How to Compare Two CSV Files in Python Using Pandas

Learn how to efficiently compare two CSV files in Python, similar to Excel's VLOOKUP, using the Pandas library. This step-by-step guide covers reading CSVs, merging dataframes, and applying custom logic for analysis. --- This video is based on the question https://stackoverflow.com/q/75764261/ asked by the user 'Blue' ( https://stackoverflow.com/u/14605324/ ) and on the answer https://stackoverflow.com/a/75764382/ provided by the user 'tmc' ( https://stackoverflow.com/u/19124198/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Compare two different csv with key column in python Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Compare Two CSV Files in Python Using Pandas If you’ve ever found yourself comparing two CSV files in Excel using VLOOKUP, you might wonder how to achieve the same task using Python. This guide firmly walks you through the process of comparing two CSV files and generates insights based on their common and differing columns using the pandas library. The Problem: Comparing CSV Files You have two CSV files, abc.csv and xyz.csv, each containing user data with a uid column as the key identifier. The goal is to compare the status of users from both files and generate a new CSV file that reflects: Users from xyz.csv and their corresponding statuses in abc.csv Specific messages for each user, indicating whether their status has changed, remained the same, or if they do not exist in abc.csv The expected output file, output.csv, should look like this: [[See Video to Reveal this Text or Code Snippet]] The Solution: Step-by-Step Implementation Step 1: Install the Pandas Library First, ensure you have Pandas installed in your Python environment. If you don't have it, you can install it via pip: [[See Video to Reveal this Text or Code Snippet]] Step 2: Import the Necessary Package Now, let's start by importing the pandas library in your Python script or Jupyter Notebook. [[See Video to Reveal this Text or Code Snippet]] Step 3: Read the CSV Files Next, we will read the content of abc.csv and xyz.csv into pandas DataFrames: [[See Video to Reveal this Text or Code Snippet]] Step 4: Merge the DataFrames To facilitate the comparison, we will merge xyz_df with abc_df based on the uid column. This allows us to align the data from both files. [[See Video to Reveal this Text or Code Snippet]] Step 5: Define a Function to Evaluate Status To determine the status of each user from the merged DataFrame, we need a function. This function checks for various conditions and returns appropriate status messages. [[See Video to Reveal this Text or Code Snippet]] Step 6: Apply the Status Function Once our function is defined, we can apply it to each row of the merged DataFrame to generate a new column abc_status. [[See Video to Reveal this Text or Code Snippet]] Step 7: Prepare the Final DataFrame Next, we will create the final DataFrame containing only the relevant columns for output: [[See Video to Reveal this Text or Code Snippet]] Step 8: Save the Output CSV File Finally, we save the resulting DataFrame to a new CSV file named output.csv. [[See Video to Reveal this Text or Code Snippet]] Conclusion And there you have it! You’ve successfully compared two CSV files in Python and generated an output CSV file that summarizes the differences in user statuses. This approach not only generalizes the process of comparison but also preserves the flexibility of Python for more extensive data manipulations. If you have any further questions or need additional assistance with data manipulation in Python, feel free to ask!