Visualizing data – overlaying charts in python

This post was originally published here

Visualizing data is vital to analyzing data.  If you can’t see your data – and see it in multiple ways – you’ll have a hard time analyzing that data.  There are quite a few ways to visualize data and, thankfully, with pandas, matplotlib and/or seaborn, you can make some pretty powerful visualizations during analysis.

One of the things I like to do when I get a new dataset is try to visualize data points against each other to see if there’s anything that jumps out at me.   To do this, I like to overlay charts against each other to find any patterns in the data / charts. With matplotlib, this is pretty easy to do but working with dual-axis can be a bit confusing at first.

Want  to learn more about data visualization and/or matplotlib? Here are a few books / websites with good info on the topic.

One chart that I like to look at for data that I know has a relationship – like sales revenue and number of widgets sold – is the dual overlay of revenue vs quantity.  An example of one of my go-to approaches for visualizing data is in Figure 1 below.

Visualizing data - revenue vs number of items
Figure 1: Visualizing data — Revenue vs Quantity chart overlay

In this chart, we have Monthly Sales Revenue (blue line) chart overlay-ed against the Number of Items Sold chart (multi-colored bar chart). This type of chart lets me quickly see if there are any easy patterns in the revenue vs # of items.

I’ve not found a quick/easy way to build the multi-colored bar chart without hacking the data and building each colored section manually…so if you know a better way that what I share below, let me know.

An example

Here’s my code for building this chart using this data.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline # needed for jupyter notebooks

plt.rcParams['figure.figsize']=(20,10) # set the figure size'fivethirtyeight') # using the fivethirtyeight matplotlib theme

sales = pd.read_csv('examples/sales.csv') # Read the data in
sales.Date = pd.to_datetime(sales.Date) #set the date column to datetime
sales.set_index('Date', inplace=True) #set the index to the date column

# now the hack for the multi-colored bar chart: 
# create fiscal year dataframes covering the timeframes you are looking for. In this case,
# the fiscal year covered October - September.
# --------------------------------------------------------------------------------
# Note: This should be set up as a function, but for this small amount of data,
# I just manually built each fiscal year. This is not very pythonic and would
# suck to do if you have many years of data, but it isn't bad for a few years of data. 
# --------------------------------------------------------------------------------

fy10_all = sales[(sales.index >= '2009-10-01') & (sales.index < '2010-10-01')]
fy11_all = sales[(sales.index >= '2010-10-01') & (sales.index < '2011-10-01')]
fy12_all = sales[(sales.index >= '2011-10-01') & (sales.index < '2012-10-01')]
fy13_all = sales[(sales.index >= '2012-10-01') & (sales.index < '2013-10-01')]
fy14_all = sales[(sales.index >= '2013-10-01') & (sales.index < '2014-10-01')]
fy15_all = sales[(sales.index >= '2014-10-01') & (sales.index < '2015-10-01')]

# Let's build our plot

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()  # set up the 2nd axis
ax1.plot(sales.Sales_Dollars) #plot the Revenue on axis #1

# the next few lines plot the fiscal year data as bar plots and changes the color for each., fy10_all.Quantity,width=20, alpha=0.2, color='orange'), fy11_all.Quantity,width=20, alpha=0.2, color='gray'), fy12_all.Quantity,width=20, alpha=0.2, color='orange'), fy13_all.Quantity,width=20, alpha=0.2, color='gray'), fy14_all.Quantity,width=20, alpha=0.2, color='orange'), fy15_all.Quantity,width=20, alpha=0.2, color='gray')

ax2.grid(b=False) # turn off grid #2

ax1.set_title('Monthly Sales Revenue vs Number of Items Sold Per Month')
ax1.set_ylabel('Monthly Sales Revenue')
ax2.set_ylabel('Number of Items Sold')

# Set the x-axis labels to be more meaningful than just some random dates.
labels = ['FY 2010', 'FY 2011','FY 2012', 'FY 2013','FY 2014', 'FY 2015']

This is just one way of visualizing data with python. Hopefully its a good example of a different approach that you may not have thought about.

The post Visualizing data – overlaying charts in python appeared first on Python Data.

Related Posts

Python and AWS Lambda – A match made in heaven In recent months, I’ve begun moving some of my analytics functions to the cloud. Specifically, I’ve been moving them many of my python scr...
Stock market forecasting with prophet In a previous post, I used stock market data to show how prophet detects changepoints in a signal (
How to Generate FiveThirtyEight Graphs in Python If you read data science articles, you may have already stumbled upon FiveThirtyEight’s content. Naturally, you were impressed by their awesome ...
psutil 5.3.0 with full Unicode support is out psutil 5.3.0 is finally out. This release is a major one, as it includes tons of improvements and bugfixes, probably like no other previous release. I...