Скачать или смотреть How to Backfill Values Based on a Category in Pandas DataFrames

Learn how to effectively backfill null values in a Pandas DataFrame by category, ensuring accurate cumulative sums for time-series data.
---
This video is based on the question https://stackoverflow.com/q/74725619/ asked by the user 'jmoore00' ( https://stackoverflow.com/u/10120211/ ) and on the answer https://stackoverflow.com/a/74725716/ provided by the user 'Panda Kim' ( https://stackoverflow.com/u/20430449/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to backfill values based on a category pandas

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Backfill Values Based on a Category in Pandas DataFrames

Handling missing values in a dataset is a common task in data analysis. One situation that often arises, especially when working with time-series data, is the need to backfill null values based on specific categories. In this guide, we'll cover a scenario where we have total vaccinations over time from different vaccine manufacturers, and we need to ensure that we correctly backfill any missing values in our dataset.

The Problem

Imagine you have collected vaccination data from various manufacturers, recorded their cumulative total vaccinations over specific dates, but there are gaps in your data. Here’s an example of how your dataset might look:

Vaccine ManufacturerDateTotal VaccinationsPfizer10/10/21100Pfizer10/11/21NaNPfizer10/12/21NaNPfizer10/13/21130Pfizer10/14/21NaNModerna10/10/21220Moderna10/11/21NaNModerna10/12/21280Moderna10/13/21NaNAs you can see, there are a few null values in the "Total Vaccinations" column, which represent missing cumulative totals. What we want to achieve is to backfill these nulls so that every date has the correct total vaccinations based on the last available data.

The Solution

To solve this, we will use a combination of forward fill (ffill) and backward fill (bfill) methods provided by Pandas. Let's break down the steps involved in this process.

Step 1: Setting Up Your DataFrame

First, let’s create a DataFrame that resembles the dataset above:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Applying the Backfill Method

Next, we will apply the groupby() method along with ffill() and bfill() to fill the missing values. Here’s the complete code to achieve that:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Reviewing the Output

After executing the above code, your DataFrame will look like this:

Vaccine ManufacturerDateTotal VaccinationsPfizer10/10/21100Pfizer10/11/21100Pfizer10/12/21100Pfizer10/13/21130Pfizer10/14/21130Moderna10/10/21220Moderna10/11/21220Moderna10/12/21280Moderna10/13/21280Now, all missing values have been backfilled appropriately, maintaining the integrity of the cumulative totals for each vaccine manufacturer.

Conclusion

In conclusion, backfilling values based on a category in a Pandas DataFrame is a straightforward process using the ffill() and bfill() functions, combined with the groupby() method. This approach ensures that your time-series data accurately reflects cumulative totals, even when some dates have missing values.

When dealing with missing data, it's essential to consider not only how to fill these gaps but also to maintain the logical flow of your dataset. By following the steps outlined above, you can ensure your vaccination data is accurate and reliable!