Interpolating Missing values after Resampling

Last update on December 21 2024 09:25:59 (UTC/GMT +8 hours)

Pandas Resampling and Frequency Conversion: Exercise-7 with Solution

Write a Pandas program to interpolate missing values after Resampling.

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='2020-01-01', end='2020-01-05', freq='D')
ts = pd.Series(np.random.randn(len(date_rng)), index=date_rng)

# Introduce some missing values
ts.iloc[2] = np.nan

# Resample the time series to hourly frequency
ts_hourly = ts.resample('H').ffill()

# Interpolate missing values
ts_interpolated = ts_hourly.interpolate()

# Display the interpolated time series
print(ts_interpolated)

Output:

2020-01-01 00:00:00    1.172812
2020-01-01 01:00:00    1.172812
2020-01-01 02:00:00    1.172812
2020-01-01 03:00:00    1.172812
2020-01-01 04:00:00    1.172812
  
2020-01-04 20:00:00   -1.386435
2020-01-04 21:00:00   -1.386435
2020-01-04 22:00:00   -1.386435
2020-01-04 23:00:00   -1.386435
2020-01-05 00:00:00   -0.720658
Freq: H, Length: 97, 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.
• Introduce missing values in the time series.
• Resample the time series data to hourly frequency by forward filling the values.
• Interpolate the missing values in the resampled time series.
• Print the interpolated time series data.

Python Code Editor:

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