How Can I Plot Multiple Pandas DataFrames in Matplotlib Subplots?

Plotting Multiple Dataframes in Subplots

When working with multiple Pandas DataFrames with a common value scale, you may desire to visualize them within the same plot using subplots.

To achieve this, you can leverage the functionality of matplotlib. Firstly, create the subplots manually using plt.subplots(), specifying the desired number of rows and columns.

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2)

Here, axes is an array containing the individual subplot axes, each of which you can access through indexing.

Now, you can plot each DataFrame on a specific subplot by passing the ax keyword argument within the plot() method. For instance, to plot df1 on the first subplot, you would use:

df1.plot(ax=axes[0,0])

To ensure a shared x-axis across all subplots, you can specify sharex=True when creating the subplots:

fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True)

By following these steps, you can effortlessly visualize multiple DataFrames within subplots, allowing for convenient comparisons and analysis.

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