Downsampling Time Series Data from Minute to Hourly Frequency

Last update on December 21 2024 09:25:57 (UTC/GMT +8 hours)

Pandas Resampling and Frequency Conversion: Exercise-5 with Solution

Write a Pandas program to downsample Time Series data from Minute to Hourly Frequency.

Sample Solution:

Python Code :

# Import necessary libraries
import pandas as pd
import numpy as np
# Create a time series data with minute frequency
date_rng = pd.date_range(start='2023-01-01', end='2023-01-01 23:59', freq='T')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)
# Downsample the time series to hourly frequency
ts_hourly = ts.resample('H').sum()

# Display the downsampled time series
print(ts_hourly)

Output:

2023-01-01 00:00:00    -9.638825
2023-01-01 01:00:00    12.406546
2023-01-01 02:00:00    -2.037772
2023-01-01 03:00:00    -5.785124
2023-01-01 04:00:00    -0.468874
2023-01-01 05:00:00    10.961871
2023-01-01 06:00:00    14.265901
2023-01-01 07:00:00    -5.936786
2023-01-01 08:00:00    -5.200742
2023-01-01 09:00:00    -0.832787
2023-01-01 10:00:00    -4.936277
2023-01-01 11:00:00     1.323612
2023-01-01 12:00:00     3.284874
2023-01-01 13:00:00     7.342899
2023-01-01 14:00:00    -3.245981
2023-01-01 15:00:00    -7.716751
2023-01-01 16:00:00    -5.579430
2023-01-01 17:00:00     1.763545
2023-01-01 18:00:00     3.903313
2023-01-01 19:00:00    -0.630663
2023-01-01 20:00:00     5.425722
2023-01-01 21:00:00    -2.147499
2023-01-01 22:00:00     3.667550
2023-01-01 23:00:00     2.219400
Freq: H, dtype: float64

Explanation:

Import Pandas and NumPy libraries.
Create a date range with minute frequency.
Generate a random time series data with the created date range.
Downsample the time series data to hourly frequency by summing the values.
Print the downsampled time series data.

Python Code Editor:

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