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Learn how to efficiently combine multiple CSV files into a single DataFrame in Pandas while renaming columns and excluding unimportant ones.
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How to Import Multiple CSV Files into One DataFrame with Different Column Names

When working with data in Python, especially in data science and data analysis tasks, it’s common to encounter situations where you need to merge multiple CSV files into a single DataFrame. However, this process can get tricky when the files have different column names and contain unnecessary data. In this article, we'll guide you through the process of importing multiple CSV files while renaming columns and excluding unimportant ones using Pandas.

The Problem: Different Column Names and Unimportant Columns

Imagine you have two CSV files in one directory, and when you import them separately, you get two distinct DataFrames:

First CSV DataFrame

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Second CSV DataFrame

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You want to combine these two DataFrames into one, and since the column names differ, you will use a dictionary to map the columns from the second file to the names you want in the combined DataFrame:

Mapping Dictionary

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Step-by-Step Solution: Merging CSV Files

Now, let’s employ Pandas to seamlessly merge these files and rename the columns according to your mapping. Here’s how you can do it:

Step 1: Import Necessary Libraries

Start by importing the required libraries:

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Step 2: Define the Path to Your CSV Files

Next, define the path to the directory where your CSV files are stored:

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Step 3: Read and Combine the CSV Files

Use the pd.concat function alongside rename to modify the column names per your mapping:

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In this snippet:

We loop through each file found in the directory.

We read each CSV file.

We rename the columns according to our Dict_col_names mapping.

Finally, these DataFrames are combined into one.

Step 4: Handling Different Column Structures

If the two CSV files have differing columns and you wish to include only the columns that are common, you can add join='inner' to the concatenate method:

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Expected Output

By carefully following these steps, you should attain a unified DataFrame that looks like this:

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Conclusion

Combining multiple CSV files with different column names in Python’s Pandas library doesn’t have to be overwhelming. With just a few steps—setting the file path, reading the files, renaming the columns, and merging—you can create a clean and organized DataFrame ready for analysis. This method is quite efficient, especially when dealing with large datasets and multiple files.

With this guide, you can now manage and integrate your data with ease, leaving more time for data analysis and insights rather than wrestling with imports. Happy coding!