ETL_Mini_Project_Starter_Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('max_colwidth', 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the crowdfunding.xlsx Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>cf_id</th>\n",
       "      <th>contact_id</th>\n",
       "      <th>company_name</th>\n",
       "      <th>blurb</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>outcome</th>\n",
       "      <th>backers_count</th>\n",
       "      <th>country</th>\n",
       "      <th>currency</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>spotlight</th>\n",
       "      <th>category &amp; sub-category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>147</td>\n",
       "      <td>4661</td>\n",
       "      <td>Baldwin, Riley and Jackson</td>\n",
       "      <td>Pre-emptive tertiary standardization</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1581573600</td>\n",
       "      <td>1614578400</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>food/food trucks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1621</td>\n",
       "      <td>3765</td>\n",
       "      <td>Odom Inc</td>\n",
       "      <td>Managed bottom-line architecture</td>\n",
       "      <td>1400</td>\n",
       "      <td>14560</td>\n",
       "      <td>successful</td>\n",
       "      <td>158</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1611554400</td>\n",
       "      <td>1621918800</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>music/rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1812</td>\n",
       "      <td>4187</td>\n",
       "      <td>Melton, Robinson and Fritz</td>\n",
       "      <td>Function-based leadingedge pricing structure</td>\n",
       "      <td>108400</td>\n",
       "      <td>142523</td>\n",
       "      <td>successful</td>\n",
       "      <td>1425</td>\n",
       "      <td>AU</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1608184800</td>\n",
       "      <td>1640844000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>technology/web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2156</td>\n",
       "      <td>4941</td>\n",
       "      <td>Mcdonald, Gonzalez and Ross</td>\n",
       "      <td>Vision-oriented fresh-thinking conglomeration</td>\n",
       "      <td>4200</td>\n",
       "      <td>2477</td>\n",
       "      <td>failed</td>\n",
       "      <td>24</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1634792400</td>\n",
       "      <td>1642399200</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>music/rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1365</td>\n",
       "      <td>2199</td>\n",
       "      <td>Larson-Little</td>\n",
       "      <td>Proactive foreground core</td>\n",
       "      <td>7600</td>\n",
       "      <td>5265</td>\n",
       "      <td>failed</td>\n",
       "      <td>53</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1608530400</td>\n",
       "      <td>1629694800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>theater/plays</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cf_id  contact_id                 company_name  \\\n",
       "0    147        4661   Baldwin, Riley and Jackson   \n",
       "1   1621        3765                     Odom Inc   \n",
       "2   1812        4187   Melton, Robinson and Fritz   \n",
       "3   2156        4941  Mcdonald, Gonzalez and Ross   \n",
       "4   1365        2199                Larson-Little   \n",
       "\n",
       "                                           blurb    goal  pledged     outcome  \\\n",
       "0           Pre-emptive tertiary standardization     100        0      failed   \n",
       "1               Managed bottom-line architecture    1400    14560  successful   \n",
       "2   Function-based leadingedge pricing structure  108400   142523  successful   \n",
       "3  Vision-oriented fresh-thinking conglomeration    4200     2477      failed   \n",
       "4                      Proactive foreground core    7600     5265      failed   \n",
       "\n",
       "   backers_count country currency  launched_at    deadline  staff_pick  \\\n",
       "0              0      CA      CAD   1581573600  1614578400       False   \n",
       "1            158      US      USD   1611554400  1621918800       False   \n",
       "2           1425      AU      AUD   1608184800  1640844000       False   \n",
       "3             24      US      USD   1634792400  1642399200       False   \n",
       "4             53      US      USD   1608530400  1629694800       False   \n",
       "\n",
       "   spotlight category & sub-category  \n",
       "0      False        food/food trucks  \n",
       "1       True              music/rock  \n",
       "2      False          technology/web  \n",
       "3      False              music/rock  \n",
       "4      False           theater/plays  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data into a Pandas DataFrame\n",
    "crowdfunding_info_df = pd.read_excel('Resources/crowdfunding.xlsx')\n",
    "crowdfunding_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   cf_id                    1000 non-null   int64 \n",
      " 1   contact_id               1000 non-null   int64 \n",
      " 2   company_name             1000 non-null   object\n",
      " 3   blurb                    1000 non-null   object\n",
      " 4   goal                     1000 non-null   int64 \n",
      " 5   pledged                  1000 non-null   int64 \n",
      " 6   outcome                  1000 non-null   object\n",
      " 7   backers_count            1000 non-null   int64 \n",
      " 8   country                  1000 non-null   object\n",
      " 9   currency                 1000 non-null   object\n",
      " 10  launched_at              1000 non-null   int64 \n",
      " 11  deadline                 1000 non-null   int64 \n",
      " 12  staff_pick               1000 non-null   bool  \n",
      " 13  spotlight                1000 non-null   bool  \n",
      " 14  category & sub-category  1000 non-null   object\n",
      "dtypes: bool(2), int64(7), object(6)\n",
      "memory usage: 103.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get a brief summary of the crowdfunding_info DataFrame.\n",
    "crowdfunding_info_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Category and Subcategory DataFrames\n",
    "---\n",
    "**Create a Category DataFrame that has the following columns:**\n",
    "- A \"category_id\" column that is numbered sequential form 1 to the length of the number of unique categories.\n",
    "- A \"category\" column that has only the categories.\n",
    "\n",
    "Export the DataFrame as a `category.csv` CSV file.\n",
    "\n",
    "**Create a SubCategory DataFrame that has the following columns:**\n",
    "- A \"subcategory_id\" column that is numbered sequential form 1 to the length of the number of unique subcategories.\n",
    "- A \"subcategory\" column that has only the subcategories. \n",
    "\n",
    "Export the DataFrame as a `subcategory.csv` CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cf_id', 'contact_id', 'company_name', 'blurb', 'goal', 'pledged',\n",
       "       'outcome', 'backers_count', 'country', 'currency', 'launched_at',\n",
       "       'deadline', 'staff_pick', 'spotlight', 'category & sub-category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the crowdfunding_info_df columns.\n",
    "crowdfunding_info_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>contact_id</th>\n",
       "      <th>company_name</th>\n",
       "      <th>blurb</th>\n",
       "      <th>goal</th>\n",
       "      <th>pledged</th>\n",
       "      <th>outcome</th>\n",
       "      <th>backers_count</th>\n",
       "      <th>country</th>\n",
       "      <th>currency</th>\n",
       "      <th>launched_at</th>\n",
       "      <th>deadline</th>\n",
       "      <th>staff_pick</th>\n",
       "      <th>spotlight</th>\n",
       "      <th>category &amp; sub-category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>147</td>\n",
       "      <td>4661</td>\n",
       "      <td>Baldwin, Riley and Jackson</td>\n",
       "      <td>Pre-emptive tertiary standardization</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1581573600</td>\n",
       "      <td>1614578400</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>food/food trucks</td>\n",
       "      <td>food</td>\n",
       "      <td>food trucks</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1621</td>\n",
       "      <td>3765</td>\n",
       "      <td>Odom Inc</td>\n",
       "      <td>Managed bottom-line architecture</td>\n",
       "      <td>1400</td>\n",
       "      <td>14560</td>\n",
       "      <td>successful</td>\n",
       "      <td>158</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1611554400</td>\n",
       "      <td>1621918800</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>music/rock</td>\n",
       "      <td>music</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1812</td>\n",
       "      <td>4187</td>\n",
       "      <td>Melton, Robinson and Fritz</td>\n",
       "      <td>Function-based leadingedge pricing structure</td>\n",
       "      <td>108400</td>\n",
       "      <td>142523</td>\n",
       "      <td>successful</td>\n",
       "      <td>1425</td>\n",
       "      <td>AU</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1608184800</td>\n",
       "      <td>1640844000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>technology/web</td>\n",
       "      <td>technology</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2156</td>\n",
       "      <td>4941</td>\n",
       "      <td>Mcdonald, Gonzalez and Ross</td>\n",
       "      <td>Vision-oriented fresh-thinking conglomeration</td>\n",
       "      <td>4200</td>\n",
       "      <td>2477</td>\n",
       "      <td>failed</td>\n",
       "      <td>24</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1634792400</td>\n",
       "      <td>1642399200</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>music/rock</td>\n",
       "      <td>music</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1365</td>\n",
       "      <td>2199</td>\n",
       "      <td>Larson-Little</td>\n",
       "      <td>Proactive foreground core</td>\n",
       "      <td>7600</td>\n",
       "      <td>5265</td>\n",
       "      <td>failed</td>\n",
       "      <td>53</td>\n",
       "      <td>US</td>\n",
       "      <td>USD</td>\n",
       "      <td>1608530400</td>\n",
       "      <td>1629694800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>theater/plays</td>\n",
       "      <td>theater</td>\n",
       "      <td>plays</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cf_id  contact_id                 company_name  \\\n",
       "0    147        4661   Baldwin, Riley and Jackson   \n",
       "1   1621        3765                     Odom Inc   \n",
       "2   1812        4187   Melton, Robinson and Fritz   \n",
       "3   2156        4941  Mcdonald, Gonzalez and Ross   \n",
       "4   1365        2199                Larson-Little   \n",
       "\n",
       "                                           blurb    goal  pledged     outcome  \\\n",
       "0           Pre-emptive tertiary standardization     100        0      failed   \n",
       "1               Managed bottom-line architecture    1400    14560  successful   \n",
       "2   Function-based leadingedge pricing structure  108400   142523  successful   \n",
       "3  Vision-oriented fresh-thinking conglomeration    4200     2477      failed   \n",
       "4                      Proactive foreground core    7600     5265      failed   \n",
       "\n",
       "   backers_count country currency  launched_at    deadline  staff_pick  \\\n",
       "0              0      CA      CAD   1581573600  1614578400       False   \n",
       "1            158      US      USD   1611554400  1621918800       False   \n",
       "2           1425      AU      AUD   1608184800  1640844000       False   \n",
       "3             24      US      USD   1634792400  1642399200       False   \n",
       "4             53      US      USD   1608530400  1629694800       False   \n",
       "\n",
       "   spotlight category & sub-category    category  subcategory  \n",
       "0      False        food/food trucks        food  food trucks  \n",
       "1       True              music/rock       music         rock  \n",
       "2      False          technology/web  technology          web  \n",
       "3      False              music/rock       music         rock  \n",
       "4      False           theater/plays     theater        plays  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the category and subcategory values to category and subcategory columns.\n",
    "crowdfunding_info_df[['category','subcategory']] = crowdfunding_info_df [\"category & sub-category\"].str.split('/' , expand=True)\n",
    "crowdfunding_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['food' 'music' 'technology' 'theater' 'film & video' 'publishing' 'games'\n",
      " 'photography' 'journalism']\n",
      "['food trucks' 'rock' 'web' 'plays' 'documentary' 'electric music' 'drama'\n",
      " 'indie rock' 'wearables' 'nonfiction' 'animation' 'video games' 'shorts'\n",
      " 'fiction' 'photography books' 'radio & podcasts' 'metal' 'jazz'\n",
      " 'translations' 'television' 'mobile games' 'world music'\n",
      " 'science fiction' 'audio']\n"
     ]
    }
   ],
   "source": [
    "# Get the unique categories and subcategories in separate lists.\n",
    "categories = crowdfunding_info_df['category'].unique()\n",
    "subcategories = crowdfunding_info_df['subcategory'].unique()\n",
    "\n",
    "print(categories)\n",
    "print(subcategories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "# Get the number of distinct values in the categories and subcategories lists.\n",
    "print(len(categories))\n",
    "print(len(subcategories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8 9]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]\n"
     ]
    }
   ],
   "source": [
    "# Create numpy arrays from 1-9 for the categories and 1-24 for the subcategories.\n",
    "category_ids = np.arange(1, 10)\n",
    "subcategory_ids = np.arange(1, 25)\n",
    "\n",
    "print(category_ids)\n",
    "print(subcategory_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']\n",
      "['subcat1', 'subcat2', 'subcat3', 'subcat4', 'subcat5', 'subcat6', 'subcat7', 'subcat8', 'subcat9', 'subcat10', 'subcat11', 'subcat12', 'subcat13', 'subcat14', 'subcat15', 'subcat16', 'subcat17', 'subcat18', 'subcat19', 'subcat20', 'subcat21', 'subcat22', 'subcat23', 'subcat24']\n"
     ]
    }
   ],
   "source": [
    "# Use a list comprehension to add \"cat\" to each category_id. \n",
    "cat_ids = [\"cat\" + str(category_id) for category_id in category_ids]\n",
    "# Use a list comprehension to add \"subcat\" to each subcategory_id.    \n",
    "scat_ids=[\"subcat\"+str(subcategory_id)for subcategory_id in subcategory_ids]\n",
    "    \n",
    "print(cat_ids)\n",
    "print(scat_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a category DataFrame with the category_id array as the category_id and categories list as the category name.\n",
    "data = {\"category_id\": cat_ids, \"category_name\": categories}\n",
    "category_df=pd.DataFrame(data)\n",
    "# Create a category DataFrame with the subcategory_id array as the subcategory_id and subcategories list as the subcategory name. \n",
    "subcategory_df={\"subcategory_id\":scat_ids,\"subcategory_name\":subcategories}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    .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>category_id</th>\n",
       "      <th>category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cat1</td>\n",
       "      <td>food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cat2</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat3</td>\n",
       "      <td>technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat4</td>\n",
       "      <td>theater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat5</td>\n",
       "      <td>film &amp; video</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat6</td>\n",
       "      <td>publishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat7</td>\n",
       "      <td>games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat8</td>\n",
       "      <td>photography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cat9</td>\n",
       "      <td>journalism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category_id category_name\n",
       "0        cat1          food\n",
       "1        cat2         music\n",
       "2        cat3    technology\n",
       "3        cat4       theater\n",
       "4        cat5  film & video\n",
       "5        cat6    publishing\n",
       "6        cat7         games\n",
       "7        cat8   photography\n",
       "8        cat9    journalism"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subcategory_id': ['subcat1',\n",
       "  'subcat2',\n",
       "  'subcat3',\n",
       "  'subcat4',\n",
       "  'subcat5',\n",
       "  'subcat6',\n",
       "  'subcat7',\n",
       "  'subcat8',\n",
       "  'subcat9',\n",
       "  'subcat10',\n",
       "  'subcat11',\n",
       "  'subcat12',\n",
       "  'subcat13',\n",
       "  'subcat14',\n",
       "  'subcat15',\n",
       "  'subcat16',\n",
       "  'subcat17',\n",
       "  'subcat18',\n",
       "  'subcat19',\n",
       "  'subcat20',\n",
       "  'subcat21',\n",
       "  'subcat22',\n",
       "  'subcat23',\n",
       "  'subcat24'],\n",
       " 'subcategory_name': array(['food trucks', 'rock', 'web', 'plays', 'documentary',\n",
       "        'electric music', 'drama', 'indie rock', 'wearables', 'nonfiction',\n",
       "        'animation', 'video games', 'shorts', 'fiction',\n",
       "        'photography books', 'radio & podcasts', 'metal', 'jazz',\n",
       "        'translations', 'television', 'mobile games', 'world music',\n",
       "        'science fiction', 'audio'], dtype=object)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subcategory_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export categories_df and subcategories_df as CSV files.\n",
    "category_df.to_csv(\"Resources/category.csv\", index=False)\n",
    "\n",
    "subcategory_df.to_csv(\"Resources/subcategory.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Campaign DataFrame\n",
    "----\n",
    "**Create a Campaign DataFrame that has the following columns:**\n",
    "- The \"cf_id\" column.\n",
    "- The \"contact_id\" column.\n",
    "- The “company_name” column.\n",
    "- The \"blurb\" column is renamed as \"description.\"\n",
    "- The \"goal\" column.\n",
    "- The \"goal\" column is converted to a `float` datatype.\n",
    "- The \"pledged\" column is converted to a `float` datatype. \n",
    "- The \"backers_count\" column. \n",
    "- The \"country\" column.\n",
    "- The \"currency\" column.\n",
    "- The \"launched_at\" column is renamed as \"launch_date\" and converted to a datetime format. \n",
    "- The \"deadline\" column is renamed as \"end_date\" and converted to a datetime format.\n",
    "- The \"category_id\" with the unique number matching the “category_id” from the category DataFrame. \n",
    "- The \"subcategory_id\" with the unique number matching the “subcategory_id” from the subcategory DataFrame.\n",
    "- And, create a column that contains the unique four-digit contact ID number from the `contact.xlsx` file.\n",
    " \n",
    "\n",
    "Then export the DataFrame as a `campaign.csv` CSV file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the crowdfunding_info_df DataFrame name campaign_df. \n",
    "campaign_df = crowdfunding_info_df.copy()\n",
    "campaign_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the blurb, launched_at, and deadline columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the goal and pledged columns to a `float` data type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the datatypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format the launched_date and end_date columns to datetime format\n",
    "from datetime import datetime as dt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Merge the campaign_df with the category_df on the \"category\" column and \n",
    "# the subcategory_df on the \"subcategory\" column.\n",
    "\n",
    "\n",
    "campaign_merged_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unwanted columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the DataFrame as a CSV file. \n",
    "campaign_cleaned.to_csv(\"Resources/campaign.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the contacts.xlsx Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data into a Pandas DataFrame. Use the `header=2` parameter when reading in the data.\n",
    "contact_info_df = pd.read_excel('Resources/contacts.xlsx', header=2)\n",
    "contact_info_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Contacts DataFrame \n",
    "---\n",
    "**Create a Contacts DataFrame that has the following columns:**\n",
    "- A column named \"contact_id\"  that contains the unique number of the contact person.\n",
    "- A column named \"first_name\" that contains the first name of the contact person.\n",
    "- A column named \"last_name\" that contains the first name of the contact person.\n",
    "- A column named \"email\" that contains the email address of the contact person\n",
    "\n",
    "Then export the DataFrame as a `contacts.csv` CSV file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 1: Use Pandas to create the contacts DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the contact_info_df and convert each row to a dictionary.\n",
    "import json\n",
    "dict_values = []\n",
    "\n",
    "\n",
    "# Print out the list of values for each row.\n",
    "print(dict_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a contact_info DataFrame and add each list of values, i.e., each row \n",
    "# to the 'contact_id', 'name', 'email' columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the datatypes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a \"first\"name\" and \"last_name\" column with the first and last names from the \"name\" column. \n",
    "\n",
    "\n",
    "# Drop the contact_name column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the datatypes one more time before exporting as CSV file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the DataFrame as a CSV file. \n",
    "contacts_df_clean.to_csv(\"Resources/contacts.csv\", encoding='utf8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: Use regex to create the contacts DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contact_info_df_copy = contact_info_df.copy()\n",
    "contact_info_df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the four-digit contact ID number.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the datatypes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the \"contact_id\" column to an int64 data type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the name of the contact and add it to a new column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the email from the contacts and add the values to a new column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the contact_info_df with the 'contact_id', 'name', 'email' columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a \"first\"name\" and \"last_name\" column with the first and last names from the \"name\" column. \n",
    "\n",
    "\n",
    "# Drop the contact_name column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the datatypes one more time before exporting as CSV file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the DataFrame as a CSV file. \n",
    "# contacts_df_clean.to_csv(\"Resources/contacts.csv\", encoding='utf8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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