Web_Scraping.ipynb

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      "metadata": {
        "id": "8Ga-1uNfgXb7"
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      "source": [
        "#Import Librairie\n",
        "from bs4 import BeautifulSoup as bs\n",
        "import requests as re \n",
        "import pandas as pd \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data parsing \n",
        "url ='https://www.cbinsights.com/research-unicorn-companies'\n",
        "unicorn_data=re.get(url).text"
      ],
      "metadata": {
        "id": "Dfcc-cX8kAzi"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_soup = bs(unicorn_data, 'html.parser')\n",
        "unicorn_table = unicorn_soup.find_all('table')"
      ],
      "metadata": {
        "id": "ujCwRZmySy_O"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(unicorn_table)"
      ],
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        "outputId": "20f96de2-d00e-4460-86bb-296931cc9e56"
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    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df =pd.read_html(str(unicorn_table),flavor='bs4')[0]\n",
        "unicorn_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sbGM0TIoTbfC",
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      "execution_count": 96,
      "outputs": [
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              "     Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0  ByteDance           $140    4/7/2017          China        Beijing   \n",
              "1     SpaceX           $127   12/1/2012  United States      Hawthorne   \n",
              "2      SHEIN           $100    7/3/2018          China       Shenzhen   \n",
              "3     Stripe            $63   1/23/2014  United States  San Francisco   \n",
              "4      Canva            $40    1/8/2018      Australia    Surry Hills   \n",
              "\n",
              "                          Industry  \\\n",
              "0          Artificial intelligence   \n",
              "1                            Other   \n",
              "2  E-commerce & direct-to-consumer   \n",
              "3                          Fintech   \n",
              "4     Internet software & services   \n",
              "\n",
              "                                    Select Investors  \n",
              "0  Sequoia Capital China, SIG Asia Investments, S...  \n",
              "1  Founders Fund, Draper Fisher Jurvetson, Rothen...  \n",
              "2  Tiger Global Management, Sequoia Capital China...  \n",
              "3        Khosla Ventures, LowercaseCapital, capitalG  \n",
              "4  Sequoia Capital China, Blackbird Ventures, Mat...  "
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              "      <th>Country</th>\n",
              "      <th>City</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ByteDance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpaceX</td>\n",
              "      <td>$127</td>\n",
              "      <td>12/1/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Hawthorne</td>\n",
              "      <td>Other</td>\n",
              "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SHEIN</td>\n",
              "      <td>$100</td>\n",
              "      <td>7/3/2018</td>\n",
              "      <td>China</td>\n",
              "      <td>Shenzhen</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canva</td>\n",
              "      <td>$40</td>\n",
              "      <td>1/8/2018</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Surry Hills</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Sequoia Capital China, Blackbird Ventures, Mat...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "(323, 7)"
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          "data": {
            "text/plain": [
              "       Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0    ByteDance           $140    4/7/2017          China        Beijing   \n",
              "1       SpaceX           $127   12/1/2012  United States      Hawthorne   \n",
              "2        SHEIN           $100    7/3/2018          China       Shenzhen   \n",
              "3       Stripe            $63   1/23/2014  United States  San Francisco   \n",
              "4        Canva            $40    1/8/2018      Australia    Surry Hills   \n",
              "..         ...            ...         ...            ...            ...   \n",
              "318     Sentry             $3   2/18/2021  United States  San Francisco   \n",
              "319     Kraken          $2.92   6/25/2019  United States  San Francisco   \n",
              "320   Workrise           $2.9   9/30/2019  United States         Austin   \n",
              "321       Loft           $2.9    1/3/2020         Brazil      Sao Paulo   \n",
              "322   BharatPe          $2.85   7/30/2021          India      New Delhi   \n",
              "\n",
              "                            Industry  \\\n",
              "0            Artificial intelligence   \n",
              "1                              Other   \n",
              "2    E-commerce & direct-to-consumer   \n",
              "3                            Fintech   \n",
              "4       Internet software & services   \n",
              "..                               ...   \n",
              "318                            Other   \n",
              "319                          Fintech   \n",
              "320     Internet software & services   \n",
              "321  E-commerce & direct-to-consumer   \n",
              "322                          Fintech   \n",
              "\n",
              "                                      Select Investors  \n",
              "0    Sequoia Capital China, SIG Asia Investments, S...  \n",
              "1    Founders Fund, Draper Fisher Jurvetson, Rothen...  \n",
              "2    Tiger Global Management, Sequoia Capital China...  \n",
              "3          Khosla Ventures, LowercaseCapital, capitalG  \n",
              "4    Sequoia Capital China, Blackbird Ventures, Mat...  \n",
              "..                                                 ...  \n",
              "318             New Enterprise Associates, Accel, Bond  \n",
              "319  Bnk To The Future, Trammell Ventures, SBI Inve...  \n",
              "320  Founders Fund, Quantum Energy Partners, Bedroc...  \n",
              "321     Monashees+, Andreessen Horowitz, QED Investors  \n",
              "322   Insight Partners, Sequoia Capital India, BEENEXT  \n",
              "\n",
              "[321 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Investors</th>\n",
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              "      <th>0</th>\n",
              "      <td>ByteDance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpaceX</td>\n",
              "      <td>$127</td>\n",
              "      <td>12/1/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Hawthorne</td>\n",
              "      <td>Other</td>\n",
              "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SHEIN</td>\n",
              "      <td>$100</td>\n",
              "      <td>7/3/2018</td>\n",
              "      <td>China</td>\n",
              "      <td>Shenzhen</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canva</td>\n",
              "      <td>$40</td>\n",
              "      <td>1/8/2018</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Surry Hills</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Sequoia Capital China, Blackbird Ventures, Mat...</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>318</th>\n",
              "      <td>Sentry</td>\n",
              "      <td>$3</td>\n",
              "      <td>2/18/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Other</td>\n",
              "      <td>New Enterprise Associates, Accel, Bond</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>Kraken</td>\n",
              "      <td>$2.92</td>\n",
              "      <td>6/25/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Bnk To The Future, Trammell Ventures, SBI Inve...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>Workrise</td>\n",
              "      <td>$2.9</td>\n",
              "      <td>9/30/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>Austin</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Founders Fund, Quantum Energy Partners, Bedroc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>Loft</td>\n",
              "      <td>$2.9</td>\n",
              "      <td>1/3/2020</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Monashees+, Andreessen Horowitz, QED Investors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>BharatPe</td>\n",
              "      <td>$2.85</td>\n",
              "      <td>7/30/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Insight Partners, Sequoia Capital India, BEENEXT</td>\n",
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              "<p>321 rows × 7 columns</p>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0780e13f-79d9-42c6-9830-cef6fb69748c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0780e13f-79d9-42c6-9830-cef6fb69748c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data processing & parsing \n",
        "continent_df = pd.read_csv('continents.csv')\n",
        "continent_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nAJ6h5XgU4nC",
        "outputId": "f6d197ad-d924-4890-a460-8b17badb444c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Entity      Code  Year Continent\n",
              "0               Abkhazia  OWID_ABK  2015      Asia\n",
              "1            Afghanistan       AFG  2015      Asia\n",
              "2  Akrotiri and Dhekelia  OWID_AKD  2015      Asia\n",
              "3                Albania       ALB  2015    Europe\n",
              "4                Algeria       DZA  2015    Africa"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Entity</th>\n",
              "      <th>Code</th>\n",
              "      <th>Year</th>\n",
              "      <th>Continent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abkhazia</td>\n",
              "      <td>OWID_ABK</td>\n",
              "      <td>2015</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2015</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Akrotiri and Dhekelia</td>\n",
              "      <td>OWID_AKD</td>\n",
              "      <td>2015</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>2015</td>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>DZA</td>\n",
              "      <td>2015</td>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bcd1d88-7878-4249-92e6-4aae30224589')\"\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-0bcd1d88-7878-4249-92e6-4aae30224589');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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",
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          },
          "metadata": {},
          "execution_count": 99
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df['Continent'] = \"\""
      ],
      "metadata": {
        "id": "-hrkLTUAXMH1"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getContinent(country):\n",
        "    con = str(continent_df.loc[(continent_df['Entity']==country),'Continent'].values)\n",
        "    list_of_chars = ['[', ']', \"''\",' ']\n",
        "    translation_table = str.maketrans('', '', ''.join(list_of_chars))\n",
        "    con = con.translate(translation_table)\n",
        "    return con"
      ],
      "metadata": {
        "id": "ZQ64DvBbY9iW"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for a in unicorn_df.index:\n",
        "    unicorn_df['Continent'][a] = getContinent(unicorn_df['Country'].values[a])\n",
        "\n",
        "unicorn_df.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        },
        "id": "RkQ8UbNcZBI2",
        "outputId": "641fff42-0c16-42f7-f80d-6ee9ebe93bbf"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Company Valuation ($B) Date Joined         Country           City  \\\n",
              "0      ByteDance           $140    4/7/2017           China        Beijing   \n",
              "1         SpaceX           $127   12/1/2012   United States      Hawthorne   \n",
              "2          SHEIN           $100    7/3/2018           China       Shenzhen   \n",
              "3         Stripe            $63   1/23/2014   United States  San Francisco   \n",
              "4          Canva            $40    1/8/2018       Australia    Surry Hills   \n",
              "5   Checkout.com            $40    5/2/2019  United Kingdom         London   \n",
              "6        Revolut            $33   4/26/2018  United Kingdom         London   \n",
              "7     Epic Games          $31.5  10/26/2018   United States           Cary   \n",
              "8     Databricks            $31    2/5/2019   United States  San Francisco   \n",
              "9       Fanatics            $31    6/6/2012   United States   Jacksonville   \n",
              "10         Chime            $25    3/5/2019   United States  San Francisco   \n",
              "11        BYJU's            $22   7/25/2017           India      Bengaluru   \n",
              "12        OpenAI            $20   7/22/2019   United States  San Francisco   \n",
              "13   Xiaohongshu            $20   3/31/2016           China       Shanghai   \n",
              "14   J&T Express            $20    4/7/2021       Indonesia        Jakarta   \n",
              "\n",
              "                               Industry  \\\n",
              "0               Artificial intelligence   \n",
              "1                                 Other   \n",
              "2       E-commerce & direct-to-consumer   \n",
              "3                               Fintech   \n",
              "4          Internet software & services   \n",
              "5                               Fintech   \n",
              "6                               Fintech   \n",
              "7                                 Other   \n",
              "8           Data management & analytics   \n",
              "9       E-commerce & direct-to-consumer   \n",
              "10                              Fintech   \n",
              "11                               Edtech   \n",
              "12              Artificial intelligence   \n",
              "13      E-commerce & direct-to-consumer   \n",
              "14  Supply chain, logistics, & delivery   \n",
              "\n",
              "                                     Select Investors     Continent  \n",
              "0   Sequoia Capital China, SIG Asia Investments, S...          Asia  \n",
              "1   Founders Fund, Draper Fisher Jurvetson, Rothen...  NorthAmerica  \n",
              "2   Tiger Global Management, Sequoia Capital China...          Asia  \n",
              "3         Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  \n",
              "4   Sequoia Capital China, Blackbird Ventures, Mat...       Oceania  \n",
              "5   Tiger Global Management, Insight Partners, DST...        Europe  \n",
              "6          index Ventures, DST Global, Ribbit Capital        Europe  \n",
              "7               Tencent Holdings, KKR, Smash Ventures  NorthAmerica  \n",
              "8   Andreessen Horowitz, New Enterprise Associates...  NorthAmerica  \n",
              "9   SoftBank Group, Andreessen Horowitz, Temasek H...  NorthAmerica  \n",
              "10   Forerunner Ventures, Crosslink Capital, Homebrew  NorthAmerica  \n",
              "11  Tencent Holdings, Lightspeed India Partners, S...          Asia  \n",
              "12                                    Khosla Ventures  NorthAmerica  \n",
              "13                     GGV Capital, ZhenFund, Tencent          Asia  \n",
              "14  Hillhouse Capital Management, Boyu Capital, Se...          Asia  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e64b4150-e275-4352-adbc-b7ad59a596d2\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Investors</th>\n",
              "      <th>Continent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ByteDance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpaceX</td>\n",
              "      <td>$127</td>\n",
              "      <td>12/1/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Hawthorne</td>\n",
              "      <td>Other</td>\n",
              "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SHEIN</td>\n",
              "      <td>$100</td>\n",
              "      <td>7/3/2018</td>\n",
              "      <td>China</td>\n",
              "      <td>Shenzhen</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canva</td>\n",
              "      <td>$40</td>\n",
              "      <td>1/8/2018</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Surry Hills</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Sequoia Capital China, Blackbird Ventures, Mat...</td>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Checkout.com</td>\n",
              "      <td>$40</td>\n",
              "      <td>5/2/2019</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Insight Partners, DST...</td>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Revolut</td>\n",
              "      <td>$33</td>\n",
              "      <td>4/26/2018</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>index Ventures, DST Global, Ribbit Capital</td>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Epic Games</td>\n",
              "      <td>$31.5</td>\n",
              "      <td>10/26/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Other</td>\n",
              "      <td>Tencent Holdings, KKR, Smash Ventures</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Databricks</td>\n",
              "      <td>$31</td>\n",
              "      <td>2/5/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Data management &amp; analytics</td>\n",
              "      <td>Andreessen Horowitz, New Enterprise Associates...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Fanatics</td>\n",
              "      <td>$31</td>\n",
              "      <td>6/6/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Jacksonville</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>SoftBank Group, Andreessen Horowitz, Temasek H...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Chime</td>\n",
              "      <td>$25</td>\n",
              "      <td>3/5/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Forerunner Ventures, Crosslink Capital, Homebrew</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>BYJU's</td>\n",
              "      <td>$22</td>\n",
              "      <td>7/25/2017</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Edtech</td>\n",
              "      <td>Tencent Holdings, Lightspeed India Partners, S...</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>OpenAI</td>\n",
              "      <td>$20</td>\n",
              "      <td>7/22/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Khosla Ventures</td>\n",
              "      <td>NorthAmerica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Xiaohongshu</td>\n",
              "      <td>$20</td>\n",
              "      <td>3/31/2016</td>\n",
              "      <td>China</td>\n",
              "      <td>Shanghai</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>GGV Capital, ZhenFund, Tencent</td>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>J&amp;T Express</td>\n",
              "      <td>$20</td>\n",
              "      <td>4/7/2021</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>Jakarta</td>\n",
              "      <td>Supply chain, logistics, &amp; delivery</td>\n",
              "      <td>Hillhouse Capital Management, Boyu Capital, Se...</td>\n",
              "      <td>Asia</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year = unicorn_df['Date Joined'].str.split('/', n=2, expand=True)\n",
        "year = year[2].astype('int32')\n",
        "unicorn_df['Year'] = year\n",
        "unicorn_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "NYa5JtmhZDeH",
        "outputId": "a6aac15b-a298-496d-b7e0-2d62f83a0926"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0  ByteDance           $140    4/7/2017          China        Beijing   \n",
              "1     SpaceX           $127   12/1/2012  United States      Hawthorne   \n",
              "2      SHEIN           $100    7/3/2018          China       Shenzhen   \n",
              "3     Stripe            $63   1/23/2014  United States  San Francisco   \n",
              "4      Canva            $40    1/8/2018      Australia    Surry Hills   \n",
              "\n",
              "                          Industry  \\\n",
              "0          Artificial intelligence   \n",
              "1                            Other   \n",
              "2  E-commerce & direct-to-consumer   \n",
              "3                          Fintech   \n",
              "4     Internet software & services   \n",
              "\n",
              "                                    Select Investors     Continent  Year  \n",
              "0  Sequoia Capital China, SIG Asia Investments, S...          Asia  2017  \n",
              "1  Founders Fund, Draper Fisher Jurvetson, Rothen...  NorthAmerica  2012  \n",
              "2  Tiger Global Management, Sequoia Capital China...          Asia  2018  \n",
              "3        Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  2014  \n",
              "4  Sequoia Capital China, Blackbird Ventures, Mat...       Oceania  2018  "
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              "      <td>1/8/2018</td>\n",
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              "                                                     [key], {});\n",
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      ]
    },
    {
      "cell_type": "code",
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        "unicorn_df.isnull().sum()"
      ],
      "metadata": {
        "id": "pCYSx1wmZJbR",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5d8d088-446a-47ae-fa50-8a7757281548"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             0\n",
              "Valuation ($B)      0\n",
              "Date Joined         0\n",
              "Country             0\n",
              "City                2\n",
              "Industry            0\n",
              "Select Investors    0\n",
              "Continent           0\n",
              "Year                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.where(unicorn_df['Select Investors'].isnull())[0]"
      ],
      "metadata": {
        "id": "xWKu0qeKZXec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "675b01ea-325e-40f0-9394-bdd90f73ce33"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df[unicorn_df['Continent'] == ''].index"
      ],
      "metadata": {
        "id": "Cu5LSw0UZam7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f763454a-56b1-418d-bd01-e6175e804a2c"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.loc[unicorn_df['Continent']=='']"
      ],
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        "id": "JgBIrXyTZgF-",
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          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "219febc0-ff2c-4012-f7cc-817eb5b36780"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Company, Valuation ($B), Date Joined, Country, City, Industry, Select Investors, Continent, Year]\n",
              "Index: []"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfcd87f5-736f-421a-b3aa-d75f8916f29f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking country name in continent table\n",
        "continent_df[continent_df['Entity'].str.contains('Czech')]"
      ],
      "metadata": {
        "id": "kZRwX27FZk9J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "9eafdc1f-33b6-4add-c509-4407cd57eec9"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Entity      Code  Year Continent\n",
              "64         Czechia       CZE  2015    Europe\n",
              "65  Czechoslovakia  OWID_CZS  2015    Europe"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e23aa44-250b-4c80-b6a8-67ef8a5ee83b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Entity</th>\n",
              "      <th>Code</th>\n",
              "      <th>Year</th>\n",
              "      <th>Continent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Czechia</td>\n",
              "      <td>CZE</td>\n",
              "      <td>2015</td>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Czechoslovakia</td>\n",
              "      <td>OWID_CZS</td>\n",
              "      <td>2015</td>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e23aa44-250b-4c80-b6a8-67ef8a5ee83b')\"\n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e23aa44-250b-4c80-b6a8-67ef8a5ee83b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e23aa44-250b-4c80-b6a8-67ef8a5ee83b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def getContinent(country):\n",
        "    if(country in continent_df['Entity'].values):\n",
        "        con = str(continent_df.loc[(continent_df['Entity']==country),'Continent'].values)\n",
        "    else:\n",
        "        if len(country.split(' '))==1:\n",
        "            con = str(continent_df.loc[(continent_df['Entity'].str.contains(country, flags=re.I, regex=True)),'Continent'].values[0])\n",
        "        else:\n",
        "            country = country.split(' ')[0]\n",
        "            con = str(continent_df.loc[(continent_df['Entity'].str.contains(country, flags=re.I, regex=True)),'Continent'].values[0])\n",
        "        \n",
        "    list_of_chars = ['[', ']', \"''\",' ']\n",
        "    translation_table = str.maketrans('', '', ''.join(list_of_chars))\n",
        "    con = con.translate(translation_table)\n",
        "    return con"
      ],
      "metadata": {
        "id": "WzhNNKGfZoSE"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.dtypes"
      ],
      "metadata": {
        "id": "S_h33wRoZ6Cg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da936acd-e5fd-4bc0-8f32-3234755bba88"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             object\n",
              "Valuation ($B)      object\n",
              "Date Joined         object\n",
              "Country             object\n",
              "City                object\n",
              "Industry            object\n",
              "Select Investors    object\n",
              "Continent           object\n",
              "Year                 int32\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.head() #check it once again"
      ],
      "metadata": {
        "id": "uVf_uFSMaBis",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "c55b612a-72ba-4581-bbf7-d47e111e4f8b"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0  ByteDance           $140    4/7/2017          China        Beijing   \n",
              "1     SpaceX           $127   12/1/2012  United States      Hawthorne   \n",
              "2      SHEIN           $100    7/3/2018          China       Shenzhen   \n",
              "3     Stripe            $63   1/23/2014  United States  San Francisco   \n",
              "4      Canva            $40    1/8/2018      Australia    Surry Hills   \n",
              "\n",
              "                          Industry  \\\n",
              "0          Artificial intelligence   \n",
              "1                            Other   \n",
              "2  E-commerce & direct-to-consumer   \n",
              "3                          Fintech   \n",
              "4     Internet software & services   \n",
              "\n",
              "                                    Select Investors     Continent  Year  \n",
              "0  Sequoia Capital China, SIG Asia Investments, S...          Asia  2017  \n",
              "1  Founders Fund, Draper Fisher Jurvetson, Rothen...  NorthAmerica  2012  \n",
              "2  Tiger Global Management, Sequoia Capital China...          Asia  2018  \n",
              "3        Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  2014  \n",
              "4  Sequoia Capital China, Blackbird Ventures, Mat...       Oceania  2018  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2e6508d-32b1-413c-858f-7985cf34bc45\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Investors</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ByteDance</td>\n",
              "      <td>$140</td>\n",
              "      <td>4/7/2017</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpaceX</td>\n",
              "      <td>$127</td>\n",
              "      <td>12/1/2012</td>\n",
              "      <td>United States</td>\n",
              "      <td>Hawthorne</td>\n",
              "      <td>Other</td>\n",
              "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SHEIN</td>\n",
              "      <td>$100</td>\n",
              "      <td>7/3/2018</td>\n",
              "      <td>China</td>\n",
              "      <td>Shenzhen</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2018</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canva</td>\n",
              "      <td>$40</td>\n",
              "      <td>1/8/2018</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Surry Hills</td>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>Sequoia Capital China, Blackbird Ventures, Mat...</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>2018</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c2e6508d-32b1-413c-858f-7985cf34bc45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr_ind = unicorn_df['Industry'].unique()\n",
        "list_ind = arr_ind.tolist()\n"
      ],
      "metadata": {
        "id": "XL_nbxSuaGjQ"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pprint #not really necessary but it will help to prettify the dictionary groups_words\n",
        "ind_lower = {v: v.lower() for v in list_ind}\n",
        "\n",
        "my_list = []\n",
        "groups_words = {}\n",
        "for key, value in ind_lower.items():\n",
        "    if value in groups_words.keys():\n",
        "        my_list = groups_words[value]\n",
        "        my_list.append(key)\n",
        "        groups_words[value] = my_list\n",
        "    else:\n",
        "        groups_words[value] = [key]\n",
        "\n",
        "pprint.pprint(groups_words)"
      ],
      "metadata": {
        "id": "z4_s8i98aMXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38c7979d-9e61-4648-a5e5-0d38897de036"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'artificial intelligence': ['Artificial intelligence'],\n",
            " 'auto & transportation': ['Auto & transportation'],\n",
            " 'consumer & retail': ['Consumer & retail'],\n",
            " 'cybersecurity': ['Cybersecurity'],\n",
            " 'data management & analytics': ['Data management & analytics'],\n",
            " 'e-commerce & direct-to-consumer': ['E-commerce & direct-to-consumer'],\n",
            " 'edtech': ['Edtech'],\n",
            " 'fintech': ['Fintech'],\n",
            " 'hardware': ['Hardware'],\n",
            " 'health': ['Health'],\n",
            " 'internet software & services': ['Internet software & services'],\n",
            " 'mobile & telecommunications': ['Mobile & telecommunications'],\n",
            " 'other': ['Other'],\n",
            " 'supply chain, logistics, & delivery': ['Supply chain, logistics, & delivery'],\n",
            " 'travel': ['Travel']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df[unicorn_df['Industry']=='Artificial intelligence'].count()"
      ],
      "metadata": {
        "id": "Jknpqa98aPqj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e538244b-7587-4fa2-c3ca-530548f649e4"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             18\n",
              "Valuation ($B)      18\n",
              "Date Joined         18\n",
              "Country             18\n",
              "City                18\n",
              "Industry            18\n",
              "Select Investors    18\n",
              "Continent           18\n",
              "Year                18\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df['Industry'].loc[unicorn_df['Industry']=='Artificial intelligence'] = 'Artificial Intelligence'"
      ],
      "metadata": {
        "id": "OJZJkqQuaT0_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e868abec-b395-4450-c52a-4c44f138160f"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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._setitem_single_block(indexer, value, name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df[unicorn_df['Industry']=='Artificial intelligence'].count()"
      ],
      "metadata": {
        "id": "tfwmYIKCaWna",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1bcb9d3-2c43-4cdb-ff74-7653ebbd2baf"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             0\n",
              "Valuation ($B)      0\n",
              "Date Joined         0\n",
              "Country             0\n",
              "City                0\n",
              "Industry            0\n",
              "Select Investors    0\n",
              "Continent           0\n",
              "Year                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.drop_duplicates()"
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      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "b35bcf6d-358f-43f6-e6ff-eb22e7199e40"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Company Valuation ($B) Date Joined        Country           City  \\\n",
              "0    ByteDance           $140    4/7/2017          China        Beijing   \n",
              "1       SpaceX           $127   12/1/2012  United States      Hawthorne   \n",
              "2        SHEIN           $100    7/3/2018          China       Shenzhen   \n",
              "3       Stripe            $63   1/23/2014  United States  San Francisco   \n",
              "4        Canva            $40    1/8/2018      Australia    Surry Hills   \n",
              "..         ...            ...         ...            ...            ...   \n",
              "318     Sentry             $3   2/18/2021  United States  San Francisco   \n",
              "319     Kraken          $2.92   6/25/2019  United States  San Francisco   \n",
              "320   Workrise           $2.9   9/30/2019  United States         Austin   \n",
              "321       Loft           $2.9    1/3/2020         Brazil      Sao Paulo   \n",
              "322   BharatPe          $2.85   7/30/2021          India      New Delhi   \n",
              "\n",
              "                            Industry  \\\n",
              "0            Artificial Intelligence   \n",
              "1                              Other   \n",
              "2    E-commerce & direct-to-consumer   \n",
              "3                            Fintech   \n",
              "4       Internet software & services   \n",
              "..                               ...   \n",
              "318                            Other   \n",
              "319                          Fintech   \n",
              "320     Internet software & services   \n",
              "321  E-commerce & direct-to-consumer   \n",
              "322                          Fintech   \n",
              "\n",
              "                                      Select Investors     Continent  Year  \n",
              "0    Sequoia Capital China, SIG Asia Investments, S...          Asia  2017  \n",
              "1    Founders Fund, Draper Fisher Jurvetson, Rothen...  NorthAmerica  2012  \n",
              "2    Tiger Global Management, Sequoia Capital China...          Asia  2018  \n",
              "3          Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  2014  \n",
              "4    Sequoia Capital China, Blackbird Ventures, Mat...       Oceania  2018  \n",
              "..                                                 ...           ...   ...  \n",
              "318             New Enterprise Associates, Accel, Bond  NorthAmerica  2021  \n",
              "319  Bnk To The Future, Trammell Ventures, SBI Inve...  NorthAmerica  2019  \n",
              "320  Founders Fund, Quantum Energy Partners, Bedroc...  NorthAmerica  2019  \n",
              "321     Monashees+, Andreessen Horowitz, QED Investors  SouthAmerica  2020  \n",
              "322   Insight Partners, Sequoia Capital India, BEENEXT          Asia  2021  \n",
              "\n",
              "[323 rows x 9 columns]"
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              "      <th>Company</th>\n",
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              "      <td>Loft</td>\n",
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              "    <tr>\n",
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              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>323 rows × 9 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d211b26-89e3-48c2-b234-e70f4e62d1a3')\"\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.to_csv('unicorn_dataset_fix.csv', index=False)"
      ],
      "metadata": {
        "id": "F3Ynr1qvafnb"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.to_csv('unicorn_dataset_fix.xlsx', index=False)"
      ],
      "metadata": {
        "id": "odDr1t8Nai4e"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Begin the exploration\n",
        "Filtering Data with Specific Conditions\n",
        "1. Display unicorn data from Asia with Artificial Intelligence industry then sort by their valuation"
      ],
      "metadata": {
        "id": "xOFtibNqa1uf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ai_asia_data = unicorn_df[(unicorn_df['Continent'] == 'Asia') & (unicorn_df['Industry'] == 'Artificial Intelligence')].sort_values(by='Valuation ($B)', ascending=False)\n",
        "ai_asia_data"
      ],
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        "colab": {
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          "height": 142
        },
        "outputId": "6dd3d77e-6b7e-4572-b50b-f23f9b486fbd"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Company Valuation ($B) Date Joined Country     City  \\\n",
              "138  Horizon Robotics             $5   2/27/2019   China  Beijing   \n",
              "192            MEGVII             $4  10/31/2017   China  Beijing   \n",
              "0           ByteDance           $140    4/7/2017   China  Beijing   \n",
              "\n",
              "                    Industry  \\\n",
              "138  Artificial Intelligence   \n",
              "192  Artificial Intelligence   \n",
              "0    Artificial Intelligence   \n",
              "\n",
              "                                      Select Investors Continent  Year  \n",
              "138  Hillhouse Capital Management, Linear Venture, ...      Asia  2019  \n",
              "192  Ant Financial Services Group, Russia-China Inv...      Asia  2017  \n",
              "0    Sequoia Capital China, SIG Asia Investments, S...      Asia  2017  "
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a30c9fb-7ba4-4bc3-b7ba-e4e937c786b0')\"\n",
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          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Display unicorn data with investors containing 'capital' word and from fintech industry"
      ],
      "metadata": {
        "id": "9N4zUk1EbBBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fintech_capital = unicorn_df[(unicorn_df['Select Investors'].str.contains('capital', flags=re.I, na=False, regex=True)) & (unicorn_df['Industry']=='Fintech')]\n",
        "fintech_capital"
      ],
      "metadata": {
        "id": "Lyvm6Nosa45z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e9e51c63-01c5-4431-d971-84ac38c995f5"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Company Valuation ($B) Date Joined         Country  \\\n",
              "3                    Stripe            $63   1/23/2014   United States   \n",
              "6                   Revolut            $33   4/26/2018  United Kingdom   \n",
              "10                    Chime            $25    3/5/2019   United States   \n",
              "21                   Ripple            $15  12/20/2019   United States   \n",
              "23                    Plaid          $13.5  12/11/2018   United States   \n",
              "29                     Brex          $12.3   10/5/2018   United States   \n",
              "35                     Deel            $12   4/21/2021   United States   \n",
              "40                     Bolt            $11   10/8/2021   United States   \n",
              "52   Digital Currency Group            $10   11/1/2021   United States   \n",
              "53                   KuCoin            $10   5/10/2022      Seychelles   \n",
              "56                      N26          $9.23   1/10/2019         Germany   \n",
              "64                    Rapyd          $8.75   12/3/2019  United Kingdom   \n",
              "71                    SumUp           $8.5   7/16/2019  United Kingdom   \n",
              "76                     Ramp           $8.1   3/29/2021   United States   \n",
              "78               Fireblocks             $8   7/27/2021   United States   \n",
              "80                  FalconX             $8   8/10/2021   United States   \n",
              "85                 Razorpay           $7.5  10/11/2020           India   \n",
              "87                    Carta           $7.4    5/6/2019   United States   \n",
              "95                ConsenSys             $7  11/17/2021   United States   \n",
              "98                   Klarna           $6.7  12/12/2011          Sweden   \n",
              "105                    CRED           $6.4    4/6/2021           India   \n",
              "107                 Upgrade          $6.28   2/12/2018   United States   \n",
              "136               Airwallex           $5.5   3/25/2019       Australia   \n",
              "137                   Mambu           $5.5    1/6/2021     Netherlands   \n",
              "146               Pine Labs             $5   1/24/2020           India   \n",
              "147                   Qonto             $5   1/11/2022          France   \n",
              "157                Creditas           $4.8  12/18/2020          Brazil   \n",
              "170                   Monzo           $4.5  10/31/2018  United Kingdom   \n",
              "196          Next Insurance             $4   10/7/2019   United States   \n",
              "213         Digit Insurance             $4   1/15/2021           India   \n",
              "256                  Upstox           $3.4  11/29/2021           India   \n",
              "266             BlockDaemon          $3.25   9/21/2021   United States   \n",
              "271                   Cedar           $3.2    3/9/2021   United States   \n",
              "273             Blockstream           $3.2   8/24/2021          Canada   \n",
              "282              HighRadius           $3.1    1/7/2020   United States   \n",
              "307             TradingView             $3  10/14/2021   United States   \n",
              "308                   Groww             $3    4/7/2021           India   \n",
              "311       Anchorage Digital             $3  12/15/2021   United States   \n",
              "312               Inxeption             $3   1/13/2022   United States   \n",
              "314             Flutterwave             $3    3/9/2021   United States   \n",
              "316        Cross River Bank             $3   2/11/2022   United States   \n",
              "317                  Remote             $3   7/13/2021   United States   \n",
              "322                BharatPe          $2.85   7/30/2021           India   \n",
              "\n",
              "              City Industry  \\\n",
              "3    San Francisco  Fintech   \n",
              "6           London  Fintech   \n",
              "10   San Francisco  Fintech   \n",
              "21   San Francisco  Fintech   \n",
              "23   San Francisco  Fintech   \n",
              "29   San Francisco  Fintech   \n",
              "35   San Francisco  Fintech   \n",
              "40   San Francisco  Fintech   \n",
              "52        New York  Fintech   \n",
              "53        Victoria  Fintech   \n",
              "56          Berlin  Fintech   \n",
              "64          London  Fintech   \n",
              "71          London  Fintech   \n",
              "76        New York  Fintech   \n",
              "78        New York  Fintech   \n",
              "80       San Mateo  Fintech   \n",
              "85       Bengaluru  Fintech   \n",
              "87   San Francisco  Fintech   \n",
              "95        New York  Fintech   \n",
              "98       Stockholm  Fintech   \n",
              "105      Bengaluru  Fintech   \n",
              "107  San Francisco  Fintech   \n",
              "136      Melbourne  Fintech   \n",
              "137      Amsterdam  Fintech   \n",
              "146          Noida  Fintech   \n",
              "147          Paris  Fintech   \n",
              "157      Sao Paulo  Fintech   \n",
              "170         London  Fintech   \n",
              "196      Palo Alto  Fintech   \n",
              "213      Bengaluru  Fintech   \n",
              "256         Mumbai  Fintech   \n",
              "266    Los Angeles  Fintech   \n",
              "271       New York  Fintech   \n",
              "273      Vancouver  Fintech   \n",
              "282        Houston  Fintech   \n",
              "307    Westerville  Fintech   \n",
              "308      Bengaluru  Fintech   \n",
              "311  San Francisco  Fintech   \n",
              "312  San Francisco  Fintech   \n",
              "314  San Francisco  Fintech   \n",
              "316       Fort Lee  Fintech   \n",
              "317  San Francisco  Fintech   \n",
              "322      New Delhi  Fintech   \n",
              "\n",
              "                                      Select Investors     Continent  Year  \n",
              "3          Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  2014  \n",
              "6           index Ventures, DST Global, Ribbit Capital        Europe  2018  \n",
              "10    Forerunner Ventures, Crosslink Capital, Homebrew  NorthAmerica  2019  \n",
              "21   IDG Capital, Venture51, Lightspeed Venture Par...  NorthAmerica  2019  \n",
              "23   New Enterprise Associates, Spar Capital, Index...  NorthAmerica  2018  \n",
              "29   DST Global, Ribbit Capital, Greenoaks Capital ...  NorthAmerica  2018  \n",
              "35    Andreessen Horowitz, Spark Capital, Y Combinator  NorthAmerica  2021  \n",
              "40   Activant Capital, Tribe Capital, General Atlantic  NorthAmerica  2021  \n",
              "52            Ribbit Capital, capitalG, Softbank Group  NorthAmerica  2021  \n",
              "53     IDG Capital, Matrix Partners China, Jump Crypto        Africa  2022  \n",
              "56   Redalpine Venture Partners, Earlybird Venture ...        Europe  2019  \n",
              "64   Target Global, General Catalyst, Durable Capit...        Europe  2019  \n",
              "71   American Express Ventures, Goldman Sachs, Bain...        Europe  2019  \n",
              "76      D1 Capital Partners, Stripe, Coatue Management  NorthAmerica  2021  \n",
              "78    Tenaya Capital, Coatue Management, Stripes Group  NorthAmerica  2021  \n",
              "80   Tiger Global Management, American Express Vent...  NorthAmerica  2021  \n",
              "85   Sequoia Capital India, Tiger Global Management...          Asia  2020  \n",
              "87   Menlo Ventures, Spark Capital, Union Square Ve...  NorthAmerica  2019  \n",
              "95    Third Point, Electric Capital, Coinbase Ventures  NorthAmerica  2021  \n",
              "98   Institutional Venture Partners, Sequoia Capita...        Europe  2011  \n",
              "105  Tiger Global Management, DST Global, Sequoia C...          Asia  2021  \n",
              "107  Union Square Ventures, Ribbit Capital, VY Capital  NorthAmerica  2018  \n",
              "136  DST Global, Sequoia Capital China, Tencent Hol...       Oceania  2019  \n",
              "137  Runa Capital, Acton Capital Partners, Point Ni...        Europe  2021  \n",
              "146    Sequoia Capital India, Temasek, PayPal Ventures          Asia  2020  \n",
              "147    Alven Capital, Valar Ventures, Tencent Holdings        Europe  2022  \n",
              "157  Kaszek Ventures, Amadeus Capital Partners, Quo...  SouthAmerica  2020  \n",
              "170  Passion Capital, Thrive Capital, Orange Digita...        Europe  2018  \n",
              "196        Zeev Ventures, Ribbit Capital, TLV Partners  NorthAmerica  2019  \n",
              "213  Fairfax Financial Holdings, A91 Partners, TVS ...          Asia  2021  \n",
              "256           Tiger Global Management, Kalaari Capital          Asia  2021  \n",
              "266  BOLDstart Ventures, Lerer Hippeau, Kenetic Cap...  NorthAmerica  2021  \n",
              "271  Thrive Capital, Founders Fund, Cocnord Health ...  NorthAmerica  2021  \n",
              "273  AME Cloud Ventures, Future Perfect Ventures, B...  NorthAmerica  2021  \n",
              "282  Susquehanna Growth Equity, Citi Ventures, ICON...  NorthAmerica  2020  \n",
              "307  Tiger Global Management, Insight Partners, Jum...  NorthAmerica  2021  \n",
              "308  Tiger Global Management, Sequoia Capital India...          Asia  2021  \n",
              "311  Andreessen Horowitz, Blockchain Capital, Lux C...  NorthAmerica  2021  \n",
              "312  Coatue Management, BMO Capital, Schonfeld Stra...  NorthAmerica  2022  \n",
              "314  Green Visor Capital, CRE Venture Capital, Grey...  NorthAmerica  2021  \n",
              "316  Battery Ventures, Andreessen Horowitz, Ribbit ...  NorthAmerica  2022  \n",
              "317  Index Ventures, Sequoia Capital, General Catalyst  NorthAmerica  2021  \n",
              "322   Insight Partners, Sequoia Capital India, BEENEXT          Asia  2021  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edbd8312-57c4-4f03-8e24-5cafa34fb7a1\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Investors</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Revolut</td>\n",
              "      <td>$33</td>\n",
              "      <td>4/26/2018</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>index Ventures, DST Global, Ribbit Capital</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Chime</td>\n",
              "      <td>$25</td>\n",
              "      <td>3/5/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Forerunner Ventures, Crosslink Capital, Homebrew</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Ripple</td>\n",
              "      <td>$15</td>\n",
              "      <td>12/20/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>IDG Capital, Venture51, Lightspeed Venture Par...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Plaid</td>\n",
              "      <td>$13.5</td>\n",
              "      <td>12/11/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>New Enterprise Associates, Spar Capital, Index...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Brex</td>\n",
              "      <td>$12.3</td>\n",
              "      <td>10/5/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>DST Global, Ribbit Capital, Greenoaks Capital ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Deel</td>\n",
              "      <td>$12</td>\n",
              "      <td>4/21/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Andreessen Horowitz, Spark Capital, Y Combinator</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Bolt</td>\n",
              "      <td>$11</td>\n",
              "      <td>10/8/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Activant Capital, Tribe Capital, General Atlantic</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Digital Currency Group</td>\n",
              "      <td>$10</td>\n",
              "      <td>11/1/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Ribbit Capital, capitalG, Softbank Group</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>KuCoin</td>\n",
              "      <td>$10</td>\n",
              "      <td>5/10/2022</td>\n",
              "      <td>Seychelles</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>IDG Capital, Matrix Partners China, Jump Crypto</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>N26</td>\n",
              "      <td>$9.23</td>\n",
              "      <td>1/10/2019</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Redalpine Venture Partners, Earlybird Venture ...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Rapyd</td>\n",
              "      <td>$8.75</td>\n",
              "      <td>12/3/2019</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Target Global, General Catalyst, Durable Capit...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>SumUp</td>\n",
              "      <td>$8.5</td>\n",
              "      <td>7/16/2019</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>American Express Ventures, Goldman Sachs, Bain...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Ramp</td>\n",
              "      <td>$8.1</td>\n",
              "      <td>3/29/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>D1 Capital Partners, Stripe, Coatue Management</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Fireblocks</td>\n",
              "      <td>$8</td>\n",
              "      <td>7/27/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tenaya Capital, Coatue Management, Stripes Group</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>FalconX</td>\n",
              "      <td>$8</td>\n",
              "      <td>8/10/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Mateo</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, American Express Vent...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Razorpay</td>\n",
              "      <td>$7.5</td>\n",
              "      <td>10/11/2020</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Sequoia Capital India, Tiger Global Management...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Carta</td>\n",
              "      <td>$7.4</td>\n",
              "      <td>5/6/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Menlo Ventures, Spark Capital, Union Square Ve...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>ConsenSys</td>\n",
              "      <td>$7</td>\n",
              "      <td>11/17/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Third Point, Electric Capital, Coinbase Ventures</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Klarna</td>\n",
              "      <td>$6.7</td>\n",
              "      <td>12/12/2011</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>CRED</td>\n",
              "      <td>$6.4</td>\n",
              "      <td>4/6/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, DST Global, Sequoia C...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Upgrade</td>\n",
              "      <td>$6.28</td>\n",
              "      <td>2/12/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Union Square Ventures, Ribbit Capital, VY Capital</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Airwallex</td>\n",
              "      <td>$5.5</td>\n",
              "      <td>3/25/2019</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>DST Global, Sequoia Capital China, Tencent Hol...</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Mambu</td>\n",
              "      <td>$5.5</td>\n",
              "      <td>1/6/2021</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Runa Capital, Acton Capital Partners, Point Ni...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Pine Labs</td>\n",
              "      <td>$5</td>\n",
              "      <td>1/24/2020</td>\n",
              "      <td>India</td>\n",
              "      <td>Noida</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Sequoia Capital India, Temasek, PayPal Ventures</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Qonto</td>\n",
              "      <td>$5</td>\n",
              "      <td>1/11/2022</td>\n",
              "      <td>France</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Alven Capital, Valar Ventures, Tencent Holdings</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Creditas</td>\n",
              "      <td>$4.8</td>\n",
              "      <td>12/18/2020</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Kaszek Ventures, Amadeus Capital Partners, Quo...</td>\n",
              "      <td>SouthAmerica</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Monzo</td>\n",
              "      <td>$4.5</td>\n",
              "      <td>10/31/2018</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Passion Capital, Thrive Capital, Orange Digita...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Next Insurance</td>\n",
              "      <td>$4</td>\n",
              "      <td>10/7/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>Palo Alto</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Zeev Ventures, Ribbit Capital, TLV Partners</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>Digit Insurance</td>\n",
              "      <td>$4</td>\n",
              "      <td>1/15/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Fairfax Financial Holdings, A91 Partners, TVS ...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Upstox</td>\n",
              "      <td>$3.4</td>\n",
              "      <td>11/29/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Kalaari Capital</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>BlockDaemon</td>\n",
              "      <td>$3.25</td>\n",
              "      <td>9/21/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>BOLDstart Ventures, Lerer Hippeau, Kenetic Cap...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>Cedar</td>\n",
              "      <td>$3.2</td>\n",
              "      <td>3/9/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Thrive Capital, Founders Fund, Cocnord Health ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>Blockstream</td>\n",
              "      <td>$3.2</td>\n",
              "      <td>8/24/2021</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Vancouver</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>AME Cloud Ventures, Future Perfect Ventures, B...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>HighRadius</td>\n",
              "      <td>$3.1</td>\n",
              "      <td>1/7/2020</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Susquehanna Growth Equity, Citi Ventures, ICON...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>TradingView</td>\n",
              "      <td>$3</td>\n",
              "      <td>10/14/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>Westerville</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Insight Partners, Jum...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>Groww</td>\n",
              "      <td>$3</td>\n",
              "      <td>4/7/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital India...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>Anchorage Digital</td>\n",
              "      <td>$3</td>\n",
              "      <td>12/15/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Andreessen Horowitz, Blockchain Capital, Lux C...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>Inxeption</td>\n",
              "      <td>$3</td>\n",
              "      <td>1/13/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Coatue Management, BMO Capital, Schonfeld Stra...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>Flutterwave</td>\n",
              "      <td>$3</td>\n",
              "      <td>3/9/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Green Visor Capital, CRE Venture Capital, Grey...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>Cross River Bank</td>\n",
              "      <td>$3</td>\n",
              "      <td>2/11/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lee</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Battery Ventures, Andreessen Horowitz, Ribbit ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>Remote</td>\n",
              "      <td>$3</td>\n",
              "      <td>7/13/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Index Ventures, Sequoia Capital, General Catalyst</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>BharatPe</td>\n",
              "      <td>$2.85</td>\n",
              "      <td>7/30/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Insight Partners, Sequoia Capital India, BEENEXT</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edbd8312-57c4-4f03-8e24-5cafa34fb7a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Display 5 countries with highest number of unicorn"
      ],
      "metadata": {
        "id": "UDwMiE62bPM5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.groupby('Country').count()['Company'].sort_values(ascending=False).head()"
      ],
      "metadata": {
        "id": "RF_9Buw6bQhT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ae07af9-9a99-49f7-a346-bc858c662e60"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United States     190\n",
              "China              32\n",
              "India              23\n",
              "United Kingdom     16\n",
              "Germany             9\n",
              "Name: Company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Display the industry that has most company number from each continent."
      ],
      "metadata": {
        "id": "UjQOwXlObU87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df = unicorn_df.groupby(['Continent','Industry']).count()\n",
        "result = grouped_df.loc[grouped_df.groupby('Continent')['Company'].idxmax()]\n",
        "result['Company']"
      ],
      "metadata": {
        "id": "np5V7McEbS39",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8a54171-bc43-4107-8a57-73cb83a338c9"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Continent     Industry                       \n",
              "Africa        Fintech                             1\n",
              "Asia          E-commerce & direct-to-consumer    11\n",
              "Europe        Fintech                            20\n",
              "NorthAmerica  Internet software & services       52\n",
              "Oceania       Fintech                             1\n",
              "SouthAmerica  E-commerce & direct-to-consumer     3\n",
              "Name: Company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Display the company name and country with highest valuation in 2019."
      ],
      "metadata": {
        "id": "l9uy4mB9baGZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df = unicorn_df[unicorn_df['Year']==2017]\n",
        "filtered_df = filtered_df.sort_values(by='Valuation ($B)',ascending=False)\n",
        "filtered_df[['Company','Country','Valuation ($B)']].head(1)"
      ],
      "metadata": {
        "id": "fR-ApSd_bXkR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "90681c38-3744-43a3-8d4d-39c4d6ef21e9"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Company        Country Valuation ($B)\n",
              "61  Niantic  United States             $9"
            ],
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              "      <th>Company</th>\n",
              "      <th>Country</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Niantic</td>\n",
              "      <td>United States</td>\n",
              "      <td>$9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr_ind = unicorn_df['Industry'].unique()\n",
        "list_ind = arr_ind.tolist()"
      ],
      "metadata": {
        "id": "QK3-I0m81nYu"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pprint #not really necessary but it will help to prettify the dictionary groups_words\n",
        "ind_lower = {v: v.lower() for v in list_ind}\n",
        "\n",
        "my_list = []\n",
        "groups_words = {}\n",
        "for key, value in ind_lower.items():\n",
        "    if value in groups_words.keys():\n",
        "        my_list = groups_words[value]\n",
        "        my_list.append(key)\n",
        "        groups_words[value] = my_list\n",
        "    else:\n",
        "        groups_words[value] = [key]\n",
        "\n",
        "pprint.pprint(groups_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nf1YOSZq1ozq",
        "outputId": "66ff0cdb-e1ec-4030-a19b-78b330806953"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'artificial intelligence': ['Artificial Intelligence'],\n",
            " 'auto & transportation': ['Auto & transportation'],\n",
            " 'consumer & retail': ['Consumer & retail'],\n",
            " 'cybersecurity': ['Cybersecurity'],\n",
            " 'data management & analytics': ['Data management & analytics'],\n",
            " 'e-commerce & direct-to-consumer': ['E-commerce & direct-to-consumer'],\n",
            " 'edtech': ['Edtech'],\n",
            " 'fintech': ['Fintech'],\n",
            " 'hardware': ['Hardware'],\n",
            " 'health': ['Health'],\n",
            " 'internet software & services': ['Internet software & services'],\n",
            " 'mobile & telecommunications': ['Mobile & telecommunications'],\n",
            " 'other': ['Other'],\n",
            " 'supply chain, logistics, & delivery': ['Supply chain, logistics, & delivery'],\n",
            " 'travel': ['Travel']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df[unicorn_df['Industry']=='Artificial intelligence'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZV3fdhC13ds",
        "outputId": "6ba95ce5-4638-43ae-aa77-5ba3f20ddc44"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             0\n",
              "Valuation ($B)      0\n",
              "Date Joined         0\n",
              "Country             0\n",
              "City                0\n",
              "Industry            0\n",
              "Select Investors    0\n",
              "Continent           0\n",
              "Year                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df['Industry'].loc[unicorn_df['Industry']=='Artificial intelligence'] = 'Artificial Intelligence'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xryFM1aQ2Bpu",
        "outputId": "651924b3-07c5-426b-9124-c21379aa883f"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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._setitem_single_block(indexer, value, name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df[unicorn_df['Industry']=='Artificial intelligence'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qvSa1xf2FuL",
        "outputId": "f98b67ea-4a1c-4c06-83c2-dac1de701292"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Company             0\n",
              "Valuation ($B)      0\n",
              "Date Joined         0\n",
              "Country             0\n",
              "City                0\n",
              "Industry            0\n",
              "Select Investors    0\n",
              "Continent           0\n",
              "Year                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fintech_capital = unicorn_df[(unicorn_df['Select Investors'].str.contains('capital', flags=re.I, na=False, regex=True)) & (unicorn_df['Industry']=='Fintech')]\n",
        "fintech_capital"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "crdMrmD12W4Y",
        "outputId": "01a42ea6-c4a9-4b6c-e548-6ada9fd48486"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Company Valuation ($B) Date Joined         Country  \\\n",
              "3                    Stripe            $63   1/23/2014   United States   \n",
              "6                   Revolut            $33   4/26/2018  United Kingdom   \n",
              "10                    Chime            $25    3/5/2019   United States   \n",
              "21                   Ripple            $15  12/20/2019   United States   \n",
              "23                    Plaid          $13.5  12/11/2018   United States   \n",
              "29                     Brex          $12.3   10/5/2018   United States   \n",
              "35                     Deel            $12   4/21/2021   United States   \n",
              "40                     Bolt            $11   10/8/2021   United States   \n",
              "52   Digital Currency Group            $10   11/1/2021   United States   \n",
              "53                   KuCoin            $10   5/10/2022      Seychelles   \n",
              "56                      N26          $9.23   1/10/2019         Germany   \n",
              "64                    Rapyd          $8.75   12/3/2019  United Kingdom   \n",
              "71                    SumUp           $8.5   7/16/2019  United Kingdom   \n",
              "76                     Ramp           $8.1   3/29/2021   United States   \n",
              "78               Fireblocks             $8   7/27/2021   United States   \n",
              "80                  FalconX             $8   8/10/2021   United States   \n",
              "85                 Razorpay           $7.5  10/11/2020           India   \n",
              "87                    Carta           $7.4    5/6/2019   United States   \n",
              "95                ConsenSys             $7  11/17/2021   United States   \n",
              "98                   Klarna           $6.7  12/12/2011          Sweden   \n",
              "105                    CRED           $6.4    4/6/2021           India   \n",
              "107                 Upgrade          $6.28   2/12/2018   United States   \n",
              "136               Airwallex           $5.5   3/25/2019       Australia   \n",
              "137                   Mambu           $5.5    1/6/2021     Netherlands   \n",
              "146               Pine Labs             $5   1/24/2020           India   \n",
              "147                   Qonto             $5   1/11/2022          France   \n",
              "157                Creditas           $4.8  12/18/2020          Brazil   \n",
              "170                   Monzo           $4.5  10/31/2018  United Kingdom   \n",
              "196          Next Insurance             $4   10/7/2019   United States   \n",
              "213         Digit Insurance             $4   1/15/2021           India   \n",
              "256                  Upstox           $3.4  11/29/2021           India   \n",
              "266             BlockDaemon          $3.25   9/21/2021   United States   \n",
              "271                   Cedar           $3.2    3/9/2021   United States   \n",
              "273             Blockstream           $3.2   8/24/2021          Canada   \n",
              "282              HighRadius           $3.1    1/7/2020   United States   \n",
              "307             TradingView             $3  10/14/2021   United States   \n",
              "308                   Groww             $3    4/7/2021           India   \n",
              "311       Anchorage Digital             $3  12/15/2021   United States   \n",
              "312               Inxeption             $3   1/13/2022   United States   \n",
              "314             Flutterwave             $3    3/9/2021   United States   \n",
              "316        Cross River Bank             $3   2/11/2022   United States   \n",
              "317                  Remote             $3   7/13/2021   United States   \n",
              "322                BharatPe          $2.85   7/30/2021           India   \n",
              "\n",
              "              City Industry  \\\n",
              "3    San Francisco  Fintech   \n",
              "6           London  Fintech   \n",
              "10   San Francisco  Fintech   \n",
              "21   San Francisco  Fintech   \n",
              "23   San Francisco  Fintech   \n",
              "29   San Francisco  Fintech   \n",
              "35   San Francisco  Fintech   \n",
              "40   San Francisco  Fintech   \n",
              "52        New York  Fintech   \n",
              "53        Victoria  Fintech   \n",
              "56          Berlin  Fintech   \n",
              "64          London  Fintech   \n",
              "71          London  Fintech   \n",
              "76        New York  Fintech   \n",
              "78        New York  Fintech   \n",
              "80       San Mateo  Fintech   \n",
              "85       Bengaluru  Fintech   \n",
              "87   San Francisco  Fintech   \n",
              "95        New York  Fintech   \n",
              "98       Stockholm  Fintech   \n",
              "105      Bengaluru  Fintech   \n",
              "107  San Francisco  Fintech   \n",
              "136      Melbourne  Fintech   \n",
              "137      Amsterdam  Fintech   \n",
              "146          Noida  Fintech   \n",
              "147          Paris  Fintech   \n",
              "157      Sao Paulo  Fintech   \n",
              "170         London  Fintech   \n",
              "196      Palo Alto  Fintech   \n",
              "213      Bengaluru  Fintech   \n",
              "256         Mumbai  Fintech   \n",
              "266    Los Angeles  Fintech   \n",
              "271       New York  Fintech   \n",
              "273      Vancouver  Fintech   \n",
              "282        Houston  Fintech   \n",
              "307    Westerville  Fintech   \n",
              "308      Bengaluru  Fintech   \n",
              "311  San Francisco  Fintech   \n",
              "312  San Francisco  Fintech   \n",
              "314  San Francisco  Fintech   \n",
              "316       Fort Lee  Fintech   \n",
              "317  San Francisco  Fintech   \n",
              "322      New Delhi  Fintech   \n",
              "\n",
              "                                      Select Investors     Continent  Year  \n",
              "3          Khosla Ventures, LowercaseCapital, capitalG  NorthAmerica  2014  \n",
              "6           index Ventures, DST Global, Ribbit Capital        Europe  2018  \n",
              "10    Forerunner Ventures, Crosslink Capital, Homebrew  NorthAmerica  2019  \n",
              "21   IDG Capital, Venture51, Lightspeed Venture Par...  NorthAmerica  2019  \n",
              "23   New Enterprise Associates, Spar Capital, Index...  NorthAmerica  2018  \n",
              "29   DST Global, Ribbit Capital, Greenoaks Capital ...  NorthAmerica  2018  \n",
              "35    Andreessen Horowitz, Spark Capital, Y Combinator  NorthAmerica  2021  \n",
              "40   Activant Capital, Tribe Capital, General Atlantic  NorthAmerica  2021  \n",
              "52            Ribbit Capital, capitalG, Softbank Group  NorthAmerica  2021  \n",
              "53     IDG Capital, Matrix Partners China, Jump Crypto        Africa  2022  \n",
              "56   Redalpine Venture Partners, Earlybird Venture ...        Europe  2019  \n",
              "64   Target Global, General Catalyst, Durable Capit...        Europe  2019  \n",
              "71   American Express Ventures, Goldman Sachs, Bain...        Europe  2019  \n",
              "76      D1 Capital Partners, Stripe, Coatue Management  NorthAmerica  2021  \n",
              "78    Tenaya Capital, Coatue Management, Stripes Group  NorthAmerica  2021  \n",
              "80   Tiger Global Management, American Express Vent...  NorthAmerica  2021  \n",
              "85   Sequoia Capital India, Tiger Global Management...          Asia  2020  \n",
              "87   Menlo Ventures, Spark Capital, Union Square Ve...  NorthAmerica  2019  \n",
              "95    Third Point, Electric Capital, Coinbase Ventures  NorthAmerica  2021  \n",
              "98   Institutional Venture Partners, Sequoia Capita...        Europe  2011  \n",
              "105  Tiger Global Management, DST Global, Sequoia C...          Asia  2021  \n",
              "107  Union Square Ventures, Ribbit Capital, VY Capital  NorthAmerica  2018  \n",
              "136  DST Global, Sequoia Capital China, Tencent Hol...       Oceania  2019  \n",
              "137  Runa Capital, Acton Capital Partners, Point Ni...        Europe  2021  \n",
              "146    Sequoia Capital India, Temasek, PayPal Ventures          Asia  2020  \n",
              "147    Alven Capital, Valar Ventures, Tencent Holdings        Europe  2022  \n",
              "157  Kaszek Ventures, Amadeus Capital Partners, Quo...  SouthAmerica  2020  \n",
              "170  Passion Capital, Thrive Capital, Orange Digita...        Europe  2018  \n",
              "196        Zeev Ventures, Ribbit Capital, TLV Partners  NorthAmerica  2019  \n",
              "213  Fairfax Financial Holdings, A91 Partners, TVS ...          Asia  2021  \n",
              "256           Tiger Global Management, Kalaari Capital          Asia  2021  \n",
              "266  BOLDstart Ventures, Lerer Hippeau, Kenetic Cap...  NorthAmerica  2021  \n",
              "271  Thrive Capital, Founders Fund, Cocnord Health ...  NorthAmerica  2021  \n",
              "273  AME Cloud Ventures, Future Perfect Ventures, B...  NorthAmerica  2021  \n",
              "282  Susquehanna Growth Equity, Citi Ventures, ICON...  NorthAmerica  2020  \n",
              "307  Tiger Global Management, Insight Partners, Jum...  NorthAmerica  2021  \n",
              "308  Tiger Global Management, Sequoia Capital India...          Asia  2021  \n",
              "311  Andreessen Horowitz, Blockchain Capital, Lux C...  NorthAmerica  2021  \n",
              "312  Coatue Management, BMO Capital, Schonfeld Stra...  NorthAmerica  2022  \n",
              "314  Green Visor Capital, CRE Venture Capital, Grey...  NorthAmerica  2021  \n",
              "316  Battery Ventures, Andreessen Horowitz, Ribbit ...  NorthAmerica  2022  \n",
              "317  Index Ventures, Sequoia Capital, General Catalyst  NorthAmerica  2021  \n",
              "322   Insight Partners, Sequoia Capital India, BEENEXT          Asia  2021  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-442a8937-9f60-46c2-ba13-932d78069e8b\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation ($B)</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>Industry</th>\n",
              "      <th>Select Investors</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>$63</td>\n",
              "      <td>1/23/2014</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Revolut</td>\n",
              "      <td>$33</td>\n",
              "      <td>4/26/2018</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>index Ventures, DST Global, Ribbit Capital</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Chime</td>\n",
              "      <td>$25</td>\n",
              "      <td>3/5/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Forerunner Ventures, Crosslink Capital, Homebrew</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Ripple</td>\n",
              "      <td>$15</td>\n",
              "      <td>12/20/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>IDG Capital, Venture51, Lightspeed Venture Par...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Plaid</td>\n",
              "      <td>$13.5</td>\n",
              "      <td>12/11/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>New Enterprise Associates, Spar Capital, Index...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Brex</td>\n",
              "      <td>$12.3</td>\n",
              "      <td>10/5/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>DST Global, Ribbit Capital, Greenoaks Capital ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Deel</td>\n",
              "      <td>$12</td>\n",
              "      <td>4/21/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Andreessen Horowitz, Spark Capital, Y Combinator</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Bolt</td>\n",
              "      <td>$11</td>\n",
              "      <td>10/8/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Activant Capital, Tribe Capital, General Atlantic</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Digital Currency Group</td>\n",
              "      <td>$10</td>\n",
              "      <td>11/1/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Ribbit Capital, capitalG, Softbank Group</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>KuCoin</td>\n",
              "      <td>$10</td>\n",
              "      <td>5/10/2022</td>\n",
              "      <td>Seychelles</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>IDG Capital, Matrix Partners China, Jump Crypto</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>N26</td>\n",
              "      <td>$9.23</td>\n",
              "      <td>1/10/2019</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Redalpine Venture Partners, Earlybird Venture ...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Rapyd</td>\n",
              "      <td>$8.75</td>\n",
              "      <td>12/3/2019</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Target Global, General Catalyst, Durable Capit...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>SumUp</td>\n",
              "      <td>$8.5</td>\n",
              "      <td>7/16/2019</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>American Express Ventures, Goldman Sachs, Bain...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Ramp</td>\n",
              "      <td>$8.1</td>\n",
              "      <td>3/29/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>D1 Capital Partners, Stripe, Coatue Management</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Fireblocks</td>\n",
              "      <td>$8</td>\n",
              "      <td>7/27/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tenaya Capital, Coatue Management, Stripes Group</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>FalconX</td>\n",
              "      <td>$8</td>\n",
              "      <td>8/10/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Mateo</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, American Express Vent...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Razorpay</td>\n",
              "      <td>$7.5</td>\n",
              "      <td>10/11/2020</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Sequoia Capital India, Tiger Global Management...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Carta</td>\n",
              "      <td>$7.4</td>\n",
              "      <td>5/6/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Menlo Ventures, Spark Capital, Union Square Ve...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>ConsenSys</td>\n",
              "      <td>$7</td>\n",
              "      <td>11/17/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Third Point, Electric Capital, Coinbase Ventures</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Klarna</td>\n",
              "      <td>$6.7</td>\n",
              "      <td>12/12/2011</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>CRED</td>\n",
              "      <td>$6.4</td>\n",
              "      <td>4/6/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, DST Global, Sequoia C...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Upgrade</td>\n",
              "      <td>$6.28</td>\n",
              "      <td>2/12/2018</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Union Square Ventures, Ribbit Capital, VY Capital</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Airwallex</td>\n",
              "      <td>$5.5</td>\n",
              "      <td>3/25/2019</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>DST Global, Sequoia Capital China, Tencent Hol...</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Mambu</td>\n",
              "      <td>$5.5</td>\n",
              "      <td>1/6/2021</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Runa Capital, Acton Capital Partners, Point Ni...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Pine Labs</td>\n",
              "      <td>$5</td>\n",
              "      <td>1/24/2020</td>\n",
              "      <td>India</td>\n",
              "      <td>Noida</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Sequoia Capital India, Temasek, PayPal Ventures</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Qonto</td>\n",
              "      <td>$5</td>\n",
              "      <td>1/11/2022</td>\n",
              "      <td>France</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Alven Capital, Valar Ventures, Tencent Holdings</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Creditas</td>\n",
              "      <td>$4.8</td>\n",
              "      <td>12/18/2020</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Kaszek Ventures, Amadeus Capital Partners, Quo...</td>\n",
              "      <td>SouthAmerica</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Monzo</td>\n",
              "      <td>$4.5</td>\n",
              "      <td>10/31/2018</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>London</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Passion Capital, Thrive Capital, Orange Digita...</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Next Insurance</td>\n",
              "      <td>$4</td>\n",
              "      <td>10/7/2019</td>\n",
              "      <td>United States</td>\n",
              "      <td>Palo Alto</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Zeev Ventures, Ribbit Capital, TLV Partners</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>Digit Insurance</td>\n",
              "      <td>$4</td>\n",
              "      <td>1/15/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Fairfax Financial Holdings, A91 Partners, TVS ...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Upstox</td>\n",
              "      <td>$3.4</td>\n",
              "      <td>11/29/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Kalaari Capital</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>BlockDaemon</td>\n",
              "      <td>$3.25</td>\n",
              "      <td>9/21/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>BOLDstart Ventures, Lerer Hippeau, Kenetic Cap...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>Cedar</td>\n",
              "      <td>$3.2</td>\n",
              "      <td>3/9/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Thrive Capital, Founders Fund, Cocnord Health ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>Blockstream</td>\n",
              "      <td>$3.2</td>\n",
              "      <td>8/24/2021</td>\n",
              "      <td>Canada</td>\n",
              "      <td>Vancouver</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>AME Cloud Ventures, Future Perfect Ventures, B...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>HighRadius</td>\n",
              "      <td>$3.1</td>\n",
              "      <td>1/7/2020</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Susquehanna Growth Equity, Citi Ventures, ICON...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>TradingView</td>\n",
              "      <td>$3</td>\n",
              "      <td>10/14/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>Westerville</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Insight Partners, Jum...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>Groww</td>\n",
              "      <td>$3</td>\n",
              "      <td>4/7/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>Bengaluru</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital India...</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>Anchorage Digital</td>\n",
              "      <td>$3</td>\n",
              "      <td>12/15/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Andreessen Horowitz, Blockchain Capital, Lux C...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>Inxeption</td>\n",
              "      <td>$3</td>\n",
              "      <td>1/13/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Coatue Management, BMO Capital, Schonfeld Stra...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>Flutterwave</td>\n",
              "      <td>$3</td>\n",
              "      <td>3/9/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Green Visor Capital, CRE Venture Capital, Grey...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>Cross River Bank</td>\n",
              "      <td>$3</td>\n",
              "      <td>2/11/2022</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lee</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Battery Ventures, Andreessen Horowitz, Ribbit ...</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>Remote</td>\n",
              "      <td>$3</td>\n",
              "      <td>7/13/2021</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Index Ventures, Sequoia Capital, General Catalyst</td>\n",
              "      <td>NorthAmerica</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>BharatPe</td>\n",
              "      <td>$2.85</td>\n",
              "      <td>7/30/2021</td>\n",
              "      <td>India</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Insight Partners, Sequoia Capital India, BEENEXT</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unicorn_df.groupby('Country').count()['Company'].sort_values(ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TX5LukK72djt",
        "outputId": "3f236493-7cee-407d-d55f-553d24b1cdfe"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United States     190\n",
              "China              32\n",
              "India              23\n",
              "United Kingdom     16\n",
              "Germany             9\n",
              "Name: Company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df = unicorn_df.groupby(['Continent','Industry']).count()\n",
        "result = grouped_df.loc[grouped_df.groupby('Continent')['Company'].idxmax()]\n",
        "result['Company']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgNssPiI2hXf",
        "outputId": "bfe43f88-5bc9-4d1a-f7c9-d64d0fb0e662"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Continent     Industry                       \n",
              "Africa        Fintech                             1\n",
              "Asia          E-commerce & direct-to-consumer    11\n",
              "Europe        Fintech                            20\n",
              "NorthAmerica  Internet software & services       52\n",
              "Oceania       Fintech                             1\n",
              "SouthAmerica  E-commerce & direct-to-consumer     3\n",
              "Name: Company, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df = unicorn_df[unicorn_df['Year']==2019]\n",
        "filtered_df = filtered_df.sort_values(by='Valuation ($B)',ascending=False)\n",
        "filtered_df[['Company','Country','Valuation ($B)']].head(1)"
      ],
      "metadata": {
        "colab": {
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          "height": 80
        },
        "id": "7KtLgrGU2l4A",
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      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Company Country Valuation ($B)\n",
              "55  HEYTEA   China          $9.28"
            ],
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              "      <td>HEYTEA</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#separating all investors and inputing into one list\n",
        "list_investor = []\n",
        "for i,a in unicorn_df.iterrows():\n",
        "    row = unicorn_df['Select Investors'][i].split(',')\n",
        "    len_row = len(row)\n",
        "    for index in range(len_row):\n",
        "        temp = row[index].strip()\n",
        "        list_investor.append(temp)\n",
        "len(list_investor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yq42hjGY4Xj_",
        "outputId": "e91718db-20b7-4b5d-979e-b626c740c976"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "917"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting unique investors (no redundant data)\n",
        "unique_investor = []\n",
        "for i,a in unicorn_df.iterrows():\n",
        "    row = unicorn_df['Select Investors'][i].split(',')\n",
        "    len_row = len(row)\n",
        "    for index in range(len_row):\n",
        "        temp = row[index].strip()\n",
        "        if temp not in unique_investor: #adding this condition to filter unique values\n",
        "            unique_investor.append(temp)\n",
        "len(unique_investor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2fBR8_u4a41",
        "outputId": "cfe5ff19-e51b-4ab1-d248-a4ed0b1d4a20"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "450"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#counting the number of companies each investor has funded and putting them into pandas dataframe\n",
        "inv_dic = {}\n",
        "for i in unique_investor:\n",
        "    inv_dic[i] = list_investor.count(i)\n",
        "\n",
        "investor_df = pd.DataFrame(list(inv_dic.items()),columns = ['Investor Name','Number of Company'])"
      ],
      "metadata": {
        "id": "LG-kqutb4efL"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "investor_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "pc3elaFb4hcm",
        "outputId": "4178bf95-2e48-4630-b2f3-8d7f06b1843d"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Investor Name  Number of Company\n",
              "0      Sequoia Capital China                 17\n",
              "1       SIG Asia Investments                  1\n",
              "2                 Sina Weibo                  2\n",
              "3             Softbank Group                  3\n",
              "4              Founders Fund                  5\n",
              "..                       ...                ...\n",
              "445  Quantum Energy Partners                  1\n",
              "446          Bedrock Capital                  1\n",
              "447               Monashees+                  1\n",
              "448            QED Investors                  1\n",
              "449                  BEENEXT                  1\n",
              "\n",
              "[450 rows x 2 columns]"
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      "source": [
        "investor_df.sort_values(by='Number of Company', ascending=False).head(10)"
      ],
      "metadata": {
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      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Investor Name  Number of Company\n",
              "36                        Accel                 25\n",
              "21          Andreessen Horowitz                 22\n",
              "42              Sequoia Capital                 21\n",
              "7       Tiger Global Management                 18\n",
              "14             Insight Partners                 17\n",
              "0         Sequoia Capital China                 17\n",
              "24               SoftBank Group                 15\n",
              "49  Lightspeed Venture Partners                 14\n",
              "57             General Catalyst                 13\n",
              "15                   DST Global                 13"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Investor Name</th>\n",
              "      <th>Number of Company</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Accel</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Andreessen Horowitz</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Sequoia Capital</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tiger Global Management</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Insight Partners</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sequoia Capital China</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>SoftBank Group</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Lightspeed Venture Partners</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>General Catalyst</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>DST Global</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8249ca7c-eeaf-4b20-a986-ec150c956b0a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "  <style>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-8249ca7c-eeaf-4b20-a986-ec150c956b0a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "investor_df[investor_df['Number of Company']==1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "6Sbcmm9L4sL4",
        "outputId": "1741b733-e79a-45f1-fdd2-ee2952ff4464"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Investor Name  Number of Company\n",
              "1       SIG Asia Investments                  1\n",
              "5    Draper Fisher Jurvetson                  1\n",
              "6        Rothenberg Ventures                  1\n",
              "10          LowercaseCapital                  1\n",
              "12        Blackbird Ventures                  1\n",
              "..                       ...                ...\n",
              "445  Quantum Energy Partners                  1\n",
              "446          Bedrock Capital                  1\n",
              "447               Monashees+                  1\n",
              "448            QED Investors                  1\n",
              "449                  BEENEXT                  1\n",
              "\n",
              "[325 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>Investor Name</th>\n",
              "      <th>Number of Company</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SIG Asia Investments</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Draper Fisher Jurvetson</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Rothenberg Ventures</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LowercaseCapital</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Blackbird Ventures</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>Quantum Energy Partners</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>446</th>\n",
              "      <td>Bedrock Capital</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>Monashees+</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>448</th>\n",
              "      <td>QED Investors</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>BEENEXT</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>325 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9b998cef-b3e8-4280-abdb-1fea0ec8c901');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? 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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "investor_df.to_csv('investor_data.csv',index=False)"
      ],
      "metadata": {
        "id": "GH7OGzVk4wOa"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "continent = [con for con, df in unicorn_df.groupby('Continent')]\n",
        "grouped_qty = unicorn_df.groupby('Continent').count()['Company']\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,10))\n",
        "plt.bar(continent, grouped_qty)    \n",
        "plt.xlabel(\"Continent Name\")\n",
        "plt.ylabel(\"Total Unicorn\")\n",
        "plt.xticks(continent, rotation = \"vertical\")\n",
        "plt.title('Unicorn Data by Continent')\n",
        "for index in range(len(continent)):\n",
        "  ax.text(index, grouped_qty[index], grouped_qty[index], size=8, ha='center', va='bottom')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "h912uE5A40Rn",
        "outputId": "5998b9ed-1d8e-4b6b-bdac-29ca77ab3496"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#grouped_year = unicorn_df[unicorn_df['Continent']=='Asia'].groupby('Year')\n",
        "grouped_year = unicorn_df.groupby('Year')\n",
        "result_year = grouped_year['Year'].count()\n",
        "years = [yr for yr, df in grouped_year]\n",
        "\n",
        "plt.plot(years, result_year)\n",
        "plt.xticks(years, rotation = \"vertical\")\n",
        "plt.title('Number of Unicorn Annual Growth')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Unicorn')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "v3P3d3pW49qF",
        "outputId": "913099ea-bc78-4dff-bee3-06b87f55e113"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}