Python pandas merge csv files in directory into one

In this tutorial, you will learn how to merge multiple CSV files from a directory into a single, consolidated CSV file using the Python Pandas library. This is a common task when working with data, as it allows you to combine data from multiple sources into one comprehensive dataset.
We will cover the following steps:
Before you begin, ensure that you have Python and the Pandas library installed on your system. You can install Pandas using pip:
Start by importing the Pandas library, which we will use for data manipulation. Additionally, we will need the os library to interact with the file system.
First, we need to obtain a list of CSV files in the target directory. You can do this using the os.listdir() function and filtering for files with a ".csv" extension. Here's how you can do it:
Make sure to replace '/path/to/your/directory' with the actual path to your directory containing the CSV files.
Now, let's read and merge the CSV files into a single DataFrame. We will use a loop to read each CSV file and append its data to the final DataFrame.
In this code, we first initialize an empty DataFrame called merged_data. Then, we loop through each CSV file, read it using pd.read_csv(), and append its data to the merged_data DataFrame using append().
Finally, we'll save the merged data to a new CSV file. You can use the to_csv() function to do this:
This will save the merged data to a CSV file named 'merged_data.csv' in the current directory. You can specify a different path or filename by changing the output_file variable.
Here is the complete code for merging CSV files in a directory:
Make sure to replace '/path/to/your/directory' and 'merged_data.csv' with your specific directory and desired output filename. Once you run this script, you will have a single merged CSV file with data from all the CSV files in the specified directory.
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