Visualizing data with Pandas and Plotnine

Overview

Teaching: 40 min
Exercises: 50 min

Questions

How can I visualize data in Python?

What is ‘grammar of graphics’?

Objectives

Describe the features of pandas built-in plotting abilities vs. using external packages.

Create simple scatterplots and histograms with pandas.

Use plotnine to customize the aesthetics of an existing plot.

Build complex and customized plots from data in a data frame.

Export plots from Jupyter to standard graphical file formats.

Visualization in Python

Matplotlib

We are going to demonstrate how to the built-in plotting tools that come with Pandas, but these functions essentially “wrap” matplotlib code to produce graphs. Even Plotnine, the ggplot-like library we’ll use later, is built on top of matplotlib.

%matplotlib inline

Pandas plotting

import pandas as pd
gh_url = 'https://raw.githubusercontent.com/datacarpentry/R-genomics/gh-pages/data/Ecoli_metadata.csv'
ecoli = pd.read_csv(gh_url)
ecoli.head()

	sample	generation	clade	strain	cit	run	genome_size
0	REL606	0	NaN	REL606	unknown	NaN	4.62
1	REL1166A	2000	unknown	REL606	unknown	SRR098028	4.63
2	ZDB409	5000	unknown	REL606	unknown	SRR098281	4.60
3	ZDB429	10000	UC	REL606	unknown	SRR098282	4.59
4	ZDB446	15000	UC	REL606	unknown	SRR098283	4.66

Pandas Histogram

ecoli['genome_size'].plot.hist()

png

ecoli['genome_size'].plot.hist(title='Genome Size Histogram')

png

Pandas Scatterplot

ecoli.plot.scatter(x='generation',y='genome_size')

png

ecoli.plot.scatter(x='generation',y='genome_size', 
                   title='Plot Title', c='green', marker='+')

png

Grammar of graphics with plotnine

Python has powerful built-in plotting capabilities such as matplotlib, but for this episode, we will be using the plotnine package, which facilitates the creation of highly-informative plots of structured data based on the R implementation of ggplot2 and The Grammar of Graphics by Leland Wilkinson. The plotnine package is built on top of Matplotlib and interacts well with Pandas.

Just as with the other packages, plotnine need to be imported. It is good practice to not just load an entire package such as from plotnine import *, but to use an abbreviation as we used pd for Pandas:

import plotnine as p9

From now on, the functions of plotnine are available using p9.. For the exercise, we will use the surveys.csv data set, with the NA values removed.

Plotnine Histogram

(p9.ggplot(data=ecoli,
           mapping=p9.aes(x='genome_size'))
    + p9.geom_histogram()
)

/Users/miketrizna/miniconda3/envs/python36/lib/python3.6/site-packages/plotnine/stats/stat_bin.py:90: UserWarning: 'stat_bin()' using 'bins = 3'. Pick better value with 'binwidth'.
  warn(msg.format(params['bins']))

png

<ggplot: (7561237964)>

(p9.ggplot(data=ecoli,
           mapping=p9.aes(x='genome_size'))
    + p9.geom_histogram(bins=15)
)

png

<ggplot: (7560944062)>

Plotnine Scatterplot

(p9.ggplot(data=ecoli,
           mapping=p9.aes(x = 'sample', y= 'genome_size'))
 + p9.geom_point()
)

png

<ggplot: (-9223372029321576718)>

(p9.ggplot(data=ecoli,
           mapping=p9.aes(x = 'sample', y= 'genome_size'))
 + p9.geom_point(alpha=0.5, color='blue')
 + p9.theme(axis_text_x = p9.element_text(angle=45, hjust=1))
)

png

<ggplot: (-9223372029308153123)>

(p9.ggplot(data=ecoli,
           mapping=p9.aes(x = 'sample', y= 'genome_size', color='generation'))
 + p9.geom_point(alpha=0.5)
 + p9.theme(axis_text_x = p9.element_text(angle=45, hjust=1))
)

png

<ggplot: (7546764513)>

Key Points

The data, aes variables and a geometry are the main elements of a plotnine graph

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