Adding Grouped Mode as Additional Column in Original Dataset with Python Pandas

When working with data in pandas, it’s often necessary to perform calculations and operations that involve grouping the data by specific columns. In this article, we’ll explore how to add a new column to an existing dataset that contains the mode of a specific numerical column grouped by two other columns.

Introduction to Grouping

Grouping is a powerful feature in pandas that allows us to aggregate data based on one or more columns. It’s commonly used for tasks such as calculating the mean, median, and standard deviation of a column, as well as performing aggregations like sum and count.

In this article, we’ll focus on how to use grouping to calculate the mode of a numerical column across multiple groups defined by two other columns.

The Challenge

Let’s consider an example dataset similar to the one provided in the Stack Overflow question:

import pandas as pd

df = pd.DataFrame({'Order ID':[555,556,557,558,559,560,561,562,563,564,565,566],
                   'State':["MA","MA","MA","MA","MA","MA","CT","CT","CT","CT","CT","CT"],
                   'County':["Essex","Essex","Essex","Worcester","Worcester","Worcester","Bristol","Bristol","Bristol","Hartford","Hartford","Hartford"],
                   'AP':[50,50,75,100,100,125,150,150,175,200,200,225]})

We want to add a new column that contains the mode of the AP column grouped by both State and County.

Solution 1: Using `GroupBy.transform`

One way to achieve this is by using the GroupBy.transform method. This method allows us to perform an operation on each group, without having to iterate over all groups individually.

Here’s how we can add a new column with the mode of AP grouped by State and County:

df['Mode'] = (df.groupby(['State', 'County']).AP
             .transform(lambda x: x.value_counts().index[0]))

This code creates a new column named Mode in the original DataFrame. The groupby function groups the data by both State and County, while the AP column is passed to the transform method. Inside this method, we use value_counts to count the occurrences of each value in the AP column, and then select the index (i.e., the mode) from the resulting Series.

Solution 2: Using `Series.mode`

Alternatively, we can use the mode function on a pandas Series. This approach provides more flexibility, as it allows us to calculate the mode for each group individually.

Here’s how we can add a new column with the mode of AP grouped by State and County, using the mode function:

df['Mode'] = df.groupby(['State', 'County']).AP.transform(lambda x: x.mode().iat[0])

This code works similarly to the previous example, but uses the mode function instead of value_counts. The iat[0] index selects the first element (i.e., the mode) from the resulting Series.

Conclusion

In this article, we explored two ways to add a new column with the mode of AP grouped by State and County, using pandas. Both approaches provide useful insights into the distribution of values in our dataset and demonstrate the flexibility and power of grouping operations in pandas.

By understanding how to perform these calculations, you can extend your data analysis skills to handle more complex tasks involving group-by operations.