How to Average Over Next n Values Within Groups

In this blog post, we’ll explore how to calculate the average of next n values in a series for each group in a Pandas DataFrame. This can be achieved using the rolling function in combination with grouping and aggregation.

Background

When working with time-series data, it’s common to want to calculate the average value of previous n observations for each unit or group in the data. For example, if we have a series of daily temperature readings, we might want to calculate the average temperature over the next 5 days for each day.

However, simply using the expanding function can lead to inefficient computations and incorrect results when n is greater than 1. This is because expanding uses a single scan of the entire dataset to compute the moving averages, which can be slow and inaccurate if the window size is large.

Instead, we’ll use the rolling function in combination with grouping and aggregation to achieve this result efficiently and accurately.

Code

import pandas as pd

# Create a sample DataFrame
ex = {'id': {0: 12,
             1: 7745,
             2: 14190,
             3: 12,
             4: 7745,
             5: 14190,
             6: 12,
             7: 7745,
             8: 14190,
             9: 12,
             10: 7745,
             11: 14190,
             12: 12,
             13: 7745,
             14: 14190,
             15: 12,
             16: 7745,
             17: 14190,
             18: 12,
             19: 7745,
             20: 14190,
             21: 12,
             22: 7745,
             23: 14190,
             24: 12,
             25: 7745,
             26: 14190,
             27: 12,
             28: 7745,
             29: 14190},
         'id2': {0: 19, 1: 18, 2: 17, 3: 16, 4: 15, 5: 14, 6: 13, 7: 12, 8: 11, 9: 10, 10: 9, 11: 8, 12: 7, 13: 6, 14: 5, 15: 4, 16: 3, 17: 2, 18: 1, 19: 0, 20: 19, 21: 18, 22: 17, 23: 16, 24: 15, 25: 14, 26: 13, 27: 12, 28: 11, 29: 10},
         'var1': {0: 63.036478, 1: 59.985110, 2: 51.892470, 3: 63.036478,
                  4: 62.959497, 5: 62.883233, 6: 62.807712, 7: 62.732956,
                  8: 62.658992, 9: 62.510738, 10: 62.364880, 11: 62.221519,
                  12: 62.080750, 13: 61.942674, 14: 61.807387, 15: 61.674987,
                  16: 61.545573, 17: 61.419242, 18: 61.296093, 19: 61.176224,
                  20: 61.059732, 21: 60.946716, 22: 60.837274, 23: 59.985110,
                  24: 59.952135, 25: 59.918906, 26: 59.885277, 27: 59.851107,
                  28: 59.816252, 29: 59.746476}}
ex = pd.DataFrame(ex).set_index(['id', 'id2'])

# Calculate the average of next n values for each group
result = ex.sort_index(ascending=False).groupby("id")["var1"].rolling(6, min_periods=1).mean().reset_index(0, drop=True)

print(result)

Output

The rolling function is used to compute the moving averages over a window of size 6. The min_periods=1 parameter ensures that all values in the window are included in the computation, even if the window is not full.

In this case, we’re using a window size of 6 because we want to calculate the average of next 5 values for each group. The result is a new DataFrame with the same index as the original DataFrame, but with an additional column containing the moving averages.

Note that the rolling function can be used with different window sizes and other parameters to achieve different results. Experimenting with different settings can help you find the optimal configuration for your specific use case.

Last modified on 2023-07-14