Data Viz using Jupyter Notebook

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Final_Project.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyM+aA3OQYfDrt5tOMthlDs4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["#Project Name: World Billionaires 2022 – Visualization Notebook"],"metadata":{"id":"nGdh4y1oAS4y"}},{"cell_type":"markdown","source":["#Project Objectives: \n","•\tI have used Seaborn library and Matplotlib to plot the graph as it provides a high-level interface for plotting the attractive and informative statistical graphs.\n","\n","•\tFurther in the visualization part, I have used bar plots to display the relationship between a numeric and a categoric variable.\n","\n","•\tThe goal is to plot a visualization graph of which industry is having more billionaires, also which country in the world is having more billionaires according to the dataset. Making use of histograms to display frequency distributions. For example, Age distribution of billionaires.\n","\n","•\tVisualizing number of billionaires by number of income sources, Country and its net worth, Industry and its net worth using bar chart.\n","\n","\n","\n"],"metadata":{"id":"3nrjazJeZYPr"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","#datadir = 'data/'\n","#imagesdir = 'images/'\n","\n","datadir = '/content/drive/My Drive/Notebooks/data/'\n","imagesdir = '/content/drive/My Drive/Notebooks/images/'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FaGuWPUv86sK","executionInfo":{"status":"ok","timestamp":1656538798125,"user_tz":240,"elapsed":1140,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"f1ad6344-7295-44e8-e875-b12ed967afd6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline"],"metadata":{"id":"XcqaJWdm9NAd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# read the dataset\n","filename = datadir + 'Billionaires_2022_Dataset.csv'\n","df = pd.read_csv(filename)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"yp9UGNKt9US4","executionInfo":{"status":"ok","timestamp":1656538802301,"user_tz":240,"elapsed":151,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"6a77afe1-4984-4c39-bb02-53a71e806456"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Unnamed: 0  Rank                       Name Networth  age  \\\n","0              0     1                 Elon Musk    $219 B   50   \n","1              1     2                Jeff Bezos    $171 B   58   \n","2              2     3  Bernard Arnault & family    $158 B   73   \n","3              3     4                Bill Gates    $129 B   66   \n","4              4     5            Warren Buffett    $118 B   91   \n","...          ...   ...                        ...      ...  ...   \n","2595        2595  2578    Jorge Gallardo Ballart      $1 B   80   \n","2596        2596  2578              Nari Genomal      $1 B   82   \n","2597        2597  2578            Ramesh Genomal      $1 B   71   \n","2598        2598  2578            Sunder Genomal      $1 B   68   \n","2599        2599  2578     Horst-Otto Gerberding      $1 B   69   \n","\n","            Country                  Source                Industry  \n","0     United States           Tesla, SpaceX             Automotive   \n","1     United States                  Amazon             Technology   \n","2            France                    LVMH       Fashion & Retail   \n","3     United States               Microsoft             Technology   \n","4     United States      Berkshire Hathaway  Finance & Investments   \n","...             ...                     ...                     ...  \n","2595          Spain         pharmaceuticals             Healthcare   \n","2596    Philippines                 apparel       Fashion & Retail   \n","2597    Philippines                 apparel       Fashion & Retail   \n","2598    Philippines                garments       Fashion & Retail   \n","2599        Germany  flavors and fragrances        Food & Beverage   \n","\n","[2600 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-b0368e42-9881-4e2e-85ff-1539c72a4e2f\">\n","    <div class=\"colab-df-container\">\n","      <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 B</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 B</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 B</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 B</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 B</td>\n","      <td>91</td>\n","      <td>United States</td>\n","      <td>Berkshire Hathaway</td>\n","      <td>Finance &amp; Investments</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","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2595</th>\n","      <td>2595</td>\n","      <td>2578</td>\n","      <td>Jorge Gallardo Ballart</td>\n","      <td>$1 B</td>\n","      <td>80</td>\n","      <td>Spain</td>\n","      <td>pharmaceuticals</td>\n","      <td>Healthcare</td>\n","    </tr>\n","    <tr>\n","      <th>2596</th>\n","      <td>2596</td>\n","      <td>2578</td>\n","      <td>Nari Genomal</td>\n","      <td>$1 B</td>\n","      <td>82</td>\n","      <td>Philippines</td>\n","      <td>apparel</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2597</th>\n","      <td>2597</td>\n","      <td>2578</td>\n","      <td>Ramesh Genomal</td>\n","      <td>$1 B</td>\n","      <td>71</td>\n","      <td>Philippines</td>\n","      <td>apparel</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2598</th>\n","      <td>2598</td>\n","      <td>2578</td>\n","      <td>Sunder Genomal</td>\n","      <td>$1 B</td>\n","      <td>68</td>\n","      <td>Philippines</td>\n","      <td>garments</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2599</th>\n","      <td>2599</td>\n","      <td>2578</td>\n","      <td>Horst-Otto Gerberding</td>\n","      <td>$1 B</td>\n","      <td>69</td>\n","      <td>Germany</td>\n","      <td>flavors and fragrances</td>\n","      <td>Food &amp; Beverage</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2600 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b0368e42-9881-4e2e-85ff-1539c72a4e2f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b0368e42-9881-4e2e-85ff-1539c72a4e2f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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dtype='object')"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["frame=df[new_cols] #new dataset name\n","frame"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"cipwsQjDMCIB","executionInfo":{"status":"ok","timestamp":1656538808948,"user_tz":240,"elapsed":177,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"2e9dbc3d-a1e3-48f6-c08c-379a22592003"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Rank                       Name Networth  age        Country  \\\n","0        1                 Elon Musk    $219 B   50  United States   \n","1        2                Jeff Bezos    $171 B   58  United States   \n","2        3  Bernard Arnault & family    $158 B   73         France   \n","3        4                Bill Gates    $129 B   66  United States   \n","4        5            Warren Buffett    $118 B   91  United States   \n","...    ...                        ...      ...  ...            ...   \n","2595  2578    Jorge Gallardo Ballart      $1 B   80          Spain   \n","2596  2578              Nari Genomal      $1 B   82    Philippines   \n","2597  2578            Ramesh Genomal      $1 B   71    Philippines   \n","2598  2578            Sunder Genomal      $1 B   68    Philippines   \n","2599  2578     Horst-Otto Gerberding      $1 B   69        Germany   \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   \n","...                      ...                     ...  \n","2595         pharmaceuticals             Healthcare   \n","2596                 apparel       Fashion & Retail   \n","2597                 apparel       Fashion & Retail   \n","2598                garments       Fashion & Retail   \n","2599  flavors and fragrances        Food & Beverage   \n","\n","[2600 rows x 7 columns]"],"text/html":["\n","  <div id=\"df-d9011b89-cfde-42a3-9532-da5587c24b47\">\n","    <div class=\"colab-df-container\">\n","      <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>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>1</td>\n","      <td>Elon Musk</td>\n","      <td>$219 B</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>2</td>\n","      <td>Jeff Bezos</td>\n","      <td>$171 B</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>3</td>\n","      <td>Bernard Arnault &amp; family</td>\n","      <td>$158 B</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>4</td>\n","      <td>Bill Gates</td>\n","      <td>$129 B</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>5</td>\n","      <td>Warren Buffett</td>\n","      <td>$118 B</td>\n","      <td>91</td>\n","      <td>United States</td>\n","      <td>Berkshire Hathaway</td>\n","      <td>Finance &amp; Investments</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","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2595</th>\n","      <td>2578</td>\n","      <td>Jorge Gallardo Ballart</td>\n","      <td>$1 B</td>\n","      <td>80</td>\n","      <td>Spain</td>\n","      <td>pharmaceuticals</td>\n","      <td>Healthcare</td>\n","    </tr>\n","    <tr>\n","      <th>2596</th>\n","      <td>2578</td>\n","      <td>Nari Genomal</td>\n","      <td>$1 B</td>\n","      <td>82</td>\n","      <td>Philippines</td>\n","      <td>apparel</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2597</th>\n","      <td>2578</td>\n","      <td>Ramesh Genomal</td>\n","      <td>$1 B</td>\n","      <td>71</td>\n","      <td>Philippines</td>\n","      <td>apparel</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2598</th>\n","      <td>2578</td>\n","      <td>Sunder Genomal</td>\n","      <td>$1 B</td>\n","      <td>68</td>\n","      <td>Philippines</td>\n","      <td>garments</td>\n","      <td>Fashion &amp; Retail</td>\n","    </tr>\n","    <tr>\n","      <th>2599</th>\n","      <td>2578</td>\n","      <td>Horst-Otto Gerberding</td>\n","      <td>$1 B</td>\n","      <td>69</td>\n","      <td>Germany</td>\n","      <td>flavors and fragrances</td>\n","      <td>Food &amp; Beverage</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2600 rows × 7 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9011b89-cfde-42a3-9532-da5587c24b47')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n","  \n","/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  self[name] = value\n"]},{"output_type":"execute_result","data":{"text/plain":["0       219.0\n","1       171.0\n","2       158.0\n","3       129.0\n","4       118.0\n","        ...  \n","2595      1.0\n","2596      1.0\n","2597      1.0\n","2598      1.0\n","2599      1.0\n","Name: Networth, Length: 2600, dtype: float64"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["frame.shape #checking the number of rows and columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A_yA1PaQCXRn","executionInfo":{"status":"ok","timestamp":1656538815813,"user_tz":240,"elapsed":192,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"94d1b53b-02e2-4b78-ae89-8dc6ddc08a1e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2600, 7)"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["#@title\n","frame.head()#default 5 rows"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"babqUybBNYer","executionInfo":{"status":"ok","timestamp":1656338097516,"user_tz":240,"elapsed":239,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"a07cba13-88f2-40e1-ea84-aaafbc6028e4","cellView":"form","collapsed":true},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Rank                       Name  Networth  age        Country  \\\n","0     1                 Elon Musk      219.0   50  United States   \n","1     2                Jeff Bezos      171.0   58  United States   \n","2     3  Bernard Arnault & family      158.0   73         France   \n","3     4                Bill Gates      129.0   66  United States   \n","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   "],"text/html":["\n","  <div id=\"df-905d3786-2031-4e7f-b91d-173eaba7dd00\">\n","    <div class=\"colab-df-container\">\n","      <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>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>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>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>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>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>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>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-905d3786-2031-4e7f-b91d-173eaba7dd00')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-905d3786-2031-4e7f-b91d-173eaba7dd00 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Investments</td>\n","      <td>386</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Technology</td>\n","      <td>329</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Manufacturing</td>\n","      <td>322</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Fashion &amp; Retail</td>\n","      <td>246</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Healthcare</td>\n","      <td>212</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Food &amp; Beverage</td>\n","      <td>201</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Real Estate</td>\n","      <td>189</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Diversified</td>\n","      <td>178</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Media &amp; Entertainment</td>\n","      <td>95</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Energy</td>\n","      <td>93</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Automotive</td>\n","      <td>69</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Metals &amp; Mining</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Service</td>\n","      <td>51</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Construction &amp; Engineering</td>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Logistics</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Telecom</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Sports</td>\n","      <td>26</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Gambling &amp; Casinos</td>\n","      <td>23</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7e3c875-5ff2-4dcf-82c4-74afdd549f62')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c7e3c875-5ff2-4dcf-82c4-74afdd549f62 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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ax.text(index,a+5,str(a),ha='center', va='center')\n","plt.xticks(rotation=90)\n","ax.set_title(\"Number of billionaires by Industry\",size='20')\n","plt.show()\n","#The grapg below shows that Finance and investments has the highest number of billionaires"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":758},"id":"UNVR9RicZtxH","executionInfo":{"status":"ok","timestamp":1656538827350,"user_tz":240,"elapsed":659,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"a2f9bd03-20c2-470a-d8e1-1a415ca5b160"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#Analysis:\n","#*Billionaires own businesses that produce goods and provide jobs for people, so many people benefit from them even if the billionaires do not simply give their money away.\n","#*The above graph here depicts that the Investment of billionaires in the Top 10 Industry which creates numerous job opportunities. "],"metadata":{"id":"1FSGXjsjUgOq"}},{"cell_type":"code","source":["# To show Number of billionaires by country need to find list of countries and numbers.\n","country_list=frame.Country.value_counts().rename_axis('Country').reset_index(name='Counts')\n","country_list # The list obtained below is long, hence taking top 10 countries and rest of the counts as others"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"_Dgt6caktfqN","executionInfo":{"status":"ok","timestamp":1656538837352,"user_tz":240,"elapsed":148,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"3b16a9d0-10d9-4baa-dd19-bb55fcc5a73b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 Country  Counts\n","0          United States     719\n","1                  China     515\n","2                  India     161\n","3                Germany     130\n","4                 Russia      81\n","..                   ...     ...\n","70             Venezuela       1\n","71              Portugal       1\n","72               Algeria       1\n","73  Eswatini (Swaziland)       1\n","74               Estonia       1\n","\n","[75 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-1fe8b618-abf3-4927-b287-2ace6cd71781\">\n","    <div class=\"colab-df-container\">\n","      <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>Country</th>\n","      <th>Counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>United States</td>\n","      <td>719</td>\n","    </tr>\n","    <tr>\n","      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particular axis with optional set logic along the other axes.\n","country_list #final list of the countries and the counts\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KLzSI_B9yUlR","executionInfo":{"status":"ok","timestamp":1656538839768,"user_tz":240,"elapsed":229,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"80940a20-c0b1-4225-9822-cfee0c494d65"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Country  Counts\n","0  United States     719\n","1          China     515\n","2          India     161\n","3        Germany     130\n","4         Russia      81\n","5      Hong Kong      67\n","6         Canada      64\n","7         Brazil      60\n","8          Italy      52\n","9         Taiwan      51\n","0         Others     700"],"text/html":["\n","  <div id=\"df-48400cc7-9a58-4f8f-95be-165cb29853ac\">\n","    <div class=\"colab-df-container\">\n","      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 <tr>\n","      <th>5</th>\n","      <td>Hong Kong</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Canada</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Brazil</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Italy</td>\n","      <td>52</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Taiwan</td>\n","      <td>51</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Others</td>\n","      <td>700</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48400cc7-9a58-4f8f-95be-165cb29853ac')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["# Number of billionaires by country\n","from pandas._libs import index\n","fig, ax = plt.subplots(figsize=(10,10))\n","sns.barplot(x='Country',y='Counts',data=country_list,edgecolor='black')\n","for index, a in enumerate(country_list['Counts']):\n","  ax.text(index, a+14,str(a),ha='center', va='center')\n","  #print(index, a)\n","plt.xticks(rotation=90)\n","\n","ax.set_title(\"Number of billionaires by country\",size='20')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":684},"id":"pcW19Fh1t4AL","executionInfo":{"status":"ok","timestamp":1656538943519,"user_tz":240,"elapsed":467,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"5f097225-d74b-417f-b939-fda2c4491091"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#Above graph describes more number of Billionaires in USA is the best example of how a country will grow economically strong by the contribution of large number of billionaires."],"metadata":{"id":"5jrMRpVxXPnW"}},{"cell_type":"code","source":["#Number of billionaires by age\n","fig, ax = plt.subplots(figsize=(10,10))\n","sns.histplot(x='age',data=frame,color='green')\n","ax.set_xlabel('Age distribution')\n","ax.set_title(\"Number of billionaires by age\",size='20')\n","plt.show()\n","#This histogram below shows the distribution of billionaires by age.\n","# graph shows that age 50-80 has more billionaires distribution.\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":627},"id":"u6exJKi8DB7M","executionInfo":{"status":"ok","timestamp":1656538949429,"user_tz":240,"elapsed":447,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"e12a47e0-6f6d-48de-d21b-041ba9dfab4c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Graph illustrates the distribution of the age of billionaires are more in the age range 50-80.\n"],"metadata":{"id":"FVsjPt87Xzpg"}},{"cell_type":"code","source":["# To show Number of billionaires by country and networth need to find list of countries and networth.\n","country_by_networth=frame.groupby(['Country']).Networth.sum().rename_axis('Country').sort_values(ascending=False).reset_index()\n","d={'Country': ['Others'],'Networth':[country_by_networth['Networth'][10:].sum()]}\n","others = pd.DataFrame(data=d)\n","country_by_networth = pd.concat([country_by_networth[0:10], others])\n","country_by_networth"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"8WqPM6eZKhzq","executionInfo":{"status":"ok","timestamp":1656538957096,"user_tz":240,"elapsed":148,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"f95383f9-2018-4c09-be83-74746ec1d6e1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          Country  Networth\n","0   United States   4685.10\n","1           China   1938.45\n","2           India    744.80\n","3         Germany    604.00\n","4          France    550.00\n","5       Hong Kong    383.40\n","6          Russia    318.20\n","7          Canada    307.90\n","8       Australia    205.10\n","9  United Kingdom    199.10\n","0          Others   2701.90"],"text/html":["\n","  <div id=\"df-b6d2b88d-266d-4ef7-80da-59b1b89a887c\">\n","    <div class=\"colab-df-container\">\n","      <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>Country</th>\n","      <th>Networth</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>United States</td>\n","      <td>4685.10</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>China</td>\n","      <td>1938.45</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>India</td>\n","      <td>744.80</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Germany</td>\n","      <td>604.00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>France</td>\n","      <td>550.00</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Hong Kong</td>\n","      <td>383.40</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Russia</td>\n","      <td>318.20</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Canada</td>\n","      <td>307.90</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Australia</td>\n","      <td>205.10</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>United Kingdom</td>\n","      <td>199.10</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Others</td>\n","      <td>2701.90</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6d2b88d-266d-4ef7-80da-59b1b89a887c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 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va='center')\n","plt.xticks(rotation=90)\n","\n","ax.set_title(\"Country and its Networth\",size='20')\n","plt.show()\n","#It is fascinating to see Networth of the top billionaires countries as below."],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":697},"id":"8wmas1OHOJvD","executionInfo":{"status":"ok","timestamp":1656538960525,"user_tz":240,"elapsed":375,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"5060cd21-a917-4d5e-f6ee-aae6215459a4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# To show Number of billionaires industry and networth need to find list of industries and networth.\n","industry_by_networth=frame.groupby(['Industry']).Networth.sum().rename_axis('Industry').sort_values(ascending=False).reset_index()\n","d={'Industry': ['Others'],'Networth':[industry_by_networth['Networth'][10:].sum()]}\n","others = pd.DataFrame(data=d)\n","industry_by_networth= pd.concat([industry_by_networth[0:10], others])\n","industry_by_networth"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"lJ3b_m0TP9x8","executionInfo":{"status":"ok","timestamp":1656538964685,"user_tz":240,"elapsed":189,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"99ee60cd-0d16-4693-f3f3-e33ad98dafb5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 Industry  Networth\n","0             Technology    2168.40\n","1  Finance & Investments    1734.30\n","2       Fashion & Retail    1613.20\n","3          Manufacturing    1079.80\n","4            Diversified     939.50\n","5        Food & Beverage     933.35\n","6             Healthcare     708.90\n","7            Real Estate     685.80\n","8             Automotive     582.60\n","9  Media & Entertainment     493.60\n","0                  Others   1698.50"],"text/html":["\n","  <div id=\"df-dd2db422-385d-49bc-a628-bbe641addc9f\">\n","    <div class=\"colab-df-container\">\n","      <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>Industry</th>\n","      <th>Networth</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Technology</td>\n","      <td>2168.40</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Finance &amp; Investments</td>\n","      <td>1734.30</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Fashion &amp; Retail</td>\n","      <td>1613.20</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Manufacturing</td>\n","      <td>1079.80</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Diversified</td>\n","      <td>939.50</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Food &amp; Beverage</td>\n","      <td>933.35</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Healthcare</td>\n","      <td>708.90</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Real Estate</td>\n","      <td>685.80</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Automotive</td>\n","      <td>582.60</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Media &amp; Entertainment</td>\n","      <td>493.60</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Others</td>\n","      <td>1698.50</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd2db422-385d-49bc-a628-bbe641addc9f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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va='center')\n","plt.xticks(rotation=90)\n","\n","ax.set_title(\"Industry and its Networth\",size='20')\n","plt.show()\n","#below graph illustrates the networth of the each industry. \n","#It is intriguing to see the how Technology networth is top contributing to the whole world."],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":736},"id":"6wp79N9JRHD2","executionInfo":{"status":"ok","timestamp":1656538968086,"user_tz":240,"elapsed":672,"user":{"displayName":"Vasavi Hegde","userId":"02520262388454139977"}},"outputId":"f8c25c25-36f1-4e95-cf7b-c386345f4ebd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#Overall,this project takes you through interesting visualization facts about how billionaires and their industry contribute to economic growth of the nation in the world.\n","#The references used for the projects are as follows:\n","https://www.kaggle.com/code/jjasnos/forbes-2022-billionaires/data \n","\n","https://www.forbes.com/billionaires/\n"],"metadata":{"id":"KeBygjFPjjNZ"}}]}