How to Plot Multiple Pandas DataFrames in Matplotlib Subplots?

Plotting Multiple DataFrames in Subplots with Pandas and Matplotlib

When working with Pandas DataFrames, it's often necessary to visualize multiple datasets simultaneously. While df.plot() provides a convenient way to plot individual DataFrames, it doesn't offer the ability to combine them into subplots.

Question:

How can we plot multiple Pandas DataFrames in subplots, using Matplotlib or any other Python library?

Answer:

For plotting multiple DataFrames in subplots, we can manually create the subplots using Matplotlib and then use the ax keyword to specify the target subplot for each DataFrame.

Implementation:

import matplotlib.pyplot as plt

# Generate some sample data
df1 = pd.DataFrame({'a': range(10), 'b': range(10, 20)})
df2 = pd.DataFrame({'c': range(20, 30), 'd': range(30, 40)})

# Create a figure
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True)

# Plot dataframes on subplots
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])

# ... (repeat for other DataFrames)

# Show the plot
plt.show()

In the above example:

• The sharex=True argument ensures that all subplots share the same x-axis scale.
• The ax keyword specifies the subplot where each DataFrame will be plotted.
• The axes array contains subplot axes, which can be accessed by indexing (e.g., axes[0, 0] for the top-left subplot).

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