account_summary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretty_print_response(response):\n",
    "  print(json.dumps(response, indent=4, sort_keys=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "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')\n",
    "\n",
    "# Oops, keys will forever be in repo history \n",
    "# print(PLAID_CLIENT_ID,PLAID_SBX_SECRET_KEY,PLAID_PUBLIC_KEY)"
   ]
  },
  {
   "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 paid\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<plaid.client.Client object at 0x0000016982554438>\n"
     ]
    }
   ],
   "source": [
    "client = plaid.Client(client_id=PLAID_CLIENT_ID, secret=PLAID_SBX_SECRET_KEY, public_key=PLAID_PUBLIC_KEY, environment='sandbox')\n",
    "print(client)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Generate a public token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "INSTITUTION_ID = \"ins_109508\"\n",
    "\n",
    "# Create public token to be exchanged for institution access token\n",
    "create_tkn_response = 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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "access-sandbox-8c97a8cb-4e26-480f-8f83-427841a970f6\n"
     ]
    }
   ],
   "source": [
    "# Exchange public token for access token\n",
    "exchange_response = client.Item.public_token.exchange(create_tkn_response['public_token'])\n",
    "# Store access token as variable\n",
    "access_token = exchange_response['access_token']\n",
    "\n",
    "print(access_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Fetch Accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accounts': [{'account_id': '5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A',\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': 'J6daVAzw7XUdgp3keGQEIRNvaZaLBxud3qygq',\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': 'kPXbALaM8BH9lWX3Qy7wfkznMwMAbxFWnKjdd',\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': 'ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7',\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': 'qM5XAWeJ8kce3E9Ma1z8C7Z8LVLpqyfdGkj7R',\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': 'K6waPGBAlXUaABxRQ4Gecn3RXaX5WvuVqb9AD',\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': 'yy93rvgk81fGzQ84yoM1CjAg838rQWuymeaXP',\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': '9DWV64eR9XTk1Mz4yBp9CXgGQVQNwdFR1avQy',\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",
       " 'item': {'available_products': ['auth',\n",
       "   'balance',\n",
       "   'credit_details',\n",
       "   'identity',\n",
       "   'investments',\n",
       "   'liabilities'],\n",
       "  'billed_products': ['assets', 'income', 'transactions'],\n",
       "  'error': None,\n",
       "  'institution_id': 'ins_109508',\n",
       "  'item_id': '15ryWKx698Srjya4JAnmcE7KXQKxb3i5Jr8A3',\n",
       "  'webhook': ''},\n",
       " 'request_id': 'RyC9ZWo1QeATyho'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": [
      "[\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 500,\n",
      "        \"category\": [\n",
      "            \"Travel\",\n",
      "            \"Airlines and Aviation Services\"\n",
      "        ],\n",
      "        \"category_id\": \"22001000\",\n",
      "        \"date\": \"2019-09-03\",\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",
      "        \"name\": \"United Airlines\",\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_id\": \"kPXbALaM8BH9lWX3Qy7wfkzQvLLlGeFW5v7xB\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 6.33,\n",
      "        \"category\": [\n",
      "            \"Travel\",\n",
      "            \"Car Service\",\n",
      "            \"Ride Share\"\n",
      "        ],\n",
      "        \"category_id\": \"22006001\",\n",
      "        \"date\": \"2019-09-01\",\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",
      "        \"name\": \"Uber 072515 SF**POOL**\",\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_id\": \"vyb5lmLg8QfmRVgxp8ZnF8rbZ44EMxfWQdbo1\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 500,\n",
      "        \"category\": [\n",
      "            \"Food and Drink\",\n",
      "            \"Restaurants\"\n",
      "        ],\n",
      "        \"category_id\": \"13005000\",\n",
      "        \"date\": \"2019-08-29\",\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",
      "        \"name\": \"Tectra Inc\",\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_id\": \"ljgWAp4my3S4kjW3AXNQSJjlyzzV5vcZ3xBL4\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 2078.5,\n",
      "        \"category\": [\n",
      "            \"Payment\"\n",
      "        ],\n",
      "        \"category_id\": \"16000000\",\n",
      "        \"date\": \"2019-08-28\",\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",
      "        \"name\": \"AUTOMATIC PAYMENT - THANK\",\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_id\": \"r1XRwAQ7mPhQAxKBNwM4fDnb3WWEejulRzbyw\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 500,\n",
      "        \"category\": [\n",
      "            \"Food and Drink\",\n",
      "            \"Restaurants\"\n",
      "        ],\n",
      "        \"category_id\": \"13005000\",\n",
      "        \"date\": \"2019-08-28\",\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",
      "        \"name\": \"KFC\",\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_id\": \"K6waPGBAlXUaABxRQ4Gecn3yVllD81uVXQo6Z\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 500,\n",
      "        \"category\": [\n",
      "            \"Shops\",\n",
      "            \"Sporting Goods\"\n",
      "        ],\n",
      "        \"category_id\": \"19046000\",\n",
      "        \"date\": \"2019-08-28\",\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",
      "        \"name\": \"Madison Bicycle Shop\",\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_id\": \"qM5XAWeJ8kce3E9Ma1z8C7Zegvv1zmfdK3lJj\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"J6daVAzw7XUdgp3keGQEIRNvaZaLBxud3qygq\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 25,\n",
      "        \"category\": [\n",
      "            \"Payment\",\n",
      "            \"Credit Card\"\n",
      "        ],\n",
      "        \"category_id\": \"16001000\",\n",
      "        \"date\": \"2019-08-19\",\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",
      "        \"name\": \"CREDIT CARD 3333 PAYMENT *//\",\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_id\": \"wwDAqypR8ZhpDrljEe4wioWKAyyZb4urX8zgb\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 5.4,\n",
      "        \"category\": [\n",
      "            \"Travel\",\n",
      "            \"Car Service\",\n",
      "            \"Ride Share\"\n",
      "        ],\n",
      "        \"category_id\": \"22006001\",\n",
      "        \"date\": \"2019-08-19\",\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",
      "        \"name\": \"Uber 063015 SF**POOL**\",\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_id\": \"R6JVGkPwxnUBPR79zMEwuG15okkdjNfRVENzo\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"qM5XAWeJ8kce3E9Ma1z8C7Z8LVLpqyfdGkj7R\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 5850,\n",
      "        \"category\": [\n",
      "            \"Transfer\",\n",
      "            \"Debit\"\n",
      "        ],\n",
      "        \"category_id\": \"21006000\",\n",
      "        \"date\": \"2019-08-18\",\n",
      "        \"iso_currency_code\": \"USD\",\n",
      "        \"location\": {\n",
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      "            \"city\": null,\n",
      "            \"country\": null,\n",
      "            \"lat\": null,\n",
      "            \"lon\": null,\n",
      "            \"postal_code\": null,\n",
      "            \"region\": null,\n",
      "            \"store_number\": null\n",
      "        },\n",
      "        \"name\": \"ACH Electronic CreditGUSTO PAY 123456\",\n",
      "        \"payment_meta\": {\n",
      "            \"by_order_of\": null,\n",
      "            \"payee\": null,\n",
      "            \"payer\": null,\n",
      "            \"payment_method\": \"ACH\",\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_id\": \"BANlk18L3XIWrmGVx75JuW3BkaaNJPiwolv7l\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"kPXbALaM8BH9lWX3Qy7wfkznMwMAbxFWnKjdd\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 1000,\n",
      "        \"category\": [\n",
      "            \"Transfer\",\n",
      "            \"Deposit\"\n",
      "        ],\n",
      "        \"category_id\": \"21007000\",\n",
      "        \"date\": \"2019-08-18\",\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",
      "        \"name\": \"CD DEPOSIT .INITIAL.\",\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_id\": \"J6daVAzw7XUdgp3keGQEIRNAEqq9jDud8PXbr\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"ljgWAp4my3S4kjW3AXNQSJjQ7w7mrpfZQ8qd7\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 78.5,\n",
      "        \"category\": [\n",
      "            \"Recreation\",\n",
      "            \"Gyms and Fitness Centers\"\n",
      "        ],\n",
      "        \"category_id\": \"17018000\",\n",
      "        \"date\": \"2019-08-17\",\n",
      "        \"iso_currency_code\": \"USD\",\n",
      "        \"location\": {\n",
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      "            \"postal_code\": null,\n",
      "            \"region\": null,\n",
      "            \"store_number\": null\n",
      "        },\n",
      "        \"name\": \"Touchstone Climbing\",\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_id\": \"zVBMzR8lDLs8mWE9pK4vhyxeRZZA5JFobndEr\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": -500,\n",
      "        \"category\": [\n",
      "            \"Travel\",\n",
      "            \"Airlines and Aviation Services\"\n",
      "        ],\n",
      "        \"category_id\": \"22001000\",\n",
      "        \"date\": \"2019-08-17\",\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",
      "        \"name\": \"United Airlines\",\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_id\": \"65JmvxPn9DSeZdpWkKvnCQJbdzzn7Wug75QrQ\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 12,\n",
      "        \"category\": [\n",
      "            \"Food and Drink\",\n",
      "            \"Restaurants\"\n",
      "        ],\n",
      "        \"category_id\": \"13005000\",\n",
      "        \"date\": \"2019-08-16\",\n",
      "        \"iso_currency_code\": \"USD\",\n",
      "        \"location\": {\n",
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      "            \"postal_code\": null,\n",
      "            \"region\": null,\n",
      "            \"store_number\": \"3322\"\n",
      "        },\n",
      "        \"name\": \"McDonald's\",\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_id\": \"X6p1rqMbjJUjEpL941qRSKl6EAAQg8SdAak5j\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 4.33,\n",
      "        \"category\": [\n",
      "            \"Food and Drink\",\n",
      "            \"Restaurants\",\n",
      "            \"Coffee Shop\"\n",
      "        ],\n",
      "        \"category_id\": \"13005043\",\n",
      "        \"date\": \"2019-08-16\",\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",
      "        \"name\": \"Starbucks\",\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_id\": \"D6KRBpWndXUDr6lqmQR3fdpvR11VegfvqMNzW\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"5Rzx53Nn98HwW48NbqK5iry3gKgQmxiZlMn7A\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": 89.4,\n",
      "        \"category\": [\n",
      "            \"Food and Drink\",\n",
      "            \"Restaurants\"\n",
      "        ],\n",
      "        \"category_id\": \"13005000\",\n",
      "        \"date\": \"2019-08-15\",\n",
      "        \"iso_currency_code\": \"USD\",\n",
      "        \"location\": {\n",
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      "            \"city\": null,\n",
      "            \"country\": null,\n",
      "            \"lat\": null,\n",
      "            \"lon\": null,\n",
      "            \"postal_code\": null,\n",
      "            \"region\": null,\n",
      "            \"store_number\": null\n",
      "        },\n",
      "        \"name\": \"SparkFun\",\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_id\": \"V6o1qVQZNJULoRMQdl97hNAXvzzLkQuWMrQ6M\",\n",
      "        \"transaction_type\": \"place\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    },\n",
      "    {\n",
      "        \"account_id\": \"J6daVAzw7XUdgp3keGQEIRNvaZaLBxud3qygq\",\n",
      "        \"account_owner\": null,\n",
      "        \"amount\": -4.22,\n",
      "        \"category\": [\n",
      "            \"Transfer\",\n",
      "            \"Credit\"\n",
      "        ],\n",
      "        \"category_id\": \"21005000\",\n",
      "        \"date\": \"2019-08-14\",\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",
      "        \"name\": \"INTRST PYMNT\",\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_id\": \"5Rzx53Nn98HwW48NbqK5iryq4WWVdofZrmXxD\",\n",
      "        \"transaction_type\": \"special\",\n",
      "        \"unofficial_currency_code\": null\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "# Get transactions for institution for specific date range\n",
    "start_date = '{:%Y-%m-%d}'.format(datetime.datetime.now() + datetime.timedelta(-90))\n",
    "end_date = '{:%Y-%m-%d}'.format(datetime.datetime.now())\n",
    "\n",
    "# Get transactions for date range\n",
    "transaction_response = client.Transactions.get(access_token,start_date,end_date)\n",
    "\n",
    "# Print JSON output\n",
    "transactions_list = json.dumps(transaction_response['transactions'],indent=4, sort_keys=True)\n",
    "print(transactions_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Print the categories for each transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Travel\n",
      "Travel\n",
      "Food and Drink\n",
      "Payment\n",
      "Food and Drink\n",
      "Shops\n",
      "Payment\n",
      "Travel\n",
      "Transfer\n",
      "Transfer\n",
      "Recreation\n",
      "Travel\n",
      "Food and Drink\n",
      "Food and Drink\n",
      "Food and Drink\n",
      "Transfer\n"
     ]
    }
   ],
   "source": [
    "transactions = transaction_response['transactions']\n",
    "all_categories = []\n",
    "for tran in transactions:\n",
    "    category = tran['category'][0]\n",
    "    all_categories.append(category)\n",
    "    print(category)\n",
    "    "
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-09-03 United Airlines \t $500 Cat: Travel\n",
      "2019-09-01 Uber 072515 SF**POOL** \t $6.33 Cat: Travel\n",
      "2019-08-29 Tectra Inc \t $500 Cat: Food and Drink\n",
      "2019-08-28 AUTOMATIC PAYMENT - THANK \t $2078.5 Cat: Payment\n",
      "2019-08-28 KFC \t $500 Cat: Food and Drink\n",
      "2019-08-28 Madison Bicycle Shop \t $500 Cat: Shops\n",
      "2019-08-19 CREDIT CARD 3333 PAYMENT *// \t $25 Cat: Payment\n",
      "2019-08-19 Uber 063015 SF**POOL** \t $5.4 Cat: Travel\n",
      "2019-08-18 ACH Electronic CreditGUSTO PAY 123456 \t $5850 Cat: Transfer\n",
      "2019-08-18 CD DEPOSIT .INITIAL. \t $1000 Cat: Transfer\n",
      "2019-08-17 Touchstone Climbing \t $78.5 Cat: Recreation\n",
      "2019-08-17 United Airlines \t $-500 Cat: Travel\n",
      "2019-08-16 McDonald's \t $12 Cat: Food and Drink\n",
      "2019-08-16 Starbucks \t $4.33 Cat: Food and Drink\n",
      "2019-08-15 SparkFun \t $89.4 Cat: Food and Drink\n",
      "2019-08-14 INTRST PYMNT \t $-4.22 Cat: Transfer\n"
     ]
    },
    {
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       "         Date                     Seller   Amount        Category\n",
       "0  2019-09-03            United Airlines   500.00          Travel\n",
       "1  2019-09-01     Uber 072515 SF**POOL**     6.33          Travel\n",
       "2  2019-08-29                 Tectra Inc   500.00  Food and Drink\n",
       "3  2019-08-28  AUTOMATIC PAYMENT - THANK  2078.50         Payment\n",
       "4  2019-08-28                        KFC   500.00  Food and Drink"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "transactions = transaction_response['transactions']\n",
    "\n",
    "all_dates = []\n",
    "all_seller_name = []\n",
    "all_amount = []\n",
    "all_categories = []\n",
    "\n",
    "for tran in transactions:\n",
    "    category = tran['category'][0]\n",
    "    all_categories.append(category)\n",
    "    all_dates.append(tran['date'])\n",
    "    all_seller_name.append(tran['name'])\n",
    "    all_amount.append(tran['amount'])\n",
    "\n",
    "for i in range(len(all_dates)):\n",
    "    print(f\"{all_dates[i]} {all_seller_name[i]} \\t ${all_amount[i]} Cat: {all_categories[i]}\")\n",
    "\n",
    "df = pd.DataFrame(list(zip(all_dates,all_seller_name,all_amount,all_categories)), \n",
    "                columns =['Date','Seller','Amount','Category'])\n",
    "df.head()"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seller       object\n",
      "Amount      float64\n",
      "Category     object\n",
      "dtype: object\n"
     ]
    },
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       "      <td>2078.50</td>\n",
       "      <td>Payment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>KFC</td>\n",
       "      <td>500.00</td>\n",
       "      <td>Food and Drink</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Seller   Amount        Category\n",
       "Date                                                          \n",
       "2019-09-03            United Airlines   500.00          Travel\n",
       "2019-09-01     Uber 072515 SF**POOL**     6.33          Travel\n",
       "2019-08-29                 Tectra Inc   500.00  Food and Drink\n",
       "2019-08-28  AUTOMATIC PAYMENT - THANK  2078.50         Payment\n",
       "2019-08-28                        KFC   500.00  Food and Drink"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Seller'].astype('str')\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date',inplace=True)\n",
    "print(df.dtypes)\n",
    "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Previous Year's Gross Income: $7,285\n",
      "2. Current Monthly Income: $500\n",
      "3. Projected Yearly Income: $6,000\n"
     ]
    }
   ],
   "source": [
    "client.Accounts.get(access_token)\n",
    "resp = client.Income.get(access_token)\n",
    "last_year_gross_income = resp['income']['last_year_income_before_tax']\n",
    "monthly_income = resp['income']['income_streams'][0]['monthly_income']\n",
    "projected_yearly_income = monthly_income * 12\n",
    "\n",
    "print(f\"1. Previous Year's Gross Income: ${last_year_gross_income:,}\")\n",
    "print(f\"2. Current Monthly Income: ${monthly_income}\")\n",
    "print(f\"3. Projected Yearly Income: ${projected_yearly_income:,}\")\n",
    "\n",
    "# I'm so digging the fstring formats in python\n",
    "# source: https://stackoverflow.com/questions/1823058/how-to-print-number-with-commas-as-thousands-separators"
   ]
  },
  {
   "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. Calulate 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Total Spending Per Category\n",
      "Category                                   \n",
      "Transfer                            6845.78\n",
      "Payment                             2103.50\n",
      "Food and Drink                      1105.73\n",
      "Shops                                500.00\n",
      "Recreation                            78.50\n",
      "Travel                                11.73\n",
      "                               Seller   Amount        Category\n",
      "Date                                                          \n",
      "2019-09-03            United Airlines   500.00          Travel\n",
      "2019-09-01     Uber 072515 SF**POOL**     6.33          Travel\n",
      "2019-08-29                 Tectra Inc   500.00  Food and Drink\n",
      "2019-08-28  AUTOMATIC PAYMENT - THANK  2078.50         Payment\n",
      "2019-08-28                        KFC   500.00  Food and Drink\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_category_sum = df.filter(['Category','Amount'])\n",
    "grouped = df_category_sum.groupby(['Category']).sum()\n",
    "grouped.columns = ['Total Spending Per Category']\n",
    "grouped.sort_values('Total Spending Per Category',ascending=False,inplace=True)\n",
    "print(grouped)\n",
    "grouped.plot.pie(subplots=True,figsize=(15,8), title=\"Spending per Category\")\n",
    "\n",
    "grouped_count = df.filter(['Category','Amount']).groupby(['Category']).count()\n",
    "grouped_count.columns = ['Transaction Count']\n",
    "grouped_count.sort_values('Transaction Count',ascending=False,inplace=True)\n",
    "grouped_count.plot.bar(figsize=(15,8), title=\"Transaction Counts Per Category\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expenses per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Amount\n",
      "Date          \n",
      "6     10645.24\n",
      "7     10645.24\n",
      "8     10645.24\n"
     ]
    }
   ],
   "source": [
    "df_amounts = df.filter(['Date','Amount'])\n",
    "df_amounts.index = pd.to_datetime(df_amounts.index)\n",
    "#print(df_amounts.head())\n",
    "#df_amounts.columns = ['Month Num','Expenses']\n",
    "#df_amounts.set_index(['Month Num'],inplace=True)\n",
    "month_grouped = df_amounts.groupby(df_amounts.index.month).sum()\n",
    "month_grouped.sort_index()\n",
    "print(month_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186ab5a00b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_grouped.plot(kind='bar',rot=0, title=\"Wait, this is the same dude that makes $500/month?\", figsize=(12,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}