30-Data-Analsis-with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4fcf9464",
   "metadata": {},
   "source": [
    "# Data Analysis with Python using Forbes 2022 Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f422390c",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "In this notebook, I'm going to talk about data analysis with Python.\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb11529",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0a1700b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "010e4103",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"forbes_2022_billionaires.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49ca38b5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>personName</th>\n",
       "      <th>age</th>\n",
       "      <th>finalWorth</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>category</th>\n",
       "      <th>source</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>organization</th>\n",
       "      <th>selfMade</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>title</th>\n",
       "      <th>philanthropyScore</th>\n",
       "      <th>residenceMsa</th>\n",
       "      <th>numberOfSiblings</th>\n",
       "      <th>bio</th>\n",
       "      <th>about</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>50.0</td>\n",
       "      <td>219000.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>Tesla, SpaceX</td>\n",
       "      <td>United States</td>\n",
       "      <td>Texas</td>\n",
       "      <td>...</td>\n",
       "      <td>Tesla</td>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>1971-06-28</td>\n",
       "      <td>CEO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elon Musk is working to revolutionize transpor...</td>\n",
       "      <td>Musk was accepted to a graduate program at Sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>58.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>United States</td>\n",
       "      <td>Washington</td>\n",
       "      <td>...</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>1964-01-12</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jeff Bezos founded e-commerce giant Amazon in ...</td>\n",
       "      <td>Growing up, Jeff Bezos worked summers on his g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>73.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>LVMH</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>LVMH Moët Hennessy Louis Vuitton</td>\n",
       "      <td>False</td>\n",
       "      <td>M</td>\n",
       "      <td>1949-03-05</td>\n",
       "      <td>Chairman and CEO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bernard Arnault oversees the LVMH empire of so...</td>\n",
       "      <td>Arnault apparently wooed his wife, Helene Merc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>United States</td>\n",
       "      <td>Washington</td>\n",
       "      <td>...</td>\n",
       "      <td>Bill &amp; Melinda Gates Foundation</td>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>1955-10-28</td>\n",
       "      <td>Cofounder</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Gates turned his fortune from software fi...</td>\n",
       "      <td>When Gates was a kid, he spent so much time re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>91.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>United States</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>...</td>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>1930-08-30</td>\n",
       "      <td>CEO</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Omaha, NE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Known as the \"Oracle of Omaha,\" Warren Buffett...</td>\n",
       "      <td>Buffett still lives in the same Omaha, Nebrask...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                personName   age  finalWorth  year  month  \\\n",
       "0     1                 Elon Musk  50.0    219000.0  2022      4   \n",
       "1     2                Jeff Bezos  58.0    171000.0  2022      4   \n",
       "2     3  Bernard Arnault & family  73.0    158000.0  2022      4   \n",
       "3     4                Bill Gates  66.0    129000.0  2022      4   \n",
       "4     5            Warren Buffett  91.0    118000.0  2022      4   \n",
       "\n",
       "                category              source        country       state  ...  \\\n",
       "0             Automotive       Tesla, SpaceX  United States       Texas  ...   \n",
       "1             Technology              Amazon  United States  Washington  ...   \n",
       "2       Fashion & Retail                LVMH         France         NaN  ...   \n",
       "3             Technology           Microsoft  United States  Washington  ...   \n",
       "4  Finance & Investments  Berkshire Hathaway  United States    Nebraska  ...   \n",
       "\n",
       "                       organization selfMade gender   birthDate  \\\n",
       "0                             Tesla     True      M  1971-06-28   \n",
       "1                            Amazon     True      M  1964-01-12   \n",
       "2  LVMH Moët Hennessy Louis Vuitton    False      M  1949-03-05   \n",
       "3   Bill & Melinda Gates Foundation     True      M  1955-10-28   \n",
       "4                Berkshire Hathaway     True      M  1930-08-30   \n",
       "\n",
       "              title philanthropyScore                 residenceMsa  \\\n",
       "0               CEO               1.0                          NaN   \n",
       "1      Entrepreneur               1.0  Seattle-Tacoma-Bellevue, WA   \n",
       "2  Chairman and CEO               NaN                          NaN   \n",
       "3         Cofounder               4.0  Seattle-Tacoma-Bellevue, WA   \n",
       "4               CEO               5.0                    Omaha, NE   \n",
       "\n",
       "   numberOfSiblings                                                bio  \\\n",
       "0               NaN  Elon Musk is working to revolutionize transpor...   \n",
       "1               NaN  Jeff Bezos founded e-commerce giant Amazon in ...   \n",
       "2               NaN  Bernard Arnault oversees the LVMH empire of so...   \n",
       "3               NaN  Bill Gates turned his fortune from software fi...   \n",
       "4               NaN  Known as the \"Oracle of Omaha,\" Warren Buffett...   \n",
       "\n",
       "                                               about  \n",
       "0  Musk was accepted to a graduate program at Sta...  \n",
       "1  Growing up, Jeff Bezos worked summers on his g...  \n",
       "2  Arnault apparently wooed his wife, Helene Merc...  \n",
       "3  When Gates was a kid, he spent so much time re...  \n",
       "4  Buffett still lives in the same Omaha, Nebrask...  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b93696c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2668, 22)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ec05c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'personName', 'age', 'finalWorth', 'year', 'month', 'category',\n",
       "       'source', 'country', 'state', 'city', 'countryOfCitizenship',\n",
       "       'organization', 'selfMade', 'gender', 'birthDate', 'title',\n",
       "       'philanthropyScore', 'residenceMsa', 'numberOfSiblings', 'bio',\n",
       "       'about'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3a8b31",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff8c82fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:,[\"rank\",\"personName\",\"age\",\"finalWorth\",\"category\",\"country\",\"gender\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f3b1a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>Elon Musk</td>\n",
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       "      <td>219000.0</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>58.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>73.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>France</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>91.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                personName   age  finalWorth               category  \\\n",
       "0     1                 Elon Musk  50.0    219000.0             Automotive   \n",
       "1     2                Jeff Bezos  58.0    171000.0             Technology   \n",
       "2     3  Bernard Arnault & family  73.0    158000.0       Fashion & Retail   \n",
       "3     4                Bill Gates  66.0    129000.0             Technology   \n",
       "4     5            Warren Buffett  91.0    118000.0  Finance & Investments   \n",
       "\n",
       "         country gender  \n",
       "0  United States      M  \n",
       "1  United States      M  \n",
       "2         France      M  \n",
       "3  United States      M  \n",
       "4  United States      M  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f623fabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.rename(columns={\"rank\":\"Sıra\",\"personName\":\"İsim\",\"age\":\"Yaş\",\n",
    "                      \"finalWorth\":\"Servet\",\"category\":\"Kategori\",\n",
    "                       \"country\":\"Ülke\", \"gender\":\"Cinsiyet\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82357bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>50.0</td>\n",
       "      <td>219000.0</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>58.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>73.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>France</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>91.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sıra                      İsim   Yaş    Servet               Kategori  \\\n",
       "0     1                 Elon Musk  50.0  219000.0             Automotive   \n",
       "1     2                Jeff Bezos  58.0  171000.0             Technology   \n",
       "2     3  Bernard Arnault & family  73.0  158000.0       Fashion & Retail   \n",
       "3     4                Bill Gates  66.0  129000.0             Technology   \n",
       "4     5            Warren Buffett  91.0  118000.0  Finance & Investments   \n",
       "\n",
       "            Ülke Cinsiyet  \n",
       "0  United States        M  \n",
       "1  United States        M  \n",
       "2         France        M  \n",
       "3  United States        M  \n",
       "4  United States        M  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0aca69fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(\"Sıra\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50810238",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>İsim</th>\n",
       "      <th>Yaş</th>\n",
       "      <th>Servet</th>\n",
       "      <th>Kategori</th>\n",
       "      <th>Ülke</th>\n",
       "      <th>Cinsiyet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sıra</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>1</th>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>50.0</td>\n",
       "      <td>219000.0</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>58.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>73.0</td>\n",
       "      <td>158000.0</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>France</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>66.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>91.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          İsim   Yaş    Servet               Kategori  \\\n",
       "Sıra                                                                    \n",
       "1                    Elon Musk  50.0  219000.0             Automotive   \n",
       "2                   Jeff Bezos  58.0  171000.0             Technology   \n",
       "3     Bernard Arnault & family  73.0  158000.0       Fashion & Retail   \n",
       "4                   Bill Gates  66.0  129000.0             Technology   \n",
       "5               Warren Buffett  91.0  118000.0  Finance & Investments   \n",
       "\n",
       "               Ülke Cinsiyet  \n",
       "Sıra                          \n",
       "1     United States        M  \n",
       "2     United States        M  \n",
       "3            France        M  \n",
       "4     United States        M  \n",
       "5     United States        M  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c634d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "İsim         object\n",
       "Yaş         float64\n",
       "Servet      float64\n",
       "Kategori     object\n",
       "Ülke         object\n",
       "Cinsiyet     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d3321dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "İsim         0\n",
       "Yaş         86\n",
       "Servet       0\n",
       "Kategori     0\n",
       "Ülke        13\n",
       "Cinsiyet    16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5594abc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c8eecbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2568, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c6b5cf",
   "metadata": {},
   "source": [
    "## Informations about gender of the richest in the world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36d3b74a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    2282\n",
       "F     286\n",
       "Name: Cinsiyet, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Cinsiyet\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58f3c8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.888629\n",
       "F    0.111371\n",
       "Name: Cinsiyet, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Cinsiyet\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "32f0282d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.826087\n",
       "F    0.173913\n",
       "Name: Cinsiyet, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Ülke\"]==\"Turkey\"].Cinsiyet.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b44be4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States', 'France', 'India', 'Mexico', 'China', 'Singapore',\n",
       "       'Spain', 'Canada', 'Germany', 'Switzerland', 'Belgium',\n",
       "       'Hong Kong', 'United Kingdom', 'Australia', 'Austria', 'Italy',\n",
       "       'Japan', 'Bahamas', 'Indonesia', 'Chile', 'Russia', 'Sweden',\n",
       "       'Czechia', 'Monaco', 'United Arab Emirates', 'Nigeria', 'Denmark',\n",
       "       'Thailand', 'Malaysia', 'Brazil', 'Colombia', 'New Zealand',\n",
       "       'South Korea', 'South Africa', 'Philippines', 'Egypt', 'Taiwan',\n",
       "       'Israel', 'Vietnam', 'Poland', 'Norway', 'Cayman Islands',\n",
       "       'Netherlands', 'Eswatini (Swaziland)', 'Peru', 'Algeria',\n",
       "       'Kazakhstan', 'Georgia', 'Portugal', 'British Virgin Islands',\n",
       "       'Turkey', 'Finland', 'Ukraine', 'Ireland', 'Bermuda', 'Lebanon',\n",
       "       'Argentina', 'Cambodia', 'Oman', 'Guernsey', 'Liechtenstein',\n",
       "       'Turks and Caicos Islands', 'Qatar', 'Morocco', 'Uruguay',\n",
       "       'Slovakia', 'Romania', 'Nepal', 'Tanzania', 'Bahrain', 'Greece',\n",
       "       'Hungary', 'Andorra'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Ülke\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e40deb6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.952381\n",
       "F    0.047619\n",
       "Name: Cinsiyet, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Ülke\"]==\"Canada\"].Cinsiyet.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be6992d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cinsiyet = df.groupby([\"Cinsiyet\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d9ffd61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cinsiyet\n",
       "F    62.937063\n",
       "M    64.409290\n",
       "Name: Yaş, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinsiyet[\"Yaş\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c5a0b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "sns.set(rc = {\"figure.dpi\":300})\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc292357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cinsiyet'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cinsiyet.size().plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbb8506",
   "metadata": {},
   "source": [
    "## Who are the top 10 richest in the world?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "222c2bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Servet', ylabel='İsim'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = df[\"İsim\"][:10], x = df[\"Servet\"][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68c5bbe",
   "metadata": {},
   "source": [
    "## Which country has the highest number of billionaires?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "592eefdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Ülke\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "735d0233",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ulke = df.groupby(\"Ülke\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5113b1c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ulke_sayi = pd.DataFrame(df_ulke.size().sort_values(ascending=False), \n",
    "                           columns = [\"Sayı\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "04656dd8",
   "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>Sayı</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ülke</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sayı\n",
       "Ülke                \n",
       "United States    742\n",
       "China            546\n",
       "India            154\n",
       "Germany           94\n",
       "United Kingdom    81"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ulke_sayi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c07bc75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sayı', ylabel='Ülke'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = df_ulke_sayi[\"Sayı\"][:10] , y = df_ulke_sayi.index[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80be375",
   "metadata": {},
   "source": [
    "## Who are the Top 10 richest in the Turkey?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c2795a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_turkiye = df[df[\"Ülke\"]==\"Turkey\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6aa8bb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_turkiye[\"İsim\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "415e05a1",
   "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>İsim</th>\n",
       "      <th>Yaş</th>\n",
       "      <th>Servet</th>\n",
       "      <th>Kategori</th>\n",
       "      <th>Ülke</th>\n",
       "      <th>Cinsiyet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sıra</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>601</th>\n",
       "      <td>Murat Ulker</td>\n",
       "      <td>63.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Ferit Faik Sahenk</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>Erman Ilicak</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Construction &amp; Engineering</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>Filiz Sahenk</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1579</th>\n",
       "      <td>Sezai Bacaksiz</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1579</th>\n",
       "      <td>Nihat Ozdemir</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>Ibrahim Erdemoglu</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>Semahat Sevim Arsel</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Hamdi Akin &amp; family</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Ahmet Calik</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     İsim   Yaş  Servet                    Kategori    Ülke  \\\n",
       "Sıra                                                                          \n",
       "601           Murat Ulker  63.0  4700.0             Food & Beverage  Turkey   \n",
       "1292    Ferit Faik Sahenk  58.0  2400.0                 Diversified  Turkey   \n",
       "1341         Erman Ilicak  54.0  2300.0  Construction & Engineering  Turkey   \n",
       "1397         Filiz Sahenk  55.0  2200.0                 Diversified  Turkey   \n",
       "1579       Sezai Bacaksiz  72.0  1900.0                 Diversified  Turkey   \n",
       "1579        Nihat Ozdemir  72.0  1900.0                 Diversified  Turkey   \n",
       "1645    Ibrahim Erdemoglu  59.0  1800.0               Manufacturing  Turkey   \n",
       "1818  Semahat Sevim Arsel  93.0  1600.0                 Diversified  Turkey   \n",
       "1929  Hamdi Akin & family  67.0  1500.0                 Diversified  Turkey   \n",
       "1929          Ahmet Calik  64.0  1500.0                 Diversified  Turkey   \n",
       "\n",
       "     Cinsiyet  \n",
       "Sıra           \n",
       "601         M  \n",
       "1292        M  \n",
       "1341        M  \n",
       "1397        F  \n",
       "1579        M  \n",
       "1579        M  \n",
       "1645        M  \n",
       "1818        F  \n",
       "1929        M  \n",
       "1929        M  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_turkiye.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "266729b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Servet', ylabel='İsim'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=df_turkiye[\"İsim\"][:10], x = df_turkiye[\"Servet\"][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1f22ec",
   "metadata": {},
   "source": [
    "## Which Industry has the most billionaires in it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "31b761ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Automotive', 'Technology', 'Fashion & Retail',\n",
       "       'Finance & Investments', 'Diversified', 'Media & Entertainment',\n",
       "       'Telecom', 'Food & Beverage', 'Logistics', 'Real Estate',\n",
       "       'Metals & Mining', 'Manufacturing', 'Gambling & Casinos',\n",
       "       'Healthcare', 'Service', 'Energy', 'Construction & Engineering',\n",
       "       'Sports'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Kategori\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f1d969c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Kategori\"]=df[\"Kategori\"].apply(lambda x:x.replace(\" \",\"\")).\\\n",
    "    apply(lambda x:x.replace(\"&\",\"_\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ddc0a2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Automotive', 'Technology', 'Fashion_Retail',\n",
       "       'Finance_Investments', 'Diversified', 'Media_Entertainment',\n",
       "       'Telecom', 'Food_Beverage', 'Logistics', 'RealEstate',\n",
       "       'Metals_Mining', 'Manufacturing', 'Gambling_Casinos', 'Healthcare',\n",
       "       'Service', 'Energy', 'Construction_Engineering', 'Sports'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Kategori\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3db37986",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kategori = df.groupby(\"Kategori\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9e966ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kategori\n",
       "Automotive                   66\n",
       "Construction_Engineering     45\n",
       "Diversified                 174\n",
       "Energy                       93\n",
       "Fashion_Retail              238\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kategori.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f934b2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kategori = df_kategori.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0a156b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kategori</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Automotive</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction_Engineering</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diversified</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fashion_Retail</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "Kategori                     \n",
       "Automotive                 66\n",
       "Construction_Engineering   45\n",
       "Diversified               174\n",
       "Energy                     93\n",
       "Fashion_Retail            238"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kategori.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd304570",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kategori = df_kategori.rename(columns={0:\"Sayi\"}).sort_values(by=\"Sayi\",\n",
    "                                                                ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "25e13b15",
   "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>Sayi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kategori</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finance_Investments</th>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fashion_Retail</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Sayi\n",
       "Kategori                 \n",
       "Finance_Investments   381\n",
       "Technology            332\n",
       "Manufacturing         319\n",
       "Fashion_Retail        238\n",
       "Healthcare            209"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kategori.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "784bc941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sayi', ylabel='Kategori'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot( x = df_kategori[\"Sayi\"][:10], y = df_kategori.index[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7a9dff",
   "metadata": {},
   "source": [
    "## Is there a relationship between money and age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5e432619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Yaş', ylabel='Servet'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df[\"Yaş\"],df[\"Servet\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20abc0f5",
   "metadata": {},
   "source": [
    "## The distribution of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ba544adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Yaş', ylabel='Count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ixYsrHSM3Nzd+9atfxZNPPhkDBw6MFi1abHCOe+21VwwdOjTGjh0be++9dw2/MgAAaHgsRwYAALCFKioqig8//DBmzZoVy5cvj3Xr1kXr1q2jXbt20bNnz5Q3XmpDSUlJfPrpp3Hccccl+q+//vrYfvvtY6+99oq2bdvW+nUBAKA+CGEAAACoU2vXro399tuvfG+ao48+Ovr37x99+/aNxo0b1/PsAACg9ghhAAAAqFOvvvpq/PznP0/p33777ePOO++MPffcsx5mBQAAtU8IAwAAQJ1atWpVLF26NBYuXBjjx4+P+++/P9atWxcREW3bto0XXnghWrduXb+TBACAWpBb3xMAAABgy9KsWbPo1KlT7LPPPnHZZZfFIYccUv7ZkiVL4qWXXqrH2QEAQO0RwgAAAFBvpkyZEu+++26ir7CwsJ5mAwAAtSuvvicAAADAluG6666Lb775JsrKymL16tUxZ86c+Pzzz1OO22effep+cgAAkAVCGAAAAOrEmDFjori4uNpjjjjiiNh3333raEYAAJBdliMDAAAg61asWLHBAOaoo46K22+/vY5mBAAA2edNGAAAALKuqKgo9t1335gzZ055INO0adPYbrvtYq+99orjjz8+9t9///qeJgAA1KqcsrKysvqeBAAAAAAAwObGcmQAAAAAAABZIIQBAAAAAADIAiEMAAAAAABAFghhAAAAAAAAskAIAwAAAAAAkAVCGAAAAAAAgCwQwgAAAAAAAGSBEAYAAAAAACALhDAAAAAAAABZIIQBAAAAAADIAiEMAAAAAABAFghhAAAAAAAAskAIAwAAAAAAkAVCGAAAAAAAgCwQwgAAAAAAAGSBEAYAAAAAACALhDAAAAAAAABZIIQBAAAAAADIAiEMAAAAAABAFghhAAAAAAAAskAIAwAAAAAAkAVCGAAAAAAAgCwQwgAAAAAAAGSBEAYAAAAAACALhDAAAAAAAABZIIQBAAAAAADIAiEMAAAAAABAFghhAAAAAAAAskAIAwAAAAAAkAX/H/bk/8+dGhPLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df[\"Yaş\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a2ffff",
   "metadata": {},
   "source": [
    "## The youngest billionaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5d11195b",
   "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>İsim</th>\n",
       "      <th>Yaş</th>\n",
       "      <th>Servet</th>\n",
       "      <th>Kategori</th>\n",
       "      <th>Ülke</th>\n",
       "      <th>Cinsiyet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sıra</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>2190</th>\n",
       "      <td>Alexandra Andresen</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Norway</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Wang Zelong</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Metals_Mining</td>\n",
       "      <td>China</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Pedro Franceschi</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Finance_Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Henrique Dubugras</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Finance_Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>Katharina Andresen</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Norway</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Ana Maria Brescia Cafferata</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Peru</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Robert Kuok</td>\n",
       "      <td>98.0</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>Diversified</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>David Murdock</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Food_Beverage</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>Charles Munger</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>Finance_Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>George Joseph</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>Finance_Investments</td>\n",
       "      <td>United States</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2568 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             İsim    Yaş   Servet             Kategori  \\\n",
       "Sıra                                                                     \n",
       "2190           Alexandra Andresen   25.0   1300.0          Diversified   \n",
       "1929                  Wang Zelong   25.0   1500.0        Metals_Mining   \n",
       "1929             Pedro Franceschi   25.0   1500.0  Finance_Investments   \n",
       "1929            Henrique Dubugras   26.0   1500.0  Finance_Investments   \n",
       "2190           Katharina Andresen   26.0   1300.0          Diversified   \n",
       "...                           ...    ...      ...                  ...   \n",
       "1929  Ana Maria Brescia Cafferata   97.0   1500.0          Diversified   \n",
       "163                   Robert Kuok   98.0  11700.0          Diversified   \n",
       "1341                David Murdock   98.0   2300.0        Food_Beverage   \n",
       "1238               Charles Munger   98.0   2500.0  Finance_Investments   \n",
       "1645                George Joseph  100.0   1800.0  Finance_Investments   \n",
       "\n",
       "               Ülke Cinsiyet  \n",
       "Sıra                          \n",
       "2190         Norway        F  \n",
       "1929          China        M  \n",
       "1929  United States        M  \n",
       "1929  United States        M  \n",
       "2190         Norway        F  \n",
       "...             ...      ...  \n",
       "1929           Peru        F  \n",
       "163       Hong Kong        M  \n",
       "1341  United States        M  \n",
       "1238  United States        M  \n",
       "1645  United States        M  \n",
       "\n",
       "[2568 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_yas = df.sort_values(by=\"Yaş\")\n",
    "df_yas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f82fbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Yaş', ylabel='İsim'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=df_yas[\"İsim\"][:10], x = df_yas[\"Yaş\"][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cea6d8e",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}