Resampling Time Series data to daily Frequency

Last update on April 11 2025 12:57:26 (UTC/GMT +8 hours)

1. Resample Time Series Data to Daily Frequency

Write a Pandas program to resample time series data to daily frequency.

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)

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

# Display the resampled time series
print(ts_daily)

Output:

2023-01-01    0.079969
2023-01-02   -0.174736
2023-01-03    0.052439
2023-01-04    0.097063
2023-01-05    0.437323
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.
• Resample the time series data to daily frequency by calculating the mean.
• Print the resampled time series data.

For more Practice: Solve these Related Problems:

• Write a Pandas program to resample a minute-level time series to daily frequency using the mean and verify the date index format.
• Write a Pandas program to convert an irregular time series to a regular daily frequency, filling missing dates with NaN.
• Write a Pandas program to resample a time series to daily frequency and forward-fill missing values after aggregation.
• Write a Pandas program to resample a time series dataset to daily frequency and then compute the daily range (max-min) for each day.

Python Code Editor:

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