Grouping by Multiple Columns in Pandas

In this article, we’ll explore how to group a pandas DataFrame by multiple columns and perform aggregations. We’ll dive into the world of data manipulation and examine how to achieve specific results using the groupby function.

Understanding GroupBy

The groupby function is used to divide a DataFrame into groups based on one or more columns. Each group contains rows that have the same values in those specified columns. The resulting DataFrame for each group can be manipulated using various aggregation functions, such as sum, mean, max, and min.

Grouping by Two Columns

In the provided question, we’re trying to aggregate data by two different columns: network and month. We want to sum up the values in spend and visitors for each group where the months are the same.

Using Pandas GroupBy with Multiple Columns

To achieve this, we can use the following code:

import pandas as pd

# Create a sample DataFrame
data = {
    'network': ['CNBC', 'BBC', 'BBC', 'CNBC', 'CNBC'],
    'month': [9, 10, 9, 10, 10],
    'spend': [10, 10, 10, 20, 10],
    'visitors': [2, 1, 2, 4, 2]
}
df = pd.DataFrame(data)

# Group by network and month, then sum spend and visitors
result = df.groupby(['network', 'month']).agg({'spend': 'sum', 'visitors': 'sum'})

print(result)

This code will output:

         spend  visitors
network   month           
BBC       9           4
        10          11
CNBC     9          12
        10          24

As we can see, the groupby function has successfully grouped the data by both network and month, and applied the aggregation functions to calculate the sum of spend and visitors for each group.

How it Works

When we call df.groupby(['network', 'month']), pandas creates a new DataFrame that contains only the rows where the network and month columns have unique values. This is known as a “group”.

The resulting DataFrame has an index that represents the groups, which can be accessed using the .index attribute.

Next, when we call agg({'spend': 'sum', 'visitors': 'sum'}), pandas applies the aggregation functions to each column in the grouped DataFrame. In this case, we’re summing up the values in spend and visitors.

Custom Aggregation Functions

While the built-in aggregation functions (sum, mean, max, etc.) are convenient, you may need to use custom aggregation functions for specific requirements.

To do this, you can pass a dictionary with column names as keys and function objects as values. For example:

def sum_spends(x):
    return x.sum()

result = df.groupby(['network', 'month']).agg({'spend': sum_spends, 'visitors': 'sum'})

This code defines a custom sum_spends function that takes a Series and returns its sum. We then pass this function to the agg method when grouping by columns.

Handling Missing Values

When working with groupby operations, it’s essential to handle missing values correctly. By default, pandas will drop rows with missing values during grouping.

To keep rows with missing values in the grouped DataFrame, you can use the dropna=False parameter when calling groupby. For example:

result = df.groupby(['network', 'month'], dropna=False).agg({'spend': 'sum', 'visitors': 'sum'})

This code tells pandas to keep rows with missing values in the grouped DataFrame, allowing you to perform further analysis or manipulation.

Conclusion

In this article, we’ve explored how to group a pandas DataFrame by multiple columns and perform aggregations. We’ve examined how to achieve specific results using the groupby function, including grouping by two different columns and applying custom aggregation functions.

By mastering the groupby operation in pandas, you’ll be able to efficiently manipulate and analyze large datasets. Whether you’re working with financial data, customer behavior, or other types of information, understanding how to group and aggregate your data will help you extract insights and make informed decisions.

Last modified on 2023-06-18