Скачать или смотреть How to Cut a Pandas DataFrame Based on Unique Values per Column Pandas DataFrame Unique Values

Learn how to split a Pandas DataFrame into separate DataFrames based on unique values in a specific column, preserving the order of the data.
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How to Cut a Pandas DataFrame Based on Unique Values per Column

When dealing with data in Python, specifically in data science and analysis, you often encounter scenarios that require reshaping or splitting your data to make it more useful or easier to analyze. One such common requirement is to cut a Pandas DataFrame into separate DataFrames based on unique values per column. This guide will guide you through the process step by step.

The Problem

Imagine you have a DataFrame that contains duplicated values in a column. For instance, consider the following DataFrame:

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

This DataFrame looks like this:

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

Your goal is to split this DataFrame into multiple DataFrames, each representing a unique combination of values in the block column. For example, you would like to achieve this:

Desired Output

The desired split for the example DataFrame should look like:

DataFrame 1:

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

DataFrame 2:

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

Solution Overview

To tackle this problem, we can make use of Python's Pandas library along with itertools. We will follow these steps:

Group the DataFrame by the target column (block).

Create combinations of rows corresponding to each group.

Concatenate these combinations to form new DataFrames.

Step-by-Step Implementation

Here's how to implement the solution using Python code:

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

Code Explanation:

Grouping: We utilize df.groupby("block") to iterate through each unique value of the block column.

Row Extraction: We extract the rows for each group into a list using list comprehension.

Combining Rows: With the help of itertools.product, we create combinations of rows from these grouped DataFrames.

Concatenation: Finally, the combinations are concatenated using pd.concat and transposed to form a DataFrame that maintains the original order.

Outputs

After executing the code, you would get separate DataFrames printed out as follows:

Output for the first example:

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

Conclusion

Now you have a method to split Pandas DataFrame into multiple DataFrames based on unique values in a specific column while preserving the order of the data. This approach is useful for data analysis where you want to analyze each unique combination of column values separately.

Feel free to use and modify this approach for your DataFrame splitting needs. Happy coding!