Скачать или смотреть How to Use np.repeat to Create a Row in a Pandas DataFrame

Learn how to easily create a specific row in a Pandas DataFrame using `np.repeat` and lists, even when starting with a DataFrame that has no data.
---
This video is based on the question https://stackoverflow.com/q/63916952/ asked by the user 'Ryo_N' ( https://stackoverflow.com/u/13579765/ ) and on the answer https://stackoverflow.com/a/63917312/ provided by the user 'Grayrigel' ( https://stackoverflow.com/u/5604562/ ) 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: using np.repeat multiple times to create a row in pandas dataframe

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.
---
Creating a Row in a Pandas DataFrame Using Numpy and Lists

If you're working with data in Python, specifically using Pandas and Numpy, you might face a scenario where you need to populate a DataFrame that starts off empty, but you want to fill it with specific repeated values across multiple columns. For instance, consider a situation where you have a DataFrame with 45 columns, and you want the first 15 columns to contain the data "A", the next 15 to have "B", and the final 15 to have "C". This post will guide you through how to achieve this efficiently using lists, avoiding the need to jump through hoops with Excel or manual data entry.

The Problem Explained

Imagine you have a DataFrame like this, with just column names but no actual data:

123...161718...434445---...---...---Your goal is to fill this DataFrame in a way that looks like this:

012345678...4344AAAAAAAAA...CCNow let's see how we can accomplish this using Python.

The Solution

To fill in the DataFrame as described, we can use Python's list features in combination with Pandas. Here’s a step-by-step approach to achieve this.

Step 1: Import Necessary Libraries

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

Make sure to have Pandas installed in your Python environment.

Step 2: Create the Data Using Lists

To create a row filled with the values "A", "B", and "C", we can use list multiplication in Python. Here’s how you can do it:

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

In this code, we create three lists: 15 'A's, 15 'B's, and 15 'C's. By concatenating them, we get a single list that contains the desired sequence of values.

Step 3: Create the DataFrame

Once you have the data list ready, creating a DataFrame from it is straightforward:

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

The pd.DataFrame(data).T line transposes the DataFrame so that our single row list becomes a row in the DataFrame rather than a column.

Step 4: Display the Result

Finally, you can print the DataFrame to see the result:

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

This will output the DataFrame structured with columns filled as required.

Example Output

When you run the code, the output will look like this:

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

Conclusion

Creating a row in a Pandas DataFrame filled with repeated values is easy and efficient using lists. Instead of relying on more complex data import methods, leveraging Python’s simplicity allows for rapid DataFrame manipulations directly in Jupyter Notebook. So the next time you need to generate structured data, remember this approach!

Feel free to ask questions or share your own methods for handling such tasks in the comments below. Happy coding!