plotnine.md

Medium uses browser cookies to give you the best possible experience. To make Medium work, we log user data and share it with processors. To use Medium, you must agree to our Privacy Policy, including cookie policy. Become a member Homepage Go to the profile of Dr. Gregor Scheithauer Dr. Gregor Scheithauer I help companies to improve their business processes using data science. - All Words are my own www.gregorscheithauer.de May 14 Data visualization in Python like in R’s ggplot2

If you love plotting your data with R’s ggplot2 but you are bound to use Python, the plotnine package is worth to look into as an alternative to matplotlib.

In this post I show you how to get started with plotnine for productive output.

If you want to follow along please find the whole script on GitHub: scheithauer/python-plotnine

python-plotnine - Data visualization in Python like in R's ggplot2 github.com ggplot2

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. Source: http://ggplot2.tidyverse.org/

ggplot output; http://ggplot2.tidyverse.org/reference/figures/README-example-1.png plotnine as an alternative to python’s matplotlib

In my experience the advantage of ggplot2 is the implementation of the grammar of graphics.

plotnine is a Grammar of Graphics for Python by Hassan Kibirige and brings the same advantages to python: Less coding and easy understanding (declarative paradigm). Setup Installing plotnine

Using pip

$ pip install plotnine # 1. should be sufficient for most $ pip install 'plotnine[all]' # 2. includes extra/optional packages

Or using conda

$ conda install -c conda-forge plotnine

Data for visualizations

I used the craft-beers-dataset from Jean-Nicholas Hould. It contains information about 2,410 US craft beers. The information includes:

abv — The alcoholic content by volume with 0 being no alcohol and 1 being pure alcohol
ibu — International bittering units, which describe how bitter a drink is.
name — Name of the beer.
style — Beer style (lager, ale, IPA, etc.)
brewery_id — Unique identifier for brewery that produces this beer
ounces — Size of beer in ounces.

Data set example entries Install necessary libs

import pandas as pd import numpy as np from plotnine import *

Define useful constants

c_remote_data ='https://raw.githubusercontent.com/nickhould/craft-beers-dataset/master/data/processed/beers.csv' c_col = ["#2f4858", "#f6ae2d", "#f26419", "#33658a", "#55dde0", "#2f4858", "#2f4858", "#f6ae2d", "#f26419", "#33658a", "#55dde0", "#2f4858"]

Useful functions

def labels(from_, to_, step_): return pd.Series(np.arange(from_, to_ + step_, step_)).apply(lambda x: '{:,}'.format(x)).tolist()

def breaks(from_, to_, step_): return pd.Series(np.arange(from_, to_ + step_, step_)).tolist()

Read data and set index

data = pd.read_csv(c_remote_data)

data = ( data.filter([ 'abv', 'ibu', 'id', 'name', 'style', 'brewery_id', 'ounces' ]). set_index('id') )

Histogram Initial

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_histogram(aes(x = 'abv')) )

Adding color

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_histogram( aes(x = 'abv'), fill = c_col[0], color = 'black' ) )

Adding labels

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_histogram( aes(x = 'abv'), fill = c_col[0], color = 'black' ) + labs( title ='Distribution of The alcoholic content by volume (abv)', x = 'abv - The alcoholic content by volume', y = 'Count', ) )

Set the axes scaling

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_histogram( aes(x = 'abv'), fill = c_col[0], color = 'black' ) + labs( title ='Distribution of The alcoholic content by volume (abv)', x = 'abv - The alcoholic content by volume', y = 'Count', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 350), labels = labels(0, 350, 50), breaks = breaks(0, 350, 50) ) )

Apply one of the available themes

theme_set( theme_538() ) # one time call

Change some theme features

theme_set( theme_538() + theme( figure_size = (8, 4), text = element_text( size = 8, color = 'black', family = 'Arial' ), plot_title = element_text( color = 'black', family = 'Arial', weight = 'bold', size = 12 ), axis_title = element_text( color = 'black', family = 'Arial', weight = 'bold', size = 6 ), ) )

Add some statistics

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_histogram( aes(x = 'abv'), fill = c_col[0], color = 'black' ) + labs( title ='Distribution of The alcoholic content by volume (abv)', x = 'abv - The alcoholic content by volume (median = dashed line; mean = solid line)', y = 'Count', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 350), labels = labels(0, 350, 50), breaks = breaks(0, 350, 50) ) + geom_vline(aes(xintercept = data.abv.mean()), color = 'gray') + geom_vline(aes(xintercept = data.abv.median()), linetype = 'dashed', color = 'gray') )

Faceting

fig = ( ggplot(data.dropna(subset = ['abv', 'style'])[data['style'].dropna().str.contains('American')]) + geom_histogram( aes(x = 'abv'), fill = c_col[0], color = 'black' ) + labs( title ='Distribution of The alcoholic content by volume (abv)', x = 'abv - The alcoholic content by volume', y = 'Count', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.07), breaks = breaks(0, 0.14, 0.07) ) + scale_y_continuous( limits = (0, 300), labels = labels(0, 300, 100), breaks = breaks(0, 300, 100) ) + theme(figure_size = (8, 12)) + facet_wrap('~style', ncol = 4) )

Scatterplots Initial

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_point( aes(x = 'abv', y = 'ibu'), fill = c_col[0], color = 'black' ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) )

Changing point sizes to a variable

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_point( aes(x = 'abv', y = 'ibu', size = 'ounces'), fill = c_col[0], color = 'black' ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) )

Changing point color to a variable

data['ounces_str'] = data['ounces'] data['ounces_str'] = data['ounces_str'].apply(str)

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_point( aes(x = 'abv', y = 'ibu', fill = 'ounces_str'), alpha = 0.5, color = 'black' ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_fill_manual( name = 'Ounces', values = c_col) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) )

Adding a linear regression line

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_point( aes(x = 'abv', y = 'ibu', fill = 'ounces_str'), alpha = 0.5, color = 'black' ) + geom_smooth( aes(x = 'abv', y = 'ibu') ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_fill_manual( name = 'Ounces', values = c_col) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) )

Faceting

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_jitter( aes(x = 'abv', y = 'ibu', fill = 'ounces_str'), width = 0.0051, height = 5, color = 'black' ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_fill_manual( guide = False, name = 'Ounces', values = c_col) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) + facet_wrap('ounces_str') )

Heatmap

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_bin2d( aes(x = 'abv', y = 'ibu') ) + labs( title ='Relationship between alcoholic content (abv) and int. bittering untis (ibu)', x = 'abv - The alcoholic content by volume', y = 'ibu - International bittering units', ) + scale_x_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) + scale_y_continuous( limits = (0, 150), labels = labels(0, 150, 30), breaks = breaks(0, 150, 30) ) + theme(figure_size = (8, 8)) )

Boxplot Generix boxplot

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_boxplot( aes(x = 'ounces_str', y = 'abv') ) + labs( title ='Distribution of alcoholic content (abv) by size', x = 'size in ounces', y = 'abv - The alcoholic content by volume', ) + scale_y_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) )

Violin boxplot

fig = ( ggplot(data.dropna(subset = ['abv'])) + geom_violin( aes(x = 'ounces_str', y = 'abv'), fill = c_col[0] ) + labs( title ='Distribution of alcoholic content (abv) by size', x = 'size in ounces', y = 'abv - The alcoholic content by volume', ) + scale_y_continuous( limits = (0, 0.14), labels = labels(0, 0.14, 0.02), breaks = breaks(0, 0.14, 0.02) ) )

Conclusion

plotnine offers a wide range of different visualizations, which are easy to adapt for customized outputs. If you have experience with ggplot in R then a switch to plotnine is effortless.