Скачать или смотреть How to Split a Column of Strings and Create Rows from Separate Elements in Pandas

Learn how to transform a DataFrame in Pandas by splitting a column of strings into multiple rows while maintaining the other column values.
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Transforming Your DataFrame: Splitting a Column into Multiple Rows

Are you working with a DataFrame in Python's Pandas library and need to split a column of strings into separate rows? If you've ever faced a situation where you wanted to divide strings in a DataFrame column while keeping other values intact, you're in the right place. In this guide, we'll walk through this process step by step, using a practical example to illustrate how it's done.

Understanding the Problem

Imagine you have a DataFrame structured like this:

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

Here, column B contains strings with periods separating individual elements. The goal is to split these strings into separate entries while preserving the values in columns A, C, and D. After transformation, your DataFrame should look something like this:

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

Now, let’s dive into how to accomplish this using Pandas' built-in methods: str.split() and explode().

Step-by-Step Solution

Step 1: Split the Strings

First, we need to prepare the column B for splitting. We'll use the str.split() method to break the strings at each period. This method will return a list for each row, which we can then explode into multiple rows in the next step.

Here’s how to do it:

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After running this code, your DataFrame will look like this:

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Step 2: Explode the List

Now that column B contains lists, we can use the explode() method to create new rows from each element of these lists. This method effectively increases the DataFrame's dimensions based on the number of elements in the specified column.

Use the following command to explode the column:

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The result will be:

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Final Output

As you can see, the DataFrame has been transformed as required! Each string component from column B has now been split into its own row, with other column values replicated accordingly. Your final DataFrame structure maintains the relationships between the original data while enhancing its flexibility and usability for analysis or further manipulation.

Conclusion

Splitting a column of strings into multiple rows while retaining other column data is seamless with Pandas using str.split() and explode(). This method enables more straightforward data manipulation and analysis, especially when working with unstructured textual data formatted in a specified way.

Feel free to try this technique on your datasets, and you’ll find it tremendously useful for cleaning and restructuring data. Happy coding!