How to apply functions in a Group in a Pandas DataFrame?

In this article, let’s see how to apply functions in a group in a Pandas Dataframe. Steps to be followed for performing this task are – 

• Import the necessary libraries.
• Set up the data as a Pandas DataFrame.
• Use apply function to find different statistical measures like Rolling Mean, Average, Sum, Maximum, and Minimum. You can use the lambda function for this.

Below is the implementation-

Let’s create the dataframe.

Python3

#import libraries
import pandas as pd

# set up the data
data_dict = {"Student House": ["Lavender", "Lavender", "Lavender",
                               "Lavender", "Daisy", "Daisy",
                               "Daisy", "Daisy", "Daffodils",
                               "Daffodils", "Daffodils", "Daffodils"],

             "Points": [10, 4, 6, 7, 3, 8, 9, 10, 4, 5, 6, 7]}

data_df = pd.DataFrame(data_dict)
print("Dataframe : ")
data_df

Output:

Example 1:

Python3

# finding rolling mean
rolling_mean = data_df.groupby("Student House")["Points"].apply(
    lambda x: x.rolling(center=False, window=2).mean())

print("Rolling Mean:")
print(rolling_mean)

Output:

Example 2:

Python3

# finding mean
mean = data_df.groupby("Student House")["Points"].apply(
  lambda x: x.mean())

print("Mean:")
print(mean)

Output:

Example 3:

Python3

# finding sum
sum = data_df.groupby("Student House")["Points"].apply(
  lambda x: x.sum())

print("Sum:")
print(sum)

Output: