Скачать или смотреть Efficient Index Search in Numpy for Faster Dataframe Value Append

Discover how to quickly append values from one dataframe to another using Numpy's efficient indexing methods. Optimize your dataframe operations today!
---
This video is based on the question https://stackoverflow.com/q/62583650/ asked by the user 'Matt W.' ( https://stackoverflow.com/u/7237997/ ) and on the answer https://stackoverflow.com/a/62583985/ provided by the user 'Quang Hoang' ( https://stackoverflow.com/u/4238408/ ) 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: index search in numpy fast

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.
---
Efficient Index Search in Numpy for Faster Dataframe Value Append

Working with large datasets can often be a tedious and time-consuming task, especially when it comes to manipulating dataframes in Python using libraries like Pandas and Numpy. If you're dealing with massive dataframes, you might find that traditional looping methods lead to significant performance bottlenecks. In this guide, we will explore how to append values from one dataframe to another based on the index using numpy's advanced indexing capabilities, ensuring the operation is both quick and efficient.

The Problem: Appending Values to DataFrames

Let's say you have two dataframes:

df - Filled with random numbers where the column headers represent city names.

df2 - Contains the names of cities but filled with unimportant letters as headers.

Your goal is to grab values from df based on the names in df2 and append them accordingly. Specifically, we want to take the value associated with a city located at a specific index in df and append it next to that city name in df2.

An Example Scenario

Consider the following dataframes:

DataFrame df:

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

DataFrame df2:

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

As you can see, Los Angeles appears in df2, and we need to retrieve the value from df corresponding to its index.

The Naïve Approach: Using Loops

Initially, one might think of using a double loop to iterate over each value, taking a lot of time, especially with larger datasets. The code might look something like this:

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

While this approach works, as datasets grow, it can become prohibitively slow.

The Efficient Solution: Using Numpy's lookup

Instead of looping through each row and performing the lookup, we can leverage Numpy's efficient indexing features for a significantly improved performance. We'll utilize the lookup method, which allows us to lookup values based on corresponding index labels.

Here’s how it works:

Step-by-Step Implementation:

Stacking DataFrame df2:

This reshapes df2 to a format suitable for further processing.

Using Numpy's lookup Method:

We utilize lookup along with stacking to create a new dataframe with the desired format.

Here’s the concise code to achieve this:

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

Execution Time

For a large dataframe, the execution time could dramatically improve. Testing on a dataframe with 10,000 rows produced an execution time of just 44 ms on average, showcasing how effective this method is compared to the naive looping method.

Output

Upon executing the efficient solution, you would get an output like this:

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

Conclusion

In this guide, we demonstrated a method for efficiently appending values from one dataframe to another using Numpy's lookup capability. By avoiding loops and utilizing Numpy's optimized functions, you can drastically reduce execution times, enabling you to work with larger datasets seamlessly.

Next time you face a similar task, consider using these techniques to improve your performance and efficiency in dataframe manipulations!