Скачать или смотреть How to Find the First Non-NaN Value in Shifted Pandas DataFrames Efficiently

Learn how to compute lagged values in Pandas while skipping NaN entries for cleaner data analysis.
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Dealing with Lagged Values in Pandas

When working with time series data in Python's Pandas library, you often need to compute lagged values. However, a common problem arises when the series contains NaN (Not a Number) values. In this guide, we'll explore a specific use-case: determining how to compute the lag of a column while skipping NaN values.

Understanding the Problem

Consider you have a DataFrame with two columns:

col1, which contains integer values, and

col2, which contains both integer values and NaN.

Here's an example of what your DataFrame might look like:

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

When you attempt to compute a lagged version of col2 using the shift method, you'll notice the presence of NaN values in the resultant column as well:

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

This will yield:

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

As demonstrated, you get lagged values where NaN persists, which is not the desired outcome. Ideally, you'd want to skip over these NaN values to find the most recent non-NaN entry.

The Desired Output

Your aim is to obtain a DataFrame that looks like this:

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

The Solution

To achieve this cleanly, you can use the ffill() method (which stands for "forward fill") alongside shift(). Here's how you can implement this approach:

Using Forward Fill (ffill())

You can combine shift() with ffill() in one line:

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

Explanation

shift(1): This method shifts the values in col2 down, introducing NaN at the first position.

ffill(): The forward fill method takes the last known non-NaN value and fills it forward for all subsequent NaN entries.

Final Code Example

Here's the complete code that demonstrates how to achieve the desired lagged DataFrame without NaN values:

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

Conclusion

This method allows you to compute lagged values in a Pandas DataFrame efficiently while skipping NaN values, providing you a cleaner dataset for further analysis.

By implementing shift() alongside ffill(), you can ensure that your lagged columns retain meaningful, non-NaN values, thus enabling more effective time series analysis in your data projects.