12. Updates & FAQ

12.1. Chap 05: matplotlib advanced layouts

12.1.1. Adding pandas plots to subplots layout

Let’s create a dataframe to plot.

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

############ here is an arbitrary dataframe to plot
df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
                   columns=['length', 'width', 'species'])
df

	length	width	species
0	5.1	3.5	0
1	4.9	3.0	0
2	7.0	3.2	1
3	6.4	3.2	1
4	5.9	3.0	2

Important

The format for matplotlib plots is:
axes_name.plot_type(xdata, ydata, options) e.g., axes1.scatter(x=df['length'], y=df['width'],'k.')
The format for pandas plots is:
dataframe.plot.type(ax=axes_name, options) e.g., df.plot.bar(ax=axes2)
Example code is given below. Notice the placement for the axes name in matplotlib vs pandas.

############# plotting side by side
fig, (axes1, axes2) = plt.subplots(nrows=1, ncols=2,figsize=(8, 4))

########## matplotlib scatter plot
axes1.scatter(x=df['length'], y=df['width'], marker='o', c='r', edgecolor='b')
axes1.set_title('Title text here...')
axes1.set_xlabel('x')
axes1.set_ylabel('y')

########### pandas bar plot
df.plot.bar(ax=axes2) #note the difference in format compared to scatter above
axes2.set_title('Title text here...')
axes2.yaxis.tick_right()
axes2.set_xlabel('x')
axes2.set_ylabel('y')
axes2.yaxis.set_label_position("right")

fig.tight_layout()