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()

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()

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 ($)")

<matplotlib.text.Text at 0x7ff9bf23c510>

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()

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

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

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()

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")

<matplotlib.text.Text at 0x7ff9bf03fc10>

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()

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")

<matplotlib.text.Text at 0x7ff9becdc210>

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()

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()

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.

fig = purchase_plot.get_figure()
fig.savefig("total-sales.png")