Resampling Time Series data to Quarterly Frequency

Last update on April 11 2025 12:56:53 (UTC/GMT +8 hours)

9. Resample Time Series Data to Quarterly Frequency

Write a Pandas program to resample Time Series data to quarterly frequency.

Sample Solution:

Python Code :

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

# Create a time series data with monthly frequency
date_rng = pd.date_range(start='2019-01-01', end='2019-12-01', freq='M')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)

# Resample the time series to quarterly frequency
ts_quarterly = ts.resample('Q').mean()

# Display the resampled time series
print(ts_quarterly)

Output:

2019-03-31    0.045935
2019-06-30   -1.032537
2019-09-30    0.062271
2019-12-31    1.227764
Freq: Q-DEC, dtype: float64

Explanation:

• Import Pandas and NumPy libraries.
• Create a date range with monthly frequency.
• Generate a random time series data with the created date range.
• Resample the time series data to quarterly frequency by calculating the mean.
• Print the resampled time series data.

For more Practice: Solve these Related Problems:

• Write a Pandas program to resample monthly data to quarterly frequency and compute the sum for each quarter.
• Write a Pandas program to resample daily time series data to quarterly frequency using the last available observation.
• Write a Pandas program to resample time series data to quarterly frequency and calculate the average quarterly growth rate.
• Write a Pandas program to resample a financial dataset to quarterly frequency and compute the quarterly volatility.

Python Code Editor:

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