How to apply function to data frame group based on whether the row meets a condition?

This blog post will show you how to apply a function to a data frame group based on whether the row meets a condition. This can be useful for tasks such as summarizing data or finding outliers.

1. Load the data

import pandas as pd

df = pd.DataFrame({
    "name": ["John", "Mary", "Bob", "Alice", "John"],
    "age": [20, 25, 30, 35, 40],
    "city": ["New York", "London", "Paris", "Berlin", "Rome"]
})

2. Create a condition

condition = df["age"] > 30

3. Apply the function to the group

df.groupby("city")[condition].mean()

This will apply the mean() function to the age column of the data frame, but only for the rows that meet the condition. The result will be a new data frame with the mean age for each city, for people who are over 30 years old.

4. Customize the function

def my_function(group):
    return group.mean() - group.min()

df.groupby("city")[condition].apply(my_function)

You can also customize the function that is applied to the group. In this example, we are using a function that calculates the difference between the mean and the minimum value of the age column.

5. Handle missing values

df["age"].fillna(df["age"].mean(), inplace=True)

df.groupby("city")[condition].apply(my_function)

If your data frame contains missing values, you will need to handle them before you can apply a function to the group. In this example, we are using the fillna() method to replace the missing values with the mean age.

Conclusion

This blog post has shown you how to apply a function to a data frame group based on whether the row meets a condition. This can be useful for tasks such as summarizing data or finding outliers.