500richest_2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "04b66698-f686-43b8-8847-122c7b41457c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the packages\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "for dirname, _, filenames in os.walk(''):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7d38da3f-9435-4665-a9eb-04013d8c35c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing data with ';' delimiter\n",
    "df=pd.read_csv('500richest_BBI/500richestpeople2021_clean.csv', sep=';') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "380b4132-d40a-4164-ab08-71015a4e04ad",
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>$188B</td>\n",
       "      <td>+$1.68B</td>\n",
       "      <td>-$2.31B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>$170B</td>\n",
       "      <td>-$2.89B</td>\n",
       "      <td>+$773M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Bernard Arnault</td>\n",
       "      <td>$155B</td>\n",
       "      <td>+$892M</td>\n",
       "      <td>+$40.9B</td>\n",
       "      <td>France</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>$144B</td>\n",
       "      <td>-$1.32B</td>\n",
       "      <td>+$12.2B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Mark Zuckerberg</td>\n",
       "      <td>$114B</td>\n",
       "      <td>+$203M</td>\n",
       "      <td>+$10.9B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank             Name Total Net Worth $ Last Change $ YTD Change  \\\n",
       "0   1.0       Jeff Bezos           $188B       +$1.68B     -$2.31B    \n",
       "1   2.0        Elon Musk           $170B       -$2.89B      +$773M    \n",
       "2   3.0  Bernard Arnault           $155B        +$892M     +$40.9B    \n",
       "3   4.0       Bill Gates           $144B       -$1.32B     +$12.2B    \n",
       "4   5.0  Mark Zuckerberg           $114B        +$203M     +$10.9B    \n",
       "\n",
       "         Country    Industry  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \n",
       "0  United States  Technology         NaN         NaN         NaN          NaN  \n",
       "1  United States  Technology         NaN         NaN         NaN          NaN  \n",
       "2         France    Consumer         NaN         NaN         NaN          NaN  \n",
       "3  United States  Technology         NaN         NaN         NaN          NaN  \n",
       "4  United States  Technology         NaN         NaN         NaN          NaN  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the head of the dataframe\n",
    "# We see unused columns in 'Unnamed 7' - 'Unnamed 10'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5cb2a8f9-cc20-49c4-a522-3749e915b655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                 4\n",
       "Name                 4\n",
       "Total Net Worth      4\n",
       "$ Last Change        4\n",
       "$ YTD Change         4\n",
       "Country              4\n",
       "Industry             4\n",
       "Unnamed: 7         503\n",
       "Unnamed: 8         503\n",
       "Unnamed: 9         503\n",
       "Unnamed: 10        503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count null fields in df\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "9c0c3a46-ffc3-48c2-a8e1-dc01d23ec201",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <th>1</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank   Name  Total Net Worth  $ Last Change  $ YTD Change  Country  \\\n",
       "0    False  False            False          False         False    False   \n",
       "1    False  False            False          False         False    False   \n",
       "2    False  False            False          False         False    False   \n",
       "3    False  False            False          False         False    False   \n",
       "4    False  False            False          False         False    False   \n",
       "..     ...    ...              ...            ...           ...      ...   \n",
       "498  False  False            False          False         False    False   \n",
       "499   True   True             True           True          True     True   \n",
       "500   True   True             True           True          True     True   \n",
       "501   True   True             True           True          True     True   \n",
       "502   True   True             True           True          True     True   \n",
       "\n",
       "     Industry  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \n",
       "0       False        True        True        True         True  \n",
       "1       False        True        True        True         True  \n",
       "2       False        True        True        True         True  \n",
       "3       False        True        True        True         True  \n",
       "4       False        True        True        True         True  \n",
       "..        ...         ...         ...         ...          ...  \n",
       "498     False        True        True        True         True  \n",
       "499      True        True        True        True         True  \n",
       "500      True        True        True        True         True  \n",
       "501      True        True        True        True         True  \n",
       "502      True        True        True        True         True  \n",
       "\n",
       "[503 rows x 11 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see that the columns 'Unnamed 7-10' are null and the rows of index 499-502 are null\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becfa546-4747-42f1-a610-ee298f2c713e",
   "metadata": {},
   "source": [
    "<h1>Data Cleaning</h1>\n",
    "<h4>1. Drop unnecessary columns</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "c8485239-b96b-4cf2-935c-0f1e21d82b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_drop = ['Unnamed: 7',\n",
    "          'Unnamed: 8',\n",
    "          'Unnamed: 9',\n",
    "          'Unnamed: 10']\n",
    "#inplace=True means we're working on the original object\n",
    "#axis=1 means we're dropping the column, not row\n",
    "df.drop(columns=to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb5275c-c796-41d6-bf85-c6228bc1c1e4",
   "metadata": {},
   "source": [
    "<h4>2. Drop necessary rows</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "c08753e7-f410-4e08-bdf7-32ca0694b222",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([499, 500, 501, 502], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "784695a0-2ea5-4626-aeb7-8bb1b5f279f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank               0\n",
       "Name               0\n",
       "Total Net Worth    0\n",
       "$ Last Change      0\n",
       "$ YTD Change       0\n",
       "Country            0\n",
       "Industry           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "54a2f247-7657-4b20-9952-68fa418ad379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank               float64\n",
       "Name                object\n",
       "Total Net Worth     object\n",
       "$ Last Change       object\n",
       "$ YTD Change        object\n",
       "Country             object\n",
       "Industry            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7c6d98-75b6-4659-912c-6a57230e550f",
   "metadata": {},
   "source": [
    "<h5>'Total Net Worth', '\\$ Last Change', and '\\$ YTD Change' are all objects. We want float for our EDA</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "30edcc55-8ff5-4cae-b021-769841f0260f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Total Net Worth'] = [x.strip('$') for x in df['Total Net Worth']]\n",
    "df[df.columns[2:5]] = df[df.columns[2:5]].replace('[\\$,]', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "bd1e516c-0e38-4daa-a3af-42e6a0348637",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>188B</td>\n",
       "      <td>+1.68B</td>\n",
       "      <td>-2.31B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>170B</td>\n",
       "      <td>-2.89B</td>\n",
       "      <td>+773M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Bernard Arnault</td>\n",
       "      <td>155B</td>\n",
       "      <td>+892M</td>\n",
       "      <td>+40.9B</td>\n",
       "      <td>France</td>\n",
       "      <td>Consumer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>144B</td>\n",
       "      <td>-1.32B</td>\n",
       "      <td>+12.2B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Mark Zuckerberg</td>\n",
       "      <td>114B</td>\n",
       "      <td>+203M</td>\n",
       "      <td>+10.9B</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank             Name Total Net Worth $ Last Change $ YTD Change  \\\n",
       "0   1.0       Jeff Bezos            188B        +1.68B      -2.31B    \n",
       "1   2.0        Elon Musk            170B        -2.89B       +773M    \n",
       "2   3.0  Bernard Arnault            155B         +892M      +40.9B    \n",
       "3   4.0       Bill Gates            144B        -1.32B      +12.2B    \n",
       "4   5.0  Mark Zuckerberg            114B         +203M      +10.9B    \n",
       "\n",
       "         Country    Industry  \n",
       "0  United States  Technology  \n",
       "1  United States  Technology  \n",
       "2         France    Consumer  \n",
       "3  United States  Technology  \n",
       "4  United States  Technology  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "573af9cf-aa42-4617-a6b7-7ec8a425107b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_to_float(x):\n",
    "    if type(x) == float or type(x) == int:\n",
    "        return x\n",
    "    if 'K' in x:\n",
    "        if len(x) > 1:\n",
    "            return float(x.replace('K', '')) * 1000\n",
    "        return 1000.0\n",
    "    if 'M' in x:\n",
    "        if len(x) > 1:\n",
    "            return float(x.replace('M', '')) * 1000000\n",
    "        return 1000000.0\n",
    "    if 'B' in x:\n",
    "        return float(x.replace('B', '')) * 1000000000\n",
    "    return 0.0\n",
    "\n",
    "df['Total Net Worth'] = df['Total Net Worth'].apply(value_to_float)\n",
    "df['$ Last Change'] = df['$ Last Change'].apply(value_to_float)\n",
    "df['$ YTD Change'] = df['$ YTD Change'].apply(value_to_float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "05b2af51-864a-4f39-b779-28c631733398",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.880000e+11</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>-2.310000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.700000e+11</td>\n",
       "      <td>-2.890000e+09</td>\n",
       "      <td>7.730000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.550000e+11</td>\n",
       "      <td>8.920000e+08</td>\n",
       "      <td>4.090000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.440000e+11</td>\n",
       "      <td>-1.320000e+09</td>\n",
       "      <td>1.220000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.140000e+11</td>\n",
       "      <td>2.030000e+08</td>\n",
       "      <td>1.090000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Net Worth  $ Last Change  $ YTD Change\n",
       "0     1.880000e+11   1.680000e+09 -2.310000e+09\n",
       "1     1.700000e+11  -2.890000e+09  7.730000e+08\n",
       "2     1.550000e+11   8.920000e+08  4.090000e+10\n",
       "3     1.440000e+11  -1.320000e+09  1.220000e+10\n",
       "4     1.140000e+11   2.030000e+08  1.090000e+10"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[2:5]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "d76ca142-145b-4af8-98b7-c6d4ab2dc9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank               float64\n",
       "Name                object\n",
       "Total Net Worth    float64\n",
       "$ Last Change      float64\n",
       "$ YTD Change       float64\n",
       "Country             object\n",
       "Industry            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3eed10-ed3b-4e2d-8e57-077e57cd8036",
   "metadata": {},
   "source": [
    "<h4>3. Identifying outliers</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "cdccf2cc-a5a3-410d-a6a7-69a4d9521481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Rank</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>499.000000</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.192385</td>\n",
       "      <td>1.619824e+10</td>\n",
       "      <td>-6.275872e+07</td>\n",
       "      <td>1.192092e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.462829</td>\n",
       "      <td>2.029522e+10</td>\n",
       "      <td>1.038117e+09</td>\n",
       "      <td>4.179780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.890000e+09</td>\n",
       "      <td>-1.830000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.500000</td>\n",
       "      <td>7.170000e+09</td>\n",
       "      <td>-1.805000e+08</td>\n",
       "      <td>-4.925000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>9.760000e+09</td>\n",
       "      <td>-7.050000e+07</td>\n",
       "      <td>7.200000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>374.500000</td>\n",
       "      <td>1.580000e+10</td>\n",
       "      <td>-7.135000e+06</td>\n",
       "      <td>1.685000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.880000e+11</td>\n",
       "      <td>2.110000e+10</td>\n",
       "      <td>4.090000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rank  Total Net Worth  $ Last Change  $ YTD Change\n",
       "count  499.000000     4.990000e+02   4.990000e+02  4.990000e+02\n",
       "mean   250.192385     1.619824e+10  -6.275872e+07  1.192092e+09\n",
       "std    144.462829     2.029522e+10   1.038117e+09  4.179780e+09\n",
       "min      1.000000     0.000000e+00  -2.890000e+09 -1.830000e+10\n",
       "25%    125.500000     7.170000e+09  -1.805000e+08 -4.925000e+06\n",
       "50%    250.000000     9.760000e+09  -7.050000e+07  7.200000e+08\n",
       "75%    374.500000     1.580000e+10  -7.135000e+06  1.685000e+09\n",
       "max    500.000000     1.880000e+11   2.110000e+10  4.090000e+10"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77531257-fd7d-4731-b65a-e1d14858f2b3",
   "metadata": {},
   "source": [
    "<h5>We can see that the minimum 'Total Net Worth' is 0.0. That doesn't make any sense.</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "dad625a0-ceb0-48f6-a9ca-bf55caa479c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Net Worth    84\n",
       "dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index of the min value in 'Total Net Worth'\n",
    "df[['Total Net Worth']].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "6a3a394b-5c77-41f6-a16a-0f8c2626df05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84.0</td>\n",
       "      <td>Stefan Quandt</td>\n",
       "      <td>2.140000e+10</td>\n",
       "      <td>-2.270000e+08</td>\n",
       "      <td>2.600000e+09</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Industrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85.0</td>\n",
       "      <td>Sara Mota de Larrea &amp; Family</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.110000e+10</td>\n",
       "      <td>6.550000e+07</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Commodities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>Alisher Usmanov</td>\n",
       "      <td>2.090000e+10</td>\n",
       "      <td>1.600000e+08</td>\n",
       "      <td>-1.920000e+08</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Diversified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank                          Name  Total Net Worth  $ Last Change  \\\n",
       "83  84.0                 Stefan Quandt     2.140000e+10  -2.270000e+08   \n",
       "84  85.0  Sara Mota de Larrea & Family     0.000000e+00   2.110000e+10   \n",
       "85  86.0               Alisher Usmanov     2.090000e+10   1.600000e+08   \n",
       "\n",
       "    $ YTD Change  Country     Industry  \n",
       "83  2.600000e+09  Germany   Industrial  \n",
       "84  6.550000e+07   Mexico  Commodities  \n",
       "85 -1.920000e+08   Russia  Diversified  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[83:86]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "32d3dd0a-2b6a-4cc4-b783-1f8f1f45b536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The outlier looks like a simple mistake. Replace with data in row index 38 from the Bloomberg Billionaire Index to date (June 10, 2021)\n",
    "df.iloc[[84],2:5] = 18.7e+10,1.96e+8,1.89e+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "97be93e4-5328-4a74-b357-621588728d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84.0</td>\n",
       "      <td>Stefan Quandt</td>\n",
       "      <td>2.140000e+10</td>\n",
       "      <td>-227000000.0</td>\n",
       "      <td>2.600000e+09</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Industrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85.0</td>\n",
       "      <td>Sara Mota de Larrea &amp; Family</td>\n",
       "      <td>1.870000e+11</td>\n",
       "      <td>196000000.0</td>\n",
       "      <td>1.890000e+10</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Commodities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>Alisher Usmanov</td>\n",
       "      <td>2.090000e+10</td>\n",
       "      <td>160000000.0</td>\n",
       "      <td>-1.920000e+08</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Diversified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank                          Name  Total Net Worth  $ Last Change  \\\n",
       "83  84.0                 Stefan Quandt     2.140000e+10   -227000000.0   \n",
       "84  85.0  Sara Mota de Larrea & Family     1.870000e+11    196000000.0   \n",
       "85  86.0               Alisher Usmanov     2.090000e+10    160000000.0   \n",
       "\n",
       "    $ YTD Change  Country     Industry  \n",
       "83  2.600000e+09  Germany   Industrial  \n",
       "84  1.890000e+10   Mexico  Commodities  \n",
       "85 -1.920000e+08   Russia  Diversified  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[83:86]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "1f1e86d4-204b-475f-a30b-24c84c434e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Total Net Worth</th>\n",
       "      <th>$ Last Change</th>\n",
       "      <th>$ YTD Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>499.000000</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.990000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.192385</td>\n",
       "      <td>1.657299e+10</td>\n",
       "      <td>-1.046505e+08</td>\n",
       "      <td>1.229836e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.462829</td>\n",
       "      <td>2.167508e+10</td>\n",
       "      <td>4.204049e+08</td>\n",
       "      <td>4.253968e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.720000e+09</td>\n",
       "      <td>-2.890000e+09</td>\n",
       "      <td>-1.830000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.500000</td>\n",
       "      <td>7.170000e+09</td>\n",
       "      <td>-1.805000e+08</td>\n",
       "      <td>-4.925000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>9.770000e+09</td>\n",
       "      <td>-7.050000e+07</td>\n",
       "      <td>7.310000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>374.500000</td>\n",
       "      <td>1.590000e+10</td>\n",
       "      <td>-7.135000e+06</td>\n",
       "      <td>1.705000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.880000e+11</td>\n",
       "      <td>4.600000e+09</td>\n",
       "      <td>4.090000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rank  Total Net Worth  $ Last Change  $ YTD Change\n",
       "count  499.000000     4.990000e+02   4.990000e+02  4.990000e+02\n",
       "mean   250.192385     1.657299e+10  -1.046505e+08  1.229836e+09\n",
       "std    144.462829     2.167508e+10   4.204049e+08  4.253968e+09\n",
       "min      1.000000     5.720000e+09  -2.890000e+09 -1.830000e+10\n",
       "25%    125.500000     7.170000e+09  -1.805000e+08 -4.925000e+06\n",
       "50%    250.000000     9.770000e+09  -7.050000e+07  7.310000e+08\n",
       "75%    374.500000     1.590000e+10  -7.135000e+06  1.705000e+09\n",
       "max    500.000000     1.880000e+11   4.600000e+09  4.090000e+10"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60985089-7178-42a1-9e6c-26889b8e0065",
   "metadata": {},
   "source": [
    "<h1>Exploratory Data Analysis</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d04de08e-7e0f-45ef-85c4-a21ddc42a66b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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