How to Compare Two CSV Files in Python and Create a Third One with Matching Columns

Learn how to efficiently compare two CSV files using Python and Pandas to create a consolidated DataFrame with matching columns. --- This video is based on the question https://stackoverflow.com/q/64855899/ asked by the user 'Davidoff' ( https://stackoverflow.com/u/12315399/ ) and on the answer https://stackoverflow.com/a/64855951/ provided by the user 'Tom Ron' ( https://stackoverflow.com/u/1481986/ ) 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 csv files and create a third where the columns match 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 and Create a Third One with Matching Columns Handling CSV files and data manipulation is a common task in data analysis. Often, you may find yourself needing to compare two datasets and extract relevant information based on specific criteria. In this guide, we will explore how to compare two CSV files and create a third file that contains only the rows where a particular column (cell_code) matches in both files. The Problem at Hand Imagine you have two large CSV files saved as DataFrames. Each of these files contains data about technology and its geographical location, identified by a unique column known as cell_code. Here’s a brief snapshot of the data in these files: First CSV Snippet technologytaccell_codelonlatLTE6710192449912.15723647.586310LTE410643827316.54101948.133775LTE410683584716.05214148.077284LTE1700630259516.38929248.229125LTE1700723885016.41375248.189886Second CSV Snippet technologytaccell_codelonlatLTE76025654116.52917647.834004LTE76025654216.52917647.834004LTE760230259516.52917647.834004LTE76021844016.92679847.838448LTE760243827316.92679847.838448From these datasets, we want to find matches in the cell_code column and create a new DataFrame that consolidates relevant data from both files. The expected result for the matching entries would look like this: Expected Result technologycell_codetac_1tac_2lon_1lon_2lat_1lat_2LTE4382734106760216.54101916.92679848.13377547.838448LTE30259517006760216.38929216.52917648.22912547.834004Let's see how we can achieve this using Python and the Pandas library. Solution Using Pandas To create a new DataFrame containing only the matching rows from both CSV files, we will utilize the merge function in Pandas. Below is a step-by-step breakdown of the code needed to accomplish this. Step 1: Import Libraries First, make sure to import the necessary library: [[See Video to Reveal this Text or Code Snippet]] Step 2: Load the CSV Files Load your datasets into two separate DataFrames: [[See Video to Reveal this Text or Code Snippet]] Step 3: Merge the DataFrames Next, use the merge method to combine the two DataFrames based on the technology and cell_code columns. This function allows you to specify how the matching should be done and can add suffixes to distinguish between columns from different DataFrames: [[See Video to Reveal this Text or Code Snippet]] Step 4: Save the Result Finally, if you wish to save the result to a new CSV file, you can do so with the following line of code: [[See Video to Reveal this Text or Code Snippet]] Conclusion You now have a powerful method of comparing two CSV files and consolidating the relevant data into a third DataFrame. By using the merge function from the Pandas library, we can efficiently handle large datasets and extract needed information based on common identifiers. If you face similar data comparison tasks in your work or studies, remember this approach to streamline your workflow effectively! Happy coding!