Скачать или смотреть How to aggregate multiple DataFrames into a summary table using Python

Learn how to effectively use Python's Pandas library to group and aggregate multiple DataFrames into a summary table for data analysis.
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How to Aggregate Multiple DataFrames into a Summary Table Using Python

When handling datasets in Python, especially when utilizing libraries like Pandas and NumPy, a common task is to aggregate and summarize data from various sources. This becomes particularly important when you want to compare different groups of data side-by-side.

In this guide, we’ll walk through the process of creating an aggregated summary table from two DataFrames, utilizing techniques that make data analysis straightforward and efficient.

The Problem

Imagine you have two datasets:

y_train: This contains class names for training data.

y_test: This contains class names for test data.

For instance:

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

Your goal is to create a summary table that counts the occurrences of each class in both datasets, like below:

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

The Solution

Step 1: Compile Lists into a DataFrame

To start, we need to combine the y_train and y_test lists into a single DataFrame with an indicator for the source. Using the Pandas library, we can concatenate the two lists and associate each with its corresponding source label.

Here’s how to do that:

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

Output:

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

Step 2: Aggregate the Data

Next, we need to aggregate counts of each category from both datasets. We will achieve this by grouping the DataFrame by category and source, and then counting the occurrences.

The aggregation can be done as follows:

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

Output:

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

Step 3: Reshape the DataFrame

Finally, to transform this DataFrame into the desired summary table format, we can use the pivot_table function.

Here’s the code for reshaping the aggregated DataFrame:

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

Output:

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

Conclusion

By following the steps outlined above, you can easily aggregate and transform different datasets into a clear and organized summary table. This simple yet powerful technique is especially helpful for data comparison and analysis, allowing you to visualize your data more effectively.

Give it a try on your own datasets, and see how this method can streamline your data analysis process with Python's Pandas library!