Pandas average every n rows

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Pandas: Average every n rows

Pandas is a popular Python library used for data manipulation and analysis. It provides a convenient way to perform operations on tabular data, including calculating averages.

To average every n rows in a pandas DataFrame, you can use the rolling() method in combination with the mean() method.

Example:

Suppose we have a DataFrame with the following data:

Index Value
0 10
1 20
2 30
3 40

We want to average every 2 rows of the DataFrame. Here’s how you can do it:

import pandas as pd
  
  # Create the DataFrame
  data = {'Value': [10, 20, 30, 40]}
  df = pd.DataFrame(data)
  
  # Average every 2 rows
  averaged_df = df['Value'].rolling(2).mean().iloc[1::2]
  
  print(averaged_df)

The output will be:

1    15.0
  3    35.0
  Name: Value, dtype: float64

The rolling(2) method creates a rolling window of size 2, which means it considers 2 consecutive rows at a time. The mean() method calculates the average of each window. Finally, iloc[1::2] selects every second row starting from the second row, since the first row doesn’t have a previous row to calculate the average. This ensures that we get the average every 2 rows.

You can adjust the window size as per your requirement. For example, if you want to average every 3 rows, use rolling(3).

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