Merge Matplotlib Subplots With Shared X-axis

I have two graphs to where both have the same x-axis, but with different y-axis scalings. The plot with regular axes is the data with a trend line depicting a decay while the y se

Solution 1:

Look at the code and comments in it:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import gridspec

# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig = plt.figure()
# set height ratios for subplots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) 

# the first subplot
ax0 = plt.subplot(gs[0])
# log scale for axis Y of the first subplot
ax0.set_yscale("log")
line0, = ax0.plot(x, y, color='r')

# the second subplot# shared axis X
ax1 = plt.subplot(gs[1], sharex = ax0)
line1, = ax1.plot(x, y, color='b', linestyle='--')
plt.setp(ax0.get_xticklabels(), visible=False)
# remove last tick label for the second subplot
yticks = ax1.yaxis.get_major_ticks()
yticks[-1].label1.set_visible(False)

# put legend on first subplot
ax0.legend((line0, line1), ('red line', 'blue line'), loc='lower left')

# remove vertical gap between subplots
plt.subplots_adjust(hspace=.0)
plt.show()

Solution 2:

Here is my solution:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True, subplot_kw=dict(frameon=False)) # frameon=False removes frames

plt.subplots_adjust(hspace=.0)
ax1.grid()
ax2.grid()

ax1.plot(x, y, color='r')
ax2.plot(x, y, color='b', linestyle='--')

One more option is seaborn.FacetGrid but this requires Seaborn and Pandas libraries.

Solution 3:

Here are some adaptions to show how the code could work to add a combined legend when plotting a pandas dataframe. ax=ax0 can be used to plot on a given ax and ax0.get_legend_handles_labels() gets the information for the legend.

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

dates = pd.date_range('20210101', periods=100, freq='D')
df0 = pd.DataFrame({'x': np.random.normal(0.1, 1, 100).cumsum(),
                    'y': np.random.normal(0.3, 1, 100).cumsum()}, index=dates)
df1 = pd.DataFrame({'z': np.random.normal(0.2, 1, 100).cumsum()}, index=dates)

fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [2, 1], 'hspace': 0})

df0.plot(ax=ax0, color=['dodgerblue', 'crimson'], legend=False)
df1.plot(ax=ax1, color='limegreen', legend=False)

# put legend on first subplot
handles0, labels0 = ax0.get_legend_handles_labels()
handles1, labels1 = ax1.get_legend_handles_labels()
ax0.legend(handles=handles0 + handles1, labels=labels0 + labels1)

# remove last tick label for the second subplot
yticks = ax1.get_yticklabels()
yticks[-1].set_visible(False)

plt.tight_layout()
plt.show()