Creating Custom Resampling periods

Last update on May 03 2025 13:02:55 (UTC/GMT +8 hours)

14. Create Custom Resampling Periods

Write a Pandas program to create custom Resampling periods.

Sample Solution:

Python Code :

# Import necessary libraries
import pandas as pd
import numpy as np

# Create a time series data with hourly frequency
date_rng = pd.date_range(start='2023-01-01', end='2023-01-10', freq='H')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)

# Define a custom function to resample using Grouper
def custom_resample(ts, freq):
    return ts.groupby(pd.Grouper(freq=freq)).mean()

# Resample the time series to a custom period of 2 days
ts_custom = custom_resample(ts, '2D')

# Display the resampled time series
print(ts_custom)

Output:

2023-01-01    0.364041
2023-01-03   -0.162548
2023-01-05   -0.121916
2023-01-07    0.009858
2023-01-09    0.269586
Freq: 2D, dtype: float64

Explanation:

Import Pandas and NumPy libraries.
Create a date range with hourly frequency.
Generate a random time series data with the created date range.
Define a custom function to resample the time series data using pd.Grouper().
Use the custom function to resample the time series data to a period of 2 days by calculating the mean.
Print the resampled time series data.

For more Practice: Solve these Related Problems:

Write a Pandas program to create custom resampling periods (e.g., 10-day periods) and aggregate data accordingly.
Write a Pandas program to resample a time series into non-standard periods using a custom date offset.
Write a Pandas program to define custom resampling intervals and compute the sum for each interval in the dataset.
Write a Pandas program to create custom resampling periods that overlap and calculate the moving average for each period.

Go to:

Previous: Handling Missing data before Resampling.
Next: Calculating Percentage change in Resampled data.

Python Code Editor:

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