Скачать с ютуб How to Compare Columns from Two Different Data Frames in Python's Pandas в хорошем качестве

How to Compare Columns from Two Different Data Frames in Python's Pandas

Learn how to effectively compare and merge columns from two Pandas DataFrames, ensuring you retain the values from the first DataFrame where applicable. --- This video is based on the question https://stackoverflow.com/q/65893739/ asked by the user 'Y4RD13' ( https://stackoverflow.com/u/11558934/ ) and on the answer https://stackoverflow.com/a/65893884/ provided by the user 'Sayandip Dutta' ( https://stackoverflow.com/u/5431791/ ) 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: How to compare columns from two different Data Frames and keep the values from the first Data Frame? 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 Columns from Two Different Data Frames in Python's Pandas In the world of data manipulation, one of the challenges many users face is merging or comparing data from multiple DataFrames. This guide will address a common problem: How can you compare columns from two different DataFrames and ensure that the values from the first DataFrame are retained? The Problem at Hand You may have a scenario where you have two DataFrames with similar columns, and you wish to join them in such a way that the data from the first DataFrame overwrites data in the second DataFrame when the keys (in this case, 'Words') match. However, if a key exists in the second DataFrame but not in the first, it should be kept in the result. DataFrames Overview Let's look at our sample DataFrames for this explanation: DataFrame 1 (df1): Wordsxyzaardvark0.9990.9990.999abalone0.8880.8880.888abandon0.7770.7770.777DataFrame 2 (df2): Wordsxyzaaaaahh0.1990.9290.993aardvark0.1110.1110.111abalone0.2220.2220.222abandon0.3330.3330.333zoo0.2320.1120.212zoom0.8420.6200.344zucchini0.9450.2650.745Expected Outcome Given the above DataFrames, the desired outcome is a new DataFrame (df_res) that looks like this: Wordsxyzaaaaahh0.1990.9290.993aardvark0.9990.9990.999abalone0.8880.8880.888abandon0.7770.7770.777zoo0.2320.1120.212zoom0.8420.6200.344zucchini0.9450.2650.745The Solution Fortunately, the Pandas library provides a straightforward way to achieve this through a combination of methods. Follow these steps to arrive at the desired DataFrame: Step-by-Step Guide Append the DataFrames: Use the append() method to combine df1 into df2. Remove Duplicates: Apply drop_duplicates() to keep the last occurrence of each 'Word' entry. The keep='last' argument ensures that the values from df1 are kept whenever there's a match. Sorting and Resetting the Index: Finally, sort the DataFrame and reset the index for clarity. Example Code Here’s how it can be implemented in Python: [[See Video to Reveal this Text or Code Snippet]] Output When you run the above code, you will get the following DataFrame: Wordsxyzaaaaahh0.1990.9290.993aardvark0.9990.9990.999abalone0.8880.8880.888abandon0.7770.7770.777zoo0.2320.1120.212zoom0.8420.6200.344zucchini0.9450.2650.745Conclusion By following the above steps, you not only solve the problem of comparing columns from two different DataFrames, but you also ensure that the values from the first DataFrame take precedence wherever applicable. This method is efficient, easy to understand, and utilizes the powerful capabilities of the Pandas library. Happy data wrangling!