Plotting Groupby Objects in Pandas

Introduction

When working with dataframes, it’s common to need to perform groupby operations and visualize the results. In this article, we’ll explore how to plot the size of each group in a groupby object using pandas.

Understanding Groupby Objects

A groupby object is an iterator that allows us to group a dataframe by one or more columns and apply aggregate functions to each group. The groupby function returns a DataFrameGroupBy object, which contains methods for performing different types of aggregations on the grouped data.

For example, let’s consider a simple dataframe:

import pandas as pd

data = {
    'branch': [0, 0, 1, 1, 1],
    'gender': ['male', 'male', 'female', 'female', 'male'],
    'listener_id': [1, 3, 2, 4, 1]
}

df = pd.DataFrame(data)

This dataframe has three columns: branch, gender, and listener_id. We can perform a groupby operation on these columns to count the number of transactions per branch and gender.

Performing Groupby Operations

To perform a groupby operation, we use the groupby function. In this case, we want to group by both branch and gender, so we pass those two columns as arguments:

grouped = df.groupby(['branch', 'gender']).agg(np.size)['listener_id']

The resulting grouped object contains the count of transactions for each branch-gender combination.

Plotting Groupby Objects

Now that we have a groupby object, we can plot it to visualize the results. However, simply plotting a grouped object using df.groupby() doesn’t give us the desired result. We need to use the groupby function again, but this time with a different aggregation method.

Unstacking and Plotting

To plot the size of each group in a groupby object, we can use the unstack method to reshape the grouped data into a long format. Then, we can use the plot method to create a bar chart.

grouped_unstacked = df.groupby(['branch', 'gender'])['listener_id'].agg(np.size).unstack()

The unstack method transforms the grouped object into a new dataframe with separate columns for each branch-gender combination.

Creating the Bar Chart

Now that we have the reshaped data, we can create the bar chart using the plot method.

grouped_unstacked.plot(kind='bar')

This will create a horizontal bar chart with the branch on the x-axis and the gender on the y-axis.

Customizing the Plot

We can customize the plot by adding labels, titles, and other visual elements. For example:

import matplotlib.pyplot as plt

plt.title('Number of Transactions per Branch and Gender')
plt.xlabel('Branch')
plt.ylabel('Number of Transactions')

This will add a title, x-label, and y-label to the plot.

Conclusion

In this article, we explored how to plot the size of each group in a groupby object using pandas. We discussed the different types of groupby operations and how to use the unstack method to reshape the data into a long format. Finally, we created a bar chart using the plot method and customized it with labels and other visual elements.

Example Use Cases

Here are some example use cases for plotting groupby objects:

• Analyzing customer demographics: You can plot the number of customers per region, age range, or purchase amount to gain insights into your target audience.
• Visualizing transaction data: You can plot the number of transactions per product category, geographic location, or time period to identify trends and patterns in your data.

Advice

When working with groupby objects, make sure to:

• Use the groupby function correctly to select the columns you want to group by.
• Choose the correct aggregation method for your use case (e.g., count, mean, sum).
• Use the unstack method to reshape the data into a long format if needed.
• Customize the plot with labels, titles, and other visual elements to make it more informative and engaging.

Last modified on 2023-07-11