Handling Missing data before Resampling
13. Handle Missing Data Before Resampling
Write a Pandas program to handle missing data before Resampling.
Sample Solution:
Python Code :
Output:
2023-01-01 0.672487 2023-01-02 -0.014764 2023-01-03 0.043870 2023-01-04 -0.190643 2023-01-05 -0.639927 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.
- Introduce missing values in the time series.
- Fill missing values using the forward fill method.
- 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 identify and fill missing values in a time series before performing resampling.
- Write a Pandas program to impute missing data using forward-fill before downsampling a time series.
- Write a Pandas program to drop rows with missing timestamps before resampling and compare the size of the original dataset.
- Write a Pandas program to apply a custom missing data imputation strategy prior to resampling a time series dataset.
Go to:
Previous: Calculating Rolling Mean of Resampled data.
Next: Creating Custom Resampling periods.
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.