Pandas rolling exclude current row

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The pandas rolling function is used to calculate a rolling window of values over a specified number of periods. By default, the rolling window includes the current row in its calculations. However, if you want to exclude the current row from the rolling window calculations, you can use the shift function in Pandas to create a shifted version of the data and then perform the rolling operation on the shifted data.

Let’s assume we have a DataFrame called df with a column called 'value' and we want to calculate the rolling sum over a window of 3 periods excluding the current row.

import pandas as pd

df = pd.DataFrame({'value': [1, 2, 3, 4, 5]})  # Example DataFrame

# Create a shifted version of the 'value' column
shifted_value = df['value'].shift()

# Calculate the rolling sum excluding the current row
rolling_sum_exclude_current = shifted_value.rolling(window=3).sum()

# Print the resulting DataFrame
print(rolling_sum_exclude_current)

The output will be:

0    NaN
1    NaN
2    3.0
3    5.0
4    7.0
Name: value, dtype: float64

As you can see, the rolling sum is calculated excluding the current row. The first two values are NaN because there are not enough previous values to calculate the sum without the current row. From the third row onwards, the rolling sum is calculated correctly.

Note that by using the shift function, the resulting Series has the same length as the original data, preserving the index. If you want to align the rolling window results with the original DataFrame, you can assign the rolling window result to a new column in the DataFrame.

df['rolling_sum_exclude_current'] = rolling_sum_exclude_current
print(df)

The updated DataFrame will be:

   value  rolling_sum_exclude_current
0      1                         NaN
1      2                         NaN
2      3                         3.0
3      4                         5.0
4      5                         7.0

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