20.11.2020 histogram, bar(h) chart .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_datareader as pdr\n",
    "from pandas_datareader import wb\n",
    "%matplotlib inline \n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hist = pd.Series([1, 1, 1, 1, 2, 2, 2, 5, 11, 11, 11, 100])\n",
    "df_hist.plot(\n",
    "    kind='hist',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tatyana/.local/lib/python3.6/site-packages/pandas_datareader/wb.py:593: UserWarning: Non-standard ISO country codes: 1A, 1W, 4E, 6F, 6N, 6X, 7E, 8S, A4, A5, A9, B1, B2, B3, B4, B6, B7, B8, C4, C5, C6, C7, C8, C9, D2, D3, D4, D5, D6, D7, D8, D9, EU, F1, F6, JG, M1, M2, N6, OE, R6, S1, S2, S3, S4, T2, T3, T4, T5, T6, T7, V1, V2, V3, V4, XC, XD, XE, XF, XG, XH, XI, XJ, XK, XL, XM, XN, XO, XP, XQ, XT, XU, XY, Z4, Z7, ZB, ZF, ZG, ZH, ZI, ZJ, ZQ, ZT\n",
      "  \"Non-standard ISO \" \"country codes: %s\" % tmp, UserWarning\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>43.244000</td>\n",
       "      <td>43.923000</td>\n",
       "      <td>44.617000</td>\n",
       "      <td>45.324000</td>\n",
       "      <td>46.040000</td>\n",
       "      <td>46.761000</td>\n",
       "      <td>47.486000</td>\n",
       "      <td>48.211000</td>\n",
       "      <td>48.930000</td>\n",
       "      <td>49.640000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.028000</td>\n",
       "      <td>61.553000</td>\n",
       "      <td>62.054000</td>\n",
       "      <td>62.525000</td>\n",
       "      <td>62.966000</td>\n",
       "      <td>63.377000</td>\n",
       "      <td>63.763000</td>\n",
       "      <td>64.130000</td>\n",
       "      <td>64.486000</td>\n",
       "      <td>64.833000</td>\n",
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       "    <tr>\n",
       "      <th>Africa Eastern and Southern</th>\n",
       "      <td>49.870693</td>\n",
       "      <td>50.115886</td>\n",
       "      <td>50.363456</td>\n",
       "      <td>50.610385</td>\n",
       "      <td>50.848335</td>\n",
       "      <td>51.058057</td>\n",
       "      <td>51.214281</td>\n",
       "      <td>51.299781</td>\n",
       "      <td>51.308595</td>\n",
       "      <td>51.251763</td>\n",
       "      <td>...</td>\n",
       "      <td>58.470697</td>\n",
       "      <td>59.353595</td>\n",
       "      <td>60.185561</td>\n",
       "      <td>60.953363</td>\n",
       "      <td>61.647367</td>\n",
       "      <td>62.259288</td>\n",
       "      <td>62.787681</td>\n",
       "      <td>63.246264</td>\n",
       "      <td>63.648988</td>\n",
       "      <td>64.005213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Western and Central</th>\n",
       "      <td>46.366104</td>\n",
       "      <td>46.798292</td>\n",
       "      <td>47.188618</td>\n",
       "      <td>47.533985</td>\n",
       "      <td>47.830536</td>\n",
       "      <td>48.079381</td>\n",
       "      <td>48.284581</td>\n",
       "      <td>48.454128</td>\n",
       "      <td>48.597421</td>\n",
       "      <td>48.719231</td>\n",
       "      <td>...</td>\n",
       "      <td>54.144307</td>\n",
       "      <td>54.649998</td>\n",
       "      <td>55.138944</td>\n",
       "      <td>55.618986</td>\n",
       "      <td>56.088269</td>\n",
       "      <td>56.542009</td>\n",
       "      <td>56.974761</td>\n",
       "      <td>57.382363</td>\n",
       "      <td>57.762347</td>\n",
       "      <td>58.115723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>70.208000</td>\n",
       "      <td>70.416000</td>\n",
       "      <td>70.635000</td>\n",
       "      <td>70.876000</td>\n",
       "      <td>71.134000</td>\n",
       "      <td>71.388000</td>\n",
       "      <td>71.605000</td>\n",
       "      <td>71.760000</td>\n",
       "      <td>71.843000</td>\n",
       "      <td>71.860000</td>\n",
       "      <td>...</td>\n",
       "      <td>76.562000</td>\n",
       "      <td>76.914000</td>\n",
       "      <td>77.252000</td>\n",
       "      <td>77.554000</td>\n",
       "      <td>77.813000</td>\n",
       "      <td>78.025000</td>\n",
       "      <td>78.194000</td>\n",
       "      <td>78.333000</td>\n",
       "      <td>78.458000</td>\n",
       "      <td>78.573000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>58.198000</td>\n",
       "      <td>59.519000</td>\n",
       "      <td>60.813000</td>\n",
       "      <td>62.029000</td>\n",
       "      <td>63.130000</td>\n",
       "      <td>64.087000</td>\n",
       "      <td>64.884000</td>\n",
       "      <td>65.545000</td>\n",
       "      <td>66.097000</td>\n",
       "      <td>66.554000</td>\n",
       "      <td>...</td>\n",
       "      <td>74.938000</td>\n",
       "      <td>75.199000</td>\n",
       "      <td>75.436000</td>\n",
       "      <td>75.661000</td>\n",
       "      <td>75.878000</td>\n",
       "      <td>76.090000</td>\n",
       "      <td>76.298000</td>\n",
       "      <td>76.499000</td>\n",
       "      <td>76.693000</td>\n",
       "      <td>76.880000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                              1980       1981       1982       1983  \\\n",
       "country                                                                   \n",
       "Afghanistan                  43.244000  43.923000  44.617000  45.324000   \n",
       "Africa Eastern and Southern  49.870693  50.115886  50.363456  50.610385   \n",
       "Africa Western and Central   46.366104  46.798292  47.188618  47.533985   \n",
       "Albania                      70.208000  70.416000  70.635000  70.876000   \n",
       "Algeria                      58.198000  59.519000  60.813000  62.029000   \n",
       "\n",
       "year                              1984       1985       1986       1987  \\\n",
       "country                                                                   \n",
       "Afghanistan                  46.040000  46.761000  47.486000  48.211000   \n",
       "Africa Eastern and Southern  50.848335  51.058057  51.214281  51.299781   \n",
       "Africa Western and Central   47.830536  48.079381  48.284581  48.454128   \n",
       "Albania                      71.134000  71.388000  71.605000  71.760000   \n",
       "Algeria                      63.130000  64.087000  64.884000  65.545000   \n",
       "\n",
       "year                              1988       1989  ...       2010       2011  \\\n",
       "country                                            ...                         \n",
       "Afghanistan                  48.930000  49.640000  ...  61.028000  61.553000   \n",
       "Africa Eastern and Southern  51.308595  51.251763  ...  58.470697  59.353595   \n",
       "Africa Western and Central   48.597421  48.719231  ...  54.144307  54.649998   \n",
       "Albania                      71.843000  71.860000  ...  76.562000  76.914000   \n",
       "Algeria                      66.097000  66.554000  ...  74.938000  75.199000   \n",
       "\n",
       "year                              2012       2013       2014       2015  \\\n",
       "country                                                                   \n",
       "Afghanistan                  62.054000  62.525000  62.966000  63.377000   \n",
       "Africa Eastern and Southern  60.185561  60.953363  61.647367  62.259288   \n",
       "Africa Western and Central   55.138944  55.618986  56.088269  56.542009   \n",
       "Albania                      77.252000  77.554000  77.813000  78.025000   \n",
       "Algeria                      75.436000  75.661000  75.878000  76.090000   \n",
       "\n",
       "year                              2016       2017       2018       2019  \n",
       "country                                                                  \n",
       "Afghanistan                  63.763000  64.130000  64.486000  64.833000  \n",
       "Africa Eastern and Southern  62.787681  63.246264  63.648988  64.005213  \n",
       "Africa Western and Central   56.974761  57.382363  57.762347  58.115723  \n",
       "Albania                      78.194000  78.333000  78.458000  78.573000  \n",
       "Algeria                      76.298000  76.499000  76.693000  76.880000  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb_countries = wb.get_countries()\n",
    "df = wb.download(indicator=\"SP.DYN.LE00.IN\", country=wb_countries['iso2c'], start=1980, end=2019)\n",
    "df.reset_index(inplace=True)\n",
    "df.rename(columns={'SP.DYN.LE00.IN':'lifespan'}, inplace=True)\n",
    "df = df.pivot(index='country', columns='year', values='lifespan')\n",
    "df.dropna(how='all', axis=1, inplace=True)\n",
    "df.dropna(how='all', axis=0, inplace=True)\n",
    "df.dropna(how='any', axis=0, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Argentina', 'Moldova', 'Afghanistan']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = [df.index[8], df.index[150], df.index[0]]\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "_, bin_edges = np.histogram(df.loc[countries], bins)\n",
    "\n",
    "df.loc[countries].T.plot(\n",
    "    kind='hist',\n",
    "    figsize=(18, 6),\n",
    "    alpha=0.6,\n",
    "    xticks=bin_edges,\n",
    "    bins=bins,\n",
    "    xlim=(bin_edges[0]-0.5, bin_edges[-1]+0.5)\n",
    "#     stacked=True\n",
    ")\n",
    "\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_h = pd.read_csv('weight-height.csv')\n",
    "df_w_h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_w_h['Height'].plot(\n",
    "    kind='hist',\n",
    "    figsize=(16, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_w_h['Height'] = df_w_h['Height'] * 2.54\n",
    "df_w_h['Height'].plot(\n",
    "    kind='hist',\n",
    "    figsize=(16, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>HM</th>\n",
       "      <th>HF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>187.571423</td>\n",
       "      <td>149.633259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>174.706036</td>\n",
       "      <td>165.684232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>188.239668</td>\n",
       "      <td>160.957270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>182.196685</td>\n",
       "      <td>163.779193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>177.499761</td>\n",
       "      <td>156.954464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              HM          HF\n",
       "5000  187.571423  149.633259\n",
       "5001  174.706036  165.684232\n",
       "5002  188.239668  160.957270\n",
       "5003  182.196685  163.779193\n",
       "5004  177.499761  156.954464"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_heights = pd.DataFrame({'HM' : df_w_h[df_w_h['Gender'] == 'Male']['Height'].values, 'HF' : df_w_h[df_w_h['Gender'] == 'Female']['Height']})\n",
    "df_heights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_heights.plot(\n",
    "    kind='hist',\n",
    "    figsize=(16, 6),\n",
    "    alpha=0.7\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['Rwanda'].plot(\n",
    "    kind='bar',\n",
    "    figsize=(18,6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sector</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AOS</th>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>Abiomed</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name       Sector\n",
       "Symbol                                  \n",
       "MMM                      3M  Industrials\n",
       "AOS             A. O. Smith  Industrials\n",
       "ABT     Abbott Laboratories  Health Care\n",
       "ABBV                 AbbVie  Health Care\n",
       "ABMD                Abiomed  Health Care"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp_names = pd.read_csv('sp500companies.csv', index_col=0)\n",
    "df_comp_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Major index membership</th>\n",
       "      <th>Market capitalization</th>\n",
       "      <th>Income (ttm)</th>\n",
       "      <th>Revenue (ttm)</th>\n",
       "      <th>Book value per share (mrq)</th>\n",
       "      <th>Cash per share (mrq)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "      <th>Dividend yield (annual)</th>\n",
       "      <th>Full time employees</th>\n",
       "      <th>Stock has options trading on a market exchange</th>\n",
       "      <th>...</th>\n",
       "      <th>Performance (Quarter)</th>\n",
       "      <th>Performance (Half Year)</th>\n",
       "      <th>Performance (Year)</th>\n",
       "      <th>Performance (Year To Date)</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Average True Range (14)</th>\n",
       "      <th>Volatility (Week, Month)</th>\n",
       "      <th>Previous close</th>\n",
       "      <th>Current stock price</th>\n",
       "      <th>Performance (today)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>39.84B</td>\n",
       "      <td>1.26B</td>\n",
       "      <td>6.53B</td>\n",
       "      <td>17.13</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.64%</td>\n",
       "      <td>17400</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>11.41%</td>\n",
       "      <td>-1.80%</td>\n",
       "      <td>-13.44%</td>\n",
       "      <td>-17.88%</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.78% 2.80%</td>\n",
       "      <td>127.71</td>\n",
       "      <td>131.11</td>\n",
       "      <td>2.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>9.20B</td>\n",
       "      <td>-1922.00M</td>\n",
       "      <td>40.72B</td>\n",
       "      <td>-12.95</td>\n",
       "      <td>18.73</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>123400</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.46%</td>\n",
       "      <td>-15.27%</td>\n",
       "      <td>-36.38%</td>\n",
       "      <td>-23.39%</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.29% 5.01%</td>\n",
       "      <td>13.97</td>\n",
       "      <td>13.76</td>\n",
       "      <td>-1.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>11.50B</td>\n",
       "      <td>570.00M</td>\n",
       "      <td>11.04B</td>\n",
       "      <td>47.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.14%</td>\n",
       "      <td>41000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.05%</td>\n",
       "      <td>-15.14%</td>\n",
       "      <td>-10.00%</td>\n",
       "      <td>-20.36%</td>\n",
       "      <td>1.23</td>\n",
       "      <td>5.43</td>\n",
       "      <td>2.77% 2.70%</td>\n",
       "      <td>185.91</td>\n",
       "      <td>191.03</td>\n",
       "      <td>2.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>DJIA S&amp;P500</td>\n",
       "      <td>2567.56B</td>\n",
       "      <td>101.94B</td>\n",
       "      <td>386.02B</td>\n",
       "      <td>4.14</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.58%</td>\n",
       "      <td>154000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50%</td>\n",
       "      <td>-1.47%</td>\n",
       "      <td>8.53%</td>\n",
       "      <td>-11.39%</td>\n",
       "      <td>1.21</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.14% 2.53%</td>\n",
       "      <td>156.79</td>\n",
       "      <td>157.35</td>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>263.34B</td>\n",
       "      <td>12.40B</td>\n",
       "      <td>56.73B</td>\n",
       "      <td>9.19</td>\n",
       "      <td>4.31</td>\n",
       "      <td>5.64</td>\n",
       "      <td>3.77%</td>\n",
       "      <td>50000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.99%</td>\n",
       "      <td>11.56%</td>\n",
       "      <td>26.32%</td>\n",
       "      <td>10.60%</td>\n",
       "      <td>0.72</td>\n",
       "      <td>3.31</td>\n",
       "      <td>2.12% 1.99%</td>\n",
       "      <td>151.15</td>\n",
       "      <td>149.75</td>\n",
       "      <td>-0.93%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Major index membership Market capitalization Income (ttm)  \\\n",
       "Company                                                             \n",
       "A                      S&P 500                39.84B        1.26B   \n",
       "AAL                    S&P 500                 9.20B    -1922.00M   \n",
       "AAP                    S&P 500                11.50B      570.00M   \n",
       "AAPL               DJIA S&P500              2567.56B      101.94B   \n",
       "ABBV                   S&P 500               263.34B       12.40B   \n",
       "\n",
       "        Revenue (ttm)  Book value per share (mrq) Cash per share (mrq)  \\\n",
       "Company                                                                  \n",
       "A               6.53B                       17.13                 3.97   \n",
       "AAL            40.72B                      -12.95                18.73   \n",
       "AAP            11.04B                       47.50                 2.30   \n",
       "AAPL          386.02B                        4.14                 3.16   \n",
       "ABBV           56.73B                        9.19                 4.31   \n",
       "\n",
       "        Dividend (annual) Dividend yield (annual)  Full time employees  \\\n",
       "Company                                                                  \n",
       "A                    0.84                   0.64%                17400   \n",
       "AAL                     -                       -               123400   \n",
       "AAP                  6.00                   3.14%                41000   \n",
       "AAPL                 0.92                   0.58%               154000   \n",
       "ABBV                 5.64                   3.77%                50000   \n",
       "\n",
       "        Stock has options trading on a market exchange  ...  \\\n",
       "Company                                                 ...   \n",
       "A                                                  Yes  ...   \n",
       "AAL                                                Yes  ...   \n",
       "AAP                                                Yes  ...   \n",
       "AAPL                                               Yes  ...   \n",
       "ABBV                                               Yes  ...   \n",
       "\n",
       "        Performance (Quarter) Performance (Half Year) Performance (Year)  \\\n",
       "Company                                                                    \n",
       "A                      11.41%                  -1.80%            -13.44%   \n",
       "AAL                   -27.46%                 -15.27%            -36.38%   \n",
       "AAP                   -15.05%                 -15.14%            -10.00%   \n",
       "AAPL                    0.50%                  -1.47%              8.53%   \n",
       "ABBV                   -4.99%                  11.56%             26.32%   \n",
       "\n",
       "        Performance (Year To Date)  Beta  Average True Range (14)  \\\n",
       "Company                                                             \n",
       "A                          -17.88%  1.05                     3.65   \n",
       "AAL                        -23.39%  1.56                     0.75   \n",
       "AAP                        -20.36%  1.23                     5.43   \n",
       "AAPL                       -11.39%  1.21                     3.78   \n",
       "ABBV                        10.60%  0.72                     3.31   \n",
       "\n",
       "        Volatility (Week, Month) Previous close Current stock price  \\\n",
       "Company                                                               \n",
       "A                    2.78% 2.80%         127.71              131.11   \n",
       "AAL                  4.29% 5.01%          13.97               13.76   \n",
       "AAP                  2.77% 2.70%         185.91              191.03   \n",
       "AAPL                 2.14% 2.53%         156.79              157.35   \n",
       "ABBV                 2.12% 1.99%         151.15              149.75   \n",
       "\n",
       "        Performance (today)  \n",
       "Company                      \n",
       "A                     2.66%  \n",
       "AAL                  -1.50%  \n",
       "AAP                   2.75%  \n",
       "AAPL                  0.36%  \n",
       "ABBV                 -0.93%  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp_descr = pd.read_csv('snp500_companies_description.csv', index_col=0)\n",
    "df_comp_descr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Market capitalization</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>39.84B</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>11.50B</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2567.56B</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>263.34B</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>30.41B</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Market capitalization Dividend (annual)\n",
       "Company                                        \n",
       "A                      39.84B              0.84\n",
       "AAP                    11.50B              6.00\n",
       "AAPL                 2567.56B              0.92\n",
       "ABBV                  263.34B              5.64\n",
       "ABC                    30.41B              1.84"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp_descr_short = df_comp_descr[df_comp_descr['Dividend (annual)'] != '-'][['Market capitalization', 'Dividend (annual)']]\n",
    "df_comp_descr_short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Market capitalization (B)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>39.84B</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>11.50B</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2567.56B</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>263.34B</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>30.41B</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Market capitalization (B) Dividend (annual)\n",
       "Company                                            \n",
       "A                          39.84B              0.84\n",
       "AAP                        11.50B              6.00\n",
       "AAPL                     2567.56B              0.92\n",
       "ABBV                      263.34B              5.64\n",
       "ABC                        30.41B              1.84"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp_descr_short.rename(columns={'Market capitalization' : 'Market capitalization (B)'}, inplace=True)\n",
    "df_comp_descr_short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Market capitalization (B)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>39.84</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>11.50</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2567.56</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>263.34</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>30.41</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Market capitalization (B) Dividend (annual)\n",
       "Company                                             \n",
       "A                            39.84              0.84\n",
       "AAP                          11.50              6.00\n",
       "AAPL                       2567.56              0.92\n",
       "ABBV                        263.34              5.64\n",
       "ABC                          30.41              1.84"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp_descr_short['Market capitalization (B)'] = df_comp_descr_short['Market capitalization (B)'].str.replace('B', '', regex=True).astype(float)\n",
    "df_comp_descr_short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp_descr_short['Dividend (annual)'] = df_comp_descr_short['Dividend (annual)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Market capitalization (B)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>79.78</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AOS</th>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>9.51</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>195.87</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>263.34</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>Abiomed</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name       Sector  Market capitalization (B)  \\\n",
       "MMM                    3M  Industrials                      79.78   \n",
       "AOS           A. O. Smith  Industrials                       9.51   \n",
       "ABT   Abbott Laboratories  Health Care                     195.87   \n",
       "ABBV               AbbVie  Health Care                     263.34   \n",
       "ABMD              Abiomed  Health Care                        NaN   \n",
       "\n",
       "      Dividend (annual)  \n",
       "MMM                5.96  \n",
       "AOS                1.12  \n",
       "ABT                1.88  \n",
       "ABBV               5.64  \n",
       "ABMD                NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_snp500 = pd.concat([df_comp_names, df_comp_descr_short], axis=1)\n",
    "df_snp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Market capitalization (B)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>79.78</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AOS</th>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>9.51</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>195.87</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>263.34</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>Accenture</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>192.09</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name                  Sector  Market capitalization (B)  \\\n",
       "MMM                    3M             Industrials                      79.78   \n",
       "AOS           A. O. Smith             Industrials                       9.51   \n",
       "ABT   Abbott Laboratories             Health Care                     195.87   \n",
       "ABBV               AbbVie             Health Care                     263.34   \n",
       "ACN             Accenture  Information Technology                     192.09   \n",
       "\n",
       "      Dividend (annual)  \n",
       "MMM                5.96  \n",
       "AOS                1.12  \n",
       "ABT                1.88  \n",
       "ABBV               5.64  \n",
       "ACN                3.88  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_snp500.dropna(how='any', inplace=True)\n",
    "df_snp500.head()"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Market capitalization (B)</th>\n",
       "      <th>Dividend (annual)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>2567.56</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLY</th>\n",
       "      <td>Eli Lilly &amp; Co</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>312.56</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>ExxonMobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>393.31</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>Home Depot</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>306.35</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>Johnson &amp; Johnson</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>456.23</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>JPMorgan Chase</td>\n",
       "      <td>Financials</td>\n",
       "      <td>338.85</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>Mastercard</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>345.84</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>2102.85</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVDA</th>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>468.58</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>Procter &amp; Gamble</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>349.65</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>UnitedHealth Group</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>509.03</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>Visa</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>445.41</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>356.58</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name                  Sector  Market capitalization (B)  \\\n",
       "AAPL               Apple  Information Technology                    2567.56   \n",
       "LLY       Eli Lilly & Co             Health Care                     312.56   \n",
       "XOM           ExxonMobil                  Energy                     393.31   \n",
       "HD            Home Depot  Consumer Discretionary                     306.35   \n",
       "JNJ    Johnson & Johnson             Health Care                     456.23   \n",
       "JPM       JPMorgan Chase              Financials                     338.85   \n",
       "MA            Mastercard  Information Technology                     345.84   \n",
       "MSFT           Microsoft  Information Technology                    2102.85   \n",
       "NVDA              Nvidia  Information Technology                     468.58   \n",
       "PG      Procter & Gamble        Consumer Staples                     349.65   \n",
       "UNH   UnitedHealth Group             Health Care                     509.03   \n",
       "V                   Visa  Information Technology                     445.41   \n",
       "WMT              Walmart        Consumer Staples                     356.58   \n",
       "\n",
       "      Dividend (annual)  \n",
       "AAPL               0.92  \n",
       "LLY                3.92  \n",
       "XOM                3.52  \n",
       "HD                 7.60  \n",
       "JNJ                4.52  \n",
       "JPM                4.00  \n",
       "MA                 1.96  \n",
       "MSFT               2.48  \n",
       "NVDA               0.16  \n",
       "PG                 3.65  \n",
       "UNH                6.60  \n",
       "V                  1.50  \n",
       "WMT                2.24  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_snp500 = df_snp500[df_snp500['Market capitalization (B)'] > 300]\n",
    "df_snp500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3.5, 8.6, '306.35')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ECay22moceeSRrLbaaoO9SZIkSZKGGIMfSRpm5s+fz1//+lf+9re/8eIXv5iddtqJiBjszZIkSZI0BBn8SNIwMn36dCZMmMCoUaM44ogjWHPNNQd7kyRJkiQNYQY/kjQMZCaXXXYZF1xwAS94wQvYZZddrPKRJEmS9G8Z/EjSEDdjxgzOPPNM5syZw1vf+lbGjh072JskSZIkaZgw+JGkISozmTx5Mn/84x/Zfffd2WOPPRgxYsRgb5YkSZKkYcTgR5KGoIceeojf/va3zJw5kze96U2su+66g71JkiRJkoYhgx9JGmKuueYazjrrLHbeeWcOPvhgRo4cOdibJEmSJGmYMviRpCHi0Ucf5fe//z333nsvr3vd69hggw0Ge5MkSZIkDXMGP5I0BNx444389re/ZbvttuOAAw5g9OjRg71JkiRJklqgX8FPRBwDvA1I4Grg8MycNRAbJknLg1mzZnH22WczZcoU/vM//5NNN910sDdJkiRJUoss8eVhImID4L+B8Zm5HTASeO1AbZgktd1tt93GiSeeyIgRIzjqqKMMfSRJkiQNuP5O9RoFrBQRc4CVgX/2f5Mkqd1mz57NH//4R2644QZe8YpXsPnmmw/2JkmSJElqqSUOfjLzroj4IvAP4DHgnMw8Z8C2TJJa6M4772TChAlssMEGHH300ay00kqDvUmSJEmSWmyJg5+IWAs4ANgMmAGcFhFvzMwf93rc24G3A2y88cZLvqWSNIzNnTuXCy64gMmTJ7PffvuxzTbbDPYmSZIkSVoOLPEaP8CLgdszc1pmzgF+DezR+0GZeVJmjs/M8euss04/fp0kDU933303J598MtOnT+foo4829JEkSZK0zPRnjZ9/ALtFxMrUVK99gIkDslWS1ALz5s3joosu4tJLL+WlL30pO+ywAxEx2JslSZIkaTnSnzV+LomIXwKXA3OBK4CTBmrDJGk4mzZtGhMmTGCllVbiyCOPZPXVVx/sTZIkSZK0HOrXVb0y8+PAxwdoWyRp2Js/fz6XXHIJF154IS960Yt4znOeY5WPJEmSpEHT38u5S5IaDzzwABMmTADgbW97G2uvvfbgbpAkSZKk5Z7BjyT1U2YyadIkzjvvPPbaay922203Rozoz9r5kiRJkjQwDH4kqR8efPBBzjzzTB599FEOP/xwvHqhJEmSpKHE4EeSlkBmcvXVV3P22Wezyy67sNdeezFy5MjB3ixJkiRJWojBjyQ9CXfccQfz589ns80245FHHuF3v/sd999/P2984xtZb731BnvzJEmSJKlPLkIhSf/G3LlzOeOMM8hMrr/+ek488UTWWmst3v72txv6SJIkSRrSrPiRpH/jsssuY6211uLKK69k6tSpHHzwwWy00UaDvVmSJEmS9G8Z/EjSYjz66KP8+c9/ZtSoUay22mrst99+jB49erA3S5IkSZKeFIMfSVqMCRMm8PjjjzN37lxuvvlm7r//frbddlue/vSnD/amSZIkSdK/ZfAjSYuxzz77sNdee7Huuuuy4oorDvbmSJIkSdJTYvAjSYux7rrrDvYmSJIkSdISM/iRNOyNPe3Uwd6EJ236QYcM9iZIkiRJWo54OXdJkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWGjXYGyBJkiRJGnrGnnbqYG/Ckzb9oEMGexOkIcuKH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKkljL4kSRJkiRJaimDH0mSJEmSpJYy+JEkSZIkSWopgx9JkiRJkqSWMviRJEmSJElqKYMfSZIkSZKklupX8BMRa0bELyPihoi4PiJ2H6gNkyRJkiRJUv+M6ufPfxU4KzNfExErACsPwDZJkiRJkiRpACxx8BMRawDPB94MkJmzgdkDs1mSJEmSJEnqr/5M9doMmAZ8LyKuiIjvRMQqA7RdkiRJkiRJ6qf+BD+jgJ2Bb2Xms4FHgA/2flBEvD0iJkbExGnTpvXj10mSJEmSJOmp6E/wMxWYmpmXNN//kgqCFpKZJ2Xm+Mwcv8466/Tj10mSJEmSJOmpWOLgJzPvAe6MiC2bm/YBrhuQrZIkSZIkSVK/9feqXu8CftJc0es24PD+b5IkSZIkSZIGQr+Cn8ycDIwfmE2RJEmSJEnSQOrPGj+SJEmSJEkawgx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklpq1GBvgCRpaBp72qmDvQlP2vSDDhnsTZAkSZKGJCt+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklqq38FPRIyMiCsi4rcDsUGSJEmSJEkaGANR8fNu4PoBeB5JkiRJkiQNoH4FPxGxIfBy4DsDszmSJEmSJEkaKP2t+DkBOA6Y3/9NkSRJkiRJ0kAataQ/GBH7A/dl5qSIeOFiHvd24O0AG2+88ZL+OkmSWmHsaacO9iY8KdMPOmSwN0GSJEkDoD8VP3sCr4yIKcDPgRdFxI97PygzT8rM8Zk5fp111unHr5MkSZIkSdJTscTBT2Z+KDM3zMxNgdcC52XmGwdsyyRJkiRJktQvSzzVS+02XKYigNMRJEmSJElalAEJfjLzAuCCgXguSZIkSZIkDYz+XtVLkiRJkiRJQ5TBjyRJkiRJUksZ/EiSJEmSJLWUwY8kSZIkSVJLGfxIkiRJkiS1lMGPJEmSJElSSxn8SJIkSZIktZTBjyRJkiRJUkuNGuwNkCRJkqT+GHvaqYO9CU/a9IMOGexNkLScseJHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrKxZ2lZciFByVJkiRJy5IVP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSowZ7AyRJkjT0jD3t1MHehCdt+kGHDPYmSJI0ZFnxI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS01arA3QJIkSZIkaUmNPe3Uwd6EJ236QYcs899pxY8kSZIkSVJLGfxIkiRJkiS1lMGPJEmSJElSSxn8SJIkSZIktZTBjyRJkiRJUkstcfATERtFxPkRcV1EXBsR7x7IDZMkSZIkSVL/9Ody7nOB92bm5RGxGjApIs7NzOsGaNskSZIkSZLUD0tc8ZOZd2fm5c3XDwHXAxsM1IZJkiRJkiSpfwZkjZ+I2BR4NnDJQDyfJEmSJEmS+q/fwU9ErAr8CnhPZj7Yx/1vj4iJETFx2rRp/f11kiRJkiRJepL6s8YPETGaCn1+kpm/7usxmXkScBLA+PHjsz+/T5Ikqbexp5062JvwpE0/6JDB3gRJkrSc6c9VvQI4Bbg+M788cJskSZIkSZKkgdCfqV57AocCL4qIyc2//QZouyRJkiRJktRPSzzVKzMvAmIAt0WSJEmSJEkDaECu6iVJkiRJkqShx+BHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppUYN9gZIkiRJkrS8GHvaqYO9CU/a9IMOGexN0ACw4keSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppUYN9gb019jTTh3sTXjSph90yGBvgiRJkiRJWo5Y8SNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLWXwI0mSJEmS1FIGP5IkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZIkSZLUUgY/kiRJkiRJLTVqsDdAkiRJWl6MPe3Uwd6EJ236QYcM9iZIkgaAFT+SJEmSJEkt1a/gJyL2jYgbI+KWiPjgQG2UJEmSJEmS+m+Jg5+IGAl8E/gPYBvgdRGxzUBtmCRJkiRJkvqnPxU/uwC3ZOZtmTkb+DlwwMBsliRJkiRJkvqrP8HPBsCdXd9PbW6TJEmSJEnSEBCZuWQ/GPEaYN/MfFvz/aHArpn5zl6Pezvw9ubbLYEbl3xzl6mnAfcP9ka0kPt14LlPlw7369Lhfl063K9Lh/t16XC/Lh3u14HnPl063K9Lh/t16RhO+3WTzFynrzv6czn3u4CNur7fsLltIZl5EnBSP37PoIiIiZk5frC3o23crwPPfbp0uF+XDvfr0uF+XTrcr0uH+3XpcL8OPPfp0uF+XTrcr0tHW/Zrf6Z6XQZsHhGbRcQKwGuBMwdmsyRJkiRJktRfS1zxk5lzI+KdwNnASOC7mXntgG2ZJEmSJEmS+qU/U73IzN8Dvx+gbRlqht30tGHC/Trw3KdLh/t16XC/Lh3u16XD/bp0uF+XDvfrwHOfLh3u16XD/bp0tGK/LvHizpIkSZIkSRra+rPGjzSkRUQM9jYMV733XUQ8PSJ2GaztabOIGB0Rq0REvyowJQ0/ETEyIjYe7O0YrqKsPNjbIUnSUGfw8yQ0DYtnRcS6ne8He5uWZ937PyLWiIjdImLbiFjo/ZyWsy2RiIjMzGa//rC5eV/gG4O5XW0RESOazl7nffwCYO/MnDuY26Uezd/H8+MSas6Z7r/FiIjRzZcvAS4ZzG0ZTpr31nMj4i8RcTrwfMBBiaeg6xzkZ3QY66MtoaWkc06LiJGDvS1Phe+RoSEiVoiI3ZqvB/Vv4UG/l+jRfVJcGfgysBf0BAoRMS4idh2kTV1uNaHE+Ii4APgL8L/Aq4Fndh7T/G2eHRFrNt970OtDU23yyog4unNbV2A2EejcPglwHz4FneNI79szc35mzuvaz5sCR0XE5yPi0GW6kcu5plH0hPd18/eZPxjbNJwsKuDJ4v5rLOI9Nqf58nLqAhnqQ+9OS3PcfA3wHeq8vzfwpog4JiJ29VxfImLliHhpRKzYBGUfi4g1YKFzkJ/RIa7TIWm+/lREHNH5vo+2hAZQ97mtc07LzHmDuU29RcRaUVfWJiJG9TEAvuA94rFx2ep13poNnNB8Paif1+V6akHzRxlB/R3mw0J/kAUf7sx8JCL+AawaNR1jDPADYBxwf0T8Bvh1Zs5YltvfJp0qk+brTah9fEtfB9mIWBs4nNrnX2tuG9c8zWjgI8DrgTuAqyLi+Mx8aBm9lEHXa1+uCGyQmbc17/fo1dibB2wMbNU8fkOAzJzaXLnvrxHxEuB2YMWIGJeZ9y3TFzTELWK/do4l83o9dnXgQGBn4Ergh8DTgF2BGcDZETHCBvnAWszfaH6vx60ArAW8GNgR+EZm/mOZbegQ1tf7snmPZ6/HrQU8B9gQ+E1mTl92Wzm4uo+93bqOx+sDG1Ch+v8DXkhdIGO1iHhaZt6/DDd3SGlC8icEhp02QGffRsQGwCuAfZp/44EVgBWBP1IDFMtNRzgingasAdzRnLNHNvvsacAHgQcz8+8RcUVz/4rU/ns59Rk9KTNPG7QXoCeIiM8Df8/M03sdT2YDmwOjgdkR8VJqcG414CeZ+b1lv7XDVyckWVR7q3N787idgE2o9tu/gA9n5mPLZENrGzqf6873zwROBB4F3gnc2V01HhGrZubDEfEC4L+BzYCJEfHlzLxhUecqPXWLO+83751tgZWALSPiYur989+Zefsy3lRgOan4iYhVI+K/I2Kd7tubBHehUY+I2CginhMRH4yIH0XEds1d91Ed6LnUh+hXmfk8atTpncDzmp83UV0CzQdkh4j4E3Au8EZgze7HdO3bnYDxmfm1ZjRwRGbel5n3Uh3qV2XmFlTjZj1guaiiiIhNImKXXgegK4CLOgemrhPZ6hExpvl+KrByRJxNdUJOiojnNj+/HvDszHyYOlhtu+xe0dAREWtGVZmt33y/0Ah0rwYCEbFBROwcEV+IiPd0PdWrgIOBO6mw5xPAl4Azgc9n5nmGPkuuGRx9QhVPXxUoEbFhRPxnRLwuetYI+QtV3bk91UH/QNPAWm4s6hzWx/4bFVVVeVREvKI5Fo8Dvkgdc58DHB4RWyz9rV42mvfXIqtzugKeNSJiu86+jIj3R8TXgJ8A+1PHgLWBtwB/Ah6jgsbW6toXEX1Xic3rOo6u2vy/UkR8OiKuBX4dEdtT7YKbgRuptte7gF8C783Mq5eX42f0rAn3AmBLmqqxrs7h6sAtwEYRsRLwvxHxCmB+c99PgOOBZ0fEm5fdlqtzHFlMf2FVqi1L1JT7zjH0amAjYFQTgO4DfBQ4Ftg6It65lDd92Io+pjw11TDd/b+REbFO17Hq+xGxI/X3+D0V+pxJHbs/FhFjl9G27wx8NyI27bp5W2Amda79V/O4V0XEpc3x8iPN4x6mlml4AfAHeq5MZV/1SYp/M8Wv67y/cURs1HwdzTn/D9Qgz0uBG4CrMvMVwJSlvuGLsFwEP8DuVInVOyJiNVjQ8R0fEe+LKoHtLK74I+C71AfqTqrhvy1wFfD0qNHMdYETo5K7Y4BzgOth8Eu4hoPFnOzGA7dn5haZ+THg4d4d7ObLWVRFEMD8Xp3uNYHrmvvmAD8Fto+WLZzb1XDo/gxfAJwbEes1j1mdOrjMpaYT0XRyL6ZGnD8ZEU8HbqI+I7/KzB2Aa4E3Nc/5Z2CP5uspVOi2XIgKgU+OiKuB86mA5tMRcUQnyY8qo39JRLwnIt6YmfMjYkvqAP9GqkO3XUR8uTlxvBM4JjO/BHwaeGvWlI8RzePG9LkxeoJFdB6zacxl1+NWiYgXRMSHIuL1TWdyQ+D/gEOA1wLHNQ+/BBiTmR+kOuUr0bz/F3PcGnaaYGKPiHhh8/3HIuJt0Pc5LCKeGRGHRMTzum5+H/B14NnUPptHTcGZARwB3Aq8jpqKM6z2Xyec6L3NncGirseNjFpf7kXNsWDXiPgDFeZ8GTioeegsaqr4sZn5ceBFwJQmqPgLcBbVMG+F7s9mZx923le9gvIVuh73qog4LyJuBd7f/NxrqJHqXal21seBu6nP7qNZlbz3URUPz+n9u4ejqLUgRjVfbx0Rx0bEDl33R0REZ3Q/M39FtU870z3eFxH/BP6Hmv6+PlV5OhLYqDnf/BbYmjru/SfV9tIA6xxHet/eNejc6TCOiFpHdO3mIRcCB0TEz4GvUaHdxtRA3jgquNsceAN1/vo8VcG1NsuxiNgyIt4dEV+PiNMj4pWd+7LXtLiIGBsRB0TERyLiWc3D3k4Faes1328C7JCZD1LHmcuaz9tnqM/Vslpb7EbquPeOrttmU+HPa4H/iarkezPVdn9uZn6oedzlwLOA06iBxh1h0VVOy7uIeFlzLlqpc1v2muIXNb1u02jW6YuId0XEldQAxP9ExEub99nTqT7qf2Tmp4AzqDYlDOL07mF9gvx3uhpte1FTszajFqmFarB+i5rutgX1YYdm9C0zv5WZH6amC70WuIZqXGxMfQgnZOaemfnippMwKCVbQ01UZcRaEbF2cwB+QoXIIjoWQSXT23Y+TJn5+CKCtEeARyJi5aYD3mkIzadKvB+LiHWbD+ozqRBvWHaon0TDYX7X/ZcC9wAviapg2IMKLG4FdmyCh/8AvtRURG0M/Be1f66g9j/UwevxiNgK+Ds9J7frgG2WxuscLIvq4DVGUQ2Aj2bmszPzBcB5wPsiYqvm/XYQNa1wFBUwHpaZNwL3Usn+/wDvbR4D1Vi4N6pK7U5gXtSi8VOBNTNz1tJ8vcNR5/PdfH1mVOlyXxUoIyNi84g4OiIOjmZ9L2r/f4QatXskqzz7GGpqxMFU52fjiHgZ9Tno/A1GUJ+d1lS5Ra199jPgb9Ro/xuiRo5nAVtExNOigswXdP3MfwHfp0aXXxkRb42IVagBkG9n5pHZM1WkU8VyOrXfvtx8PSQHRZq31ibRK3BdRIC4dtRg0cciYs+IeA4VRnwX2I/qjN0OvCkzx1ODDq+PiG2oIPgKKggGuJ9qj3TcCGzHENd9nGz23eoRsW80lZAd3Z/NrpB866iq3nUj4hsRcRtwSkTs1Zzzdwa+n5nPzMyPN/v+GcA9WRWnZ1D78CBqP28SNRB3D9UJWqn37x5uIuL5VHDaaaeuRnXwN2nu37J5b2ZEvDwifh410HMK8KyI2Bx4bWauT3UCpwNbZa0vcQ91/oE6H20BfAX4AFUxpCUQEWOiKh8Pjl5rfvYKOTvVwGMjYpuI+HhEfLQJHS4ATqUqA6AGnbcGPpiZu1ADyx/KzClUh3F96lj7D+A24MNUZfYnl/LLHbKipjx+mBqcnAh8E9i02d+rRsQ+EfG/EXFx1EyOY6gBiqcBn4+qcr+GqojrBGiX0yyDQLWvV2u+vg/4J82A6tKWmY8APwMObt5vI6iK5D9Rx76Rmfk4FTS8B3hVRGzRtPmfQw2+fCZrYJfoCbqWS1FXKd6naXt3buuc23agZvCs2dy+SdTAztej1kw7mHpfnEwNHAJMAJ7XfFYfpN5bAH+lChA6rqAnZB+081Srg58uq1Gd1vOoD8QqVAPs7sz8HNUpeDwiXkU1iFeBBXPO/051du+i5tWuCFwM7BkR20fEjhFxFDWFY7kTtTjwfhHx2qjE+bPUKN1salRzw+ZxT+v6me0i4vvdz9M08n5HHcx+FxFbN4/dLyIOjJ4roECVu46lJ7nuNIQ2oapVNgQOan7nXlQH/GGGgd4hRHfDoesxK0aNMv93RLyXnpPPP6n378bUlU7GUZ3dP1Cdii2pz/yk5vE/pU4Ua1Cl851G4f3UCMEj1AFu6+b2m4DdBuzFLkNNJ2WRU4AW0SmdCkyjOXBHhTU/okbjXtkcR46mGt1XUZUPhzU/+y/g5qh52TOpcG2T5nEHNoHdKlSDbk2q0mSXiPhlRLxmQF/8MBFVYbVjdFUBwBMCg/nADk2H85CmsdbpMJ1ENZy3p8KHzsLkawFnZeZHMvOM5rZ7gcebr++iGnW7Uu/3zZvbH6eCn007mzggL3Qpat7nixtJOhr4V2Zuk5kvpdYAmUE1eHegKio+ChwbEW9tfuYnVJny16h9eUTTEL0SOCaqmu0LTRAyhRoUeXlmHpGZP6EnSBt00etqRs1760jqvNN5TETETlHTs46JCsagqoZPoTpes6l15s7OzF0z832ZOY06XhwQVSV4JHVhiJ2pztkomrYF1WDfJmpqHNR77DlL63X3R3O+WQEWhDjHRlWUPI+qJp1LrTPRefx6URcMeE1Udd2bqAbvZ6grmO0HjM3MZ1DB2aepc1YAR0dV5e0QVf1wP9WQhnqfQlW23En9HcZR4catwCER8cmoaRHD1c3UINXBUdNIbqXOHas3x7rfdz12LaoKYDp1PFubCr8663zMpjq/nfdv5zFQHd6TM/MCqh01NnothaDFa44T76HO6V+iKm7+o7MfoxbX3iMi/isiOuf8tajP/jHU+3d7KvB5TWY+B9gnqmL4Fipc6KyP9hMqxFiB+kysT4XFs4E/ZuYVmTk7KoBaddnsgWUjIraKmja/4iLu7/Rjv0j16Q7PzB9k5h+pgYnpVB/u49Rx9lVU+3Yz4NDMPIYK0A6mPk+jqEENqHNcZ7DzBmrgFOoztgl1XF8mMvNKqg/7zqZPsD91PJgBrBE1m+X1wEVUH+giqh+wN/DPzPxLRDyDOid1qiOHfJtmSUVNRe9U5Lw4Ir4VPcHsy6l9NaPrRzrvr2up4+jsiDiECm8657qPAftk5mZUe+h9zc88DHw9IiZSA+UbRFVtXgF0H1cvp851MIjr0LU6+GkaKWOphucFmfkD6oB6GPUB6jQoHqT+2FsBk6kFmNZoKkaeRU0/eow6IT+r+QAeSY2WnEBVVrR+AdCo6QE/6e5YZJUNXwn8rkmc/wms1gQtdwNrRcQewOld4c/LgavjidOvPkJ1TH5FfYieR02XWTUz53Q6Nc1B74fUNLwXRiX6r24eO4dKvLenql1uoNYMGlKa1xK9bhvZxyjz5hFxeETs23z/DOo993Wq4bcbPdVq11EHk8up8G0e1TC4hXpvP0B1PDq/9zYqrLiNOqi9sWlY7ACs0lSkXE9Px/cyqnJuSOsKeRbM517ECP6IqClA748q9+0dOMwBHqLWSFihK4C7hjqYdzoYXwReSVVKdebYTwH2z57y0JupkO2bwN4R8eXm8X/OqhA6hyq/v5I6FrXSIt73nfPQwVSpdXdIPK7p8GzY3HRR87jjqbU9jm2OLzdQ1VlXZuZ/UZ+PTiPtr1RH6uvNvxdT4fF2AM2xanMqrJsCrB8961/dQ3O1wKFUSdDXfoQnTkXq9TPPol7zd5rvR2Xm9CbEuYl6T1+TVdn2VaoaEKoR9Csq1L+XOj6Qmd/PzJ2b51sTeCu1uO4eEfHGiDgsIr5FDbYstam23fshekbVR0Yfl6vOrqsZNbtwVWBP4IcRcWITGmxBnUPmUsH5u6Iqgm4BHs7M46n3yQjqmEhXp2QrKhB6bmbuTnUKN6POiyOpYzbUe/Jk4PcRcWnz3NcM3F4ZGM05+DHggK6b16Re+7aZ+SjVQe0M1LwQ+DUVvG5CswgtNcXoVVlTXLehphBDdVxupAYlvkqd2zYEvkftx8tppss1v2tv4IqmPbYG8MzmmP4D6j08lwrfhqv7qRDtHOBbTaf1YXoWBD8/IjqDC3sC5zfVPLOo8wvAjIhYJ2sq2Fhq/b61qYB75ahq4F9QUxL+0PzcfXSFn8u7qIqAPWLxlRG7U+eiHTPzRZl5GPBzasAMqtL0XdT57NUR8cLMfIDqK9ydVQ38aaqd1ml7TKSCuAeoY0Znit9YeirXb6M+e9dSg3pfjJoieWOzPcPuCoHNsXiLqAGdsVGVlWs0d78ROLjpXzxBcyzvhF1X9Lqv8zO3UmHxDVkXJxkNrND8PaD2447U+W0uPZ3zGdSAENSxfOeI+DhVfRiZefaSv+olcgI16Lg51ZZ/lDp3ADwjM2/NzB9n5uepCqEtqHPyWhFxAxVUXNj87JCswh0IEbEZtfD185ubnk71+zvfP0adq98UEZc05+B3NffdRlXvrkv9zWcDP24CwvPpqeC5HBjT9Bv2pt5P47PW/h1HhbO3AKtEz8DJNOCciLgEODOapWeWtVate7III6grtMyMiDdSJbSzgXfTlPBl5oymgXNWZt4fEbOB45vG3q70TNP4IzWKP6JJk/+4jF/LUhU1UvEItX++ChzXdAoAyMyZUVd42pCaAtexGvDaiPgGdeDctGmM3wdsmJk/j4h7qIXRvkPt019kXWGie1X9LYF/ZOaJEXET8Dbguc22dHQ6X18AXgZ8qNmeG6kpBTOaA/uRA7RblkjTuNqSStZXp65ANqW57wlXx4kKGmdGTY17FfWat6NChVvrIfH8zPxwVIp9T2Z+onn816JGki6lRvKOoRrqH6XKvRPYNDPvbv4OR1Ah22bUSMCsiLiPamz8jhoR+AxAZj4QTWCXmXc12zNkNO+foObRLlhDgideaWhj6v2SwA+bxvJe1Gf7Sqpze0xEfD8z742eKyjcTXWIV6E+FzT/j6IabJOpKxed1fyeTif0QmpNsbupaomp1PzwOc2+finwbZoOULM9v2r+tUpUVcN7qUsv/x74HLVfe7uamtrRWdj1PVQQfCdwW0R8gAp+3g28KzOPjYh3AYdl5pHNMWNK13Ot0nyuTgNOi1qk+XXUCf71wAkR8XqqI7stcEJmPhgRU6jG+lTqM7VgnYDB0OsYSfN1X9Nl16Q62y+ljg8/iljoahOzqUDiiuY55nb9+B1UyHl58/smUo3FFaiO93WZ+b7m9xzTfJ4eohpHY6nG8uWZeU9E7Ec1MFemwuiLev2ufmuClszM2ZlVhUItcHo4cO5iwq/nAu+nwoczqIU65zbb+lUq/Hsj1eA/j1pTobMo5qXUvoUaHeysKdfdwbiHmmowKmpR0N2pv8VjzX5dq3n8LGqa09+Bu3LoXhH05ub/PSPil1QDem3gx/RMgXwtMLJpzL4D+Gxmntl5goi4hqZyoXk/rUpNdx2VmdOb88sc4IHMPLV53L3U3+gyYEJEnEud58+lGtw0v+tGWBDedhYzHbaa88Oj1Hoc74q6atNMKtRegVqk9SsRcRoVCny/+dH7qc7G9VR77MCoqurdqU7s5lR7bC6wedN26AwOXdDVAV6uNefvI6k21I3UNM5bFvHwQ6mqqQVXdsrMG7ruP4E6rjyLqs44iJrSdS89A8UPUO27zanzzZ1UB352RDxAtSFubH7Xzc35aRpVUTA6Mz/fhNUrU6H9jP7tgWWjj3bbulS17snUwNe+VKgxkxrUGR8RH6EGNK8HPpeZV3W108ZSx+7OOlfrU9UZzwMubtoHN9LTf7iWGuDZKGuAcyawbmY+FBFXUIHAutQxZ0TUmoA3NP+uoy4C0TkOLRPNufzKiLgO+F/qvfNQ8y+B9SIiqb7R2lRg/NPMvCLqKnFPAyY3AXrn+VoZ/FCh6R3UBX7+1Hx9NRWE0Xz/dOr88hvqOHhiRLyc6tcntYj6Bc3j740qeLiJqswflZl3NceLdZt/D0ZNu30udSzemmpDdApIOmvPvpM6Rt/aux+4rCwPwc+j1IlvF6qTdgTwSeoyqptHxEepP0KnNA7qj3s/lR6f2DmYZ+aJvZ+86wCWg/VHfKqaJH1bqoG6A3VC+RI1Uvf5zPxt1EjtY10/0wkrbqIaZHd03bYBFXCsT524tqMauLdTa/asRHX23hQRr6X+Dp191X3gmURN0fpN83tupMKJj0XE+zPz+s4Dm47ybyLiD4vqVPQVCiwLEbEn1fE/nTr4/ommuixqXaJHm8Zup4xwa+C/IuJA6m+yPTXN5GAqIPtq1Jzk7zV/l6voWXzuLuo9/szm9k4p9xeoz/fV1Pt7o6hRkU9Q85mvoDqCnekcvwZ+lrWI3UKyFtAcFBGxevc2RVU+7Qd8IDMf6+sz1+yrTaiKj5dTn/dNqU7aeOpA/D2quuQa6nP+HqrxdRHVMOsEOLdTjefVqRMt1Gdm5cz8a1S1ybubhsFOVEn+B+gZKbiLquQ5K6uCiMy8lOpE9vV6R7KM368Doa9GRNdtz6f27e7U536hTnnX33AKtZ9XjqpsOyIzt2ye64vUWjyfpjqJ10VVkVxOndyhOutrN8/5j6YRtF5z/NmYOk5tSP0tHmlO8h+kOkNfoRreZOZeXdv2KF3TWJamphGxyEvOd463UQMS21GNmLObzvMOwNnU9M0VgP0j4p7MPLfref4RNaK6btYVEBc8ZxPCP0hTWdB0MGZQ+2susGJEHEDtxzHUZ+A6qgpoJPV3+F3ze26lZ5rdgIsapPkzdfzsrC20JnW8exa1wP0e1Of7GdQx8Hiqcf8fVKP93VTQMKvpUHwqM6+Pmn65LbV/V6eODydS4fCz6BkpnRoR5wPvjYjrqXPf+pn546ZNcRl1vDgZ+FdzLjqy97E0a9R+KJtOHcduBI6iprrtToVn/9085kZgp+a9uSdwaPNeXrEJuKZSa/Jt0DSWJ1HB65+pztqGVCixbUQcR+3nFYF3NO/LT1LtgHs6HdvmfXvJMnj9g+ESalDyLdTx7hZqQG7jzJwcEdOpYGIKVbkAFThuQ+23I6lj5Yepyujjm/v/kZlv6/ySzPzlMngtQ07nHAtPDNCbEPn91FVjH4haymChxcm72r27Umt70JyL9qICi+up6VsbUwNoD1DHg5WiLqZxLdX3GEG1C++lBgnPp9obnTWvLqTagF+h/s6nNJv5ta6gmcxcpgHEU9HsuxH0atP00W57hDqfbEK1Dz4AHBkRv6IWvn9m8zwHUgMx36RCnU47bQa1L5/V9XzfpSrbvtXcdg89Hf/rqePP/0QtyrsPFaaQmT+Mmt67GnU+/VxzvF+JOidMzEG6DHfj61TwvkZmToqeC7TMogKNzwM3ZeZUWNAOu6l5zALDrY35VGTm41FXKf4Z1bZfhxrkWTUi/oN631xG9fM/S53vnwZcn5m/i4iZVNHC4xHxELVO2sSI6ISL61Ph7Vwq6Pkuda6/mBpQ+iQ1U2hOROzRKxy+h3ovDprlIfjZCTgjMxdUgETEV6mG39VUufCt1IH6ruYh91Kfi9/TS/Sq1hguYU8vnRXiz6UatOc3t99Cz9zW51GNrd8034+gTpY3UyWRf+h6vn9SH6CNqQ/DaCqYuJmaBrdRc4DakjoRnktzMG5OtJ2D97eoxvnvm///RF21YAMWMerSNAw7J5fuS5kO5t/mTuCvmfmf3TdGrUPytYh4A7WPDqNe8y1Ug+1U6r33CZoqHeBHzcjONVEjPeOofb1lRKyYVa3WaRRObPbFtpl5cUS8twmZ5lAHo7GZeUdE/DfwUK/38VAtj78jIl6UTZUC1clcmXp/3RY1bedQ6j3yjcycQJ3E30A1lL5NHZT/npnvilq3ZMcmHHqMqogaRzUCPkGN6EBPOHEbFRKvETVt9BXUiHUnBD6BCjLfQB1PLs2qrBoBrJ11BYgniD6qOJrv+6xUGIq6G8RdjeIFAVBXw2I01Sm+v6+AqMud1Pt+Ayr4nNk85yhqKsfnM/Ph5v28cWbeElU9NSLqCnZ3U9WGT8vM+5vfuzX1d3kPdfyaTBMWZOYtVFVhn69taTWMOgFPsw29q3h6h2ebUe/3V2Xmp6JKg39NjfLdAewXEYdT+2514AdZI6HvB14YEX/PGsXsjIxeQy3+/vPMnNt01jenjkE3UA3nUVS49jCwX2Z+IyI+Q3UkT6YqEi/Nqqx81WJeZ2fKwUAHmYurQulMjRhFTaW8ipoS89XMfH3TSN6B2l+XR8RdVPiwTnM8fSSq2uTarMGQzmtZi2qsrRQRz8wqqT81Kkz/FvV+Pb0J5b7W/L7e559BC9CXVNNwnUmds79NhWZXUg3eEVFVZndTx0ioc9NuWWvHdNZ2epieKYJ3UY3xDaj9tSk1penK5vj6DWBq1ih853P4OD3H5c52Dcd215N1HrWOxJcj4jKqUvJOquMCVZn2f9T+7QyGXUp1fkc1bYXO1SKXa80xKHsdZ7uvzjMKmNe0QzvHyFnU1OBTFrEPO23hKdSA59lU+21bavr306m/1cFUBfqboyoNv0J1Lm+gPi8rU22QB+lZR/ECaqAhaK4i2kcboc8pT8tS735Qc9uaVLB+b2aeBwvOafP6eNxLqL7ZzMz8f9R+uIsKPH9LHW/OzcxvNY8/gjom3xFV7XZ88/xzm/9nRsRFwGci4qSs6vTONNxRURX4D1AXcVgrq8LtwxHxPmpw4Izm99I832d7v+asqs3v0lQkL2td59DrqOB3zeb2e1i4Ev+8RfzcciUzL42IxyJiF+rccxHVJtyN6kf8nQrJOjMl3kxN5RtN9WM7U2f/QYU7E6lz2ar09HX/D7i/CYiOyqaaqtd2PNb7tsG2PAQ/T6c6j2Ooxuz8zDw9arTuk9Scz1NgobUmTqVngec2BD29XU91os7qdfvN9Cxktg31YflN01HpHDyuoGeB384JcCbVqH0G1dCeS43iXUOlo2Obx59LnRiek5nTOp2rro7iAxHxf5n5zV7bdefiXkxfJ5dBdg81orM+tcD1VtRr/ws1HeG71Aj5Cc376SGqmuc11KVV35o9Jd/bZ8+IzjOp/X0nPVeYu5l6X3euynEGPSHYjOb/OTSjs80+n7n0XvqAu4IapekEP1dRo2obRE1bO7i571RqpHl1qlTzxVTn9OqI+C09C1ffQjXWxlIjb9OzrsoHLCgR/mfX759C7dsJ1Kj0PcApmXkRLGh4PGHaZ9bI9uVR05ymNbctbsRryOkOP/oKQrruW5E6XszPWv+st3OowPN7mXl48zPrU8HyX7s+/49FxMPU6MxtVOnsxlmVKhtTHXSo9//21N/yQWqa3pbUiXg9eq7gdxg16jKTnum6T3iN1Oeld+dgQBpLTTDwOeCSrIXBFxXwrEYFkPtRjZPOaOVYqmPxwahpiFOj1jNbiQpp/gR8NzPPi4ir6LnqyC1UpdXTqeNLJ1z/OFWtsUZE/JoK8fejrr51O/V3HEkdUz5IdeTJusLlol5jnwFPLr0Qc3FVKO9uHnMxVRH0Feq8tHr0XHzg1VQn7f9RlZG/oN4DK1MBzo+p6TSrUB263alR9gkR8Ud6FmiG2ven0G6XUe/XS6kKklOoKp7HqcGJqdTVCdengsHDm+PeWlQn+qfUZ/MZUFeoiarg+x4V8nSqL6ZTFydYYDnttPyNei9/mQrCnk5Na/tm1/3voQKL6QCZ+XeqI0Pz/XIR+nQPPkSN5F/RdIRpbu8OeToVk4dQ+/QF1HHyM1RoNoKeauBPAk+PiG9l5n1RU4pvylrTpdNPOJ9az6wTJHwzIn5IBXMbUQMOL46arvca6jO0EzXIdA8V0j0cNRD9aLO9Vzc/Bz1Ty4eMqLWits4aWHwNcFBmdq5stArVQb4LOK8J1XalgvdVgC835+K9qMG5y6jp2F/JzGMi4k5g7+Z8P4mmf5o1uDkDmBY1xeaeKGM77/+mffLHiHgd8Imoyoz1m3/fyhoAvZr6O3dXS/W5dEFXu6D3Oe1z/d6J/dRszwWDvR3DxOlUf3UPahH0P0bNGtiXqkp+PbU+7dwmnN2OaofdS89gxl/oyUouB45tjgnR3VftK/QZqpaH4OfXi2g8zKI6fnNg4YAnM7/fedBw6KAtgUnU3OHR1NzjucD/UCN5nYUDL6BS0I7s+tlDYaG0/Z9R5ccbUgfW+dTUjvOokdhxzeOmRcR5VAe6z0ZdV2dyBDXtYV5fnc6hLGt+9miqsqAzZe2O5jWcFxG/o0YsvgYLFnWeR3XQ/gz8b0T8L9XAPqoZrViT6szcQgVAd9CzCv1HaaYhZeax/2bbhs1+bFxOXRL5IepAvBu1L35NHcyfmZlvhwWNkkOpkGYmPQ20K6kKNqjqvpWpBt7ZwE+jRvg3pTrSX6PWPeqEEfdFxKFUqr/IqxNF32sNDeraMP3Vq8HT+VxuSu2LhyPi2VRJ9kiqIT0qIt6YNdWn2/bUiNT6EXEm1Yk5nPobXQrM6Tr+di45fGPzs2+IiB9QDefO9Lh76Ak6H6A6Sbdk5mU0lYjN803u3oi+AoqBCI0jYrWsqpp1qDU3fpM904I3oBZJXjMiftY0MJ5FdTo2otaIuIsqM9+SOl78jBrR+1tmfjyqSuVgapriVGpNlbdRU0JuoBrX5zU/uzN1nJhGHbOfToXDnXPb+VFTuo6kQpPbmp+dTgWaCzobmfnXRe2/rn23zKvUcvFVKERNo92UWtPrB9S57EJgu8ycRLNmXNM5eBbV6fo7tdjiP6jj6VupqWKzqPfuhc3v7ize3tmW4XY8XRLnUwMYn6YGMe6lwtd/UQHaWVTYuldmfj0i/pNaJ2kmdQ6cB/xX83frDPY8Qs8iuFrYLTQL3GdVMnyUqmbtrGc0lwp8l3u9Pn+HUIH2L6kLjDwQEe+gqnHnU6PzP6WOka+i1ok7u+u55kRVP65GrQX3QioA/gvVtu3MAOiEar+gjuHfb4KhZ1Bhe1DH6VupY/o7qcGPL1HreT1MHXs7v3f6wOyNpacJXOZSn/efUq/7HirUARYMdl1Jz7l5C6qK4hrqePEzapDhXCrc2ptql+0UER+iwuFVmvbuTKoKc7Ws6bH3U+2Clah28jTqvPf7Xn23t0ZN4d6VaiNekc20rKwlOxaqHGxeW19VYUNtMFlL5jfU9MBn0bM8xvXUOo9bU+f6/41anuEmqlJv5ebn/gKQVZFG8/XDVNXPsD73tz74aUYC+hqtnkU1GjvftzHgWZQ/UZUn11Nv9ouz1om4lVrQcy1qSsQm8IQR6muo0dMx1IF4J6qD3alOySZZn9acSI+hWcguanHVN1Eh02L1cRAebi4CPpiZnQW9ugOetaiA550R8ePM/FtEHE2VzB8BfIoKII6j5vMeTh1svtacBCc3/zojHXcss1e17HXeqzdTYdcFVGNje2pNkc26Hvs34H+y1ieZT89lFG+kuSpUVsXEGtRi1xc11RPvoqodTqfXiHPnZ2DR1SHNY4bV8aPrvbio+8dR1R9/z1oH5SgqLJgPXNZ0Rh6nFrs9MOtSod+hrjjx7awRts7v+CA16vb5iHgvNaVuD+DNzTFiJD3Hl9upK82tTnW+3039nc+jZ6rD2zohXBNULBRQNLc/4e8xEAFFVNnw9VQYsDkVat/aHOemU52K7SPijMy8mwrWf0Ttqw2iphZ9khrNfYgqTf9E8xqeDXw9awrbO2iu8pQ1wnketU7dBOr9/7msytXj6Wl830iFQ1Cd8lWoY/iFvY6nkyLi6CezP7rPncs64Pk3FlWFMpc6vv4HdeWcM6Omda4NbBERU6lFgLegQrHPNmHc56hQ45bsWb/hY8vyBQ1hfwWOycyTI+Ld1HtrJHU8Htu0HU5qvidremvvKa59rqmiJ8qapvqCru9n0kx7XR4059knrHXWfX/TzhxBjdCvQ4UQ46gp2B+h1m65kWqjvpDq5P13VEXpWdT+Pbf3c1JLP7woMz9ODdLt3zznZZl5a6/j4b0R8SlqLabTm22YCPxfZnaqUz89YDtmKejdN4o+pnBBzyAv9RofbNr/l1Frpoyi9u8OVPg2tXnsR6nO8z3UeXHfiNiKCm0+S72nv0dVXm5PBWWPUW2126nO+jOptu6D1Pl2beq8+VeajnwfbbHf0aw39+9eb/P4oXRe0wDKmu71EmogqJN3/JUKdmZl5jlRF2CZkc304i5DPpBdUq0PfsDGRh+uB+7LzON63X4b1aDbKJsV82PhhUCjGYFakQpzplPTYn5EHWh/AZCZ3+k8YdY6Gh1vojrw19N+51MjGguCn6zqpV2p0bzDqEXqjouIE6lGyy1ZlQOfoRog05p9f+Gifsly8N7uvFff17khauX8ranGQUTENk3Atj3NvG7qfbxZ0yiZCkyJusTtNKpB8g+AppO32CqpjuE2CtQErYdk5md639dp7EQtWLgFdYWBh6OuZPEj6v33GPCWpkLkAGqx5YlRi+YdQs2J/ws9l6K9hDrBjqWC4E7p/B3UFMYzMvNLTUjyEmoh93sz8/bm7zSfGu0+ABiRVQmz0DGqOQYtsvJqaelqEF/YbPuu1FTN7zef36Op9Z7OpEYxX0iNcK5PNXAfocKsFwOPZrPIatSaAQdTUwWvp6dK7ToqCOp4jAouRlAh8HZN1ct4esLPq6nGMVQj+rMsYhHB5ljU59povR43VI8vi6pCmU51QE6npsddRlUDTaI6DQ82P3sqcFVzvI2sKrXelWoqN9Os/ZeZ/+i6fcFxJTP/sqw3SsNX1NXddqQ+qxsDv8ya5rSoqbCbACtl5g1N6LM5NY1zXerz/BOqM7dqZj6/+Zm3U+2s9ZvfM5dqp95BVQT1Nbh4AzCrqQD4JXX++gt1kYAvUdWl3ZWwD0bEVzPzhIHaN0tTVMXuXlmL0D+PGoBYsEh6Z59EVa/OyGbKYHOOewE1oLk+dWW4q5u2wY3UsfM2qq21ShMMrUINZJ5PtdV2ycwbIuKD1DpAxzTns88BW2bmZVFXVN49M38QES8Ezo+ITze/dwQ90+Y7U8b7DKr6quJpfm6ons+09PySei92qnavotqYNN9fvYifa63lIvjRwjJzShPe9L59dkTMpUb6r6I6dNtSjepuz6NCicVOfenjoPvxfm/88PF36mpk51OlhhtQ6+9sAjycNY/5h1SH765sSo6b/XYvT9zny6XmvTomItbMnkuVPkDPVXyOp+Z0d9ZIeXvzmJ9RjY15WVVS+8KC/bvQlXRikK7+NhCaCpTLmsZw7yqetalRtq9krZ+zHhWizYuIL1Al69tQDfDvRsT/o6YRnQN8pSscejnVsJvRPO/3m58bS1UMbkG9jztX9FuTnoWaoabWHB4Rf6VKta+ipnt1FhKGnkBtRyr0+Vevkd3B/vt01sg5n9ru8+l5fedQ76+VqM/tZGrKwZuarzenQq31qMqfp3U979nUtJiTqMbJms3t5wL7NPt+BhXEbZG1RsW3qWkLW9Osn9K8r39Dsxh/1gKgUxb3goZbkNnLoqpQpgCbZOavoi5huylVtda9eP3p3U803D7zy1r2qkCRBsBHm3+foNpGxzUBysSI2J5qY86lAp0J1Ln9toi4IDO/Sl0a/ebM3L/zhBFxP1W92xnQmEcF6CdSg2oL1oMBRncNBC3QDH68hWpX/JBar+YE6rg+cxHVMEPu+NG8Rui7QrlTIf5sYGxzfr03M2+Lqj59GXUs/UVzrtmROje9iBrE2I+qPr2aZoHbzPxp83s3AT5EBXJTqKlWx3dt1whqiY2to6osgwqfdqHWVvsdPWssfjwz37+Y17jIqmWreNSRNUV2kWsULo8MfpZfM6OmCcykpgo8RnWaz6GZw0h1Zjpl791rOiy44gYtmfqyFHSu3PBxetbRuDwzz4EFAcSDNJd573C/9elBqpFyfvP9DCq02KQZGbqFaqhc0wQGIzLzt72fpAkS+mq4Dbt93hWs/oXqlF3SXcWTdSWB+6jR+u801T8rU2vJnEYz9YW6SllQUwtfTE11+zSwexNa/p2qVplLhQ9Qx4ztqOlO99Es2ko1ktejGvJX04QKmXld06DcjqosesIV5JqAZ2NqJOZrndua/4fC36fTuJ9MNVInUqE41AjoyllTtO6nGr1/pfbzqdSI04FU2fr5VIenYwXqePsQFRx1rqo4iWqsfIHarxdRayFEM0L1PPqwqBHQFnoyVSjdC6VKGjquopYY6AzafAh4eVOR8hUqBLieqhK/PjNfErU22tlRi9JvD3yrCRJGZebsrDVmVoyI9bPWnZxIBUHXNoHOOtQVTy9ogqHt6XUFJFjQvn3rUt8D/RQRq2atOdL5/q3UYMwJ2bNWW+8A5B5qKvKG1DTtPanpyO+PiK2p0OXFzf66jmr/bwT8I2vqMlGXWN+peb4rm+f4afP9aKq9sDW10PsHIuIEKtzZETg1M78bEUktsn8Gdb7vVPL8qLOh2SyW2z0A1KtKy3BHWgIGP8uvX1Or659HTe2Y1HQWF6xyn7UY2iIN8xHjpSrrKg8rL+b+5aFzNlDOodbt6PhLNpcLbRoDF3c/OHvKlReazz0UR+b6oTOi92fqCnLXUmuW7UmtTfBNqlG2BjAnM3drKlBeHbXA8kXA07KumDGGWk/mBZl5XEQcRnWqX09N6foIFb4dQQUSm1MVfzOboGOXZltupRrsV8ETFod+mK6rzsCC0b/sCnj+Qa3NMhR1XsvfqEDmh1THYw/qGPjJ5v7rqUuvXxW10OXemfk/zeM2ocri/xYRX6Yq154HfCBrraNraa5c1jR0L6emla1FLRB6ChVwzu0O3ZvHz+/+v+2sQpGGtYk0iwBnTbdcn1qvayawQmZ+CCAifkxdgGFkE6xfSw0sjKTOX/NZ+OpX91KXZF6VCn1/APwkIjaiLsP8DWqdwMOpypNhqQmuLomIVzcVDVAX+1ibWoNzVaoy9XnUNOvvAt+iFq7+L6qK6QRqavWXsyrQP9Tc94amTfAQFfyMpWdhXKiKoc5Umb9S1bsd/6IGiXbNzLMi4sPUhQgepKYeXwbQDMw9YXCueW29A56hNAAkDXsGP8upXMzVn5ajUWMNA9mzHkrnijBzu+7rM+Rp7mtT0NNbdwXKC6gpRI9l5qYRcSwVRLyeKnXvVJF0Rue2phrFnQbbbKoqbefm+1mZeUVTLb4xVbL9f8Cnoi4Zfjs9l83+BVWO3xmh6yzA/AR9BHHD5hjTtd2TqDV17qbWM9ivqdzruJG6gtdI4L00i4pTo6CbUCXzb6XCtJFU0H5l8zt6T4Vdn7oSzIbAFcBJ2XMlRUN3ScNS9kzhfiNVMTqOWhB8HeCKTtVO8/A16Vkvbi0qvPgBcGREPE4FFM+gpncfD7yPqlJ5c2Z+JSLOAh7K5iINze//49J/lUtP1tTtW6hzeSf4uYNa72wEVWH6Zmow6O3UoM1Rzf64lto/v6OubrY9NWVrKjA9mzWSOqLW3dkvIg6kpiZv1vwM1FVXt+x6+ANUwPRgs513UlXvTxCLXodn2LQLpOHI4Gc5FrWgavLESxl64NWQsrj53ND6kKcvndd7AfAdaupLZ4HV06lpXLtQ1TcHNrff0fzcRlSl3+oRsXLWFbjuohqNAB+JuhLCTOD0zlSaiHhH1toxPRvRlGN39BXAdT122P+Nsq4KtwIV2tzdub3r/Xk/VWW1ZdaC450rRZwLnN9Vmv+/fT1/r/13Z2YetDRehyQNstupQOJM4Pisq2StSFWNbE5NO/oDNRXp/oi4h5pa/EDWWkCjqMuir0KFRitlXaXn3M4xtDmetvViIrdSU+AnNN/fQ52XNqQWbD4AOLeplNqamtb9FWrq8NZU1epcKkgDuBg4OiJe2uzH3YGtMvN7zXo8X6KudHgG8OWIWCEzr2+qtYAF5/gp3RsZi1ijz6la0uAw+FmOdVdOSEOZjYSFdTWgrqBGSdehp3T9TqpSZyZVCbRqE0zMiLrM/RaZ+Zuoy9o/k6r+uRU4ISJGA5/MzPd2/76mAf148/VIFrHQchvCnUXpqoS8m1o0eC6wOzUK2plq9WjUJYBv7v7ZzLyn13P1uT5ar4Zxa/elpOXeFOpKj9/tuu0hqlpkB2oa86nU+pPHUVO1vpWZEwEy8wwqhFjIcnQMvZbmohWNu6nq3Y2oqXRzqatrQrUT3tN8fRt1ha3ZTZi2a9QVIm+iqqXeH3Whh0eAXzVth781gdDD9NJ7MKiP+x1IloaQEf/+IZKkoagJFB6kQp7XN8HNCGqa0D1UBco69CzAfAk1/QtgN2ptHzLz3sz8XmbOycyZUEFHE/L0bkzPa3mD+t+ZTC3sfDC1LkXv/XNdNpfBXZRmyuI8G8WSllMTqWMozZoyUCHPJJorSGbm3Mz8dWa+ODN3y8wfDMqWDk3XUQMQncGYf1EVvaOpECiApzePvYWagrxSc98zmoGfk6kpyP8FrJWZfwUOz8ydMnPPzPxy1lVAoxP6RBm5zF6lpAFlxY8kDUNdFSj3UGHEXGq619OBz2Yt2Lgy8Baaq/N1N5wz8/ZFPF/nfkOJLlmXUl+XCtr+2buKR5L0pF1AVZySmbO6/j95ELdpOJkIbBA9l6WfFxH7AL9szlUjga0i4orMfDAiplDTwK4DPgg83gzyvLr7STNzBvRM0eo90NN8bQW2NEzF8j1wK0nDUyeoiYhfAOdl5okRsVHWgor/9ueW0Wa2RrOmxOeoNXze1emsSJK0rEXEScDD1LSsrah1kT6Rmf9qrs55dWZe/iSeZ5HTtyW1i8GPJA1TTQXKJ4E/ZeYvum5f5CLLkiRp+GuujLYZVclzfhP69Hn+79xu+0Bafhn8SNIwZAWKJEnqi9W9knoz+JEkSZKkYaaZqpWGPJL+HYMfSZIkSZKklvJy7pIkSZIkSS1l8CNJkiRJktRSBj+SJEmSJEktZfAjSZJaLSIyIr7U9f37IuL4QdwkSZKkZcbgR5Iktd3jwKsj4mmDvSGSJEnLmsGPJElqu7nAScAxve+IiFdExCURcUVE/DEi1m1uPz4ifhARF0bEHRHx6oj4fxFxdUScFRGjm8c9JyL+HBGTIuLsiFhv2b40SZKkxTP4kSRJy4NvAm+IiDV63X4RsFtmPhv4OXBc133PBF4EvBL4MXB+Zm4PPAa8vAl/vg68JjOfA3wX+PTSfRmSJElPzajB3gBJkqSlLTMfjIgfAv9NBTcdGwKnNpU6KwC3d933h8ycExFXAyOBs5rbrwY2BbYEtgPOjQiax9y9NF+HJEnSU2XFjyRJWl6cALwVWKXrtq8D32gqeY4ExnTd9zhAZs4H5mRmNrfPpwbPArg2M3dq/m2fmS9dyq9BkiTpKTH4kSRJy4XM/BfwCyr86VgDuKv5+rCn+JQ3AutExO4AETE6Irbt94ZKkiQNIIMfSZK0PPkS0H11r+OB0yJiEnD/U3mizJwNvAb4fERcCUwG9hiYzZQkSRoY0VO1LEmSJEmSpDax4keSJEmSJKmlDH4kSZIkSZJayuBHkiRJkiSppQx+JEmSJEmSWsrgR5IkSZIkqaUMfiRJkiRJklrK4EeSJEmSJKmlDH4kSZIkSZJa6v8DpCgonINvDtAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_snp500.plot(\n",
    "    kind='bar', \n",
    "    figsize=(20, 7),\n",
    "    x='Name',\n",
    "    y='Dividend (annual)', \n",
    "    rot=10,\n",
    "    color='#12AAA8',\n",
    "    ylim=(0, 10)\n",
    ")\n",
    "\n",
    "index = 3\n",
    "div = df_snp500.iloc[index]['Dividend (annual)']\n",
    "\n",
    "plt.annotate(\n",
    "    df_snp500.iloc[index]['Market capitalization (B)'],\n",
    "    (index, div),\n",
    "    xytext=(index+0.5, div+1),\n",
    "    arrowprops=dict(arrowstyle='->', color='grey', lw=1)\n",
    "\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_snp500.plot(\n",
    "    kind='barh', \n",
    "    figsize=(24, 11),\n",
    "    x='Name',\n",
    "    y='Dividend (annual)', \n",
    "    color='#12AAA8',\n",
    ")\n",
    "plt.ylabel('')\n",
    "\n",
    "for index, value in enumerate(df_snp500['Dividend (annual)']):\n",
    "    label = int(df_snp500.iloc[index]['Market capitalization (B)'])\n",
    "    plt.annotate(\n",
    "        label,\n",
    "        (value-0.145, index-0.1), \n",
    "        color='white'\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>75.278</td>\n",
       "      <td>75.439</td>\n",
       "      <td>75.598</td>\n",
       "      <td>75.756</td>\n",
       "      <td>75.913</td>\n",
       "      <td>76.068</td>\n",
       "      <td>76.221</td>\n",
       "      <td>76.372</td>\n",
       "      <td>76.520</td>\n",
       "      <td>76.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moldova</th>\n",
       "      <td>69.616</td>\n",
       "      <td>70.119</td>\n",
       "      <td>70.581</td>\n",
       "      <td>70.968</td>\n",
       "      <td>71.267</td>\n",
       "      <td>71.478</td>\n",
       "      <td>71.617</td>\n",
       "      <td>71.717</td>\n",
       "      <td>71.808</td>\n",
       "      <td>71.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>61.028</td>\n",
       "      <td>61.553</td>\n",
       "      <td>62.054</td>\n",
       "      <td>62.525</td>\n",
       "      <td>62.966</td>\n",
       "      <td>63.377</td>\n",
       "      <td>63.763</td>\n",
       "      <td>64.130</td>\n",
       "      <td>64.486</td>\n",
       "      <td>64.833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year           2010    2011    2012    2013    2014    2015    2016    2017  \\\n",
       "country                                                                       \n",
       "Argentina    75.278  75.439  75.598  75.756  75.913  76.068  76.221  76.372   \n",
       "Moldova      69.616  70.119  70.581  70.968  71.267  71.478  71.617  71.717   \n",
       "Afghanistan  61.028  61.553  62.054  62.525  62.966  63.377  63.763  64.130   \n",
       "\n",
       "year           2018    2019  \n",
       "country                      \n",
       "Argentina    76.520  76.667  \n",
       "Moldova      71.808  71.901  \n",
       "Afghanistan  64.486  64.833  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_countries = df.loc[countries, df.columns[30:]]\n",
    "df_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='year'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_countries.T.plot(\n",
    "    kind='barh', \n",
    "    figsize=(20, 12),\n",
    "    color=['#12AAA8', 'lightcoral', 'silver'],\n",
    "    stacked=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}