

In [2]:

    
import plotnine as gg
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# Ignore some deprecation warnings from plotnine 
# using deprecated numpy functions in matplotlib
import warnings
import matplotlib.cbook
warnings.filterwarnings("ignore",category=matplotlib.cbook.mplDeprecation)
warnings.filterwarnings("ignore",category=FutureWarning)

Plotnine

plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot. - Plotnine Official Website

Plotting with a grammar is powerful, it makes custom (and otherwise complex) plots are easy to think about and then create, while the simple plots remain simple.

Let's take a create a quick example using the mtcars dataset.



In [3]:

    
DATASET_URL = 'https://gist.githubusercontent.com/ZeccaLehn/4e06d2575eb9589dbe8c365d61cb056c/raw/64f1660f38ef523b2a1a13be77b002b98665cdfe/mtcars.csv'
mtcars = pd.read_csv(DATASET_URL).rename(columns={'Unnamed: 0': 'brand'})

mtcars.head()









    Out[3]:







  
    
      
      brand
      mpg
      cyl
      disp
      hp
      drat
      wt
      qsec
      vs
      am
      gear
      carb
    
  
  
    
      0
      Mazda RX4
      21.0
      6
      160.0
      110
      3.90
      2.620
      16.46
      0
      1
      4
      4
    
    
      1
      Mazda RX4 Wag
      21.0
      6
      160.0
      110
      3.90
      2.875
      17.02
      0
      1
      4
      4
    
    
      2
      Datsun 710
      22.8
      4
      108.0
      93
      3.85
      2.320
      18.61
      1
      1
      4
      1
    
    
      3
      Hornet 4 Drive
      21.4
      6
      258.0
      110
      3.08
      3.215
      19.44
      1
      0
      3
      1
    
    
      4
      Hornet Sportabout
      18.7
      8
      360.0
      175
      3.15
      3.440
      17.02
      0
      0
      3
      2

Scatter Plot



In [15]:

    
(gg.ggplot(mtcars, gg.aes('disp', 'mpg', color='factor(gear)'))
 + gg.geom_point()
 + gg.geom_smooth(method="lm")
 + gg.facet_wrap("~gear"))









    












    Out[15]:





<ggplot: (-9223371849348229408)>

The equivalent in plain matplotlib would be



In [10]:

    
plt.scatter(mtcars['disp'], mtcars['mpg'])
plt.xlabel('Disp')
plt.ylabel('MPG')
plt.title('Disp vs MPG')
plt.show()

Box Plot

Let's build a boxplot, and see the distribution of horspower on wether a car is automatic or manual



In [11]:

    
(gg.ggplot(mtcars, gg.aes(y='hp', x='factor(gear)'))
 + gg.geom_boxplot())









    












    Out[11]:





<ggplot: (-9223371849349535196)>

The matplotlib equivalent



In [34]:

    
auto_tmission = mtcars.loc[lambda df: df['am'] == 0].hp
man_tmission = mtcars.loc[lambda df: df['am'] == 1].hp

fig, axes = plt.subplots()
axes.set(xticklabels=['0', '1',], xlabel='am', ylabel='hp')
axes.boxplot([auto_tmission, man_tmission])

plt.show()

Bar Chart

Let's plot the weight of each brand



In [40]:

    
(gg.ggplot(mtcars, gg.aes(x='brand', y='wt'))
    + gg.geom_bar(stat='identity'))









    












    Out[40]:





<ggplot: (-9223371906734255728)>

The matplotlib equivalent



In [42]:

    
plt.bar(mtcars['brand'], mtcars['wt'])
plt.xlabel('Brand')
plt.ylabel('wt')
plt.title('Brand by weight')
plt.show()

The brands are all quished together, let's try making the bar graph horizontal



In [41]:

    
(gg.ggplot(mtcars, gg.aes(x='brand', y='wt'))
    + gg.geom_bar(stat='identity')
    + gg.coord_flip())









    












    Out[41]:





<ggplot: (-9223371906733997520)>



In [35]:

    
(gg.ggplot(mtcars, gg.aes(x='gear', y='hp', fill="factor(brand)"))
    + gg.geom_bar(stat='identity', color="black")
    + gg.coord_flip()
    + gg.theme_xkcd())









    












    Out[35]:





<ggplot: (-9223371849348881064)>



In [43]:

    
plt.barh(mtcars['brand'], mtcars['wt'])
plt.xlabel('Brand')
plt.ylabel('wt')
plt.title('Brand by weight')
plt.show()

Histogram

Let's look at the distribution of weight for all the cars



In [31]:

    
(gg.ggplot(mtcars, gg.aes(x='wt'))
    + gg.geom_histogram(bins=10, fill="blue"))









    












    Out[31]:





<ggplot: (-9223371921100933236)>



In [15]:

    
plt.hist(mtcars['wt'], 10)
plt.xlabel('history')
plt.ylabel('wt')
plt.title('Histogram')
plt.show()

Pie Chart

Let's see the most common gear in this dataset

Unfortunately, the function coord_polar which is needed to created piecharts is not in the plotnine API, so pie charts:

Check out this issue

	brand	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2