Resampling Time Series data with Custom functions

Last update on April 11 2025 12:56:54 (UTC/GMT +8 hours)

8. Resample Time Series Data with Custom Functions

Write a Pandas program to resample Time Series data with custom functions.

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='2023-01-05', freq='H')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)

# Define a custom function for resampling
def custom_resample(array):
    return np.sum(array) / len(array)

# Resample the time series to daily frequency using the custom function
ts_custom = ts.resample('D').apply(custom_resample)

# Display the resampled time series
print(ts_custom)

Output:

2023-01-01    0.147705
2023-01-02   -0.022656
2023-01-03   -0.131713
2023-01-04    0.053327
2023-01-05    0.562042
Freq: D, 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 for resampling.
• Resample the time series data to daily frequency using the custom function.
• Print the resampled time series data.

For more Practice: Solve these Related Problems:

• Write a Pandas program to resample a time series to daily frequency using a custom function that computes the range of values.
• Write a Pandas program to apply a custom aggregation during resampling that calculates the interquartile range for each period.
• Write a Pandas program to resample data and apply a lambda function that computes the weighted average for each period.
• Write a Pandas program to resample a dataset with a custom function that returns both the mean and median for each resampled period.

Python Code Editor:

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