How to Extract DataFrame Names and Column Names Using Pandas

Learn how to efficiently extract `DataFrame` names and their respective column names into a structured format using Python's Pandas library.
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Extracting DataFrame Names and Column Names Using Pandas

When working with multiple data frames in Python, it is often necessary to understand the structure of your data. For instance, if you have a list of data frames, you might want to compile their names alongside their respective column names into a single organized format. This not only makes data management easier but also aids in data analysis. In this guide, we will tackle the problem of how to extract DataFrame names and column names and present them in a consolidated table.

The Problem

You have a list of various data frames, each with different sets of column names. For example, consider the following data frames:

master: Contains columns such as ID, Type, Year, and Score.

vertical_list: Consists of columns like ID and Winner_ID.

The task is to generate a structured summary showing which columns belong to each data frame in a readable format, like this:

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

The Solution

To achieve this, we can utilize a dictionary to store our data frames. This enables us to efficiently iterate through the data frames and extract the required information. Below, we will detail the steps to implement this solution.

Step 1: Create a Dictionary of DataFrames

First, create a dictionary that contains the names of your data frames as keys and their corresponding DataFrame objects as values. Below is an example:

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

Step 2: Extract Column Names into a DataFrame

Next, we will loop through this dictionary and extract the column names along with the respective DataFrame names. Here's how it's done:

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

This code creates a list of DataFrames, each containing two columns: df_name and column_name.

Step 3: Combine All DataFrames

Once we have our list of DataFrames, we can concatenate them into a single DataFrame:

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

Output

The printed output will look like this:

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

Optional: Exporting DataFrames to CSV Files

If you want to export these DataFrames to CSV files, we can extend our approach as follows:

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

This code will create separate .csv files for each data frame with their respective names and column names.

Conclusion

In this guide, we walked through an effective solution to extract and organize DataFrame names and their corresponding column names into a cohesive format using Pandas. Whether you're managing a small number of dataframes or working with a large data collection, this approach will simplify your workflow and enhance data analysis processes. Happy coding!

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