companies.ipynb

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        "<a href=\"https://colab.research.google.com/github/JayJay9x/JAYSTORE_YOUBUYSOMETHING/blob/main/companies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "Making investing firm by diving in the dataset of Unicorn Companies."
      ],
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      }
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "companies = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Unicorn_Companies_project/Unicorn_Companies.csv')"
      ],
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        "id": "FZHqusqW9XN_"
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      "outputs": []
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      "source": [
        "# Let see what we have!\n",
        "companies.head()"
      ],
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      "execution_count": 5,
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            "text/plain": [
              "     Company Valuation Date Joined                         Industry  \\\n",
              "0  Bytedance     $180B      4/7/17          Artificial intelligence   \n",
              "1     SpaceX     $100B     12/1/12                            Other   \n",
              "2      SHEIN     $100B      7/3/18  E-commerce & direct-to-consumer   \n",
              "3     Stripe      $95B     1/23/14                          Fintech   \n",
              "4     Klarna      $46B    12/12/11                          Fintech   \n",
              "\n",
              "            City Country/Region      Continent  Year Founded Funding  \\\n",
              "0        Beijing          China           Asia          2012     $8B   \n",
              "1      Hawthorne  United States  North America          2002     $7B   \n",
              "2       Shenzhen          China           Asia          2008     $2B   \n",
              "3  San Francisco  United States  North America          2010     $2B   \n",
              "4      Stockholm         Sweden         Europe          2005     $4B   \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  Institutional Venture Partners, Sequoia Capita...  "
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              "      <td>Bytedance</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "companies",
              "summary": "{\n  \"name\": \"companies\",\n  \"rows\": 1074,\n  \"fields\": [\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1073,\n        \"samples\": [\n          \"Pat McGrath Labs\",\n          \"Intercom\",\n          \"Yunxuetang\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valuation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"$3B\",\n          \"$15B\",\n          \"$7B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date Joined\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 639,\n        \"samples\": [\n          \"4/6/21\",\n          \"11/16/20\",\n          \"8/5/21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Industry\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Artificial intelligence\",\n          \"Other\",\n          \"Supply chain, logistics, & delivery\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 256,\n        \"samples\": [\n          \"Jeju-do\",\n          \"London\",\n          \"Eden Prairie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country/Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"Senegal\",\n          \"Austria\",\n          \"Ireland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"North America\",\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year Founded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1919,\n        \"max\": 2021,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1995,\n          2003,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Funding\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 539,\n        \"samples\": [\n          \"$243M\",\n          \"$644M\",\n          \"$115M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Select Investors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1058,\n        \"samples\": [\n          \"Carsales\",\n          \"Goldman Sachs Asset Management\",\n          \"Iconiq Capital\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "# More detail.\n",
        "companies.info()"
      ],
      "metadata": {
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        "id": "bdpUBvWs93CI",
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1074 entries, 0 to 1073\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   Company           1074 non-null   object\n",
            " 1   Valuation         1074 non-null   object\n",
            " 2   Date Joined       1074 non-null   object\n",
            " 3   Industry          1074 non-null   object\n",
            " 4   City              1058 non-null   object\n",
            " 5   Country/Region    1074 non-null   object\n",
            " 6   Continent         1074 non-null   object\n",
            " 7   Year Founded      1074 non-null   int64 \n",
            " 8   Funding           1074 non-null   object\n",
            " 9   Select Investors  1073 non-null   object\n",
            "dtypes: int64(1), object(9)\n",
            "memory usage: 84.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Date Joined to datetime.\n",
        "companies['Date Joined'] = pd.to_datetime(companies['Date Joined'])\n",
        "# Any duplicated record here?\n",
        "companies.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5jSWPTs-CsR",
        "outputId": "cf7a5da9-9fa4-45dc-87d2-75752307de96"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-c0d71b48e1b2>:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  companies['Date Joined'] = pd.to_datetime(companies['Date Joined'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no need to drop_duplicates right!\n",
        "\n",
        "Let begin the EDA journey!"
      ],
      "metadata": {
        "id": "f2jDNXWK-X7W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Discover the unicorn companies history.\n",
        "companies['Year Founded'].sort_values().value_counts()\n",
        "sns.histplot(companies['Year Founded'])\n",
        "plt.title('History of Unicorn Companies')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EGu7rDju-T_y",
        "outputId": "7e978297-e598-4c3c-9960-c945b0668aef"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have the statement that the old comapanies have the less chance to become Unicorn."
      ],
      "metadata": {
        "id": "PdT6L5CsAQCe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "''' To make deeper understanding in the route of Unicorn companies,\n",
        "I will consider how long it takes to become Unicorn of each Insdustry.'''\n",
        "\n",
        "companies['Year To Join'] = companies['Date Joined'].dt.year - companies['Year Founded']\n",
        "route_to_unicorn = companies.groupby('Industry')['Year To Join'].mean().round(2).sort_values(ascending=True).reset_index()"
      ],
      "metadata": {
        "id": "RwTg6qeZ_28Z"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: convert the currency as B to billion in Valuation and M to Million in Funding\n",
        "\n",
        "def convert_currency(val):\n",
        "  \"\"\"Converts currency values from 'B' to billion and 'M' to million.\"\"\"\n",
        "  if isinstance(val, str):\n",
        "    if val.endswith('B'):\n",
        "      return float(int(val[1:-1])) * 1000000000\n",
        "    elif val.endswith('M'):\n",
        "      return float(int(val[1:-1])) * 1000000\n",
        "    else:\n",
        "      return 0\n",
        "  return val\n",
        "\n",
        "\n",
        "companies['Valuation'] = companies['Valuation'].apply(convert_currency).astype(float)\n",
        "companies['Funding'] = companies['Funding'].apply(convert_currency).astype(float)\n",
        "companies['Profit Returns'] = companies['Valuation'] - companies['Funding']\n",
        "companies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "cTce4GAuBwr-",
        "outputId": "2d2fd82c-2fbf-45ba-c269-62690f183ad3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Company     Valuation Date Joined                         Industry  \\\n",
              "0       Bytedance  1.800000e+11  2017-04-07          Artificial intelligence   \n",
              "1          SpaceX  1.000000e+11  2012-12-01                            Other   \n",
              "2           SHEIN  1.000000e+11  2018-07-03  E-commerce & direct-to-consumer   \n",
              "3          Stripe  9.500000e+10  2014-01-23                          Fintech   \n",
              "4          Klarna  4.600000e+10  2011-12-12                          Fintech   \n",
              "...           ...           ...         ...                              ...   \n",
              "1069     Zhaogang  1.000000e+09  2017-06-29  E-commerce & direct-to-consumer   \n",
              "1070  Zhuan Zhuan  1.000000e+09  2017-04-18  E-commerce & direct-to-consumer   \n",
              "1071     Zihaiguo  1.000000e+09  2021-05-06                Consumer & retail   \n",
              "1072         Zopa  1.000000e+09  2021-10-19                          Fintech   \n",
              "1073        Zwift  1.000000e+09  2020-09-16  E-commerce & direct-to-consumer   \n",
              "\n",
              "               City  Country/Region      Continent  Year Founded  \\\n",
              "0           Beijing           China           Asia          2012   \n",
              "1         Hawthorne   United States  North America          2002   \n",
              "2          Shenzhen           China           Asia          2008   \n",
              "3     San Francisco   United States  North America          2010   \n",
              "4         Stockholm          Sweden         Europe          2005   \n",
              "...             ...             ...            ...           ...   \n",
              "1069       Shanghai           China           Asia          2012   \n",
              "1070        Beijing           China           Asia          2015   \n",
              "1071      Chongqing           China           Asia          2018   \n",
              "1072         London  United Kingdom         Europe          2005   \n",
              "1073     Long Beach   United States  North America          2014   \n",
              "\n",
              "           Funding                                   Select Investors  \\\n",
              "0     8.000000e+09  Sequoia Capital China, SIG Asia Investments, S...   \n",
              "1     7.000000e+09  Founders Fund, Draper Fisher Jurvetson, Rothen...   \n",
              "2     2.000000e+09  Tiger Global Management, Sequoia Capital China...   \n",
              "3     2.000000e+09        Khosla Ventures, LowercaseCapital, capitalG   \n",
              "4     4.000000e+09  Institutional Venture Partners, Sequoia Capita...   \n",
              "...            ...                                                ...   \n",
              "1069  3.790000e+08    K2 Ventures, Matrix Partners China, IDG Capital   \n",
              "1070  9.900000e+08                           58.com, Tencent Holdings   \n",
              "1071  8.000000e+07  Xingwang Investment Management, China Capital ...   \n",
              "1072  7.920000e+08  IAG Capital Partners, Augmentum Fintech, North...   \n",
              "1073  6.200000e+08    Novator Partners, True, Causeway Media Partners   \n",
              "\n",
              "      Year To Join  Profit Returns  \n",
              "0                5    1.720000e+11  \n",
              "1               10    9.300000e+10  \n",
              "2               10    9.800000e+10  \n",
              "3                4    9.300000e+10  \n",
              "4                6    4.200000e+10  \n",
              "...            ...             ...  \n",
              "1069             5    6.210000e+08  \n",
              "1070             2    1.000000e+07  \n",
              "1071             3    9.200000e+08  \n",
              "1072            16    2.080000e+08  \n",
              "1073             6    3.800000e+08  \n",
              "\n",
              "[1074 rows x 12 columns]"
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              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Valuation</th>\n",
              "      <th>Date Joined</th>\n",
              "      <th>Industry</th>\n",
              "      <th>City</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bytedance</td>\n",
              "      <td>1.800000e+11</td>\n",
              "      <td>2017-04-07</td>\n",
              "      <td>Artificial intelligence</td>\n",
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              "      <td>2012</td>\n",
              "      <td>8.000000e+09</td>\n",
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              "      <th>1</th>\n",
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              "      <td>2012-12-01</td>\n",
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              "      <td>Hawthorne</td>\n",
              "      <td>United States</td>\n",
              "      <td>North America</td>\n",
              "      <td>2002</td>\n",
              "      <td>7.000000e+09</td>\n",
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              "      <td>9.300000e+10</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SHEIN</td>\n",
              "      <td>1.000000e+11</td>\n",
              "      <td>2018-07-03</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Shenzhen</td>\n",
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              "      <td>2008</td>\n",
              "      <td>2.000000e+09</td>\n",
              "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
              "      <td>10</td>\n",
              "      <td>9.800000e+10</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stripe</td>\n",
              "      <td>9.500000e+10</td>\n",
              "      <td>2014-01-23</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>United States</td>\n",
              "      <td>North America</td>\n",
              "      <td>2010</td>\n",
              "      <td>2.000000e+09</td>\n",
              "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
              "      <td>4</td>\n",
              "      <td>9.300000e+10</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Klarna</td>\n",
              "      <td>4.600000e+10</td>\n",
              "      <td>2011-12-12</td>\n",
              "      <td>Fintech</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2005</td>\n",
              "      <td>4.000000e+09</td>\n",
              "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
              "      <td>6</td>\n",
              "      <td>4.200000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1069</th>\n",
              "      <td>Zhaogang</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>2017-06-29</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Shanghai</td>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2012</td>\n",
              "      <td>3.790000e+08</td>\n",
              "      <td>K2 Ventures, Matrix Partners China, IDG Capital</td>\n",
              "      <td>5</td>\n",
              "      <td>6.210000e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>Zhuan Zhuan</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>2017-04-18</td>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2015</td>\n",
              "      <td>9.900000e+08</td>\n",
              "      <td>58.com, Tencent Holdings</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1071</th>\n",
              "      <td>Zihaiguo</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>2021-05-06</td>\n",
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              "      <td>Chongqing</td>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
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              "      <td>8.000000e+07</td>\n",
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              "      <td>3</td>\n",
              "      <td>9.200000e+08</td>\n",
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              "    <tr>\n",
              "      <th>1072</th>\n",
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              "      <td>16</td>\n",
              "      <td>2.080000e+08</td>\n",
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              "    <tr>\n",
              "      <th>1073</th>\n",
              "      <td>Zwift</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>2020-09-16</td>\n",
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              "      <td>6.200000e+08</td>\n",
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              "      <td>6</td>\n",
              "      <td>3.800000e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1074 rows × 12 columns</p>\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('companies');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "companies",
              "summary": "{\n  \"name\": \"companies\",\n  \"rows\": 1074,\n  \"fields\": [\n    {\n      \"column\": \"Company\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1073,\n        \"samples\": [\n          \"Pat McGrath Labs\",\n          \"Intercom\",\n          \"Yunxuetang\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valuation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8547022216.45056,\n        \"min\": 1000000000.0,\n        \"max\": 180000000000.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          3000000000.0,\n          15000000000.0,\n          7000000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date Joined\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2007-07-02 00:00:00\",\n        \"max\": \"2022-04-05 00:00:00\",\n        \"num_unique_values\": 639,\n        \"samples\": [\n          \"2021-04-06 00:00:00\",\n          \"2020-11-16 00:00:00\",\n          \"2021-08-05 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Industry\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Artificial intelligence\",\n          \"Other\",\n          \"Supply chain, logistics, & delivery\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 256,\n        \"samples\": [\n          \"Jeju-do\",\n          \"London\",\n          \"Eden Prairie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country/Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"Senegal\",\n          \"Austria\",\n          \"Ireland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"North America\",\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year Founded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1919,\n        \"max\": 2021,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1995,\n          2003,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Funding\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 807719436.3261343,\n        \"min\": 0.0,\n        \"max\": 14000000000.0,\n        \"num_unique_values\": 538,\n        \"samples\": [\n          243000000.0,\n          644000000.0,\n          115000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Select Investors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1058,\n        \"samples\": [\n          \"Carsales\",\n          \"Goldman Sachs Asset Management\",\n          \"Iconiq Capital\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year To Join\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": -4,\n        \"max\": 98,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          24,\n          12,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit Returns\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8089590800.214347,\n        \"min\": -1000000000.0,\n        \"max\": 172000000000.0,\n        \"num_unique_values\": 764,\n        \"samples\": [\n          1555000000.0,\n          2830000000.0,\n          377000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ROI_unicorn = companies.groupby('Industry')['Profit Returns'].mean().round(2).sort_values(ascending=True).reset_index()\n",
        "result_table = pd.merge(route_to_unicorn, ROI_unicorn, on='Industry', how='left')\n",
        "result_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eO--kR80GDO3",
        "outputId": "f074b56a-f580-45af-e2e4-eaf445ab1957"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Industry  Year To Join  Profit Returns\n",
              "0                 Auto & transportation          5.03    2.062129e+09\n",
              "1               Artificial Intelligence          5.36    1.193000e+09\n",
              "2                              Hardware          5.94    2.282794e+09\n",
              "3               Artificial intelligence          5.97    4.343493e+09\n",
              "4           Mobile & telecommunications          6.32    1.950421e+09\n",
              "5       E-commerce & direct-to-consumer          6.43    3.167234e+09\n",
              "6                               Fintech          6.54    3.455375e+09\n",
              "7                                Travel          6.57    2.383857e+09\n",
              "8                         Cybersecurity          6.80    2.141360e+09\n",
              "9   Supply chain, logistics, & delivery          6.93    2.326439e+09\n",
              "10                               Edtech          7.71    2.790179e+09\n",
              "11                                Other          7.78    3.654414e+09\n",
              "12         Internet software & services          7.87    2.546561e+09\n",
              "13          Data management & analytics          8.07    2.878512e+09\n",
              "14                    Consumer & retail          8.08    3.220320e+09\n",
              "15                               Health          8.19    2.216973e+09"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry</th>\n",
              "      <th>Year To Join</th>\n",
              "      <th>Profit Returns</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Auto &amp; transportation</td>\n",
              "      <td>5.03</td>\n",
              "      <td>2.062129e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Artificial Intelligence</td>\n",
              "      <td>5.36</td>\n",
              "      <td>1.193000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hardware</td>\n",
              "      <td>5.94</td>\n",
              "      <td>2.282794e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artificial intelligence</td>\n",
              "      <td>5.97</td>\n",
              "      <td>4.343493e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mobile &amp; telecommunications</td>\n",
              "      <td>6.32</td>\n",
              "      <td>1.950421e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>E-commerce &amp; direct-to-consumer</td>\n",
              "      <td>6.43</td>\n",
              "      <td>3.167234e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fintech</td>\n",
              "      <td>6.54</td>\n",
              "      <td>3.455375e+09</td>\n",
              "    </tr>\n",
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              "      <th>7</th>\n",
              "      <td>Travel</td>\n",
              "      <td>6.57</td>\n",
              "      <td>2.383857e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Cybersecurity</td>\n",
              "      <td>6.80</td>\n",
              "      <td>2.141360e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Supply chain, logistics, &amp; delivery</td>\n",
              "      <td>6.93</td>\n",
              "      <td>2.326439e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Edtech</td>\n",
              "      <td>7.71</td>\n",
              "      <td>2.790179e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Other</td>\n",
              "      <td>7.78</td>\n",
              "      <td>3.654414e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Internet software &amp; services</td>\n",
              "      <td>7.87</td>\n",
              "      <td>2.546561e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Data management &amp; analytics</td>\n",
              "      <td>8.07</td>\n",
              "      <td>2.878512e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Consumer &amp; retail</td>\n",
              "      <td>8.08</td>\n",
              "      <td>3.220320e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Health</td>\n",
              "      <td>8.19</td>\n",
              "      <td>2.216973e+09</td>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_616b4c48-5e5a-4200-a3c8-e5999baf6182 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result_table');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_table",
              "summary": "{\n  \"name\": \"result_table\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"Industry\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Auto & transportation\",\n          \"Artificial Intelligence\",\n          \"E-commerce & direct-to-consumer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year To Join\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0072237007404727,\n        \"min\": 5.03,\n        \"max\": 8.19,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          5.03,\n          5.36,\n          6.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit Returns\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 770816219.6052251,\n        \"min\": 1193000000.0,\n        \"max\": 4343493150.68,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          2062129032.26,\n          1193000000.0,\n          3167234234.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78bcc4cfe680>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "result_table['Year To Join'].plot(kind='hist', bins=20, title='Year To Join')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "YCxCBQAAGItQAQAAxvp/CtX/sYPdYP4AAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-edd5b1c5-1579-44b8-8fa0-80f7e0fc3153\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-edd5b1c5-1579-44b8-8fa0-80f7e0fc3153\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "result_table['Profit Returns'].plot(kind='hist', bins=20, title='Profit Returns')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-11c7d91d-d0eb-4c2b-8619-da17ff46c22e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-11c7d91d-d0eb-4c2b-8619-da17ff46c22e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78bcc4cfe920>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "result_table.plot(kind='scatter', x='Year To Join', y='Profit Returns', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f3b10d0f-965b-4acb-a925-43a918dc2238\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f3b10d0f-965b-4acb-a925-43a918dc2238\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78bcc4cfe6b0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['Year To Join']\n",
              "  ys = series['Profit Returns']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = result_table.sort_values('Year To Join', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('Year To Join')\n",
              "_ = plt.ylabel('Profit Returns')"
            ],
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-7858a032-9f4c-42ea-84e6-cb31d6d091b7\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-7858a032-9f4c-42ea-84e6-cb31d6d091b7\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  counted = (series['Year To Join']\n",
              "                .value_counts()\n",
              "              .reset_index(name='counts')\n",
              "              .rename({'index': 'Year To Join'}, axis=1)\n",
              "              .sort_values('Year To Join', ascending=True))\n",
              "  xs = counted['Year To Join']\n",
              "  ys = counted['counts']\n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = result_table.sort_values('Year To Join', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('Year To Join')\n",
              "_ = plt.ylabel('count()')"
            ],
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              "AAAAhRHzAAAAUJj/BwHyj55VLkkKAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-860e88bb-6cf1-41af-ada7-258c94e2218e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-860e88bb-6cf1-41af-ada7-258c94e2218e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x78bcc4cfebc0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "result_table['Year To Join'].plot(kind='line', figsize=(8, 4), title='Year To Join')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "ozvkaTEAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-2a1055ff-3a75-4945-8065-90f707562683\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-2a1055ff-3a75-4945-8065-90f707562683\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "result_table['Profit Returns'].plot(kind='line', figsize=(8, 4), title='Profit Returns')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-6fe80bbe-d8b6-4884-82ad-0388bbf66684\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-6fe80bbe-d8b6-4884-82ad-0388bbf66684\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The average time and ammount for those investing firm\n",
        "avg_investing = result_table.describe()\n",
        "avg_investing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dURzZ7OwKqZ-",
        "outputId": "574571f3-68cd-45cb-dc3d-f7a4a0b9d371"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Year To Join  Profit Returns\n",
              "count     16.000000    1.600000e+01\n",
              "mean       6.849375    2.663316e+09\n",
              "std        1.007224    7.708162e+08\n",
              "min        5.030000    1.193000e+09\n",
              "25%        6.232500    2.198070e+09\n",
              "50%        6.685000    2.465209e+09\n",
              "75%        7.802500    3.180506e+09\n",
              "max        8.190000    4.343493e+09"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year To Join</th>\n",
              "      <th>Profit Returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>1.600000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.849375</td>\n",
              "      <td>2.663316e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.007224</td>\n",
              "      <td>7.708162e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.030000</td>\n",
              "      <td>1.193000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.232500</td>\n",
              "      <td>2.198070e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.685000</td>\n",
              "      <td>2.465209e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.802500</td>\n",
              "      <td>3.180506e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "avg_investing",
              "summary": "{\n  \"name\": \"avg_investing\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Year To Join\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.194586175777295,\n        \"min\": 1.0072237007404727,\n        \"max\": 16.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.849375,\n          6.6850000000000005,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit Returns\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1396957260.6079807,\n        \"min\": 16.0,\n        \"max\": 4343493150.68,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2663316302.189375,\n          2465209059.235,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=result_table, x= 'Year To Join', y= 'Profit Returns', palette='Set2', showfliers=True)\n",
        "plt.xticks(rotation=45)\n",
        "plt.xlabel('Year to Join')\n",
        "plt.ylabel('Profit Returns')\n",
        "plt.title('Profit Returns vs Year to Join')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "sMZG_GT2O6RC",
        "outputId": "53a90fcc-4ac3-48f9-ca62-71eb74ae1d14"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-a7c627fd4064>:1: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=result_table, x= 'Year To Join', y= 'Profit Returns', palette='Set2', showfliers=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hmb7ZapjUx0x"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}