Resampling Time Series data with different Aggregation methods

Pandas Resampling and Frequency Conversion: Exercise-3 with Solution

Write a Pandas program to resample Time Series Data with different Aggregation methods.

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 using different aggregation methods
ts_daily_mean = ts.resample('D').mean()
ts_daily_sum = ts.resample('D').sum()
ts_daily_max = ts.resample('D').max()

# Display the resampled time series
print("Daily Mean:\n", ts_daily_mean)
print("Daily Sum:\n", ts_daily_sum)
print("Daily Max:\n", ts_daily_max)

Output:

Daily Mean:
 2023-01-01    0.224578
2023-01-02   -0.077165
2023-01-03    0.052121
2023-01-04   -0.005321
2023-01-05   -0.192389
Freq: D, dtype: float64
Daily Sum:
 2023-01-01    5.389876
2023-01-02   -1.851952
2023-01-03    1.250895
2023-01-04   -0.127700
2023-01-05   -0.192389
Freq: D, dtype: float64
Daily Max:
 2023-01-01    3.698549
2023-01-02    1.527495
2023-01-03    3.101162
2023-01-04    1.254722
2023-01-05   -0.192389
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 using mean, sum, and max aggregation methods.
• Print the resampled time series data for each aggregation method.

Python Code Editor:

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