Source

Learn Pandas / lessons / 12 - Lesson.ipynb

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{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Case Study  \n",
      "\n",
      "Company ABC has some data they have in an excel file that they want to get analyzed. A representative of Company ABC takes action and contacts David Rojas LLC at HernanDRojas@gmail dot com. The initial email contains the following:\n",
      "\n",
      "> Hi David,\n",
      "\n",
      "> We have a contact in the Bay area that recommended you to us. Our company collects a lot of financial data from our customers and there has been a recent interest from the executives to leverage the financial data to our benefit. At this point we are not sure what to do with the data but maybe someone with your expertise can do some initial exploratory analysis. I have attached a sample excel file so you can have an intial idea of what the data looks like.  \n",
      "\n",
      "> Tony  \n",
      "Marketing Analytics Manager  \n",
      "Company ABC  \n",
      "408 444 4444  \n",
      "\n",
      "After I received the email from Tony, I decided to give him a call the following business day. In our conversation I asked him the following questions:  \n",
      "\n",
      "1. Can you send me the definitions of each of the columns of the excel workbook?    \n",
      "2. What exactly are you expecting to get out of the analysis?    \n",
      "3. How are you envisioning data adding value to Company ABC?    \n",
      "4. How do you want me to deliver you the results?  \n",
      "\n",
      "After several attempts I soon realized that Tony really had no expectations on what he wanted to get out of the data analysis. He told me that they have never truly embraced data driven decisions thus this was all new to him and the company. I reminded Tony that a contract would have to be signed by both parties before I can begin my work. Tony also mentioned that he will get back with me on the column definitions and I can directly email him the results of the data analysis.  \n",
      "\n",
      "Second email from Company ABC:\n",
      "\n",
      "> Hi David,\n",
      "\n",
      "> It was refreshing talking to you last Friday. I am sure the company can make better decisions if we simply leverage the data we are already collecting, but we need your help. I have attached the signed contract with the rate we both agreed to over the phone. Call me on my direct line if you have any additional questions. I will be patiently awaiting the results.   \n",
      "\n",
      "> **Column Definitions**:  \n",
      "\n",
      "> * *Date* - Date of transaction   \n",
      "* *Description* - Decription of transaction  \n",
      "* *Amount* - USD dollar amount of transaction  \n",
      "* *Transaction Type* - Credit vs debit  \n",
      "* *Category* - List of specific groups an item belongs to  \n",
      "\n",
      "> Tony  \n",
      "Marketing Analytics Manager  \n",
      "Company ABC  \n",
      "408 444 4444  \n",
      "\n",
      "\n",
      "Below are the results from the data anaysis that were delivered to Tony:  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import libraries\n",
      "from pandas import DataFrame, read_csv, to_datetime, merge\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set inline plotting\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# File location\n",
      "f = r'C:\\Users\\david\\Downloads\\transactions.csv'\n",
      "\n",
      "# csv file is tab seperated\n",
      "# make the Date column the index of the dataframe\n",
      "# ask Pandas to identify date columns \n",
      "raw = read_csv(f, sep = '\\t', index_col = 'Date', parse_dates = True)\n",
      "#raw"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us take a peek at the data we will be working with. Note that I had to do a small amount of work in cleaning the data before I could begin my analysis.    \n",
      "\n",
      "**Steps I took to clean the data:**  \n",
      "\n",
      "* Sort the Date column ascending   \n",
      "* Make the Amount column positive for credits and negative for debits  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we create a new dataframe with the date column ordered\n",
      "df = raw.sort_index()\n",
      "#df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# debit = -1 & credit = 1\n",
      "AddSigns = df['Transaction Type'].apply(lambda x: -1 if x == 'debit' else 1).values\n",
      "\n",
      "# Add signs to Amount column\n",
      "df['Amount'] = df['Amount']*AddSigns\n",
      "\n",
      "df.head(15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Description</th>\n",
        "      <th>Amount</th>\n",
        "      <th>Transaction Type</th>\n",
        "      <th>Category</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>2010-01-04</th>\n",
        "      <td>                            Exxon</td>\n",
        "      <td>  -25.86</td>\n",
        "      <td>  debit</td>\n",
        "      <td>      Gas &amp; Fuel</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                      Cinos Pizza</td>\n",
        "      <td>   -9.37</td>\n",
        "      <td>  debit</td>\n",
        "      <td>   Food &amp; Dining</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                             IKEA</td>\n",
        "      <td>  -13.83</td>\n",
        "      <td>  debit</td>\n",
        "      <td>     Furnishings</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                  Mellow Mushroom</td>\n",
        "      <td>  -26.61</td>\n",
        "      <td>  debit</td>\n",
        "      <td>   Food &amp; Dining</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                   ATM Withdrawal</td>\n",
        "      <td>  -60.00</td>\n",
        "      <td>  debit</td>\n",
        "      <td>        Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                      Globe Globe</td>\n",
        "      <td>  -16.00</td>\n",
        "      <td>  debit</td>\n",
        "      <td>        Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                           Publix</td>\n",
        "      <td>  -48.32</td>\n",
        "      <td>  debit</td>\n",
        "      <td>       Groceries</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-04</th>\n",
        "      <td>                             IKEA</td>\n",
        "      <td>   -4.00</td>\n",
        "      <td>  debit</td>\n",
        "      <td>   Food &amp; Dining</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td>                    Maui Teriyaki</td>\n",
        "      <td>   -8.46</td>\n",
        "      <td>  debit</td>\n",
        "      <td>   Food &amp; Dining</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-05</th>\n",
        "      <td>                     Panera Bread</td>\n",
        "      <td>   -8.73</td>\n",
        "      <td>  debit</td>\n",
        "      <td>   Food &amp; Dining</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-06</th>\n",
        "      <td>                           Target</td>\n",
        "      <td>   -2.12</td>\n",
        "      <td>  debit</td>\n",
        "      <td>        Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td>                            Exxon</td>\n",
        "      <td>  -25.04</td>\n",
        "      <td>  debit</td>\n",
        "      <td>      Gas &amp; Fuel</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-07</th>\n",
        "      <td>                        Check 334</td>\n",
        "      <td> -694.00</td>\n",
        "      <td>  debit</td>\n",
        "      <td> Mortgage &amp; Rent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-08</th>\n",
        "      <td>           Infinite Payrollppd Id</td>\n",
        "      <td> 1343.74</td>\n",
        "      <td> credit</td>\n",
        "      <td>        Paycheck</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-01-11</th>\n",
        "      <td> Transfer from FREE CLASSIC CKING</td>\n",
        "      <td>  500.00</td>\n",
        "      <td> credit</td>\n",
        "      <td>        Transfer</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                                 Description   Amount Transaction Type         Category\n",
        "Date                                                                                   \n",
        "2010-01-04                             Exxon   -25.86            debit       Gas & Fuel\n",
        "2010-01-04                       Cinos Pizza    -9.37            debit    Food & Dining\n",
        "2010-01-04                              IKEA   -13.83            debit      Furnishings\n",
        "2010-01-04                   Mellow Mushroom   -26.61            debit    Food & Dining\n",
        "2010-01-04                    ATM Withdrawal   -60.00            debit         Shopping\n",
        "2010-01-04                       Globe Globe   -16.00            debit         Shopping\n",
        "2010-01-04                            Publix   -48.32            debit        Groceries\n",
        "2010-01-04                              IKEA    -4.00            debit    Food & Dining\n",
        "2010-01-05                     Maui Teriyaki    -8.46            debit    Food & Dining\n",
        "2010-01-05                      Panera Bread    -8.73            debit    Food & Dining\n",
        "2010-01-06                            Target    -2.12            debit         Shopping\n",
        "2010-01-07                             Exxon   -25.04            debit       Gas & Fuel\n",
        "2010-01-07                         Check 334  -694.00            debit  Mortgage & Rent\n",
        "2010-01-08            Infinite Payrollppd Id  1343.74           credit         Paycheck\n",
        "2010-01-11  Transfer from FREE CLASSIC CKING   500.00           credit         Transfer"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that the data is clean, we can begin our analysis.  \n",
      "\n",
      "We simply start out by plotting the only numerical column, Amount! It seems like there is a steady positive cashflow thoughout the year. On the debit side, the winter and summer months seem to have slightly more activity than the other months. April, June, September, and November are months we can keep an eye out for. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Amount'].plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wEpQaAbkvUEkapasimfPYqgwE1Nc7zqNGGkc/FLUh97kpKQeWDIcUKKnQpZQVZ6RRYgTk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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5abf930>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are working with a data set of 823 data points, we will ignore the mean and standard deviation since the distribution from the plot above tells us these numbers are not valid. Most of the transactions seem to be between \\$5 and \\$28 dollars. Other figures to keep an eye out for are 40, 1,343.74 (credit), and 694."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Amount'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "count     823.000000\n",
        "mean        7.824241\n",
        "std       291.397559\n",
        "min     -1343.740000\n",
        "25%       -28.930000\n",
        "50%       -13.420000\n",
        "75%        -5.780000\n",
        "max      2269.950000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The numbers below represent the frequency of individual transactions. For example, a debit of ***\\$2*** has occurred in the year 2010 a total of ***17 times***."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Amount'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "-2.00       17\n",
        "-40.00      15\n",
        "-25.00      14\n",
        " 1343.74    12\n",
        "-5.33       11\n",
        "-694.00     11\n",
        "-18.00      11\n",
        "-10.00      10\n",
        "-45.00       9\n",
        " 1343.73     8\n",
        "-6.39        7\n",
        "-121.83      7\n",
        "-3.00        7\n",
        "-7.89        7\n",
        "-3.09        6\n",
        "...\n",
        "-54.54      1\n",
        "-25.42      1\n",
        "-79.63      1\n",
        "-24.99      1\n",
        "-3.82       1\n",
        "-31.43      1\n",
        "-11.91      1\n",
        "-3.98       1\n",
        "-21.58      1\n",
        "-7.47       1\n",
        "-165.56     1\n",
        " 2269.95    1\n",
        "-5.31       1\n",
        "-26.61      1\n",
        "-22.50      1\n",
        "Length: 552, dtype: int64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most of the transactions in the file are ATM fees! I would recommend you to find a way to show your customers how much money they are spending every year on ATM fees, in this case $34 dollars.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TwoDollars = df['Amount'].isin([-2]) \n",
      "df[TwoDollars]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Description</th>\n",
        "      <th>Amount</th>\n",
        "      <th>Transaction Type</th>\n",
        "      <th>Category</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>2010-03-01</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-04-26</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-01</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-01</th>\n",
        "      <td> ATM Withdrawal</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-18</th>\n",
        "      <td> ATM Withdrawal</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-18</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-06</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-19</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-11</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-11</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-30</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-01</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-08</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-23</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-29</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-29</th>\n",
        "      <td>        ATM Fee</td>\n",
        "      <td>-2</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "               Description  Amount Transaction Type Category\n",
        "Date                                                        \n",
        "2010-03-01         ATM Fee      -2            debit  ATM Fee\n",
        "2010-04-26         ATM Fee      -2            debit  ATM Fee\n",
        "2010-06-01         ATM Fee      -2            debit  ATM Fee\n",
        "2010-06-01  ATM Withdrawal      -2            debit  ATM Fee\n",
        "2010-06-18  ATM Withdrawal      -2            debit  ATM Fee\n",
        "2010-06-18         ATM Fee      -2            debit  ATM Fee\n",
        "2010-07-06         ATM Fee      -2            debit  ATM Fee\n",
        "2010-07-19         ATM Fee      -2            debit  ATM Fee\n",
        "2010-08-11         ATM Fee      -2            debit  ATM Fee\n",
        "2010-08-11         ATM Fee      -2            debit  ATM Fee\n",
        "2010-08-30         ATM Fee      -2            debit  ATM Fee\n",
        "2010-09-20         ATM Fee      -2            debit  ATM Fee\n",
        "2010-11-01         ATM Fee      -2            debit  ATM Fee\n",
        "2010-11-08         ATM Fee      -2            debit  ATM Fee\n",
        "2010-12-23         ATM Fee      -2            debit  ATM Fee\n",
        "2010-12-29         ATM Fee      -2            debit  ATM Fee\n",
        "2010-12-29         ATM Fee      -2            debit  ATM Fee"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add them up\n",
      "#df['Amount'][TwoDollars].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also notice that we have several other atm fees that are grater an \\$2 dollars and inclusing these we now have a total of \\$61.25 in atm fees."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get all ATM Fee transactions\n",
      "atm = df['Category'] == 'ATM Fee'\n",
      "\n",
      "df[atm]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Description</th>\n",
        "      <th>Amount</th>\n",
        "      <th>Transaction Type</th>\n",
        "      <th>Category</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>2010-03-01</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-03-01</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-3.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-04-26</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-04-26</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-2.25</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-01</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-01</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-18</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-18</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-06</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  30680007/0319 W UNIV</td>\n",
        "      <td>-3.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-06</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-19</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  33904307/1719 W UNIV</td>\n",
        "      <td>-3.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-19</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-09</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-3.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-11</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-11</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-30</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-30</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-2.50</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-2.50</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.50</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-01</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  37164710/30116 SE 1S</td>\n",
        "      <td>-2.50</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-01</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-08</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-23</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-23</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-3.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-29</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-29</th>\n",
        "      <td>                                      ATM Fee</td>\n",
        "      <td>-2.00</td>\n",
        "      <td> debit</td>\n",
        "      <td> ATM Fee</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                                             Description  Amount Transaction Type Category\n",
        "Date                                                                                      \n",
        "2010-03-01                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-03-01                                ATM Withdrawal   -3.00            debit  ATM Fee\n",
        "2010-04-26                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-04-26                                ATM Withdrawal   -2.25            debit  ATM Fee\n",
        "2010-06-01                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-06-01                                ATM Withdrawal   -2.00            debit  ATM Fee\n",
        "2010-06-18                                ATM Withdrawal   -2.00            debit  ATM Fee\n",
        "2010-06-18                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-07-06  NON-CHASE ATM WITHDRAW  30680007/0319 W UNIV   -3.00            debit  ATM Fee\n",
        "2010-07-06                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-07-19  NON-CHASE ATM WITHDRAW  33904307/1719 W UNIV   -3.00            debit  ATM Fee\n",
        "2010-07-19                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-08-09                                       ATM Fee   -3.00            debit  ATM Fee\n",
        "2010-08-11                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-08-11                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-08-30                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-08-30                                ATM Withdrawal   -2.50            debit  ATM Fee\n",
        "2010-09-20                                ATM Withdrawal   -2.50            debit  ATM Fee\n",
        "2010-09-20                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-09-20                                       ATM Fee   -2.50            debit  ATM Fee\n",
        "2010-11-01  NON-CHASE ATM WITHDRAW  37164710/30116 SE 1S   -2.50            debit  ATM Fee\n",
        "2010-11-01                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-11-08                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-12-23                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-12-23                                       ATM Fee   -3.00            debit  ATM Fee\n",
        "2010-12-29                                       ATM Fee   -2.00            debit  ATM Fee\n",
        "2010-12-29                                       ATM Fee   -2.00            debit  ATM Fee"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add them up\n",
      "#df['Amount'][atm].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Forty dollars was another number that looked interesting. As shown below we can conclude that these are atm withdrawals that total up to \\$600 dollars. More ATM madness... it seems like every time there is an ATM withdrawal, there is an evil ATM fee. Let us verify this..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "FortyDollars = df['Amount'].isin([-40]) \n",
      "df[FortyDollars]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Description</th>\n",
        "      <th>Amount</th>\n",
        "      <th>Transaction Type</th>\n",
        "      <th>Category</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>2010-03-01</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-04-26</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-01</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-06-21</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-06</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  30680007/0319 W UNIV</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td> Entertainment</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-07-19</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  33904307/1719 W UNIV</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td> Entertainment</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-11</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-08-30</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-20</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-09-27</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-10-18</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-11-01</th>\n",
        "      <td> NON-CHASE ATM WITHDRAW  37164710/30116 SE 1S</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td> Entertainment</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-23</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-12-29</th>\n",
        "      <td>                               ATM Withdrawal</td>\n",
        "      <td>-40</td>\n",
        "      <td> debit</td>\n",
        "      <td>      Shopping</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "                                             Description  Amount Transaction Type       Category\n",
        "Date                                                                                            \n",
        "2010-03-01                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-04-26                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-06-01                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-06-21                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-07-06  NON-CHASE ATM WITHDRAW  30680007/0319 W UNIV     -40            debit  Entertainment\n",
        "2010-07-19  NON-CHASE ATM WITHDRAW  33904307/1719 W UNIV     -40            debit  Entertainment\n",
        "2010-08-11                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-08-30                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-09-20                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-09-20                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-09-27                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-10-18                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-11-01  NON-CHASE ATM WITHDRAW  37164710/30116 SE 1S     -40            debit  Entertainment\n",
        "2010-12-23                                ATM Withdrawal     -40            debit       Shopping\n",
        "2010-12-29                                ATM Withdrawal     -40            debit       Shopping"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add up the forty amounts\n",
      "#df['Amount'][FortyDollars].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "-600.0"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As you can see we are on the money a good chunck of the time. Lets try to see if we can find the missing atm withdrawls."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# since there are multiple transaction per day, we can resample the data so\n",
      "# that transactions that happen on the same day are aggregated.\n",
      "dailyatm = df[atm].resample(rule='d', how='sum')\n",
      "\n",
      "#Create a figure\n",
      "fig, axes = plt.subplots(2,1,figsize=(12,5))\n",
      "\n",
      "# plot\n",
      "df['Amount'][FortyDollars].plot(kind='bar', color='r', ax = axes[0], title = 'FortyDollars');\n",
      "dailyatm['Amount'].dropna().plot(kind='bar',ax = axes[1], title = 'ATM Fees');\n",
      "\n",
      "# Adjust plots\n",
      "plt.subplots_adjust(wspace=0, hspace=2.5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5EjNnzgQA3Hfffbjllltw++23f7/TJp5aLVhNPd1IsNirFnvVYq9a7FVLp16dWgH2qhb2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\nmIiIiIj6rf8BQjevGi9NOrwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5ab8630>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get all ATM withdrawals\n",
      "withdrawals = df['Description'].apply(lambda x: x.lower()).str.contains('withdraw')\n",
      "\n",
      "# problem is this is also picking up atm fees, we fix that here\n",
      "#df[withdrawals & (df['Category'] <> 'ATM Fee')]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Things are getting a little better for us, we are definitely seeing a pattern here. Although it does seem that not every withdrawal ties to an ATM fee. It is also not clear from the graph, so lets try a little harder... "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Create a figure\n",
      "fig, axes = plt.subplots(2,1,figsize=(12,5))\n",
      "\n",
      "# plot\n",
      "df['Amount'][withdrawals & ~(df['Description'] == 'Withdrawal') & (df['Category'] <> 'ATM Fee')].plot(kind='bar', color='r',ax = axes[0]);\n",
      "dailyatm['Amount'].dropna().plot(kind='bar',ax = axes[1]);\n",
      "\n",
      "# adjust plots\n",
      "plt.subplots_adjust(wspace=0, hspace=1.7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IgIZzxMj3VqVfJ31ukDxgwAA8++yzKCwsxN13342ZM2eivr7e0IZx48ahT58+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\nAAAADPMf/wZ3hMDqMS4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6f7ffd0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We make dataframes out of the two slices of data\n",
      "w = df['Amount'][withdrawals & ~(df['Description'] == 'Withdrawal') & (df['Category'] <> 'ATM Fee')]\n",
      "f = dailyatm['Amount'].dropna()\n",
      "\n",
      "w = DataFrame(w)\n",
      "f = DataFrame(f)\n",
      "\n",
      "# We combine the data sets by their index\n",
      "combined = merge(w,f, left_index=True, right_index=True, how='outer', suffixes=('_Withdrawals', '_Fees'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is soo much easier to understand. We can now clearly see which were the days that we saw both fees and withdrawls. \n",
      "\n",
      "**Possible reasons for the missing fees:**  \n",
      "\n",
      "* Customer used an ATM that does not charge them a fee  \n",
      "* The category \"ATM Fee\" is not complete. There might be atm fees that were not tagged as such.  \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combined.plot(kind='bar',figsize=(12,5));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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108iRIy+4/y6H62/EtYEGcxpMYMJ+oIEGVyZ00GDfhmrPSa7IqnD5jKKiIt1+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\n/w9QTDf4OPLgugAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9f83f70>"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is were we will stop for the example, but as you can see there are still many more questions to be answered from this small data set. At this point I may simply email Tony the current results to see if he wants me to digg a little deeper into the atm topic or to move on.  \n",
      "\n",
      "As a manager you can already see that with more information, you can make better decisions and you don't always have to go with your gut. At David Rojas LLC, we can empower you with data so that you can make the daily decisions with confidence. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}
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