Sometimes, the operation that you want to perform is more complicated than mean or count. In those cases, you can use the apply method and lambda functions, just like we did for individual column operations. Note that the input to our lambda function will always be a list of values.

A great example of this is calculating percentiles. Suppose we have a DataFrame of employee information called df that has the following columns:

• id: the employee’s id number
• name: the employee’s name
• wage: the employee’s hourly wage
• category: the type of work that the employee does

Our data might look something like this:

If we want to calculate the 75th percentile (i.e., the point at which 75% of employees have a lower wage and 25% have a higher wage) for each category, we can use the following combination of apply and a lambda function:

# np.percentile can calculate any percentile over an array of values
high_earners = df.groupby('category').wage
    .apply(lambda x: np.percentile(x, 75))
    .reset_index()

The output, high_earners might look like this: