Validate and clean your data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1WD1hC5MTRMS"
   },
   "source": [
    "# Activity: Validate and clean your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ee31943b-274e-4c28-bce6-155cb6f4c334"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "In this activity, you will use input validation and label encoding to prepare a dataset for analysis. These are fundamental techniques used in all types of data analysis, from simple linear regression to complex neural networks. \n",
    "\n",
    "In this activity, you are a data professional an investment firm that is attempting to invest in private companies with a valuation of at least $1 billion. These are often known as \"unicorns.\" Your client wants to develop a better understanding of unicorns, with the hope they can be early investors in future highly successful companies. They are particularly interested in the investment strategies of the three top unicorn investors: Sequoia Capital, Tiger Global Management, and Accel. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4bb9630e-b940-4835-a389-d39def1fa848"
   },
   "source": [
    "## Step 1: Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bc180686-4ae2-47ea-a292-3588ac60a820"
   },
   "source": [
    "Import relevant Python libraries and packages: `numpy`, `pandas`, `seaborn`, and `pyplot` from `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "8e7f1f2c-bbcc-4f3c-b927-b090ee8334c4"
   },
   "outputs": [],
   "source": [
    "# Import libraries and packages.\n",
    "\n",
    "### YOUR CODE HERE ### \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7d29c6bd-f194-46bb-b794-56dc20130473"
   },
   "source": [
    "### Load the dataset\n",
    "\n",
    "The data contains details about unicorn companies, such as when they were founded, when they achieved unicorn status, and their current valuation. The dataset `Modified_Unicorn_Companies.csv` is loaded as `companies`, now display the first five rows. The variables in the dataset have been adjusted to suit the objectives of this lab, so they may be different from similar data used in prior labs. As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell so pandas displays all columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "3bd40d44-080f-4e49-b0c2-160dedc3c4df"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</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>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</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>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stripe</td>\n",
       "      <td>95</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>$2B</td>\n",
       "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Klarna</td>\n",
       "      <td>46</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>$4B</td>\n",
       "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \n",
       "3     Stripe         95  2014-01-23                          FinTech   \n",
       "4     Klarna         46  2011-12-12                          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...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RUN THIS CELL TO IMPORT YOUR DATA. \n",
    "companies = pd.read_csv('Modified_Unicorn_Companies.csv')\n",
    "\n",
    "# Display the first five rows.\n",
    "### YOUR CODE HERE ### \n",
    "companies.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95bf39c9-d05c-4324-a40b-bc692607b61b"
   },
   "source": [
    "## Step 2: Data cleaning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad17d09c-c829-4e88-9089-83060d1adf91"
   },
   "source": [
    "Begin by displaying the data types of the columns in `companies`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "5340b9fd-a9e1-4943-b546-eece0049dc3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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   int64 \n",
      " 2   Date Joined       1074 non-null   object\n",
      " 3   Industry          1074 non-null   object\n",
      " 4   City              1057 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  1074 non-null   object\n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 84.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display the data types of the columns.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f155353c-d39d-436e-b0de-3cb0777c831e"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Review what you have learned about exploratory data analysis in Python.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a0c1a503-ccec-44a9-baaf-a89eabf363cf"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "There is a `pandas` DataFrame property that displays the data types of the columns in the specified DataFrame.\n",
    " \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0566888c-aa4d-4dec-966b-d99959fc05f0"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "  The `pandas` DataFrame `dtypes` property will be helpful.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0dbc048f-cd6d-4e52-93a9-21ec30ce055b"
   },
   "source": [
    "### Modify the data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad280560-d6db-4fcd-ba70-2c16389e12fd"
   },
   "source": [
    "Notice that the data type of the `Date Joined` column is an `object`&mdash;in this case, a string. Convert this column to `datetime` to make it more usable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1793ad8d-659a-4fc9-aa8a-570f063ca0d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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   int64         \n",
      " 2   Date Joined       1074 non-null   datetime64[ns]\n",
      " 3   Industry          1074 non-null   object        \n",
      " 4   City              1057 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  1074 non-null   object        \n",
      "dtypes: datetime64[ns](1), int64(2), object(7)\n",
      "memory usage: 84.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Apply necessary datatype conversions.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies['Date Joined'] = pd.to_datetime(companies['Date Joined'])\n",
    "companies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ea7364c-a5be-4c3a-ae8d-24a0ac497973"
   },
   "source": [
    "### Create a new column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ed05e76-0a66-4ea3-9796-39afb48eeb93"
   },
   "source": [
    "Add a column called `Years To Unicorn`, which is the number of years between when the company was founded and when it became a unicorn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "3d495d71-39af-4d3f-80fd-0d0a22b01532"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</td>\n",
       "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpaceX</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</td>\n",
       "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHEIN</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stripe</td>\n",
       "      <td>95</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>$2B</td>\n",
       "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Klarna</td>\n",
       "      <td>46</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>$4B</td>\n",
       "      <td>Institutional Venture Partners, Sequoia Capita...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \n",
       "3     Stripe         95  2014-01-23                          FinTech   \n",
       "4     Klarna         46  2011-12-12                          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  Years To Unicorn  \n",
       "0  Sequoia Capital China, SIG Asia Investments, S...                 5  \n",
       "1  Founders Fund, Draper Fisher Jurvetson, Rothen...                10  \n",
       "2  Tiger Global Management, Sequoia Capital China...                10  \n",
       "3        Khosla Ventures, LowercaseCapital, capitalG                 4  \n",
       "4  Institutional Venture Partners, Sequoia Capita...                 6  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the column Years To Unicorn.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies['Years To Unicorn'] = companies['Date Joined'].dt.year - companies['Year Founded']\n",
    "companies.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4a576b71-1047-48ab-abe9-c5e8d1b67d26"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Extract just the year from the `Date Joined` column. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "21584921-1d6a-42ab-8f04-9793c7d5cee2"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "  Use `dt.year` to access the year of a datetime object.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aea4d087-51f5-4b0f-b4c4-18b1acfe0c0d"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "Subtract the `Year Founded` from the `Date Joined`, and save it to a new column called `Years To Unicorn`.\n",
    "  \n",
    "Ensure you're properly extracting just the year (as an integer) from `Date Joined`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fb06c26b-70b7-4b05-9347-6675b8b329d3"
   },
   "source": [
    "**QUESTION: Why might your client be interested in how quickly a company achieved unicorn status?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ac6a0172-611e-4a1c-9afd-a39b489cbdee"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fc9ddf1-ece0-40fb-9281-0808c193f5da",
    "tags": []
   },
   "source": [
    "### Input validation\n",
    "\n",
    "The data has some issues with bad data, duplicate rows, and inconsistent `Industry` labels.\n",
    "\n",
    "Identify and correct each of these issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "db0651e7-36ef-4c10-a3ba-97373167da64",
    "tags": []
   },
   "source": [
    "#### Correcting bad data\n",
    "\n",
    "Get descriptive statistics for the `Years To Unicorn` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "8b1b2ad0-a668-4290-9404-5a2541955c0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1074.000000\n",
       "mean        7.013035\n",
       "std         5.331842\n",
       "min        -3.000000\n",
       "25%         4.000000\n",
       "50%         6.000000\n",
       "75%         9.000000\n",
       "max        98.000000\n",
       "Name: Years To Unicorn, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify and correct the issue with Years To Unicorn.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies['Years To Unicorn'].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "80974b46-b7f9-4181-a90d-a6fa927a710b"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `describe()` method on the series. Considering the results, does anything seem problematic?\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7d7e351d-368e-45ab-8e8a-5f1aded15655"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "A company cannot reach unicorn status before it is founded. In other words, `Years to Unicorn` cannot be less than 0.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "Using the `describe()` method on the `Years To Unicorn` series shows that the minimum value is `-3`. Since there cannot be negative time, this value and possibly others are problematic.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8b325a5a-67d2-4f92-8159-a9f4c21b1779"
   },
   "source": [
    "Isolate all rows where the `Years To Unicorn` column contains a negative value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "b02348fb-dde8-43e7-9368-4d771bf6e5a7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>InVision</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>Internet software &amp; services</td>\n",
       "      <td>New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2020</td>\n",
       "      <td>$349M</td>\n",
       "      <td>FirstMark Capital, Tiger Global Management, IC...</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company  Valuation Date Joined                      Industry      City  \\\n",
       "527  InVision          2  2017-11-01  Internet software & services  New York   \n",
       "\n",
       "    Country/Region      Continent  Year Founded Funding  \\\n",
       "527  United States  North America          2020   $349M   \n",
       "\n",
       "                                      Select Investors  Years To Unicorn  \n",
       "527  FirstMark Capital, Tiger Global Management, IC...                -3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolate any rows where `Years To Unicorn` is negative\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies[companies['Years To Unicorn'] <0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: How many rows have negative values in the `Years To Unicorn` column, and what companies are they for?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "739430f4-97ce-43d4-a982-3f75f3823702"
   },
   "source": [
    "An internet search reveals that InVision was founded in 2011. Replace the value at `Year Founded` with `2011` for InVision's row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "6b89463a-6426-4797-a62e-b7b26f0b2f3b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>InVision</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>Internet software &amp; services</td>\n",
       "      <td>New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2011</td>\n",
       "      <td>$349M</td>\n",
       "      <td>FirstMark Capital, Tiger Global Management, IC...</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company  Valuation Date Joined                      Industry      City  \\\n",
       "527  InVision          2  2017-11-01  Internet software & services  New York   \n",
       "\n",
       "    Country/Region      Continent  Year Founded Funding  \\\n",
       "527  United States  North America          2011   $349M   \n",
       "\n",
       "                                      Select Investors  Years To Unicorn  \n",
       "527  FirstMark Capital, Tiger Global Management, IC...                -3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace InVision's `Year Founded` value with 2011\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies.loc[(companies['Company'] == 'InVision') , 'Year Founded'] = 2011\n",
    "\n",
    "# Verify the change was made properly\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies[companies['Company'] == 'InVision']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "65909471-2b17-4d8d-b734-6d895029dc7d"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "To overwrite data in a dataframe in a situation like this, you should use `loc[]` or `iloc[]` selection statements. Otherwise, you might overwrite to a view of the dataframe, which means that you're not overwriting the data in the dataframe itself, and the change will not take permanent effect.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c12d89ac-bcd3-4912-9faf-507b70b4b060"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "The following code will **not** work:\n",
    "    \n",
    "    companies[companies['Company']=='InVision']['Year Founded'] = 2011\n",
    "    \n",
    "You must use either `loc[]` or `iloc[]`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "121e1b7c-845f-4cee-a973-aebd41411dd2"
   },
   "source": [
    "Now, recalculate all the values in the `Years To Unicorn` column to remove the negative value for InVision. Verify that there are no more negative values afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "0850371d-4cf8-4f06-9e41-b3baa842d86c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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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",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Company, Valuation, Date Joined, Industry, City, Country/Region, Continent, Year Founded, Funding, Select Investors, Years To Unicorn]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recalculate all values in the `Years To Unicorn` column\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies['Years To Unicorn'] = companies['Date Joined'].dt.year - companies['Year Founded']\n",
    "# Verify that there are no more negative values in the column\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies[companies['Years To Unicorn'] <0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0b6edaeb-6561-4201-b2d8-2c11ed8837c1"
   },
   "source": [
    "#### Issues with `Industry` labels\n",
    "\n",
    "The company provided you with the following list of industry labels to identify in the data for `Industry`. \n",
    "\n",
    "**Note:** Any labels in the `Industry` column that are not in `industry_list` are misspellings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List provided by the company of the expected industry labels in the data\n",
    "industry_list = ['Artificial intelligence', 'Other','E-commerce & direct-to-consumer', 'Fintech',\\\n",
    "       'Internet software & services','Supply chain, logistics, & delivery', 'Consumer & retail',\\\n",
    "       'Data management & analytics', 'Edtech', 'Health', 'Hardware','Auto & transportation', \\\n",
    "        'Travel', 'Cybersecurity','Mobile & telecommunications']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, check if there are values in the `Industry` column that are not in `industry_list`. If so, what are they?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stripe</td>\n",
       "      <td>95</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>$2B</td>\n",
       "      <td>Khosla Ventures, LowercaseCapital, capitalG</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Databricks</td>\n",
       "      <td>38</td>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2013</td>\n",
       "      <td>$3B</td>\n",
       "      <td>Andreessen Horowitz, New Enterprise Associates...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chime</td>\n",
       "      <td>25</td>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2013</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Forerunner Ventures, Crosslink Capital, Homebrew</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Brex</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2017</td>\n",
       "      <td>$1B</td>\n",
       "      <td>DST Global, Ribbit Capital, Greenoaks Capital ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Celonis</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-06-26</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>$1B</td>\n",
       "      <td>Accel, 83North</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Toss</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-12-09</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2013</td>\n",
       "      <td>$844M</td>\n",
       "      <td>Bessemer Venture Partners, Qualcomm Ventures, ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Airwallex</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2015</td>\n",
       "      <td>$802M</td>\n",
       "      <td>DST Global, Sequoia Capital China, Tencent Hol...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>FiveTran</td>\n",
       "      <td>6</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2012</td>\n",
       "      <td>$728M</td>\n",
       "      <td>Matrix Partners, Andreessen Horowitz, General ...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Pine Labs</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Noida</td>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1998</td>\n",
       "      <td>$903M</td>\n",
       "      <td>Sequoia Capital India, Temasek, PayPal Ventures</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Chainalysis</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2014</td>\n",
       "      <td>$367M</td>\n",
       "      <td>Addition, Benhcmark, Accel</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>dbt Labs</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2016</td>\n",
       "      <td>$414M</td>\n",
       "      <td>Andreessen Horowitz, Amplify Partners, Sequoia...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>BitPanda</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2014</td>\n",
       "      <td>$546M</td>\n",
       "      <td>Speedinvest, Valar Ventures, Uniqa Ventures</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Uniphore</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2008</td>\n",
       "      <td>$607M</td>\n",
       "      <td>Chiratae Ventures, March Capital Partners, Nat...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>Circle</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Boston</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2013</td>\n",
       "      <td>$711M</td>\n",
       "      <td>General Catalyst, Digital Currency Group, Accel</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Flutterwave</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2016</td>\n",
       "      <td>$474M</td>\n",
       "      <td>Green Visor Capital, CRE Venture Capital, Grey...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Inxeption</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2017</td>\n",
       "      <td>$187M</td>\n",
       "      <td>Coatue Management, BMO Capital, Schonfeld Stra...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>NotCo</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-07-26</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>South America</td>\n",
       "      <td>2015</td>\n",
       "      <td>$370M</td>\n",
       "      <td>Kaszek Ventures, SOSV, Tiger Global Management</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>HomeLight</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-09-02</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2012</td>\n",
       "      <td>$219M</td>\n",
       "      <td>Zeev Ventures, Menlo Ventures,Crosslink Capital</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Addepar</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2009</td>\n",
       "      <td>$676M</td>\n",
       "      <td>8VC, D1 Capital Partners, Sway Ventures</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>ConcertAI</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-03-29</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Boston</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2018</td>\n",
       "      <td>$300M</td>\n",
       "      <td>Sixth Street Partners, Declaration Partners, M...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>CredAvenue</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-03-07</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2017</td>\n",
       "      <td>$227M</td>\n",
       "      <td>Insight Partners, B Capital Group, Lightspeed ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Shukun Technology</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-08-16</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2017</td>\n",
       "      <td>$211M</td>\n",
       "      <td>Marathon Venture Partners, Huagai Capital, Chi...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>SumUp</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-07-16</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2011</td>\n",
       "      <td>$1B</td>\n",
       "      <td>American Express Ventures, Goldman Sachs, Bain...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>TUNGEE</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Guangzhou</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2016</td>\n",
       "      <td>$171M</td>\n",
       "      <td>UNITY VENTURES, Qiming Venture Partners, GGV C...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>Firebolt</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>Israel</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>$264M</td>\n",
       "      <td>TLV Partners, Zeev Ventures, Bessemer Venture ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>SmartMore</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>$300M</td>\n",
       "      <td>IDG Capital, ZhenFund, Sequoia Capital China</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Guideline</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>San Mateo</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2015</td>\n",
       "      <td>$339M</td>\n",
       "      <td>Pelion Venture Partners, Foundation Capital, T...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>Amount</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-05-17</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2019</td>\n",
       "      <td>$243M</td>\n",
       "      <td>Invus Group, Hanaco Venture Capital, WestCap G...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Anyscale</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-07</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2019</td>\n",
       "      <td>$160M</td>\n",
       "      <td>Andreessen Horowitz, Intel Capital, Foundation...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>CommerceIQ</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-03-21</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2012</td>\n",
       "      <td>$196M</td>\n",
       "      <td>Trinity Ventures, Madrona Venture Group, Shast...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Dune Analytics</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Data management and analytics</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2018</td>\n",
       "      <td>$79M</td>\n",
       "      <td>Multicoin Capital, Coatue Management, Dragonfl...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>Haomao.AI</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>$157M</td>\n",
       "      <td>Qualcomm Ventures, Nine Intelligence Capital, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Owkin</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2016</td>\n",
       "      <td>$254M</td>\n",
       "      <td>Google Ventures, Cathay Innovation, NJF Capital</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>WeLab</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2013</td>\n",
       "      <td>$871M</td>\n",
       "      <td>Sequoia Capital China, ING, Alibaba Entreprene...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>Wrapbook</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-11-10</td>\n",
       "      <td>FinTech</td>\n",
       "      <td>New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2018</td>\n",
       "      <td>$131M</td>\n",
       "      <td>Equal Ventures, Uncork Capital, Andreessen Hor...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Company  Valuation Date Joined                       Industry  \\\n",
       "3                Stripe         95  2014-01-23                        FinTech   \n",
       "9            Databricks         38  2019-02-05  Data management and analytics   \n",
       "14                Chime         25  2019-03-05                        FinTech   \n",
       "33                 Brex         12  2018-10-05                        FinTech   \n",
       "44              Celonis         11  2018-06-26  Data management and analytics   \n",
       "91                 Toss          7  2018-12-09                        FinTech   \n",
       "105           Airwallex          6  2019-03-25                        FinTech   \n",
       "110            FiveTran          6  2020-06-30  Data management and analytics   \n",
       "161           Pine Labs          5  2020-01-24                        FinTech   \n",
       "207         Chainalysis          4  2020-11-23                        FinTech   \n",
       "208            dbt Labs          4  2021-06-30  Data management and analytics   \n",
       "215            BitPanda          4  2021-03-16                        FinTech   \n",
       "269            Uniphore          3  2022-02-16        Artificial Intelligence   \n",
       "329              Circle          3  2018-05-15                        FinTech   \n",
       "335         Flutterwave          3  2021-03-09                        FinTech   \n",
       "339           Inxeption          3  2022-01-13                        FinTech   \n",
       "412               NotCo          2  2021-07-26        Artificial Intelligence   \n",
       "444           HomeLight          2  2021-09-02                        FinTech   \n",
       "514             Addepar          2  2021-06-15                        FinTech   \n",
       "522           ConcertAI          2  2022-03-29        Artificial Intelligence   \n",
       "608          CredAvenue          1  2022-03-07                        FinTech   \n",
       "636   Shukun Technology          1  2021-08-16        Artificial Intelligence   \n",
       "640               SumUp          1  2019-07-16                        FinTech   \n",
       "642              TUNGEE          1  2021-12-15        Artificial Intelligence   \n",
       "697            Firebolt          1  2022-01-26  Data management and analytics   \n",
       "788           SmartMore          1  2021-06-24        Artificial Intelligence   \n",
       "801           Guideline          1  2021-06-16                        FinTech   \n",
       "860              Amount          1  2021-05-17                        FinTech   \n",
       "862            Anyscale          1  2021-12-07        Artificial Intelligence   \n",
       "895          CommerceIQ          1  2022-03-21        Artificial Intelligence   \n",
       "902      Dune Analytics          1  2022-02-02  Data management and analytics   \n",
       "928           Haomao.AI          1  2021-12-22        Artificial Intelligence   \n",
       "990               Owkin          1  2021-11-18        Artificial Intelligence   \n",
       "1061              WeLab          1  2017-11-08                        FinTech   \n",
       "1063           Wrapbook          1  2021-11-10                        FinTech   \n",
       "\n",
       "               City  Country/Region      Continent  Year Founded Funding  \\\n",
       "3     San Francisco   United States  North America          2010     $2B   \n",
       "9     San Francisco   United States  North America          2013     $3B   \n",
       "14    San Francisco   United States  North America          2013     $2B   \n",
       "33    San Francisco   United States  North America          2017     $1B   \n",
       "44           Munich         Germany         Europe          2011     $1B   \n",
       "91            Seoul     South Korea           Asia          2013   $844M   \n",
       "105       Melbourne       Australia        Oceania          2015   $802M   \n",
       "110         Oakland   United States  North America          2012   $728M   \n",
       "161           Noida           India           Asia          1998   $903M   \n",
       "207        New York   United States  North America          2014   $367M   \n",
       "208    Philadelphia   United States  North America          2016   $414M   \n",
       "215          Vienna         Austria         Europe          2014   $546M   \n",
       "269       Palo Alto   United States  North America          2008   $607M   \n",
       "329          Boston   United States  North America          2013   $711M   \n",
       "335   San Francisco   United States  North America          2016   $474M   \n",
       "339   San Francisco   United States  North America          2017   $187M   \n",
       "412        Santiago           Chile  South America          2015   $370M   \n",
       "444   San Francisco   United States  North America          2012   $219M   \n",
       "514   Mountain View   United States  North America          2009   $676M   \n",
       "522          Boston   United States  North America          2018   $300M   \n",
       "608         Chennai           India           Asia          2017   $227M   \n",
       "636         Beijing           China           Asia          2017   $211M   \n",
       "640          London  United Kingdom         Europe          2011     $1B   \n",
       "642       Guangzhou           China           Asia          2016   $171M   \n",
       "697        Tel Aviv          Israel           Asia          2019   $264M   \n",
       "788        Shenzhen           China           Asia          2019   $300M   \n",
       "801       San Mateo   United States  North America          2015   $339M   \n",
       "860         Chicago   United States  North America          2019   $243M   \n",
       "862        Berkeley   United States  North America          2019   $160M   \n",
       "895       Palo Alto   United States  North America          2012   $196M   \n",
       "902            Oslo          Norway         Europe          2018    $79M   \n",
       "928         Beijing           China           Asia          2019   $157M   \n",
       "990        New York   United States  North America          2016   $254M   \n",
       "1061            NaN       Hong Kong           Asia          2013   $871M   \n",
       "1063       New York   United States  North America          2018   $131M   \n",
       "\n",
       "                                       Select Investors  Years To Unicorn  \n",
       "3           Khosla Ventures, LowercaseCapital, capitalG                 4  \n",
       "9     Andreessen Horowitz, New Enterprise Associates...                 6  \n",
       "14     Forerunner Ventures, Crosslink Capital, Homebrew                 6  \n",
       "33    DST Global, Ribbit Capital, Greenoaks Capital ...                 1  \n",
       "44                                       Accel, 83North                 7  \n",
       "91    Bessemer Venture Partners, Qualcomm Ventures, ...                 5  \n",
       "105   DST Global, Sequoia Capital China, Tencent Hol...                 4  \n",
       "110   Matrix Partners, Andreessen Horowitz, General ...                 8  \n",
       "161     Sequoia Capital India, Temasek, PayPal Ventures                22  \n",
       "207                          Addition, Benhcmark, Accel                 6  \n",
       "208   Andreessen Horowitz, Amplify Partners, Sequoia...                 5  \n",
       "215         Speedinvest, Valar Ventures, Uniqa Ventures                 7  \n",
       "269   Chiratae Ventures, March Capital Partners, Nat...                14  \n",
       "329     General Catalyst, Digital Currency Group, Accel                 5  \n",
       "335   Green Visor Capital, CRE Venture Capital, Grey...                 5  \n",
       "339   Coatue Management, BMO Capital, Schonfeld Stra...                 5  \n",
       "412      Kaszek Ventures, SOSV, Tiger Global Management                 6  \n",
       "444     Zeev Ventures, Menlo Ventures,Crosslink Capital                 9  \n",
       "514             8VC, D1 Capital Partners, Sway Ventures                12  \n",
       "522   Sixth Street Partners, Declaration Partners, M...                 4  \n",
       "608   Insight Partners, B Capital Group, Lightspeed ...                 5  \n",
       "636   Marathon Venture Partners, Huagai Capital, Chi...                 4  \n",
       "640   American Express Ventures, Goldman Sachs, Bain...                 8  \n",
       "642   UNITY VENTURES, Qiming Venture Partners, GGV C...                 5  \n",
       "697   TLV Partners, Zeev Ventures, Bessemer Venture ...                 3  \n",
       "788        IDG Capital, ZhenFund, Sequoia Capital China                 2  \n",
       "801   Pelion Venture Partners, Foundation Capital, T...                 6  \n",
       "860   Invus Group, Hanaco Venture Capital, WestCap G...                 2  \n",
       "862   Andreessen Horowitz, Intel Capital, Foundation...                 2  \n",
       "895   Trinity Ventures, Madrona Venture Group, Shast...                10  \n",
       "902   Multicoin Capital, Coatue Management, Dragonfl...                 4  \n",
       "928   Qualcomm Ventures, Nine Intelligence Capital, ...                 2  \n",
       "990     Google Ventures, Cathay Innovation, NJF Capital                 5  \n",
       "1061  Sequoia Capital China, ING, Alibaba Entreprene...                 4  \n",
       "1063  Equal Ventures, Uncork Capital, Andreessen Hor...                 3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which values are in `Industry` but not in `industry_list`\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies[~companies['Industry'].isin(industry_list)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>HINT 1</strong></h4></summary>\n",
    "\n",
    "There are many ways to do this. One approach is to consider what data type reduces iterable sequences to their unique elements and allows you to compare membership.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>HINT 2</strong></h4></summary>\n",
    "\n",
    "A set is a data type that consists of unique elements and supports membership testing with other sets.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>HINT 3</strong></h4></summary>\n",
    "\n",
    "Set A &ndash; Set B will result in all the elements that are in Set A but not in Set B. Convert `industry_list` to a set and subtract it from the set of the values in the `Industry` series.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Which values currently exist in the `Industry` column that are not in `industry_list`?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, correct the bad entries in the `Industry` column by replacing them with an approved string from `industry_list`. To do this, use the [`replace()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.replace.html) `Series` method on the `Industry` series. When you pass a dictionary to the method, it will replace the data in the series where that data matches the dictionary's keys. The values that get imputed are the values of the dictionary. If a value is not specified in the dictionary, the series' original value is retained.\n",
    "</br></br>\n",
    "\n",
    "\n",
    "```\n",
    "Example:\n",
    "\n",
    " [IN]: column_a = pd.Series(['A', 'B', 'C', 'D'])\n",
    "       column_a\n",
    "\n",
    "[OUT]: 0    A\n",
    "       1    B\n",
    "       2    C\n",
    "       3    D\n",
    "       dtype: object\n",
    "\n",
    " [IN]: replacement_dict = {'A':'z', 'B':'y', 'C':'x'}\n",
    "       column_a = column_a.replace(replacement_dict)\n",
    "       column_a\n",
    "       \n",
    "[OUT]: 0    z\n",
    "       1    y\n",
    "       2    x\n",
    "       3    D\n",
    "       dtype: object\n",
    "\n",
    "```\n",
    "\n",
    "1. Create a dictionary called `replacement_dict` whose keys are the incorrect spellings in the `Industry` series and whose values are the correct spelling, as indicated in `industry_list`.\n",
    "\n",
    "2. Call the `replace()` method on the `Industry` series and pass to it `replacement_dict` as its argument. Reassign the result back to the `Industry` column.\n",
    "\n",
    "3. Verify that there are no longer any elements in `Industry` that are not in `industry_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Company, Valuation, Date Joined, Industry, City, Country/Region, Continent, Year Founded, Funding, Select Investors, Years To Unicorn]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create `replacement_dict`\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "replacement_dict = {'FinTech':'Fintech', 'Artificial Intelligence':'Artificial intelligence', 'Data management and analytics':'Data management & analytics'}\n",
    "# 2. Replace the incorrect values in the `Industry` column\n",
    "companies['Industry'] = companies['Industry'].replace(replacement_dict)\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "# 3. Verify that there are no longer any elements in `Industry` that are not in `industry_list`\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies[~companies['Industry'].isin(industry_list)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Refer to the example provided for how to use the `replace()` `Series` method.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "When you define the `replacement_dict` dictionary, the misspellings should be the keys and the correct spellings should be the values.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>   \n",
    "\n",
    "When you call `replace()` on the `Industry` series and pass to it the `replacement_dict` dictionary as an argument, you must reassign the result back to `companies['Industry']` for the change to take effect.   \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling duplicate rows\n",
    "\n",
    "The business mentioned that no company should appear in the data more than once.\n",
    "\n",
    "Verify that this is indeed the case, and if not, clean the data so each company appears only once.\n",
    "\n",
    "Begin by checking which, if any, companies are duplicated. Filter the data to return all occurrences of those duplicated companies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Company  Valuation Date Joined                 Industry         City  \\\n",
      "385      BrewDog          2  2017-04-10        Consumer & retail     Aberdeen   \n",
      "386      BrewDog          2  2017-04-10        Consumer & retail     Aberdeen   \n",
      "510       ZocDoc          2  2015-08-20                   Health     New York   \n",
      "511       ZocDoc          2  2015-08-20                   Health          NaN   \n",
      "1031  SoundHound          1  2018-05-03  Artificial intelligence  Santa Clara   \n",
      "1032  SoundHound          1  2018-05-03                    Other  Santa Clara   \n",
      "\n",
      "      Country/Region      Continent  Year Founded Funding  \\\n",
      "385   United Kingdom         Europe          2007   $233M   \n",
      "386    UnitedKingdom         Europe          2007   $233M   \n",
      "510    United States  North America          2007   $374M   \n",
      "511    United States  North America          2007   $374M   \n",
      "1031   United States  North America          2005   $215M   \n",
      "1032   United States  North America          2005   $215M   \n",
      "\n",
      "                                       Select Investors  Years To Unicorn  \n",
      "385                    TSG Consumer Partners, Crowdcube                10  \n",
      "386                               TSG Consumer Partners                10  \n",
      "510       Founders Fund, Khosla Ventures, Goldman Sachs                 8  \n",
      "511                                       Founders Fund                 8  \n",
      "1031  Tencent Holdings, Walden Venture Capital, Glob...                13  \n",
      "1032                                   Tencent Holdings                13  \n"
     ]
    }
   ],
   "source": [
    "# Isolate rows of all companies that have duplicates\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "# Find all occurrences of duplicated companies\n",
    "duplicated_companies = companies[companies.duplicated('Company', keep=False)]\n",
    "\n",
    "# Display the result\n",
    "print(duplicated_companies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Check for duplicated values specifically in the `Company` column, not entire rows that are duplicated. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "The pandas [`duplicated()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.duplicated.html#pandas.DataFrame.duplicated) `DataFrame` method can indentify duplicated rows. Apply it to the `Company` column in `companies` to find which companies appear more than once.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "* To specify that you want to check for duplicates only in the `Company` column, indicate this with the `subset` parameter.\n",
    "* To return _all_ occurrences of duplicates, set the `keep` parameter to `False`. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Do these duplicated companies seem like legitimate data points, or are they problematic data?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep the first occurrence of each duplicate company and drop the subsequent rows that are copies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows of duplicate companies after their first occurrence\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "companies = companies.drop_duplicates('Company', keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the [`drop_duplicates()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html) `DataFrame` method. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Make sure to subset `Company` and reassign the results back to the `companies` dataframe for the changes to take effect.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Why is it important to perform input validation?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What steps did you take to perform input validation for this dataset?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w_HtFyHrXVAc"
   },
   "source": [
    "### Convert numerical data to categorical data\n",
    "\n",
    "Sometimes, you'll want to simplify a numeric column by converting it to a categorical column. To do this, one common approach is to break the range of possible values into a defined number of equally sized bins and assign each bin a name. In the next step, you'll practice this process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3c03f2b7-8262-4ea5-94dd-53ee5f219cc7"
   },
   "source": [
    "#### Create a `High Valuation` column\n",
    "\n",
    "The data in the `Valuation` column represents how much money (in billions, USD) each company is valued at. Use the `Valuation` column to create a new column called `High Valuation`. For each company, the value in this column should be `low` if the company is in the bottom 50% of company valuations and `high` if the company is in the top 50%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "646aaace-82a5-4b73-822a-27475822de4a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "      <th>High Valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</td>\n",
       "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpaceX</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</td>\n",
       "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHEIN</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \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",
       "\n",
       "                                    Select Investors  Years To Unicorn  \\\n",
       "0  Sequoia Capital China, SIG Asia Investments, S...                 5   \n",
       "1  Founders Fund, Draper Fisher Jurvetson, Rothen...                10   \n",
       "2  Tiger Global Management, Sequoia Capital China...                10   \n",
       "\n",
       "  High Valuation  \n",
       "0           High  \n",
       "1           High  \n",
       "2           High  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new `High Valuation` column\n",
    "# Use qcut to divide Valuation into 'high' and 'low' Valuation groups\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "median_valuation = companies['Valuation'].median()\n",
    "companies['High Valuation'] = companies['Valuation'].apply(lambda x: 'High' if x > median_valuation else 'Low')\n",
    "companies.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3f610ee9-ef9d-4451-8667-edc8b4d803b9"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "There are multiple ways to complete this task. Review what you've learned about organizing data into equal quantiles.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "97caea9d-0d55-45ba-be01-d90260610d95"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Consider using the pandas [`qcut()`](https://pandas.pydata.org/docs/reference/api/pandas.qcut.html) function. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a606f612-aa34-46e3-b39f-c6f39b7e5dbd"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "Use `pandas` `qcut()` to divide the data into two equal-sized quantile buckets. Use the `labels` parameter to define the output labels. The values you give for `labels` will be the values that are inserted into the new column. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6f5107f1-3694-4296-b857-b1e44ce2b09b"
   },
   "source": [
    "### Convert categorical data to numerical data\n",
    "\n",
    "Three common methods for changing categorical data to numerical are:\n",
    "\n",
    "1. Label encoding: order matters (ordinal numeric labels)\n",
    "2. Label encoding: order doesn't matter (nominal numeric labels)\n",
    "3. Dummy encoding: order doesn't matter (creation of binary columns for each possible category contained in the variable)\n",
    "\n",
    "The decision on which method to use depends on the context and must be made on a case-to-case basis. However, a distinction is typically made between categorical variables with equal weight given to all possible categories vs. variables with a hierarchical structure of importance to their possible categories.  \n",
    "\n",
    "For example, a variable called `subject` might have possible values of `history`, `mathematics`, `literature`. In this case, each subject might be **nominal**&mdash;given the same level of importance. However, you might have another variable called `class`, whose possible values are `freshman`, `sophomore`, `junior`, `senior`. In this case, the class variable is **ordinal**&mdash;its values have an ordered, hierarchical structure of importance. \n",
    "\n",
    "Machine learning models typically need all data to be numeric, and they generally use ordinal label encoding (method 1) and dummy encoding (method 3). \n",
    "\n",
    "In the next steps, you'll convert the following variables: `Continent`, `Country/Region`, and `Industry`, each using a different approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert `Continent` to numeric\n",
    "\n",
    "For the purposes of this exercise, suppose that the investment group has specified that they want to give more weight to continents with fewer unicorn companies because they believe this could indicate unrealized market potential. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Which type of variable would this make the `Continent` variable in terms of how it would be converted to a numeric data type?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rank the continents in descending order from the greatest number of unicorn companies to the least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "bbd72ed4-5cac-41f7-a816-3a4d28e34eb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Continent  Unicorn Count\n",
      "0  North America            586\n",
      "1           Asia            310\n",
      "2         Europe            143\n",
      "3  South America             21\n",
      "4        Oceania              8\n",
      "5         Africa              3\n"
     ]
    }
   ],
   "source": [
    "# Rank the continents by number of unicorn companies\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "# Count the number of unicorn companies by continent\n",
    "continent_counts = companies['Continent'].value_counts()\n",
    "\n",
    "# Convert the counts to a DataFrame for better readability\n",
    "continent_counts_df = continent_counts.reset_index()\n",
    "continent_counts_df.columns = ['Continent', 'Unicorn Count']\n",
    "\n",
    "# Sort the DataFrame in descending order by 'Unicorn Count'\n",
    "ranked_continents = continent_counts_df.sort_values(by='Unicorn Count', ascending=False)\n",
    "\n",
    "# Display the ranked continents\n",
    "print(ranked_continents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34f1f513-2d9f-4da7-b884-6a6714b7652e"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint</strong></h4></summary>\n",
    "\n",
    "Use the `value_counts()` method on the `Continent` series.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2c95b77d-7cda-46ba-975f-8632aaa6158d",
    "tags": []
   },
   "source": [
    "Now, create a new column called `Continent Number` that represents the `Continent` column converted to numeric in the order of their number of unicorn companies, where North America is encoded as `1`, through Africa, encoded as `6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "c3bd716a-7845-4c3c-988c-3b227c10e036"
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "      <th>High Valuation</th>\n",
       "      <th>Continent Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</td>\n",
       "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpaceX</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</td>\n",
       "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHEIN</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \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",
       "\n",
       "                                    Select Investors  Years To Unicorn  \\\n",
       "0  Sequoia Capital China, SIG Asia Investments, S...                 5   \n",
       "1  Founders Fund, Draper Fisher Jurvetson, Rothen...                10   \n",
       "2  Tiger Global Management, Sequoia Capital China...                10   \n",
       "\n",
       "  High Valuation  Continent Number  \n",
       "0           High                 2  \n",
       "1           High                 1  \n",
       "2           High                 2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create numeric `Continent Number` column\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "ranked_continents['Continent Number'] = range(1, len(ranked_continents) + 1)\n",
    "\n",
    "# Create the 'Continent Number' column using the mapping\n",
    "# Merge the companies dataset with the continent summary dataset\n",
    "companies = companies.drop(columns=['Continent Number_y'])\n",
    "companies = companies.drop(columns=['Continent Number_x'])\n",
    "companies = companies.merge(ranked_continents[['Continent', 'Continent Number']], on='Continent', how='left')\n",
    "\n",
    "# Display the updated companies DataFrame\n",
    "companies.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ce4eb04d-d2d3-454c-a273-0815b1d1a95a"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint </strong></h4></summary>\n",
    "\n",
    "Create a mapping dictionary and use the `replace()` method on the `Category` column. Refer to the example provided above for more information about `replace()`.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert `Country/Region` to numeric\n",
    "\n",
    "Now, suppose that within a given continent, each company's `Country/Region` is given equal importance. For analytical purposes, you want to convert the values in this column to numeric without creating a large number of dummy columns. Use label encoding of this nominal categorical variable to create a new column called `Country/Region Numeric`, wherein each unique `Country/Region` is assigned its own number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame with label encoding using LabelEncoder:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "      <th>High Valuation</th>\n",
       "      <th>Continent Number</th>\n",
       "      <th>Country/Region Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</td>\n",
       "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpaceX</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</td>\n",
       "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHEIN</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \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",
       "\n",
       "                                    Select Investors  Years To Unicorn  \\\n",
       "0  Sequoia Capital China, SIG Asia Investments, S...                 5   \n",
       "1  Founders Fund, Draper Fisher Jurvetson, Rothen...                10   \n",
       "2  Tiger Global Management, Sequoia Capital China...                10   \n",
       "\n",
       "  High Valuation  Continent Number  Country/Region Numeric  \n",
       "0           High                 2                       9  \n",
       "1           High                 1                      44  \n",
       "2           High                 2                       9  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `Country/Region Numeric` column\n",
    "# Create numeric categories for Country/Region\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "companies['Country/Region Numeric'] = label_encoder.fit_transform(companies['Country/Region'])\n",
    "\n",
    "# Display the DataFrame with label encoding\n",
    "print(\"\\nDataFrame with label encoding using LabelEncoder:\")\n",
    "companies.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Review what you have learned about converting a variable with a string/object data type to a category.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "To use label encoding, apply `.astype('category').cat.codes` to the `Country/Region` in `companies`.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2eeff065-469f-4a43-bf2a-87920c4d7bc0"
   },
   "source": [
    "### Convert `Industry` to numeric\n",
    "\n",
    "Finally, create dummy variables for the values in the `Industry` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "4bca4703-9f76-4b59-bc5e-df0097e44b4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame with label encoding using LabelEncoder:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Valuation</th>\n",
       "      <th>Date Joined</th>\n",
       "      <th>Industry</th>\n",
       "      <th>City</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year Founded</th>\n",
       "      <th>Funding</th>\n",
       "      <th>Select Investors</th>\n",
       "      <th>Years To Unicorn</th>\n",
       "      <th>High Valuation</th>\n",
       "      <th>Continent Number</th>\n",
       "      <th>Country/Region Numeric</th>\n",
       "      <th>Industry Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bytedance</td>\n",
       "      <td>180</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Artificial intelligence</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2012</td>\n",
       "      <td>$8B</td>\n",
       "      <td>Sequoia Capital China, SIG Asia Investments, S...</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpaceX</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hawthorne</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "      <td>2002</td>\n",
       "      <td>$7B</td>\n",
       "      <td>Founders Fund, Draper Fisher Jurvetson, Rothen...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHEIN</td>\n",
       "      <td>100</td>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>E-commerce &amp; direct-to-consumer</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2008</td>\n",
       "      <td>$2B</td>\n",
       "      <td>Tiger Global Management, Sequoia Capital China...</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  Valuation Date Joined                         Industry  \\\n",
       "0  Bytedance        180  2017-04-07          Artificial intelligence   \n",
       "1     SpaceX        100  2012-12-01                            Other   \n",
       "2      SHEIN        100  2018-07-03  E-commerce & direct-to-consumer   \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",
       "\n",
       "                                    Select Investors  Years To Unicorn  \\\n",
       "0  Sequoia Capital China, SIG Asia Investments, S...                 5   \n",
       "1  Founders Fund, Draper Fisher Jurvetson, Rothen...                10   \n",
       "2  Tiger Global Management, Sequoia Capital China...                10   \n",
       "\n",
       "  High Valuation  Continent Number  Country/Region Numeric  Industry Number  \n",
       "0           High                 2                       9                0  \n",
       "1           High                 1                      44               12  \n",
       "2           High                 2                       9                5  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert `Industry` to numeric data\n",
    "# Create dummy variables with Industry values\n",
    "companies['Industry Number'] = label_encoder.fit_transform(companies['Industry'])\n",
    "\n",
    "# Display the DataFrame with label encoding\n",
    "print(\"\\nDataFrame with label encoding using LabelEncoder:\")\n",
    "companies.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the first few rows of `companies`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### YOUR CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "adc26824-609c-4a9b-a5d7-c3abfb5258e1"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Consider using `pd.get_dummies` on the specified column. \n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5f2bf2c6-fc4b-4d5f-b985-dd8fe5a7d87d"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "When you call `pd.get_dummies()` on a specified series, it will return a dataframe consisting of each possible category contained in the series represented as its own binary column. You'll then have to combine this new dataframe of binary columns with the existing `companies` dataframe.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fa14d383-ff64-4249-b51b-8026618a4571"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "You can use `pd.concat([col_a, col_b])` to combine the two dataframes. Remember to specify the correct axis of concatenation and to reassign the result back to the `companies` dataframe.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f7edb136-45f0-4ff1-84d7-ff6bcc29dd43"
   },
   "source": [
    "**Question: Which categorical encoding approach did you use for each variable? Why?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qNl-29W8ejOX"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: How does label encoding change the data?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What are the benefits of label encoding?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What are the disadvantages of label encoding?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c38f5e8d-89fa-4eaf-acd3-4841583e427c",
    "tags": []
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "**What are some key takeaways that you learned during this lab?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9sAADZgr-YI"
   },
   "source": [
    "**Reference**\n",
    "\n",
    "[Bhat, M.A. *Unicorn Companies*](https://www.kaggle.com/datasets/mysarahmadbhat/unicorn-companies)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations!** You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged."
   ]
  }
 ],
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