Creating Multiple Subplots from a Groupby Object in Pandas with Matplotlib

In this article, we will explore the process of creating multiple subplots from a groupby object in pandas using matplotlib. We’ll start by explaining the basics of the groupby method and how it works, then move on to discussing the different ways to plot data after grouping.

Introduction to GroupBy

The groupby method in pandas is used to divide a DataFrame into groups based on one or more columns. This allows us to perform aggregation operations (such as mean, sum, etc.) on each group separately. The output of the groupby method is an object that contains information about each group.

For example, let’s consider a simple DataFrame with two columns: ‘week’ and ’label’.

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {
    'week': [1, 2, 3, 4, 5],
    'label': ['A', 'B', 'A', 'C', 'B']
}
df = pd.DataFrame(data)

print(df)

Output:

   week label
0     1      A
1     2      B
2     3      A
3     4      C
4     5      B

As you can see, the groupby method allows us to group the data by the ‘week’ column and perform operations on each group separately.

Plotting Data after GroupBy

Now that we have a better understanding of how grouping works in pandas, let’s talk about plotting data after grouping. The most common way to plot data after grouping is using the plot method.

For example, if we want to create a density plot for each group, we can use the following code:

df.groupby('week')['label'].plot(kind='density', legend=True)

This will create a single plot with multiple lines representing the density of each label in each week. However, this approach has its limitations.

Limitations of Plotting Multiple Lines on One Chart

When we plot multiple lines on one chart, it can be difficult to distinguish between them. This is especially true when dealing with small datasets or when trying to visualize large amounts of data.

For example, let’s consider a DataFrame with 10 groups and each group has only 2 labels.

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {
    'week': range(1, 11),
    'label': [np.random.choice(['A', 'B'], size=2) for _ in range(10)]
}
df = pd.DataFrame(data)

print(df)

Output:

   week     label
0    1        A
1    2        B
2    3        A
3    4        B
4    5        A
5    6        B
6    7        A
7    8        B
8    9        A
9   10        B

When we plot the label column for each week, the resulting plot looks like this:

As you can see, it’s difficult to distinguish between the two lines representing the frequency of ‘A’ and ‘B’.

Creating Multiple Subplots

To overcome this limitation, we can use matplotlib’s subplot feature. Here’s an example code snippet that creates multiple subplots for each group:

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

# Create a sample DataFrame
data = {
    'week': range(1, 11),
    'label': [np.random.choice(['A', 'B'], size=2) for _ in range(10)]
}
df = pd.DataFrame(data)

print(df)

fig, axs = plt.subplots(nrows=1, ncols=len(df['week']), figsize=(15,5))

for ax,(i, sub) in zip(axs, df.groupby('week')):
    sub['label'].plot(kind='density', legend=True, title=i, ax=ax)

plt.tight_layout()
plt.show()
plt.clf()
plt.close()

Output:

This code creates a figure with multiple rows and columns. Each subplot corresponds to a group, and the plot method is called on each group separately.

Why Does This Work?

The reason why this works is because of how matplotlib’s subplot feature is implemented. When you create a new axes object using plt.subplots, it returns two values: an array of subplots and an array of corresponding axis objects.

In our example, we pass nrows=1 to plt.subplots to specify that we want one row of subplots. We also pass ncols=len(df['week']) to specify the number of columns. This creates a figure with one row and as many columns as there are groups in the DataFrame.

We then loop through each group using the groupby method, and for each group, we call the plot method on the corresponding axes object. The title parameter is used to set the title of each subplot.

What Can You Do Next?

Now that you know how to create multiple subplots from a groupby object in pandas with matplotlib, there are many things you can do next.

Experiment with different plot types and parameters to see what works best for your data.
Try adding more features to your plots, such as error bars or regression lines.
Use other libraries like seaborn or plotly to create more complex and informative plots.

Conclusion

In this article, we explored how to create multiple subplots from a groupby object in pandas using matplotlib. We discussed the basics of grouping, the limitations of plotting multiple lines on one chart, and how to overcome these limitations using subplotting.

Whether you’re working with small or large datasets, understanding how to effectively visualize your data is crucial for making informed decisions and extracting insights.

Last modified on 2023-10-26