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

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

This recipe helps you apply functions in a Group in a Pandas DataFrame

This data science python source code does the following: 1. Creates groups of selected variable for data preprocessing. 2.Creats your own data dictionary and converts them into Pandas dataframe format. 3. Applying groupby function to selected columns.

In [1]:

```
## How to apply functions in a Group in a Pandas DataFrame
def Kickstarter_Example_69():
print()
print(format('How to apply functions in a Group in a Pandas DataFrame','*^82'))
import warnings
warnings.filterwarnings("ignore")
# load libraries
import pandas as pd
# Create an example dataframe
data = {'EmployeeGroup': ['A','A','A','A','A','A','B','B','B','B','B','C','C','C','C','C'],
'Points': [10,40,50,70,50,50,60,10,40,50,60,70,40,60,40,60]}
df = pd.DataFrame(data)
print('\nThe Original DataFrame'); print(df)
# Apply A Function (Rolling Mean) To The DataFrame, By Group
print('\nRolling Mean:'); print(df.groupby('EmployeeGroup')['Points'].apply(lambda x:x.rolling(center=False,window=2).mean()))
# Apply A Function (Mean) To The DataFrame, By Group
print('\nAverage:'); print(df.groupby('EmployeeGroup')['Points'].apply(lambda x:x.mean()))
# Apply A Function (Sum) To The DataFrame, By Group
print('\nSum:'); print(df.groupby('EmployeeGroup')['Points'].apply(lambda x:x.sum()))
# Apply A Function (Max) To The DataFrame, By Group
print('\nMaximum:'); print(df.groupby('EmployeeGroup')['Points'].apply(lambda x:x.max()))
# Apply A Function (Min) To The DataFrame, By Group
print('\nMinimum:'); print(df.groupby('EmployeeGroup')['Points'].apply(lambda x:x.min()))
Kickstarter_Example_69()
```

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