{"id":138500,"date":"2026-02-20T08:22:32","date_gmt":"2026-02-20T02:52:32","guid":{"rendered":"https:\/\/www.vskills.in\/certification\/tutorial\/?page_id=138500"},"modified":"2026-02-20T08:22:32","modified_gmt":"2026-02-20T02:52:32","slug":"concat-dataframes","status":"publish","type":"page","link":"https:\/\/www.vskills.in\/certification\/tutorial\/concat-dataframes\/","title":{"rendered":"Concat DataFrames"},"content":{"rendered":"\n

Concat DataFrames in Pandas means joining two or more DataFrames by stacking them together. It is most commonly used when you have data split across multiple files (for example, monthly sales files) and you want to combine them into one single dataset for analysis.<\/p>\n\n\n\n

There are two main ways concatenation happens:<\/p>\n\n\n\n

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  1. Row-wise concatenation (stacking rows)
    This is the most common use case. You combine DataFrames with the same columns and add their rows one after another. For example, you might have January, February, and March data as three separate DataFrames and you want one DataFrame for Q1. When stacking row-wise, it is important that the column names match, otherwise Pandas may create extra columns and fill missing values.<\/li>\n\n\n\n
  2. Column-wise concatenation (adding columns side-by-side)
    This is used when DataFrames represent different information for the same set of rows and you want to bring them together as new columns. For example, one DataFrame contains customer details and another contains customer scores, and both have the same row order. In this case, you must ensure the indexes align correctly, otherwise values can shift and you may get mismatched results.<\/li>\n<\/ol>\n\n\n\n

    Key points to remember while concatenating:<\/p>\n\n\n\n