account_summary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import plaid\n",
    "import requests\n",
    "import datetime\n",
    "import json\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretty_print_response(response):\n",
    "    '''\n",
    "       This funtions takes in a response in json format and \n",
    "       prints out a more readable version\n",
    "    '''\n",
    "    print(json.dumps(response, indent=4, sort_keys=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish Credentials\n",
    "PLAID_CLIENT_ID = os.getenv('PLAID_CLIENT_ID')\n",
    "PLAID_SBX_SECRET_KEY = os.getenv('PLAID_SBX_SECRET_KEY')\n",
    "PLAID_PUBLIC_KEY = os.getenv('PLAID_PUBLIC_KEY')\n",
    "PLAID_ENV = os.getenv('PLAID_ENV', 'sandbox')\n",
    "PLAID_PRODUCTS = os.getenv('PLAID_PRODUCTS', 'transactions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plaid Access Token\n",
    "\n",
    "In this section, you will use the plaid-python api to generate the correct authentication tokens to access data in the free developer Sandbox. This mimics how you might connect to your own account or a customer account, but due to privacy issues, this homework will only require connecting to and analyzing the fake data from the developer sandbox that Plaid provides. \n",
    "\n",
    "Complete the following steps to generate an access token:\n",
    "1. Create a client to connect to plaid\n",
    "2. Use the client to generate a public token and request the following items: \n",
    "['transactions', 'income', 'assets']\n",
    "3. Exchange the public token for an access token\n",
    "4. Test the access token by requesting and printing the available test accounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create a client to connect to plaid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "INSTITUTION_ID = \"ins_109508\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Generate a public token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "client = plaid.Client(client_id=PLAID_CLIENT_ID, secret=PLAID_SBX_SECRET_KEY, public_key=PLAID_PUBLIC_KEY, environment='sandbox')\n",
    "create_public_token = client.Sandbox.public_token.create(INSTITUTION_ID, ['transactions', 'income', 'assets'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exchange the public token for an access token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'access_token': 'access-sandbox-04ae0cb2-f6d4-4489-8683-22218b9ceac2',\n",
       " 'item_id': 'pz8kdBKd7GFz9zEJAMn5cqZvKm4bKziLNEDer',\n",
       " 'request_id': 'Ilsr1pqHAtnKGzA'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "exchange_reponse = client.Item.public_token.exchange(create_public_token['public_token'])\n",
    "exchange_reponse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'access-sandbox-04ae0cb2-f6d4-4489-8683-22218b9ceac2'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_token = exchange_reponse['access_token']\n",
    "access_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Fetch Accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accounts': [{'account_id': 'pz8kdBKd7GFz9zEJAMn5cqZvqDpKZXSLnyZqB',\n",
       "   'balances': {'available': 100,\n",
       "    'current': 110,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '0000',\n",
       "   'name': 'Plaid Checking',\n",
       "   'official_name': 'Plaid Gold Standard 0% Interest Checking',\n",
       "   'subtype': 'checking',\n",
       "   'type': 'depository'},\n",
       "  {'account_id': 'orgdkzqkjAurJrG5mPdNiPdvPwXBd9iRyxdPE',\n",
       "   'balances': {'available': 200,\n",
       "    'current': 210,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '1111',\n",
       "   'name': 'Plaid Saving',\n",
       "   'official_name': 'Plaid Silver Standard 0.1% Interest Saving',\n",
       "   'subtype': 'savings',\n",
       "   'type': 'depository'},\n",
       "  {'account_id': 'g8elLgnLB1fe1eEqAMloH3oj3gAZoecg1axGw',\n",
       "   'balances': {'available': None,\n",
       "    'current': 1000,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '2222',\n",
       "   'name': 'Plaid CD',\n",
       "   'official_name': 'Plaid Bronze Standard 0.2% Interest CD',\n",
       "   'subtype': 'cd',\n",
       "   'type': 'depository'},\n",
       "  {'account_id': '83Ly8rM87zCdQdwV9kjDI8JZ84rKJXfwm4bRV',\n",
       "   'balances': {'available': None,\n",
       "    'current': 410,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': 2000,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '3333',\n",
       "   'name': 'Plaid Credit Card',\n",
       "   'official_name': 'Plaid Diamond 12.5% APR Interest Credit Card',\n",
       "   'subtype': 'credit card',\n",
       "   'type': 'credit'},\n",
       "  {'account_id': 'E1xlgQmgAZf5Z5kgAod6HlB7l8JgBjtXlvqMJ',\n",
       "   'balances': {'available': 43200,\n",
       "    'current': 43200,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '4444',\n",
       "   'name': 'Plaid Money Market',\n",
       "   'official_name': 'Plaid Platinum Standard 1.85% Interest Money Market',\n",
       "   'subtype': 'money market',\n",
       "   'type': 'depository'},\n",
       "  {'account_id': 'WdEqexjeZrF5e5kzQDlpHPAmPkEwAXilEAk51',\n",
       "   'balances': {'available': None,\n",
       "    'current': 320.76,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '5555',\n",
       "   'name': 'Plaid IRA',\n",
       "   'official_name': None,\n",
       "   'subtype': 'ira',\n",
       "   'type': 'investment'},\n",
       "  {'account_id': 'AeryvPAvgjcZgZKwJpLBtXw9XvBawgf1PKQkP',\n",
       "   'balances': {'available': None,\n",
       "    'current': 23631.9805,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '6666',\n",
       "   'name': 'Plaid 401k',\n",
       "   'official_name': None,\n",
       "   'subtype': '401k',\n",
       "   'type': 'investment'},\n",
       "  {'account_id': 'GpaKEM9Eq1f5k5K6l1yAHGaRGbgyaXF13r5Lo',\n",
       "   'balances': {'available': None,\n",
       "    'current': 65262,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '7777',\n",
       "   'name': 'Plaid Student Loan',\n",
       "   'official_name': None,\n",
       "   'subtype': 'student',\n",
       "   'type': 'loan'},\n",
       "  {'account_id': 'XalKDd7DMkilKlbQZJeos1gLLrpnPwUdEzmrv',\n",
       "   'balances': {'available': None,\n",
       "    'current': 56302.06,\n",
       "    'iso_currency_code': 'USD',\n",
       "    'limit': None,\n",
       "    'unofficial_currency_code': None},\n",
       "   'mask': '8888',\n",
       "   'name': 'Plaid Mortgage',\n",
       "   'official_name': None,\n",
       "   'subtype': 'mortgage',\n",
       "   'type': 'loan'}],\n",
       " 'item': {'available_products': ['auth',\n",
       "   'balance',\n",
       "   'credit_details',\n",
       "   'identity',\n",
       "   'investments',\n",
       "   'liabilities'],\n",
       "  'billed_products': ['assets', 'income', 'transactions'],\n",
       "  'consent_expiration_time': None,\n",
       "  'error': None,\n",
       "  'institution_id': 'ins_109508',\n",
       "  'item_id': 'pz8kdBKd7GFz9zEJAMn5cqZvKm4bKziLNEDer',\n",
       "  'webhook': ''},\n",
       " 'request_id': 'ENph5SEUyP7YTSF'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "client.Accounts.get(access_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Account Transactions with Plaid\n",
    "\n",
    "In this section, you will use the Plaid Python SDK to connect to the Developer Sandbox account and grab a list of transactions. You will need to complete the following steps:\n",
    "\n",
    "\n",
    "1. Use the access token to fetch the transactions for the last 90 days\n",
    "2. Print the categories for each transaction type\n",
    "3. Create a new DataFrame using the following fields from the JSON transaction data: `date, name, amount, category`. (For categories with more than one label, just use the first category label in the list)\n",
    "4. Convert the data types to the appropriate types (i.e. datetimeindex for the date and float for the amount)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Fetch the Transactions for the last 90 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-05-06 2020-08-04\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "end_date = datetime.datetime.now()\n",
    "start_date = end_date + datetime.timedelta(-90)\n",
    "start_date = start_date.strftime('%Y-%m-%d')\n",
    "end_date = end_date.strftime('%Y-%m-%d')\n",
    "print(start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Print the categories for each transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['accounts', 'item', 'request_id', 'total_transactions', 'transactions'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "transaction_response = client.Transactions.get(access_token, start_date, end_date)\n",
    "transaction_response.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['account_id', 'account_owner', 'amount', 'authorized_date', 'category', 'category_id', 'date', 'iso_currency_code', 'location', 'merchant_name', 'name', 'payment_channel', 'payment_meta', 'pending', 'pending_transaction_id', 'transaction_code', 'transaction_id', 'transaction_type', 'unofficial_currency_code'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_response['transactions'][0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"account_id\": \"83Ly8rM87zCdQdwV9kjDI8JZ84rKJXfwm4bRV\",\n",
      "    \"account_owner\": null,\n",
      "    \"amount\": 500,\n",
      "    \"authorized_date\": null,\n",
      "    \"category\": [\n",
      "        \"Travel\",\n",
      "        \"Airlines and Aviation Services\"\n",
      "    ],\n",
      "    \"category_id\": \"22001000\",\n",
      "    \"date\": \"2020-07-29\",\n",
      "    \"iso_currency_code\": \"USD\",\n",
      "    \"location\": {\n",
      "        \"address\": null,\n",
      "        \"city\": null,\n",
      "        \"country\": null,\n",
      "        \"lat\": null,\n",
      "        \"lon\": null,\n",
      "        \"postal_code\": null,\n",
      "        \"region\": null,\n",
      "        \"store_number\": null\n",
      "    },\n",
      "    \"merchant_name\": \"United Airlines\",\n",
      "    \"name\": \"United Airlines\",\n",
      "    \"payment_channel\": \"in store\",\n",
      "    \"payment_meta\": {\n",
      "        \"by_order_of\": null,\n",
      "        \"payee\": null,\n",
      "        \"payer\": null,\n",
      "        \"payment_method\": null,\n",
      "        \"payment_processor\": null,\n",
      "        \"ppd_id\": null,\n",
      "        \"reason\": null,\n",
      "        \"reference_number\": null\n",
      "    },\n",
      "    \"pending\": false,\n",
      "    \"pending_transaction_id\": null,\n",
      "    \"transaction_code\": null,\n",
      "    \"transaction_id\": \"83Ly8rM87zCdQdwV9kjDI8JXEKkVoKcwVBxd9\",\n",
      "    \"transaction_type\": \"special\",\n",
      "    \"unofficial_currency_code\": null\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pretty_print_response(transaction_response['transactions'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Travel', 'Airlines and Aviation Services']\n",
      "['Travel', 'Taxi']\n",
      "['Food and Drink', 'Restaurants']\n",
      "['Payment']\n",
      "['Food and Drink', 'Restaurants', 'Fast Food']\n",
      "['Shops', 'Sporting Goods']\n",
      "['Payment', 'Credit Card']\n",
      "['Travel', 'Taxi']\n",
      "['Transfer', 'Debit']\n",
      "['Transfer', 'Deposit']\n",
      "['Recreation', 'Gyms and Fitness Centers']\n",
      "['Travel', 'Airlines and Aviation Services']\n",
      "['Food and Drink', 'Restaurants', 'Fast Food']\n",
      "['Food and Drink', 'Restaurants', 'Coffee Shop']\n",
      "['Food and Drink', 'Restaurants']\n",
      "['Transfer', 'Credit']\n"
     ]
    }
   ],
   "source": [
    "for transaction in transaction_response['transactions']:\n",
    "    print(transaction['category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Create a new DataFrame using the following fields from the JSON transaction data: date, name, amount, category. \n",
    "\n",
    "(For categories with more than one label, just use the first category label in the list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Date</th>\n",
       "      <th>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>United Airlines</td>\n",
       "      <td>500</td>\n",
       "      <td>Travel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Uber 072515 SF**POOL**</td>\n",
       "      <td>6.33</td>\n",
       "      <td>Travel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>Tectra Inc</td>\n",
       "      <td>500</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>AUTOMATIC PAYMENT - THANK</td>\n",
       "      <td>2078.5</td>\n",
       "      <td>Payment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-23</td>\n",
       "      <td>KFC</td>\n",
       "      <td>500</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date                       Name  Amount        Category\n",
       "0  2020-07-29            United Airlines     500          Travel\n",
       "1  2020-07-27     Uber 072515 SF**POOL**    6.33          Travel\n",
       "2  2020-07-24                 Tectra Inc     500  Food and Drink\n",
       "3  2020-07-23  AUTOMATIC PAYMENT - THANK  2078.5         Payment\n",
       "4  2020-07-23                        KFC     500  Food and Drink"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "transactions_df = pd.DataFrame(columns=['Date', 'Name', 'Amount', 'Category'])\n",
    "transactions = transaction_response['transactions']\n",
    "\n",
    "for index, transaction in enumerate(transactions): \n",
    "    transactions_df.loc[index] = [transaction['date'], transaction['name'], transaction['amount'], transaction['category'][0]]\n",
    "\n",
    "transactions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Name        0\n",
       "Amount      0\n",
       "Category    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Convert the data types to the appropriate types \n",
    "\n",
    "(i.e. datetimeindex for the date and float for the amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        object\n",
       "Name        object\n",
       "Amount      object\n",
       "Category    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "transactions_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>INTRST PYMNT</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>Transfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>SparkFun</td>\n",
       "      <td>89.4</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>McDonald's</td>\n",
       "      <td>12</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4.33</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Touchstone Climbing</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Recreation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Amount        Category\n",
       "Date                                                  \n",
       "2020-07-09         INTRST PYMNT  -4.22        Transfer\n",
       "2020-07-10             SparkFun   89.4  Food and Drink\n",
       "2020-07-11           McDonald's     12  Food and Drink\n",
       "2020-07-11            Starbucks   4.33  Food and Drink\n",
       "2020-07-12  Touchstone Climbing   78.5      Recreation"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_df['Date'] = pd.to_datetime(transactions_df['Date'])\n",
    "transactions_df.set_index('Date', inplace = True)\n",
    "transactions_df.sort_index(inplace  = True)\n",
    "transactions_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Income Analysis with Plaid\n",
    "\n",
    "In this section, you will use the Plaid Sandbox to complete the following:\n",
    "1. Determine the previous year's gross income and print the results\n",
    "2. Determine the current monthly income and print the results\n",
    "3. Determine the projected yearly income and print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"income\": {\n",
      "        \"income_streams\": [\n",
      "            {\n",
      "                \"confidence\": 0.99,\n",
      "                \"days\": 690,\n",
      "                \"monthly_income\": 500,\n",
      "                \"name\": \"UNITED AIRLINES\"\n",
      "            }\n",
      "        ],\n",
      "        \"last_year_income\": 6000,\n",
      "        \"last_year_income_before_tax\": 7285,\n",
      "        \"max_number_of_overlapping_income_streams\": 1,\n",
      "        \"number_of_income_streams\": 1,\n",
      "        \"projected_yearly_income\": 6085,\n",
      "        \"projected_yearly_income_before_tax\": 7389\n",
      "    },\n",
      "    \"request_id\": \"35llJN80dci3htm\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "income_response = client.Income.get(access_token)\n",
    "pretty_print_response(income_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last year's gross income: $7285\n"
     ]
    }
   ],
   "source": [
    "print(f\"Last year's gross income: ${income_response['income']['last_year_income_before_tax']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current monthly income: $500\n"
     ]
    }
   ],
   "source": [
    "print(f\"Current monthly income: ${income_response['income']['income_streams'][0]['monthly_income']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected yearly gross income: $7389\n"
     ]
    }
   ],
   "source": [
    "print(f\"Projected yearly gross income: ${income_response['income']['projected_yearly_income_before_tax']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Budget Analysis\n",
    "In this section, you will use the transactions DataFrame to analyze the customer's budget\n",
    "\n",
    "1. Calculate the total spending per category and print the results (Hint: groupby or count transactions per category)\n",
    "2. Generate a bar chart with the number of transactions for each category \n",
    "3. Calculate the expenses per month\n",
    "4. Plot the total expenses per month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expenses per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>INTRST PYMNT</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>Transfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>SparkFun</td>\n",
       "      <td>89.4</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>McDonald's</td>\n",
       "      <td>12</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4.33</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Touchstone Climbing</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Recreation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Amount        Category\n",
       "Date                                                  \n",
       "2020-07-09         INTRST PYMNT  -4.22        Transfer\n",
       "2020-07-10             SparkFun   89.4  Food and Drink\n",
       "2020-07-11           McDonald's     12  Food and Drink\n",
       "2020-07-11            Starbucks   4.33  Food and Drink\n",
       "2020-07-12  Touchstone Climbing   78.5      Recreation"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "transactions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Name</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Food and Drink</th>\n",
       "      <td>SparkFunMcDonald'sStarbucksKFCTectra Inc</td>\n",
       "      <td>1105.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment</th>\n",
       "      <td>CREDIT CARD 3333 PAYMENT *//AUTOMATIC PAYMENT ...</td>\n",
       "      <td>2103.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recreation</th>\n",
       "      <td>Touchstone Climbing</td>\n",
       "      <td>78.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shops</th>\n",
       "      <td>Madison Bicycle Shop</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transfer</th>\n",
       "      <td>INTRST PYMNTACH Electronic CreditGUSTO PAY 123...</td>\n",
       "      <td>6845.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel</th>\n",
       "      <td>United AirlinesUber 063015 SF**POOL**Uber 0725...</td>\n",
       "      <td>11.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Name   Amount\n",
       "Category                                                                  \n",
       "Food and Drink           SparkFunMcDonald'sStarbucksKFCTectra Inc  1105.73\n",
       "Payment         CREDIT CARD 3333 PAYMENT *//AUTOMATIC PAYMENT ...  2103.50\n",
       "Recreation                                    Touchstone Climbing    78.50\n",
       "Shops                                        Madison Bicycle Shop   500.00\n",
       "Transfer        INTRST PYMNTACH Electronic CreditGUSTO PAY 123...  6845.78\n",
       "Travel          United AirlinesUber 063015 SF**POOL**Uber 0725...    11.73"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transactions_df.groupby(by = 'Category').sum()['Amount']\n",
    "transactions_df.groupby(by='Category').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb57e63908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#transactions_df.groupby(by = 'Category').sum().plot.pie(y='Amount', legend=False)\n",
    "transactions_df.groupby('Category').sum()['Amount'].plot.pie(title='Expenses Per Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb5859b308>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fzCwjTvpmZhlx0jczy4iTvplZRpz0zcwy4qRvZpYRJ30zs4x0KOlLWijpfklzJc1OZZtKminpkfQ8MJVL0oWS5ku6T9JeFccZn7Z/RNL4rjklMzNrS2dq+gdGxPCIGJGWJwKzImIYMCstAxwCDEuPCcBFUHxJAGcA+wAjgTNavyjMzKwx1qV5ZwwwNb2eChxeUX5pFG4HBkgaDBwEzIyI5RGxApgJHLwO729mZp3U0aQfwP9KmiNpQirbMiKeAEjPW6TyrYHFFfu2pLK2ylcjaYKk2ZJmL126tONnYmZmNfXp4Hbvj4glkrYAZkp6qJ1tVaUs2ilfvSBiEjAJYMSIEW9Zb2Zma69DNf2IWJKenwKuomiTfzI125Cen0qbtwDbVOw+BFjSTrmZmTVIzaQvaQNJG7W+BkYDDwBXA609cMYDM9Lrq4FxqRfPvsCzqfnnWmC0pIHpAu7oVGZmZg3SkeadLYGrJLVu/8uI+JOku4ArJJ0IPA4clba/BjgUmA+8CJwAEBHLJZ0D3JW2OzsiltftTMzMrKaaST8iFgB7VilfBoyqUh7AyW0cawowpfNhmplZPXhErplZRpz0zcwy4qRvZpYRJ30zs4w46ZuZZcRJ38wsI076ZmYZcdI3M8uIk76ZWUac9M3MMuKkb2aWESd9M7OMOOmbmWXESd/MLCNO+mZmGXHSNzPLiJO+mVlGnPTNzDLipG9mlhEnfTOzjDjpm5llxEnfzCwjHU76knpLukfS79PydpLukPSIpF9JWi+V90vL89P6oRXH+Foqf1jSQfU+GTMza19navpfAOZVLH8H+H5EDANWACem8hOBFRGxA/D9tB2SdgXGAu8CDgZ+LKn3uoVvZmad0aGkL2kIcBhwcVoW8EHgyrTJVODw9HpMWiatH5W2HwNMj4hXIuIxYD4wsh4nYWZmHdPRmv75wFeBN9LyZsAzEfFaWm4Btk6vtwYWA6T1z6btV5VX2WcVSRMkzZY0e+nSpZ04FTMzq6Vm0pf0EeCpiJhTWVxl06ixrr193iyImBQRIyJixKBBg2qFZ2ZmndCnA9u8H/iopEOB/sDGFDX/AZL6pNr8EGBJ2r4F2AZokdQH2ARYXlHeqnIfMzNrgJo1/Yj4WkQMiYihFBdir4uIY4DrgSPTZuOBGen11WmZtP66iIhUPjb17tkOGAbcWbczMTOzmjpS02/LvwPTJX0LuAeYnMonA5dJmk9Rwx8LEBF/lXQF8CDwGnByRLy+Du9vZmad1KmkHxE3ADek1wuo0vsmIl4Gjmpj/3OBczsbpJmZ1YdH5JqZZcRJ38wsI076ZmYZcdI3M8uIk76ZWUac9M3MMuKkb2aWESd9M7OMOOmbmWXESd/MLCNO+mZmGXHSNzPLiJO+mVlGnPTNzDLipG9mlhEnfTOzjDjpm5llxEnfzCwjTvpmZhlx0jczy4iTvplZRmomfUn9Jd0p6V5Jf5V0VirfTtIdkh6R9CtJ66Xyfml5flo/tOJYX0vlD0s6qKtOyszMqutITf8V4IMRsScwHDhY0r7Ad4DvR8QwYAVwYtr+RGBFROwAfD9th6RdgbHAu4CDgR9L6l3PkzEzs/bVTPpReCEt9k2PAD4IXJnKpwKHp9dj0jJp/ShJSuXTI+KViHgMmA+MrMtZmJlZh3SoTV9Sb0lzgaeAmcCjwDMR8VrapAXYOr3eGlgMkNY/C2xWWV5ln8r3miBptqTZS5cu7fwZmZlZmzqU9CPi9YgYDgyhqJ3vUm2z9Kw21rVVvuZ7TYqIERExYtCgQR0Jz8zMOqhTvXci4hngBmBfYICkPmnVEGBJet0CbAOQ1m8CLK8sr7KPmZk1QEd67wySNCC9fhvwIWAecD1wZNpsPDAjvb46LZPWXxcRkcrHpt492wHDgDvrdSJmZlZbn9qbMBiYmnra9AKuiIjfS3oQmC7pW8A9wOS0/WTgMknzKWr4YwEi4q+SrgAeBF4DTo6I1+t7OmZm1p6aST8i7gPeXaV8AVV630TEy8BRbRzrXODczodpZmb14BG5ZmYZcdI3M8uIk76ZWUac9M3MMuKkb2aWESd9M7OMOOmbmWXESd/MLCNO+mZmGXHSNzPLiJO+mVlGnPTNzDLipG9mlhEnfTOzjDjpm5llxEnfzCwjTvpmZhlx0jczy4iTvplZRpz0zcwy4qRvZpaRPrU2kLQNcCnwduANYFJEXCBpU+BXwFBgIfCJiFghScAFwKHAi8DxEXF3OtZ44Ovp0N+KiKn1PZ08DJ34h4a+38LzDmvo+5lZ1+lITf814EsRsQuwL3CypF2BicCsiBgGzErLAIcAw9JjAnARQPqSOAPYBxgJnCFpYB3PxczMaqiZ9CPiidaaekQ8D8wDtgbGAK019anA4en1GODSKNwODJA0GDgImBkRyyNiBTATOLiuZ2NmZu3qVJu+pKHAu4E7gC0j4gkovhiALdJmWwOLK3ZrSWVtla/5HhMkzZY0e+nSpZ0Jz8zMauhw0pe0IfAb4IsR8Vx7m1Ypi3bKVy+ImBQRIyJixKBBgzoanpmZdUCHkr6kvhQJ//KI+J9U/GRqtiE9P5XKW4BtKnYfAixpp9zMzBqkZtJPvXEmA/Mi4r8rVl0NjE+vxwMzKsrHqbAv8Gxq/rkWGC1pYLqAOzqVmZlZg9Tssgm8HzgOuF/S3FR2OnAecIWkE4HHgaPSumsoumvOp+iyeQJARCyXdA5wV9ru7IhYXpezMDOzDqmZ9CPiZqq3xwOMqrJ9ACe3cawpwJTOBGhmZvXjEblmZhlx0jczy4iTvplZRpz0zcwy4qRvZpYRJ30zs4w46ZuZZcRJ38wsI076ZmYZcdI3M8uIk76ZWUac9M3MMuKkb2aWESd9M7OMOOmbmWXESd/MLCNO+mZmGXHSNzPLiJO+mVlGnPTNzDLipG9mlpE+tTaQNAX4CPBUROyWyjYFfgUMBRYCn4iIFZIEXAAcCrwIHB8Rd6d9xgNfT4f9VkRMre+pvGnoxD901aGrWnjeYQ19PzOztdWRmv7PgYPXKJsIzIqIYcCstAxwCDAsPSYAF8GqL4kzgH2AkcAZkgaua/BmZtY5NZN+RNwELF+jeAzQWlOfChxeUX5pFG4HBkgaDBwEzIyI5RGxApjJW79IzMysi61tm/6WEfEEQHreIpVvDSyu2K4llbVV/haSJkiaLWn20qVL1zI8MzOrpt4XclWlLNopf2thxKSIGBERIwYNGlTX4MzMcre2Sf/J1GxDen4qlbcA21RsNwRY0k65mZk10Nom/auB8en1eGBGRfk4FfYFnk3NP9cCoyUNTBdwR6cyMzNroI502ZwGHABsLqmFohfOecAVkk4EHgeOSptfQ9Fdcz5Fl80TACJiuaRzgLvSdmdHxJoXh83MrIvVTPoR8ak2Vo2qsm0AJ7dxnCnAlE5FZ2ZmdeURuWZmGXHSNzPLiJO+mVlGnPTNzDLipG9mlhEnfTOzjDjpm5llxEnfzCwjTvpmZhlx0jczy0jNaRjMrL58O08rk2v6ZmYZcdI3M8uIk76ZWUac9M3MMuKkb2aWESd9M7OMOOmbmWXESd/MLCNO+mZmGXHSNzPLiJO+mVlGGj73jqSDgQuA3sDFEXFeo2Ow7s1z05h1nYbW9CX1Bn4EHALsCnxK0q6NjMHMLGeNrumPBOZHxAIASdOBMcCDDY7DzKzTmuFXqCKi7gdt882kI4GDI+KktHwcsE9EnFKxzQRgQlrcCXi4YQHC5sDTDXy/RvP59WzNfH7NfG7Q+PN7R0QMqrai0TV9VSlb7VsnIiYBkxoTzuokzY6IEWW8dyP4/Hq2Zj6/Zj436F7n1+jeOy3ANhXLQ4AlDY7BzCxbjU76dwHDJG0naT1gLHB1g2MwM8tWQ5t3IuI1SacA11J02ZwSEX9tZAw1lNKs1EA+v56tmc+vmc8NutH5NfRCrpmZlcsjcs3MMuKkb2aWESd9M2soSb0lTS07jlw1fO6d7kTS2RHxzYrl3sClEXFMiWGZNbWIeF3SYEl9I2Jl2fHUk6Q92lsfEfc1Kpa2ZJ30gW0lfS0ivi2pH/Br4O6yg6onSZdFxHG1ynqy9GW9JRX/niPi8fIiqg9J7wfmRsQ/JB0L7AVcEBGLSg6tHhYAf5E0A/hHa2FEXFheSHXxo3bWBfCBRgXSlqx770gScDlwP3Ag8MeI+H65UdWXpLsjYq+K5d7A/RHRFBPdSToVOAN4EngjFUdEtFvj6gkk3QfsCewBXAZMBj4WEfuXGlgdSDqnWnlEfKPRseQmy6Qvaa+Kxb7AT4FbKP5TERE9vrYv6WvA6cDbgBdbi4FXgUkR8bWyYqsnSfMp5m9aVnYs9db6hS3pm8D/RcTkNb/EezpJ/SLilbLjqDdJbwO+QDEHzucl7QAMi4g/lhxatkn/+nZWR0R8sGHBdDFJ326WBF9N+lt+OCJeKzuWepN0I/An4NPAPwFLKZp7di81sDqQNJKikrVJRGwraU/gpIg4teTQ6kLSNIoWhKMjYjdJ6wO3RMS7Sw4tz6SfG0lbA+9g9Tbvm8qLqH4kTaaYjfUPwKoaY0T8d2lB1YmktwNHA3dFxF8kbQscEBGXlhzaOpN0O/BJ4LetiVDSAxGxW7mR1UfrBGuS7qk4v7kRMbzs2LK+kJsu3n4cGMrqCfHssmKqN0nnUcxx9CDweioOoCmSPvB4eqyXHk0jIv4u6ZfASEn/TJH8e3zCT3pFxKListoqr7e1cQ/0qqT+pFmEJW1H0bRauqyTPjADeBaYQ0UtsckcAezUjO2mABFxFoCkjYrFeKHkkOpG0knAN4HrKK7H/CB1M55SbmR1sTg18UTqXHAq8LeSY6qncyia5oakMQn7AyeWG1Ih6+adZvo52RZJfwSOaqZkWEnSbhQ9WzZNRU8D47rZRH5rRdLDwPtaL1JL2gy4NSJ2KjeydSdpC+BC4EOp6M/AKRHRNDdSkTQIeB/FF/atEfFUySEBrunfKmn3iLi/7EC60IvAXEmzWL3N+7TyQqqrScC/RcT1AJIOAH5G8Z+tp2sBnq9Yfh5YXFIsdSHpPyLidGC/iBhbdjxdRdJvgOnA7yPipbLjqZR7Tf9BYAfgMYqEKJqkj3crSeOrlUdEUwyDl3RvROxZq6wnknQpsDtFM2RQ3E/6TlIzSE+8WC3pfmA4xfWJpul6uiZJoyguVB8E3Ar8CrgmIkpv18+9pn9I2QF0tYiYmvoMbxsRjbzfcKMskPQNiiYegGMpvsSbwaPp0WpGet6ohFjqZSawHFhf0vKK8tYK16bVd+tZImIWMEtSH+DDFO35PwcGlBkXZFrTl7RxRDwnqeo/sIhYXq28J0q9Pr4HrBcR20kaDpwdER8tObS6kDQQOAvYjyJx3AScGRErSg2sjprtInUaCf974C3/BiOiaXrwpN6Bh1HU+PehGPH/+XKjyjfp/z4iPiLpMYqfzZX9xiIi3llSaHUnaQ7wQeCGiv7C9zfDAJ9m1+QXqd8GvBwRIWl7irEW/9ssg+wkXU5REZkJXAHM6i5faFk276SEL2D/ZpiYq4bXIuLZNfpD9/hveknnR8QXJf2OKufTJL9kmvki9V+AD0jaBLgRuIdiPMm4UqOqn2nA8d1xFtEskz4U1XlJVwHvKTuWLvaApKOB3pKGAadRXFjq6Vrb8L9XahRda4PWhA8QETdI2qDMgOqoV0S8KOnTwA8j4jxJc8sOql4i4veSdpa0K9C/ovyXJYYF+CYqt0vau+wgutipwLsoeidNA54DvlhqRHUQEXPSy+ERcWPlg6J3SDNYIOkbkoamx9dpnovUvdL/vaMp2vcBepcYT12lv9Uk4CcUHUbOB44sNagkyzb9VqnL5k7AQoo5vZuuy2azqzbrZOV8Jz1ZM1+klvRB4MsUk5CdK+mdwJcj4l9KDq0uKrqm3h0Re0oaDPy0OzQ75p7031GtPJrjJhUASBpBMcXyUFafX6hHf7FJ+hRFLXE/ivbhVhsBr0fEh6ruaNYAku6MiJGpI8UBwAsU97EofQaAbNv0k42BndPreRHxQJnBdJHLga9QTPP6Ro1te5JbgSeAzYH/qih/Hij9lnT1IGlHitrwUFb/wu7xU3+n+eX/jbee2+iyYqqzeyQNAKYAsymaVbvFfYeLUpYAAAqkSURBVDqyrOmnHgMzgG0oEoQoRj4+DoyJiOdKDK+uJN0cEfuVHYd1nqR7KdqE51AxA2XF9YweK120ncxbz+2O0oKqk9Qz8O0R8URa3gHYOLrJzZlyTfoXUkxz+tWIeCOV9QLOA97WLDdygFXDwT8FrDn3zv+UFlQdSdoX+AGwC8XUyr2Bf0TExqUGVgeS5kREU/Yua7Y7gK2pO//tcm3e+RCwR2vCB4iINySdTtEM0kxOoGjC6kvFPWSBpkj6wA8p+nf/GhhB0c97h1IjWkcVI8V/J+lfgKtY/Qu7GUaMz5A0gbeeW7P8yr5T0l7dpXZfKdeafpt3sOkud7epl2YffVtxh6L7Wi9OS7o1InrsAKYqI8VX+0/aDCPGJVWbLTQiYtuGB1NHkvpExGup984uFHMnVfYMLP3XTa41/f6S3s3q0y+QlvuVEE9Xul3SrhHxYNmBdJEXJa1HMX30f1Jc3O3pA5g+CSyuaBMeT3GHt4XAmeWFVT8RsU3ZMXSRO4G9gMPLDqQtudb027sxOhFxYKNi6WqS5gHb06TTR6dut09StOf/K7AJ8OOImF9qYOtA0t3AhyJiuaQPUMzLfipFv+9dIqJbDPJZV5J2BrrdiNV10RPGiGSZ9HOSyViEppo6uvJ+AJJ+BCyNiDPTclM0P6YRq6MprjddSzHv/M0R8bFSA1tHklqANu9z0B3ugZD7NAw5OJViDpdFlY+yg6qXNHX0XIr7kSJpuKSry41qnfVO87ADjKK4R26rZmmS/SRwIPBERBwH7ElznFtvYEOKQYLVHqVrhg/Z2vcQ8LOURC4BpkXEsyXHVE9nAiOBGwAiYq6koeWFUxfTgBslPQ28RBpxnPp7N8vf7qWIeF3Sa+l+AX8HevwFaoovsbPLDqI9TvpNLiIuBi6WtBNF9837JN0C/KxyBscerNrU0T1amotmFjCYYo751jbYXhS/3JpBtx2xuo66/T/ELNv0JbXbbao79q1dF5J6Ax+hSPrbUNzUYT+KQUw9+ubUkiZTDDybSNHD5TSgb0R8rtTArE3dfcTqupC0aXcfR5Fr0m+t4fanGNBzL8U39B7AHc00bYGk/6a4Ld0sYHJE3Fmx7uGI2Km04OpA0vrA/6O4KAjFRcFvRcTL5UVltXTnEavNLsvmndYumZKmAxMi4v60vBvFBFfN5AHg6xHxYpV1IxsdTD2lXzBnRcRXKBK/9RzddsRqs8uypt+qWve3ZukSVynNyz6M1ftD31ReRPUj6bpmmHUyFz1hxGqzy7KmX2GepIuBX1AMdT8WmFduSPUl6STgC8AQiq6N+wK3UdwsvRnck7po/poieQDNM6FcE+r2I1abXe41/f7A54EPpKKbgIuaqT041aj2Bm6PiOFpFORZEfHJkkOrC0mXVCmOiPh0w4OxmnrCiNVml3XSz4GkuyJi7zR/+T4R8UozNmFZz9ATRqw2u6xH5Ep6v6SZkv4maUHro+y46qwl9Yf+LTBT0gxgSckx1Y2kqen8WpcHSppSZkzWrm4/YrXZZV3Tl/QQxSRda969Z1lpQXUhSftTTEj2p4h4tex46qFac4GbELqvZr95Sk+Q+4XcZyPij2UH0RXS9YrPUdxQ5H6KPvo3lhtVl+glaWBErIBVNyDJ/d91d9btR6w2u9z/c1wv6bsUd5GqvHtPM/QdngqspJi35RCKKWy/UGpEXeO/gFslXUnRA+sTwLnlhmTtGFV2ALnLvXmn2twz0Qz9vivvmJUmW7uzWX9WS9qVoguqgFlNfMMYs3WWdU2/mW6WUsXK1hdpMEyZsXS1TSnmEbpE0iBJ20XEY2UHZdYdZV3TB5B0GPAuVh+t2q2nRu0ISa/z5mAlAW8DXuTNkY8blxVbPUk6g2L+pJ0iYkdJWwG/joj3lxyaWbeUdU1f0k+A9Slu5nAxcCTFiMEeLyJ6lx1DgxwBvJs0LW9ELEnzs5tZFVn30wfeFxHjgBURcRbwXoqph63neDXNNx8Aknr6TdHNulTuSf+l9PxiahZYCWxXYjzWeVdI+ikwQNJngD9T/Gozsyqybt4Bfp9Gc36XonkggJ+VG5J1RkR8T9KHKe68tBPwzYiYWXJYZt1W9hdyW0nqB/RvsvvHZifNsT82Ii4vOxaz7ij35p1VIuIVJ/yeQ9LGkr4m6YeSRqtwCrCAYoCWmVXhmr71SGniuBUU9wYYBQwE1gO+EBFzy4zNrDtz0rceaY0Rx72Bp4FtI+L5ciMz696yvJArqd3pCJpk7p1mVzni+HVJjznhm9WWZU2/Ys6d/hSjOe+lGKm6B3BHROxXVmzWMbmMODartyxr+q1z7kiaDkyIiPvT8m7Al8uMzTomoxHHZnWVe++dnVsTPkBEPAD4NoJm1rSyrOlXmCfpYuAXFAOzjgXmlRuSmVnXybJNv1W6u9TngQ+kopuAiyLi5fKiMjPrOlknfQBJ61EM3w/g4YhYWWMXM7MeK+ukL+kAitsKLqTo9bENMD4ibioxLDOzLpN70p8DHB0RD6flHYFpEfGeciMzM+sauffe6dua8AEi4m9A3xLjMTPrUrn33pktaTJwWVo+BphTYjxmZl0q9+adfsDJwH4Ubfo3AT+OiFdKDczMrItknfTBvXfMLC9ZJ3333jGz3OSe9N17x8yy4t477r1jZhlx7x333jGzjOTevOPeO2aWlSyTvqRtI+LxsuMwM2u0XNv0f9v6QtJvygzEzKyRck36qnj9ztKiMDNrsFyTfrTx2sysqeXapt96U+3KG2qDb6ptZk0uy6RvZparXJt3zMyy5KRvZpYRJ30zs4w46VsWJL1d0nRJj0p6UNI1aYK9atsOkPQvjY7RrBGc9K3pSRJwFXBDRGwfEbsCpwNbtrHLAKDLk76k3Oe+shI46VsODgRWRsRPWgsiYi5wj6RZku6WdL+kMWn1ecD2kuZK+i6ApK9IukvSfZLOaj2OpG9IekjSTEnTJH05lQ+XdHva/ipJA1P5DZL+Q9KNwP+T9JikvmndxpIWti6bdQXXNCwHu1F99tSXgSMi4jlJmwO3S7oamAjsFhHDASSNBoYBIynGclwt6QMU4zs+Dryb4v/S3RXvcylwakTcKOls4Azgi2ndgIjYPx17KHAYxdQgY4Hf+O5t1pWc9C1nAv4jJfA3gK2p3uQzOj3uScsbUnwJbATMiIiXACT9Lj1vQpHYb0zbTwV+XXG8X1W8vhj4KkXSPwH4zLqfllnbnPQtB38FjqxSfgwwCHhPRKyUtBDoX2U7Ad+OiJ+uVij961rG84/WFxFxi6ShkvYHekfEA2t5TLMOcZu+5eA6oJ+kVbVoSXsD7wCeSgn/wLQM8DxFLb7VtcCnJW2Y9t1a0hbAzcA/S+qf1h0GEBHPAisk/VPa/zjgRtp2KTANuGQdz9OsJtf0relFREg6Ajhf0kSKtvyFwJnAhZJmA3OBh9L2yyTdIukB4I8R8RVJuwC3FR2BeAE4NiLuStcA7gUWAbOBZ9Pbjgd+Iml9YAFF001bLge+RZH4zbqU594xWweSNoyIF1JyvwmYEBF3d/IYRwJjIuK4LgnSrIJr+mbrZpKkXSmuBUxdi4T/A+AQ4NCuCM5sTa7pm5llxBdyzcwy4qRvZpYRJ30zs4w46ZuZZcRJ38wsI/8fjItXv2Cklb4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions_df.groupby(by = 'Category').sum().plot.bar(title='Expenses Per Category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expenses per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>INTRST PYMNT</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>SparkFun</td>\n",
       "      <td>89.4</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>McDonald's</td>\n",
       "      <td>12</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4.33</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Touchstone Climbing</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Recreation</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Amount        Category  Month\n",
       "Date                                                         \n",
       "2020-07-09         INTRST PYMNT  -4.22        Transfer      7\n",
       "2020-07-10             SparkFun   89.4  Food and Drink      7\n",
       "2020-07-11           McDonald's     12  Food and Drink      7\n",
       "2020-07-11            Starbucks   4.33  Food and Drink      7\n",
       "2020-07-12  Touchstone Climbing   78.5      Recreation      7"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "transactions_df['Month'] = pd.DatetimeIndex(data=transactions_df.index).month\n",
    "transactions_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>INTRST PYMNT</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-10</th>\n",
       "      <td>SparkFun</td>\n",
       "      <td>89.4</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>McDonald's</td>\n",
       "      <td>12</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-11</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>4.33</td>\n",
       "      <td>Food and Drink</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Touchstone Climbing</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Recreation</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name Amount        Category Month\n",
       "Date                                                        \n",
       "2020-07-09         INTRST PYMNT  -4.22        Transfer  July\n",
       "2020-07-10             SparkFun   89.4  Food and Drink  July\n",
       "2020-07-11           McDonald's     12  Food and Drink  July\n",
       "2020-07-11            Starbucks   4.33  Food and Drink  July\n",
       "2020-07-12  Touchstone Climbing   78.5      Recreation  July"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the reference below to change month number into names\n",
    "#https://stackoverflow.com/questions/6557553/get-month-name-from-number\n",
    "import calendar\n",
    "\n",
    "transactions_df['Month'] = transactions_df.Month.apply(lambda x: calendar.month_name[x])\n",
    "transactions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Name</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>INTRST PYMNTSparkFunMcDonald'sStarbucksTouchst...</td>\n",
       "      <td>10645.24</td>\n",
       "      <td>TransferFood and DrinkFood and DrinkFood and D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Name    Amount  \\\n",
       "Month                                                                \n",
       "July   INTRST PYMNTSparkFunMcDonald'sStarbucksTouchst...  10645.24   \n",
       "\n",
       "                                                Category  \n",
       "Month                                                     \n",
       "July   TransferFood and DrinkFood and DrinkFood and D...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_df.groupby(by='Month').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb5865bb48>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions_df.groupby(by='Month').sum().plot.bar(title='Expenses Per Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving data for future use\n",
    "transactions_df.to_csv('budget_analysis_data.csv')"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "dev"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}