How Can I Efficiently Plot Multiple Pandas DataFrames as Subplots in Python?

Plotting Multiple DataFrames as Subplots in Python

When working with multiple Pandas DataFrames, it is often useful to visualize them in the same plot as subplots. This article provides a comprehensive guide to creating subplots for multiple dataframes, allowing for easy comparison and analysis of different data sets.

Problem

To plot multiple dataframes in subplots, you may think of using df.plot() function. However, this approach will result in separate plot images for each dataframe. The challenge lies in finding a way to combine these dataframes into a single plot with multiple subplots, where each dataframe is plotted in its own subplot.

Solution: Creating Subplots Manually

The key to creating subplots for multiple dataframes is to manually create the subplots using matplotlib. This can be done using the subplots() function from matplotlib.pyplot module:

import matplotlib.pyplot as plt

# Create a figure and a set of subplots
fig, axes = plt.subplots(nrows=2, ncols=2)

# Plot each dataframe on a specific subplot
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...

In this example, the subplots() function creates a 2x2 grid of subplots, with axes being an array holding the individual subplot axes. You can access a specific subplot by indexing axes and plot the desired dataframe on each subplot using the ax keyword.

Considerations

• Shared X-Axis: If you want the subplots to share the same x-axis, you can set sharex=True when creating the subplots:

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

• Different Indices: If the dataframes have different indices, you can manually adjust the indices before plotting to ensure they have a common index.

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