BillionaireOnForbes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 1000 Richest Person in the World EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in this project, i will analyze top 1000 Richest person in the world based on forbes ranking, i am interest about knowing the following question\n",
    "\n",
    "- which industry has the most richest person ?\n",
    "- which country has the most richest person ?\n",
    "- percentage between under 50 age and over 50 age billionaire ?\n",
    "\n",
    "My analysis will help me to answer these question through using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for dataset, i use Forbes Billionaire 2022 Datasets obtained from this link                                       \n",
    "https://www.kaggle.com/datasets/jjdaguirre/forbes-billionaires-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  1000 non-null   int64 \n",
      " 1   rank        1000 non-null   int64 \n",
      " 2   name        1000 non-null   object\n",
      " 3   networth    1000 non-null   object\n",
      " 4   age         1000 non-null   int64 \n",
      " 5   country     1000 non-null   object\n",
      " 6   source      1000 non-null   object\n",
      " 7   industry    1000 non-null   object\n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#import module and framework\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#import csv dataset\n",
    "df = pd.read_csv(\"CSV file/ForbesBillionaire.csv\")\n",
    "df = df[:1000]\n",
    "\n",
    "#null values check\n",
    "df.isnull().sum()\n",
    "\n",
    "#explore data\n",
    "df.head()\n",
    "df.describe()\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cause there is datapoint which should have Dtype int (networth columns), i will convert it into int so later it will not become problem"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>networth</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>source</th>\n",
       "      <th>industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>219.0</td>\n",
       "      <td>50</td>\n",
       "      <td>United States</td>\n",
       "      <td>Tesla, SpaceX</td>\n",
       "      <td>Automotive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>171.0</td>\n",
       "      <td>58</td>\n",
       "      <td>United States</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>158.0</td>\n",
       "      <td>73</td>\n",
       "      <td>France</td>\n",
       "      <td>LVMH</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>129.0</td>\n",
       "      <td>66</td>\n",
       "      <td>United States</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>118.0</td>\n",
       "      <td>91</td>\n",
       "      <td>United States</td>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  rank                       name  networth  age        country  \\\n",
       "0           0     1                 Elon Musk      219.0   50  United States   \n",
       "1           1     2                Jeff Bezos      171.0   58  United States   \n",
       "2           2     3  Bernard Arnault & family      158.0   73         France   \n",
       "3           3     4                Bill Gates      129.0   66  United States   \n",
       "4           4     5            Warren Buffett      118.0   91  United States   \n",
       "\n",
       "               source                industry  \n",
       "0       Tesla, SpaceX             Automotive   \n",
       "1              Amazon             Technology   \n",
       "2                LVMH       Fashion & Retail   \n",
       "3           Microsoft             Technology   \n",
       "4  Berkshire Hathaway  Finance & Investments   "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove dollar sign from data in column networth\n",
    "df.networth = df[\"networth\"].apply(lambda x: x.split(\" \")[0])\n",
    "df.networth = df[\"networth\"].apply(lambda x: x.split(\"$\")[1])\n",
    "\n",
    "#convert datapoint from object to numerical\n",
    "df['networth'] = pd.to_numeric(df['networth'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "because there are too much unique data in the country column, I will change the data in the country column with a small amount (countries that only have the richest number of people less than 10) to other data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check amount of unique values in country column\n",
    "len(df.country.unique())\n",
    "\n",
    "#make country elimination list \n",
    "country_elim = df.groupby(df.country)[\"country\"].agg(\"count\").sort_values(ascending = False)\n",
    "country_less_10 = country_elim[country_elim < 10]\n",
    "\n",
    "#convert country which has under 10 rich person to \"other\" category\n",
    "df[\"country\"] = df.country.apply(lambda x: \"other\" if x in country_less_10 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to find out the percentage of rich people aged over 50 years and under 50 years, then I will create a new column to categorize by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set label and bin\n",
    "bins= [0, 50, 100]\n",
    "labels = ['age under 50','age over 50']\n",
    "\n",
    "#make category for data age and assign to age_category column\n",
    "df['age_category'] = pd.cut(df['age'], bins=bins, labels=labels, right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='industry'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#separate data for visualization\n",
    "over50 = df[df.age_category == \"age under 50\"][\"age_category\"]\n",
    "under50 = df[df.age_category == \"age over 50\"][\"age_category\"]\n",
    "\n",
    "#setup percentage and label for pie chart\n",
    "data = [over50.count(), under50.count()]\n",
    "label = [\"age under 50\", \"age over 50\"]\n",
    "\n",
    "#comparating number of richest person with age under 50 and over 50\n",
    "plt.pie(data, labels=label, colors=sns.color_palette(\"bright\"), autopct='%.0f%%', explode=[0, 0.05])\n",
    "plt.show()\n",
    "\n",
    "#visualization the distribution of the billionaire in every country\n",
    "pd.crosstab(df.country, df.age_category).plot(kind=\"bar\", stacked=True)\n",
    "\n",
    "#visualization the distribution of the billionaire in every industry\n",
    "pd.crosstab(df.industry, df.age_category).plot(kind=\"bar\", stacked=True, )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "2c0848194679cc856cdb22afd850d5f70cd56543c051b91d6fd67c617f7ae4ab"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}