNefWCPfdLUbmqC/z3HxbuSpMpkeGlH3z0kTCGl8OorcP+dLt6VJFUew0s76toNhh+Qrv5fLnHxriSp8hhe2tnew6FPvzS1p06Byy9y8a4kqbL4WmtnHevggEPT1b/vdhj9NJRcvCtJqhCGlwi+sQustU66+hf8Ltw+7fSRJKkSGF4iKBTgBz9NV//TCXDZBU4fSZIqg6+zSIYMhU03T1ff6SNJUqUwvET0gyPSjn44fSRJqgSGl4jWHAjb75quvtNHkqRK4GsssgN+DHV16erPnz7y6gBJUl4ZXiLr3Sfce5TSuafDjBlOH0mS8snwksDwA6B7z3T1J0+Ec05x+kiSlE++vhLo3AUOPSptD6NHwfV/g7KjL5KknDG8JLLdTrDpsLQ9XHMFvDbW7dOSpHwxvCR0xLHQsWO6+k2NcNbJMGeO618kSflheElo5VVhv4PT9vDpBDj3NNe/SJLyw1dWYt/5PgxYI20PTz0K/7zB0RdJUj4YXhKrrYWfHQc1NWn7+Msl8MZYz3+RJGWf4SUDNvwK7P6ttD2UGuD0E2DqFGhsTNuLJElLY3jJiEMOh74rpe1h8iQ49biwkNcpJElSVhleMqK+Mxx5XOou4M3X4LwzXMArScouX1EZsunmsNMeqbuAR0bC9VdBuZy6E0mSFmV4yZgfHQk9e6XuAq6+HJ5+3PUvkqTsMbxkzAorht1HqZXL4f6jj8Z7Aq8kKVsMLxm0+dbwzW+n7gJmzYTf/jJ8dgRGkpQVhpeM+tGRsNY6qbuAj8fDSceErdQGGElSFhheMqpDRzjhNKivT91J2IF02glA2S3UkqT0DC8Z1n8AHH5s6i6C55+GEd6BJEnKAF9FGbfDbrD9rqm7CB4ZCZedn7oLSVK1M7zkwBHHQv/VUncR3HYj3HB16i4kSdXM8JID9Z3hhNPDOpgsuOqPcP+drn+RJKVheMmJtdaBH/40dRcLXHg2jHrMACNJis/wkiN7fge22CZ1F0FTI/zuJHjmCQOMJCkuw0vOHH1i+tun5yuV4Mxfw3OjDDCSpHgMLzmzYlf471OgWEzdSVBqgDNOhOefMcBIkuIwvOTQBoPDCbxZ0TAXTjseRjsCI0mKwPCSU3sNhz0ycP/RfA1zwym8roGRJLU3w0uOHXY0bDosdRcLlBrgzF+5C0mS1L5qyuVyOXUTar0Z0+HYw+D9d1N3skCxCEceDzvtkboTSVIlMrxUgI/Gwy9+DJ9PSd3JFx14GOx3YOouJEmVxvBSIca8CL86Oqw9yZK9hsNhx4RpJC91lCS1BV8nFWKDjeGo41N3sajbboSzfwPlJmhsTN2NJKkSGF4qyPa7wr4ZnKZ5ZCT85tiwoNcAI0laXoaXCnPgj2Grb6TuYlHPPwPHHQEzZ4STeSVJai3DS4WpqYFjT4Z11kvdyaLefA2O+RF8/KEjMJKk1jO8VKC6OvjN2dC7b+pOFvXxeDjmh/Dsk+BScUlSa7jbqIKNewdO+BlMnZK6k0XV1MD+P4LvHuxOJElSyxheKtzbb8CJR8L0aak7WbxtdoBfnASFYnYum5QkZZvhpQq8PhZ+fXRYLJtFAwfBb8+Bbt2hWJu6G0lS1jlYXwXWXR9OHQGd6lN3snhvvgZHHRI+eyeSJGlZHHmpIi88C6f8EuZm7BTe+Wo7wCGHwz77uQ5GkrRkhpcq88yTcPoJ4cC4rNp867Ddu64T1DqNJEn6EsNLFXriEfjdSdk+a6VPPzjhdBi0PtQ4AiNJWojhpUo98gD8/jRoynCAKRbh+z+CfQ8I00juRpIkgeGlqo28G/5wZvYPixsyDI47Bbp0cTeSJMndRlVth93gp79M3cWyjR4FR+wPLzwX/rM7kiSpujnyIu66FS49Lx+hYOc94bCjoUMHR2EkqVoZXgTAYw+GNTANGd1GvbA+/eDoE2HIULdUS1I1Mrzo/7zwLJxxYnZP4v2y+aMwtR3cUi1J1cTwoi9463X47S9h8sTUnTSPozCSVH0ML1rER+Ph5J+Hz3mx855w6JHhYDu3VEtSZTO8aLGmTIbf/CKMxORFt+5w8OGw8zfDAXyGGEmqTIYXLdHMGWENzAvPpu6kZdbbEH52HKw50KkkSapEhhctVcNcGHE6PPrv1J20TKEAu+4FBx8BnZxKkqSKYnjRMjU1wR//AHfekrqTluvaHQ7+CeyyJzSWPBtGkiqB4UXNdt1f4JorUnfROoM2gEOPgvUHux5GkvLO8KIWefRBOP9MmDUrdSetM3QL+MFPYcCaroeRpLwyvKjF3nsbzvgVfPh+6k5ap1CAbXeCgw6D3n3DxZSGGEnKD8OLWmXGdPj9qfD046k7ab3aDrDb3vD9H8IKK0JNTeqOJEnN4c+bapUuK8Bvz4Hv/SC/L/1SA9x+EzzyQH7/P0hSNXLkRcvtqUdhxGn5uRNpYcUi3Dwy3FItScoHR1603DbfGs6/AlZbI3UnLXfYzw0ukpQ3hhe1if4D4A+Xw1bbpe6k+Tp0hF2+mboLSVJLGV7UZuo7w6/ODLt48rB754hfhkW7kqR8cc2L2sWzT4V1MJ9PSd3J4tXVwY33eeKuJOVRDn4+Vh59dXO4+G8wdMvUnSzekScYXCQprxx5Ubu75za44sLsnMpb3wWuv9srAiQprxx5UbvbdS+46K/hXqEsOOZEg4sk5ZkjL4qmsRFu/jv8/c9QKqXpYcUV4dq78rGgWJK0eD7CFU2xCPseCOddDquvmaaHn59kcJGkvHPkRUk0zIWr/gT/uj7c7hxDtx5wzW2GF0nKO8OLknppNJx3BnzycfvXOu1c+OrX2r+OJKl9+TOokho8JGyp3nH39q3Tqw9sOqx9a0iS4nDkRZnx4nPwx/PgvXfa/vf+nwvhK19t+99XkhSf4UWZ0liC226Ca69su1uq+60Mf74Ramra5veTJKVleFEmTZoIV14MD967/L/XOZfChhsv/+8jScoGw4sy7eXn4Y9/gHfebN3/fpVV4U//cNRFkiqJ4UWZ19gId94C11wBM6a37H973p9g0Ibt05ckKQ13GynzikXYazhcdh3ssGvzR1EGrAXrbtC+vUmS4nPkRbkz5kW45NxlTyVdcCUMHBSnJ0lSPIYX5VJjIzxwF1z3F/h0wqL//cBBcP6fXesiSZXI8KJca5gLd9wCN14NU6cs+PWL/wZrrJ2sLUlSOzK8qCLMmgn/vB5uuQ5WWwPOvcxRF0mqVIYXVZRpn4cRmFUHpO5EktReDC+SJClX3CotSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJyxfAiSZJy5f8DfC9hSAy2cqAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_fig = plt.figure()\n",
    "ax = p_fig.add_subplot(1,1,1)\n",
    "col = colors=colors[:3]\n",
    "col.append('white')\n",
    "ax.pie([13049,4870+1418], colors=['#4A3AFF','white'])\n",
    "ax.add_artist(plt.Circle((0,0), 0.87, color='white'))\n",
    "p_fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>prof</th>\n",
       "      <th>id</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2022</td>\n",
       "      <td>Break-even</td>\n",
       "      <td>1418</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2022</td>\n",
       "      <td>Not Profitable</td>\n",
       "      <td>4870</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2022</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>13049</td>\n",
       "      <td>67.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year            prof     id    pct\n",
       "51  2022      Break-even   1418   7.33\n",
       "52  2022  Not Profitable   4870  25.18\n",
       "53  2022      Profitable  13049  67.48"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prof_count_22 = prof_count[prof_count['year'] == 2022].copy()\n",
    "prof_count_22['pct'] = round(((prof_count_22['id']/ 19337 * 100)),2)\n",
    "sns.barplot(prof_count_22, x='year', y='id', hue='prof', palette=colors)\n",
    "plt.show()\n",
    "prof_count_22"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biggest Employers, Profit Margin % Companies that aren't employers\n",
    "\n",
    "Are there Large companies in other locations except the top 3 ones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57 unique locations\n",
      "139 companies have no employees\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>employees</th>\n",
       "      <th>turnover</th>\n",
       "      <th>profit</th>\n",
       "      <th>debts</th>\n",
       "      <th>size</th>\n",
       "      <th>employer</th>\n",
       "      <th>prof</th>\n",
       "      <th>prof_margin</th>\n",
       "      <th>location_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170310</th>\n",
       "      <td>2022</td>\n",
       "      <td>1246560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5842872</td>\n",
       "      <td>0</td>\n",
       "      <td>Small</td>\n",
       "      <td>No</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Municipiul Târnăveni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173571</th>\n",
       "      <td>2022</td>\n",
       "      <td>16170338</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>955706</td>\n",
       "      <td>573305</td>\n",
       "      <td>Small</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170335</th>\n",
       "      <td>2022</td>\n",
       "      <td>1251469</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>888183</td>\n",
       "      <td>1611</td>\n",
       "      <td>Small</td>\n",
       "      <td>No</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Sat Vînatori, Comuna Vînatori</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year        id  employees  turnover   profit   debts   size employer  \\\n",
       "170310  2022   1246560          0         0  5842872       0  Small       No   \n",
       "173571  2022  16170338          1         0   955706  573305  Small      Yes   \n",
       "170335  2022   1251469          0         0   888183    1611  Small       No   \n",
       "\n",
       "              prof  prof_margin                     location_y  \n",
       "170310  Profitable        100.0           Municipiul Târnăveni  \n",
       "173571  Profitable        100.0         Municipiul Târgu Mureş  \n",
       "170335  Profitable        100.0  Sat Vînatori, Comuna Vînatori  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prof_locs = pd.merge(profitable,loc_df[['id', 'location_y']], on='id', how='left')\n",
    "prof_22 = prof_locs[(prof_locs['year'] == 2022) & (prof_locs['prof'] == 'Profitable')].copy()\n",
    "\n",
    "prof_22_100 = prof_22[prof_22['prof_margin'] == 100] # profit margin 100%\n",
    "\n",
    "print(len(prof_22_100['location_y'].unique()), 'unique locations')\n",
    "print(len(prof_22_100[prof_22_100['employer'] == 'No']), 'companies have no employees')\n",
    "\n",
    "prof_22_100.sort_values(by='profit', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 Biggest Employers and their data - 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>id</th>\n",
       "      <th>employees</th>\n",
       "      <th>turnover</th>\n",
       "      <th>profit</th>\n",
       "      <th>debts</th>\n",
       "      <th>prof</th>\n",
       "      <th>prof_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171974</th>\n",
       "      <td>10976687</td>\n",
       "      <td>5384</td>\n",
       "      <td>349288436</td>\n",
       "      <td>-25278897</td>\n",
       "      <td>521349204</td>\n",
       "      <td>Not Profitable</td>\n",
       "      <td>-7.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175438</th>\n",
       "      <td>21408186</td>\n",
       "      <td>1608</td>\n",
       "      <td>136669019</td>\n",
       "      <td>2955927</td>\n",
       "      <td>71232688</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178417</th>\n",
       "      <td>32039122</td>\n",
       "      <td>1224</td>\n",
       "      <td>99607465</td>\n",
       "      <td>-3805483</td>\n",
       "      <td>87206406</td>\n",
       "      <td>Not Profitable</td>\n",
       "      <td>-3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169797</th>\n",
       "      <td>1200490</td>\n",
       "      <td>960</td>\n",
       "      <td>439877457</td>\n",
       "      <td>6035143</td>\n",
       "      <td>285992908</td>\n",
       "      <td>Profitable</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170222</th>\n",
       "      <td>1235668</td>\n",
       "      <td>928</td>\n",
       "      <td>175562007</td>\n",
       "      <td>-18591667</td>\n",
       "      <td>106073364</td>\n",
       "      <td>Not Profitable</td>\n",
       "      <td>-10.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  employees   turnover    profit      debts            prof  \\\n",
       "171974  10976687       5384  349288436 -25278897  521349204  Not Profitable   \n",
       "175438  21408186       1608  136669019   2955927   71232688      Profitable   \n",
       "178417  32039122       1224   99607465  -3805483   87206406  Not Profitable   \n",
       "169797   1200490        960  439877457   6035143  285992908      Profitable   \n",
       "170222   1235668        928  175562007 -18591667  106073364  Not Profitable   \n",
       "\n",
       "        prof_margin  \n",
       "171974        -7.24  \n",
       "175438         2.16  \n",
       "178417        -3.82  \n",
       "169797         1.37  \n",
       "170222       -10.59  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_22 = profitable[profitable['year'] == 2022].copy()\n",
    "big_22 = big_22[big_22['size'] == 'Large'].sort_values(by='employees', ascending=False).head(5)\n",
    "big_22 = big_22.drop(columns=['year','size','employer'])\n",
    "big_22['pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_fig = big_22.plot.bar(x='id', y=['employees', 'turnover','profit','debts'], color=colors)\n",
    "b_fig.get_legend().remove()\n",
    "b_fig.yaxis.set_major_formatter('{x:1.0f}')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 Locations breakdown by Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>location_y</th>\n",
       "      <th>size</th>\n",
       "      <th>id_x</th>\n",
       "      <th>id_y</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>Small</td>\n",
       "      <td>7579</td>\n",
       "      <td>7677</td>\n",
       "      <td>98.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>Medium</td>\n",
       "      <td>89</td>\n",
       "      <td>7677</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>Large</td>\n",
       "      <td>9</td>\n",
       "      <td>7677</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>Medium</td>\n",
       "      <td>23</td>\n",
       "      <td>1479</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>Small</td>\n",
       "      <td>1453</td>\n",
       "      <td>1479</td>\n",
       "      <td>98.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>Large</td>\n",
       "      <td>3</td>\n",
       "      <td>1479</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>Small</td>\n",
       "      <td>1072</td>\n",
       "      <td>1086</td>\n",
       "      <td>98.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>Medium</td>\n",
       "      <td>12</td>\n",
       "      <td>1086</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>Large</td>\n",
       "      <td>2</td>\n",
       "      <td>1086</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location_y    size  id_x  id_y   pct\n",
       "93  Municipiul Târgu Mureş   Small  7579  7677  98.7\n",
       "92  Municipiul Târgu Mureş  Medium    89  7677   1.2\n",
       "91  Municipiul Târgu Mureş   Large     9  7677   0.1\n",
       "86       Municipiul Reghin  Medium    23  1479   1.6\n",
       "87       Municipiul Reghin   Small  1453  1479  98.2\n",
       "85       Municipiul Reghin   Large     3  1479   0.2\n",
       "90   Municipiul Sighişoara   Small  1072  1086  98.7\n",
       "89   Municipiul Sighişoara  Medium    12  1086   1.1\n",
       "88   Municipiul Sighişoara   Large     2  1086   0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_sum4 = prof_locs[prof_locs['year'] == 2022].groupby(['location_y', 'size'])[['id']].count().reset_index()\n",
    "size_sum4 = pd.merge(size_sum4,prof_locs[prof_locs['year'] == 2022].groupby(['location_y'])[['id']].count().reset_index(), on='location_y', how='left')\n",
    "size_sum4['pct'] = round((size_sum4['id_x'] / size_sum4['id_y'] * 100),1)\n",
    "size_sum4 = size_sum4.sort_values(by='id_y', ascending=False)\n",
    "display(size_sum4.head(9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 locations by company size\n",
    "\n",
    "We can see nothing is changed in the top 3, compared to top cities with the total number of companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>location_y</th>\n",
       "      <th>id_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location_y  id_x\n",
       "91  Municipiul Târgu Mureş     9\n",
       "85       Municipiul Reghin     3\n",
       "88   Municipiul Sighişoara     2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>location_y</th>\n",
       "      <th>id_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location_y  id_x\n",
       "92  Municipiul Târgu Mureş    89\n",
       "86       Municipiul Reghin    23\n",
       "89   Municipiul Sighişoara    12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>location_y</th>\n",
       "      <th>id_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>7579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>1453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location_y  id_x\n",
       "93  Municipiul Târgu Mureş  7579\n",
       "87       Municipiul Reghin  1453\n",
       "90   Municipiul Sighişoara  1072"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc_big = size_sum4[size_sum4['size'] == 'Large'][['location_y','id_x']].sort_values(by='id_x', ascending=False)\n",
    "loc_med = size_sum4[size_sum4['size'] == 'Medium'][['location_y','id_x']].sort_values(by='id_x', ascending=False)\n",
    "loc_small = size_sum4[size_sum4['size'] == 'Small'][['location_y','id_x']].sort_values(by='id_x', ascending=False)\n",
    "display(loc_big.head(3),loc_med.head(3),loc_small.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 3 locations for Companies with 100% Profit Margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>index</th>\n",
       "      <th>location_y</th>\n",
       "      <th>id</th>\n",
       "      <th>profit</th>\n",
       "      <th>pct_count</th>\n",
       "      <th>pct_prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Municipiul Târgu Mureş</td>\n",
       "      <td>75</td>\n",
       "      <td>2786226</td>\n",
       "      <td>39.5</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>Municipiul Sighişoara</td>\n",
       "      <td>13</td>\n",
       "      <td>42649</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Municipiul Târnăveni</td>\n",
       "      <td>10</td>\n",
       "      <td>5845552</td>\n",
       "      <td>5.3</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Municipiul Reghin</td>\n",
       "      <td>8</td>\n",
       "      <td>227266</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>Sat Livezeni, Comuna Livezeni</td>\n",
       "      <td>7</td>\n",
       "      <td>1726114</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                     location_y  id   profit  pct_count  pct_prof\n",
       "0     11         Municipiul Târgu Mureş  75  2786226       39.5      22.0\n",
       "1     10          Municipiul Sighişoara  13    42649        6.8       0.3\n",
       "2     12           Municipiul Târnăveni  10  5845552        5.3      46.2\n",
       "3      9              Municipiul Reghin   8   227266        4.2       1.8\n",
       "4     38  Sat Livezeni, Comuna Livezeni   7  1726114        3.7      13.7"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prof_22_100_summed = pd.merge(prof_22_100.groupby('location_y')['id'].count().reset_index(), prof_22_100.groupby('location_y')['profit'].sum().reset_index(), on='location_y', how='left')\n",
    "top5_profit = prof_22_100_summed.sort_values(by='profit', ascending=False).head(5).reset_index()\n",
    "top5_count = prof_22_100_summed.sort_values(by='id', ascending=False).head(5).reset_index()\n",
    "\n",
    "top5_count['pct_count'] = round((top5_count['id'] / prof_22_100_summed['id'].sum() * 100),1)\n",
    "top5_profit['pct_count'] = round((top5_profit['id'] / prof_22_100_summed['id'].sum() * 100),1)\n",
    "\n",
    "top5_count['pct_prof'] = round((top5_count['profit'] / prof_22_100_summed['profit'].sum() * 100),1)\n",
    "top5_profit['pct_prof'] = round((top5_profit['profit'] / prof_22_100_summed['profit'].sum() * 100),1)\n",
    "top5_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 3 locations by number of companies with 100% margin\n",
    "loc_100_count = top5_count.head(3).plot.bar(x='location_y', y=['pct_count','pct_prof'], color=colors)\n",
    "loc_100_count.get_legend().remove()\n",
    "loc_100_count.yaxis.set_major_formatter('{x:1.0f}%')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 3 locations by profit of companies with 100% margin\n",
    "loc_100_pr = top5_profit.head(3).plot.bar(x='location_y', y=['pct_count','pct_prof'], color=colors)\n",
    "loc_100_pr.get_legend().remove()\n",
    "loc_100_pr.yaxis.set_major_formatter('{x:1.0f}%')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### % Non Employers - Profitability Break Down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>prof</th>\n",
       "      <th>id</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Break-even</td>\n",
       "      <td>1393</td>\n",
       "      <td>19.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Profitable</td>\n",
       "      <td>2239</td>\n",
       "      <td>31.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Profitable</td>\n",
       "      <td>3553</td>\n",
       "      <td>49.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             prof    id    pct\n",
       "0      Break-even  1393  19.39\n",
       "1  Not Profitable  2239  31.16\n",
       "2      Profitable  3553  49.45"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Companies with no employees in 2022. How many are profitable\n",
    "no_empl = profitable[profitable['employer'] == 'No'].copy()\n",
    "no_empl_22 = no_empl[no_empl['year'] == 2022].groupby('prof')['id'].count().reset_index()\n",
    "no_empl_22['pct'] = round(((no_empl_22['id']/ no_empl_22['id'].sum() * 100)),2)\n",
    "no_empl_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_fig = plt.figure()\n",
    "ax = pr_fig.add_subplot(1,1,1)\n",
    "col = colors=colors[:3]\n",
    "col.append('white')\n",
    "ax.pie([3553,2239,1393], colors=['#4A3AFF','#0B0086','#E1DEFF'])\n",
    "ax.add_artist(plt.Circle((0,0), 0.87, color='white'))\n",
    "pr_fig.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}