In this tutorial, we will learn how to compare multiple CSV files in Python. CSV (Comma Separated Values) files are commonly used for storing and transferring data in a tabular format, and it’s a scenario commonly encountered when dealing with data analysis or data science tasks.
Python, being a powerful programming language with vast libraries, offers an easy approach to dealing with such tasks.
Step 1: Importing Necessary Libraries
The very first step is to import the necessary Python libraries. For this task, we will be using the Pandas library, a flexible and powerful data analysis library.
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import pandas as pd |
Step 2: Load CSV Files
After importing the necessary libraries, we will load the CSV files we want to compare. Here we’re assuming that we have two CSV files, ‘file1.csv’ and ‘file2.csv’. You would have to replace these with the actual paths of your CSV files.
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df1 = pd.read_csv('file1.csv') df2 = pd.read_csv('file2.csv') |
file1.csv:
Name,Age,City Alice,25,New York Bob,30,Los Angeles Charlie,35,Chicago
file2.csv:
Name,Age,City David,28,Houston Eve,22,Miami Frank,40,Denver
Step 3: Compare the CSV Files
Next, we’ll use pandas to compare the files. We will find all the differences between the two dataframes and save them in a third dataframe.
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df3 = df1.compare(df2) |
Step 4: Display the Differences
Finally, we’ll display the differences between the two CSV files.
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print(df3) |
The full code
Putting it all together, here’s the complete Python source code:
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import pandas as pd df1 = pd.read_csv('file1.csv') df2 = pd.read_csv('file2.csv') df3 = df1.compare(df2) print(df3) |
Name Age City self other self other self other 0 Alice David 25 28 New York Houston 1 Bob Eve 30 22 Los Angeles Miami 2 Charlie Frank 35 40 Chicago Denver
Conclusion
Comparing multiple CSV files in Python is very straightforward thanks to pandas. With just a few lines of code, you can load your CSV files, compare them, and display any differences.
Understanding how to compare CSV files can be incredibly useful, especially when dealing with large datasets in data analysis and data science.
For more in-depth details, you can refer to the official pandas documentation.