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Discover how to *efficiently read multiple Excel files* into separate Pandas dataframes using Python with this easy-to-follow guide.
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How to Read Multiple Excel Files into Different Pandas Dataframes

When working with data analysis in Python, especially using libraries like Pandas, you may find yourself in a situation where you have multiple Excel files that contain similar datasets. If you want to analyze this data in Python, the first thing you need to do is read these files into separate Pandas dataframes. In this guide, we'll walk through how to do just that effectively.

The Problem

Imagine you have a collection of Excel files — let’s call them data1.xls, data2.xls, and so on — that are stored in a folder on your computer. These files contain similar types of data that you want to analyze independently. You might find the task of reading each Excel file cumbersome, especially if there are many files involved. The goal is to streamline this process so that you can convert each file into its own dataframe without losing track of any data.

The Solution

To solve this problem, we'll utilize the glob library to access the files and the pandas library to manage the dataframes. Here’s a step-by-step breakdown of how to read multiple Excel files into different Pandas dataframes:

Step 1: Import Necessary Libraries

We begin by importing the necessary libraries glob and pandas. The glob library will help us fetch file names, and pandas will handle the data in our dataframes.

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Step 2: Define the File Path

Specify the directory path where your Excel files are located. Be sure to use double backslashes (\) in the file path to avoid escape character issues in Python strings.

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Step 3: Fetch Excel Files with Glob

Use the glob method along with os.path.join() to fetch all Excel files in the specified directory. The *.xls pattern ensures that we only get files with the Excel extension.

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Step 4: Read Each File into a Dataframe

Now, we loop through the list of files and read each Excel file into a Pandas dataframe. Instead of assigning each dataframe to a variable, we will store each dataframe in a list dfl. This way, each dataframe can be accessed later using its index in the list.

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Summary of the Code

Here's the complete code put together:

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Conclusion

By following these steps, you can easily read multiple Excel files into different Pandas dataframes. This approach not only saves you time but also helps keep your data organized. Whether you are conducting a simple analysis or preparing for a more complex data manipulation task, managing multiple dataframes effectively is key.

Utilizing Python’s robust libraries allows you to streamline your workflow and focus on deriving insights from your data rather than getting bogged down in the data input process. Happy coding!