Скачать или смотреть A Better Way to Fill a DataFrame Column than iterrows in Python & Pandas

Discover efficient methods to fill a DataFrame column in Python using Pandas without utilizing the slow `iterrows` approach.
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A Better Way to Fill a DataFrame Column than iterrows in Python & Pandas

When working with data in Python, especially using the Pandas library, you may encounter scenarios where you need to fill a DataFrame column based on the results of other columns. A common question arises: How can you achieve this efficiently? The traditional method—using iterrows—can be slow and inefficient, particularly with larger data sets. Here, we’ll explore a better approach to fill a DataFrame column in a way that's both simpler and faster.

Understanding the Problem

Let's consider a practical example:

You have a DataFrame structured with three key columns:

Date: When the activity occurs

In: Indicates the start of an activity (1 when the activity starts, 0 otherwise)

Out: Indicates the end of an activity (1 when it ends, 0 otherwise)

The objective is to create a new column, Res, that tracks the ongoing status of the activity. The Res column should register:

A value of 1 when an activity is going on (when In is 1 or an activity has started and not yet ended)

A value of 0 when there are no ongoing activities

Sample DataFrame Example

Consider the following initial DataFrame:

dateinout2020-01-01002020-01-02002020-01-03102020-01-04002020-01-05002020-01-06012020-01-07002020-01-08102020-01-09012020-01-1000The expected result after processing should look like this:

dateinoutres2020-01-010002020-01-020002020-01-031012020-01-040012020-01-050012020-01-060112020-01-070002020-01-081012020-01-090112020-01-10000The Inefficiency of iterrows

Using iterrows for such a task would look like this:

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

While effective, iterating through rows can become a performance bottleneck, especially with larger datasets.

A More Efficient Approach

Instead of using iterrows, we can take advantage of Pandas' vectorized operations, which are significantly faster. Here’s how to fill the Res column using a more efficient method:

Step-by-Step Solution

Compute an Auxiliary Column: First, create a temporary column that combines the in and out values.

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

Cumulative Sum: Use the cumulative sum and apply conditions to track ongoing activities.

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

Assign the Result: Finally, assign the computed res column back to the original DataFrame.

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

Final Code Example

Here’s the complete code that implements the above logic:

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

Conclusion

By leveraging Pandas' vectorized methods, you can significantly improve the performance of DataFrame manipulations. Avoiding iterrows not only enhances efficiency but also simplifies your code. This approach enables you to handle larger datasets effectively without compromising on speed.

Don't forget to practice this method in your own projects to get accustomed to its benefits!