Resampling a Pandas DataFrame While Forward Filling (ffill) the Values

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is resampling, which allows us to change the frequency of our data. However, when we resample, we often need to handle missing values. In this article, we will explore how to resample a Pandas DataFrame while forward filling (ffill) the values.

Understanding Resampling

Resampling in Pandas involves changing the frequency of your data. For example, if you have a minute-level dataset and you want to aggregate it to every 2 minutes, you would resample on a frequency of ‘2T’. Similarly, if you want to change the sampling rate from every 5 seconds to every 10 seconds, you would resample on a frequency of ‘2S’.

Resampling with Forward Filling

When we resample our data, we often encounter missing values. These missing values can be handled in different ways depending on our specific requirements. In this article, we will explore how to forward fill the values when resampling.

Forward Filling (ffill)

Forward filling is a method of handling missing values where the next available value is used to fill in the gap. For example, if we have a dataset with a missing value at index 2 and the next available value is at index 3, we would use the value at index 3 to fill in the missing value at index 2.

The ffill() method in Pandas can be used to forward fill the values. However, it needs to be applied after resampling. Here’s an example:

import pandas as pd

# Create a sample dataset
data = {'Time': ['2019-01-01 11:48:50', '2019-01-01 11:48:52', '2019-01-01 11:48:53', '2019-01-01 11:48:54'],
        'Temperature': [23.798, 23.832, None, 23.817]}
df = pd.DataFrame(data)

# Resample on a frequency of '2S'
resampled_df = df.resample('2S').mean()

# Forward fill the values
resampled_filled_df = resampled_df.ffill()

In this example, we first create a sample dataset with missing values. We then resample the data on a frequency of ‘2S’ and calculate the mean of each group. Finally, we forward fill the values using the ffill() method.

Reindexing and Forward Filling

Another approach to handle missing values when resampling is to reindex the data before forward filling. Here’s an example:

import pandas as pd

# Create a sample dataset
data = {'Time': ['2019-01-01 11:48:50', '2019-01-01 11:48:52', '2019-01-01 11:48:53', '2019-01-01 11:48:54'],
        'Temperature': [23.798, 23.832, None, 23.817]}
df = pd.DataFrame(data)

# Resample on a frequency of '2S'
resampled_idx = df.resample('2S').asfreq().index

# Reindex the data with the new index
reindexed_df = df.reindex(df.index.union(resampled_idx))

# Forward fill the values
filled_df = reindexed_df.bfill()

# Resample on a frequency of '2S'
resampled_filled_df = filled_df.resample('2S').first()

In this example, we first create a sample dataset with missing values. We then resample the data on a frequency of ‘2S’ and get the index of each group. We reindex the original data with the new index and forward fill the values using the bfill() method. Finally, we resample the filled data on a frequency of ‘2S’ and take the first value of each group.

Conclusion

Resampling a Pandas DataFrame while handling missing values can be achieved through various methods. We have explored three approaches: forward filling using the ffill() method, reindexing and forward filling using the bfill() method, and interpolating the data using the interpolate() method. By understanding these different methods, we can choose the best approach for our specific use case.

References

Last modified on 2023-09-13