I will walk through how to start doing some simple graphing and plotting of data in pandas. I am using a new data file that is the same format as my previous article but includes data for only 20 customers. If you would like to follow along, the file is available here .

This article is a follow on to my previous article on analyzing data with python. I am going to build on my basic intro of IPython , notebooks and pandas to show how to visualize the data you have processed with these tools. I hope that this will demonstrate to you (once again) how powerful these tools are and how much you can get done with such little code. I ultimately hope these articles will help people stop reaching for Excel every time they need to slice and dice some files. The tools in the python environment can be so much more powerful than the manual copying and pasting most people do in excel.

I am showing the output of dtypes so that you can see that the date column is a datetime field. I also scan this to make sure that any columns that have numbers are floats or ints so that I can do additional analysis in the future.

I can see that my average price is $56.18 but it ranges from $10.06 to $99.97.

If we want we can look at a single column as well:

We can actually learn some pretty helpful info from this simple command:

Now that we have read in the data, we can do some quick analysis

We will read in the file like we did in the previous article but I’m going to tell it to treat the date column as a date field (using parse_dates ) so I can do some re-sampling later.

First we are going to import pandas, numpy and matplot lib. I am also showing the pandas version I’m using so you can make sure yours is compatible.

As described in the previous article , I’m using an IPython notebook to explore my data.

Plotting Some Data

We have our data read in and have completed some basic analysis. Let’s start plotting it.

First remove some columns to make additional analysis easier.

customers = sales [[ 'name' , 'ext price' , 'date' ]] customers . head ()

name ext price date 0 Carroll PLC 578.24 2014-09-27 07:13:03 1 Heidenreich-Bosco 1018.78 2014-07-29 02:10:44 2 Kerluke, Reilly and Bechtelar 289.92 2014-03-01 10:51:24 3 Waters-Walker 413.40 2013-11-17 20:41:11 4 Waelchi-Fahey 1793.52 2014-01-03 08:14:27

This representation has multiple lines for each customer. In order to understand purchasing patterns, let’s group all the customers by name. We can also look at the number of entries per customer to get an idea for the distribution.

customer_group = customers . groupby ( 'name' ) customer_group . size ()

name Berge LLC 52 Carroll PLC 57 Cole-Eichmann 51 Davis, Kshlerin and Reilly 41 Ernser, Cruickshank and Lind 47 Gorczany-Hahn 42 Hamill-Hackett 44 Hegmann and Sons 58 Heidenreich-Bosco 40 Huel-Haag 43 Kerluke, Reilly and Bechtelar 52 Kihn, McClure and Denesik 58 Kilback-Gerlach 45 Koelpin PLC 53 Kunze Inc 54 Kuphal, Zieme and Kub 52 Senger, Upton and Breitenberg 59 Volkman, Goyette and Lemke 48 Waelchi-Fahey 54 Waters-Walker 50 dtype: int64

Now that our data is in a simple format to manipulate, let’s determine how much each customer purchased during our time frame.

The sum function allows us to quickly sum up all the values by customer. We can also sort the data using the sort command.

sales_totals = customer_group . sum () sales_totals . sort ( columns = 'ext price' ) . head ()

ext price name Davis, Kshlerin and Reilly 19054.76 Huel-Haag 21087.88 Gorczany-Hahn 22207.90 Hamill-Hackett 23433.78 Heidenreich-Bosco 25428.29

Now that we know what the data look like, it is very simple to create a quick bar chart plot. Using the IPython notebook, the graph will automatically display.

my_plot = sales_totals . plot ( kind = 'bar' )

Unfortunately this chart is a little ugly. With a few tweaks we can make it a little more impactful. Let’s try:

sorting the data in descending order

removing the legend

adding a title

labeling the axes

my_plot = sales_totals . sort ( columns = 'ext price' , ascending = False ) . plot ( kind = 'bar' , legend = None , title = "Total Sales by Customer" ) my_plot . set_xlabel ( "Customers" ) my_plot . set_ylabel ( "Sales ($)" )

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This actually tells us a little about our biggest customers and how much difference there is between their sales and our smallest customers.

Now, let’s try to see how the sales break down by category.

customers = sales [[ 'name' , 'category' , 'ext price' , 'date' ]] customers . head ()

name category ext price date 0 Carroll PLC Belt 578.24 2014-09-27 07:13:03 1 Heidenreich-Bosco Shoes 1018.78 2014-07-29 02:10:44 2 Kerluke, Reilly and Bechtelar Shirt 289.92 2014-03-01 10:51:24 3 Waters-Walker Shirt 413.40 2013-11-17 20:41:11 4 Waelchi-Fahey Shirt 1793.52 2014-01-03 08:14:27

We can use groupby to organize the data by category and name.

category_group = customers . groupby ([ 'name' , 'category' ]) . sum () category_group . head ()

ext price name category Berge LLC Belt 6033.53 Shirt 9670.24 Shoes 14361.10 Carroll PLC Belt 9359.26 Shirt 13717.61

The category representation looks good but we need to break it apart to graph it as a stacked bar graph. unstack can do this for us.

category_group . unstack () . head ()

ext price category Belt Shirt Shoes name Berge LLC 6033.53 9670.24 14361.10 Carroll PLC 9359.26 13717.61 12857.44 Cole-Eichmann 8112.70 14528.01 7794.71 Davis, Kshlerin and Reilly 1604.13 7533.03 9917.60 Ernser, Cruickshank and Lind 5894.38 16944.19 5250.45

Now plot it.

my_plot = category_group . unstack () . plot ( kind = 'bar' , stacked = True , title = "Total Sales by Customer" ) my_plot . set_xlabel ( "Customers" ) my_plot . set_ylabel ( "Sales" )

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In order to clean this up a little bit, we can specify the figure size and customize the legend.

my_plot = category_group . unstack () . plot ( kind = 'bar' , stacked = True , title = "Total Sales by Customer" , figsize = ( 9 , 7 )) my_plot . set_xlabel ( "Customers" ) my_plot . set_ylabel ( "Sales" ) my_plot . legend ([ "Total" , "Belts" , "Shirts" , "Shoes" ], loc = 9 , ncol = 4 )

<matplotlib.legend.Legend at 0x7ff9bed5f710>

Now that we know who the biggest customers are and how they purchase products, we might want to look at purchase patterns in more detail.

Let’s take another look at the data and try to see how large the individual purchases are. A histogram allows us to group purchases together so we can see how big the customer transactions are.

purchase_patterns = sales [[ 'ext price' , 'date' ]] purchase_patterns . head ()

ext price date 0 578.24 2014-09-27 07:13:03 1 1018.78 2014-07-29 02:10:44 2 289.92 2014-03-01 10:51:24 3 413.40 2013-11-17 20:41:11 4 1793.52 2014-01-03 08:14:27

We can create a histogram with 20 bins to show the distribution of purchasing patterns.

purchase_plot = purchase_patterns [ 'ext price' ] . hist ( bins = 20 ) purchase_plot . set_title ( "Purchase Patterns" ) purchase_plot . set_xlabel ( "Order Amount($)" ) purchase_plot . set_ylabel ( "Number of orders" )

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In looking at purchase patterns over time, we can see that most of our transactions are less than $500 and only a very few are about $1500.

Another interesting way to look at the data would be by sales over time. A chart might help us understand, “Do we have certain months where we are busier than others?”

Let’s get the data down to order size and date.

purchase_patterns = sales [[ 'ext price' , 'date' ]] purchase_patterns . head ()

ext price date 0 578.24 2014-09-27 07:13:03 1 1018.78 2014-07-29 02:10:44 2 289.92 2014-03-01 10:51:24 3 413.40 2013-11-17 20:41:11 4 1793.52 2014-01-03 08:14:27

If we want to analyze the data by date, we need to set the date column as the index using set_index .

purchase_patterns = purchase_patterns . set_index ( 'date' ) purchase_patterns . head ()

ext price date 2014-09-27 07:13:03 578.24 2014-07-29 02:10:44 1018.78 2014-03-01 10:51:24 289.92 2013-11-17 20:41:11 413.40 2014-01-03 08:14:27 1793.52

One of the really cool things that pandas allows us to do is resample the data. If we want to look at the data by month, we can easily resample and sum it all up. You’ll notice I’m using ‘M’ as the period for resampling which means the data should be resampled on a month boundary.

purchase_patterns . resample ( 'M' , how = sum )

Plotting the data is now very easy

purchase_plot = purchase_patterns . resample ( 'M' , how = sum ) . plot ( title = "Total Sales by Month" , legend = None )

Looking at the chart, we can easily see that December is our peak month and April is the slowest.

Let’s say we really like this plot and want to save it somewhere for a presentation.