This (article] [http://pbpython.com/simple-graphing-pandas.html] will walk through how to start doing some simple graphing 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. First we are going to import pandas, numpy and matplot lib. I am also showing the versions I'm testing so you can make sure yours is compatible.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.__version__

Out[1]:
'0.18.1'

Next, enable iPython to display matplotlib graphs. As an alternative you can run ipython notebook.

In [2]:
%matplotlib inline


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 so I can do some re-sampling later.

In [3]:
sales=pd.read_csv("sample-salesv2.csv",parse_dates=['date'])

Out[3]:
account number name sku category quantity unit price ext price date
0 199372 Cruickshank and Sons FQ-39509 Belt 12 65.26 783.12 2014-12-06 15:02:14
1 518580 Hessel-Lind XV-77832 Shirt 14 28.26 395.64 2014-10-29 14:54:18
2 368293 Hodkiewicz-Kozey BC-56182 Belt 4 54.93 219.72 2015-03-11 21:45:30
3 677566 Boyle-Wiegand BH-22979 Belt 4 65.37 261.48 2014-05-21 16:07:44
4 677566 Boyle-Wiegand FQ-39509 Belt 19 35.34 671.46 2015-04-15 12:50:05

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

In [4]:
sales.describe()

Out[4]:
account number quantity unit price ext price
count 1000.000000 1000.000000 1000.000000 1000.00000
mean 496998.477000 10.450000 55.465770 580.21763
std 271588.313857 5.777272 25.621962 445.17188
min 62767.000000 1.000000 10.350000 12.85000
25% 200626.000000 5.000000 33.900000 213.72000
50% 518580.000000 11.000000 56.400000 462.37500
75% 687215.000000 15.000000 77.610000 872.76750
max 968503.000000 20.000000 99.580000 1906.60000

We can actually learn some pretty helpful info from this simple command: For example, we can tell that customers on average purchases 10.3 items per transaction and that the average cost of the transaction was $579.84. It is also easy to see the min and max so you understand the range of the data. In [5]: sales['unit price'].describe()  Out[5]: count 1000.000000 mean 55.465770 std 25.621962 min 10.350000 25% 33.900000 50% 56.400000 75% 77.610000 max 99.580000 Name: unit price, dtype: float64 It is easy to call describe on a single column too. I can see that my average price is \$56.18 but it ranges from \$10.06 to \$99.97.

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.

In [6]:
sales.dtypes

Out[6]:
account number             int64
name                      object
sku                       object
category                  object
quantity                   int64
unit price               float64
ext price                float64
date              datetime64[ns]
dtype: object

Now we remove some columns to make additional analysis easier.

In [7]:
customers = sales[['name','ext price','date']]

Out[7]:
name ext price date
0 Cruickshank and Sons 783.12 2014-12-06 15:02:14
1 Hessel-Lind 395.64 2014-10-29 14:54:18
2 Hodkiewicz-Kozey 219.72 2015-03-11 21:45:30
3 Boyle-Wiegand 261.48 2014-05-21 16:07:44
4 Boyle-Wiegand 671.46 2015-04-15 12:50:05

This representation has multiple lines for each customer. In order to understand purchasing patterns, let's group all the customers by name.

In [8]:
customer_group = customers.groupby('name')
customer_group.size()

Out[8]:
name
Boyer-Kerluke                         50
Boyle-Wiegand                         53
Braun-Wehner                          65
Crona-Leannon                         51
Cruickshank and Sons                  46
Feest LLC                             54
Heller Inc                            38
Hessel-Lind                           57
Hills Ltd                             52
Hodkiewicz-Kozey                      36
Johnston-Deckow                       54
Kautzer, Kunze and Eichmann           48
Keebler PLC                           51
Kiehn, Metz and Turcotte              49
Leuschke Inc                          55
Little and Sons                       57
Mitchell PLC                          41
Moore, Stehr and Carroll              56
Nolan, Koepp and McDermott            49
Schulist, Christiansen and Douglas    38
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.

In [9]:
sales_totals = customer_group.sum()

Out[9]:
ext price
name
Mitchell PLC 18855.52
Hodkiewicz-Kozey 19900.27
Schulist, Christiansen and Douglas 22122.12
Cruickshank and Sons 22716.09
Heller Inc 23224.74

Now that we know what the data look like, tt is very simple to create a quick bar chart plot.

In [10]:
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
• Labeling the axes
In [11]:
my_plot = sales_totals.sort_values(by=['ext price'],ascending=False).plot(kind='bar',legend=None,title="Total Sales by Customer")
my_plot.set_xlabel("Customers")
my_plot.set_ylabel("Sales ($)")  Out[11]: <matplotlib.text.Text at 0x7fc74b8725c0> 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. In [12]: customers = sales[['name','category','ext price','date']] customers.head()  Out[12]: name category ext price date 0 Cruickshank and Sons Belt 783.12 2014-12-06 15:02:14 1 Hessel-Lind Shirt 395.64 2014-10-29 14:54:18 2 Hodkiewicz-Kozey Belt 219.72 2015-03-11 21:45:30 3 Boyle-Wiegand Belt 261.48 2014-05-21 16:07:44 4 Boyle-Wiegand Belt 671.46 2015-04-15 12:50:05 We can use groupby to organize the data by category and name. In [13]: category_group=customers.groupby(['name','category']).sum() category_group.head()  Out[13]: ext price name category Boyer-Kerluke Belt 13201.21 Shirt 19043.45 Shoes 3748.47 Boyle-Wiegand Belt 10959.84 Shirt 12274.01 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. In [14]: category_group.unstack().head()  Out[14]: ext price category Belt Shirt Shoes name Boyer-Kerluke 13201.21 19043.45 3748.47 Boyle-Wiegand 10959.84 12274.01 4199.82 Braun-Wehner 13841.64 15916.63 6861.65 Crona-Leannon 12754.64 9863.25 6332.65 Cruickshank and Sons 10770.14 5914.07 6031.88 Now plot it. In [15]: 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")  Out[15]: <matplotlib.text.Text at 0x7fc74b763ac8> Now clean some of this up a little bit. We can specify the figure size and customize the legend. In [16]: 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)  Out[16]: <matplotlib.legend.Legend at 0x7fc74b7703c8> 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. In [17]: purchase_patterns = sales[['ext price','date']] purchase_patterns.head()  Out[17]: ext price date 0 783.12 2014-12-06 15:02:14 1 395.64 2014-10-29 14:54:18 2 219.72 2015-03-11 21:45:30 3 261.48 2014-05-21 16:07:44 4 671.46 2015-04-15 12:50:05 In [18]: 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")

Out[18]:
<matplotlib.text.Text at 0x7fc74b5c3128>

After looking at this group

We can look 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. Do we have certain months where we are busier than others?

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

In [19]:
purchase_patterns = sales[['ext price','date']]

Out[19]:
ext price date
0 783.12 2014-12-06 15:02:14
1 395.64 2014-10-29 14:54:18
2 219.72 2015-03-11 21:45:30
3 261.48 2014-05-21 16:07:44
4 671.46 2015-04-15 12:50:05

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

In [20]:
purchase_patterns = purchase_patterns.set_index('date')

Out[20]:
ext price
date
2014-12-06 15:02:14 783.12
2014-10-29 14:54:18 395.64
2015-03-11 21:45:30 219.72
2014-05-21 16:07:44 261.48
2015-04-15 12:50:05 671.46

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.

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

Plotting the data is now very easy

In [21]:
purchase_plot = purchase_patterns.resample('M').sum().plot(title="Total Sales by Month",legend=None)


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.

In [22]:
fig = purchase_plot.get_figure()
fig.savefig("total-sales.png")

In [ ]: