Calculating Percentage change in Resampled data

Last update on April 11 2025 12:56:49 (UTC/GMT +8 hours)

15. Calculate Percentage Change in Resampled Data

Write a Pandas program to calculate percentage change in Resampled data.

Sample Solution:

Python Code :

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

# Create a time series data with daily frequency
date_rng = pd.date_range(start='2021-01-01', end='2021-01-10', freq='D')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)

# Resample the time series to daily frequency
ts_daily = ts.resample('D').mean()

# Calculate the percentage change in the resampled data
ts_pct_change = ts_daily.pct_change()

# Display the percentage change in the resampled time series
print(ts_pct_change)

Output:

2021-01-01         NaN
2021-01-02   -4.219191
2021-01-03   -0.483793
2021-01-04    1.751098
2021-01-05    0.088296
2021-01-06   -2.842570
2021-01-07   -1.521794
2021-01-08   -1.391348
2021-01-09   -4.379459
2021-01-10   -1.437542
Freq: D, dtype: float64

Explanation:

Import Pandas and NumPy libraries.
Create a date range with daily frequency.
Generate a random time series data with the created date range.
Resample the time series data to daily frequency by calculating the mean.
Calculate the percentage change in the resampled data.
Print the percentage change in the resampled time series data.

For more Practice: Solve these Related Problems:

Write a Pandas program to resample a time series to monthly frequency and calculate the month-over-month percentage change.
Write a Pandas program to compute the percentage change in resampled quarterly data and then visualize the trend using a line plot.
Write a Pandas program to calculate the percentage change of yearly resampled data and compare it with the absolute differences.
Write a Pandas program to resample a financial time series to daily frequency and then compute the rolling percentage change over a specified window.

Python Code Editor:

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