{"id":138504,"date":"2026-02-20T08:24:38","date_gmt":"2026-02-20T02:54:38","guid":{"rendered":"https:\/\/www.vskills.in\/certification\/tutorial\/?page_id=138504"},"modified":"2026-02-20T08:24:39","modified_gmt":"2026-02-20T02:54:39","slug":"apply-function","status":"publish","type":"page","link":"https:\/\/www.vskills.in\/certification\/tutorial\/apply-function\/","title":{"rendered":"Apply Function"},"content":{"rendered":"\n

The apply function in Pandas is used to run a custom operation on data and create a transformed output. It is commonly used when built-in Pandas operations are not enough and you need logic that is specific to your dataset. Apply is useful for cleaning values, creating new columns, categorising records, and performing row-wise or column-wise transformations.<\/p>\n\n\n\n

There are two common ways apply is used:<\/p>\n\n\n\n

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  1. Applying to a Series (a single column)
    This is the most common use case. You apply a function to each value in a column. For example, you can convert text to a standard format, extract part of a string, or create a label based on rules. This is often used to build new columns such as \u201cHigh\u201d or \u201cLow\u201d based on a numeric threshold, or to standardise categories like city names.<\/li>\n\n\n\n
  2. Applying to a DataFrame (rows or columns)
    You can apply a function across rows or across columns. Row-wise apply is used when a calculation depends on multiple columns together, such as creating a new field based on both sales and cost, or generating a combined identifier from multiple fields. Column-wise apply is less common in analysis but can be used for transformations across many columns.<\/li>\n<\/ol>\n\n\n\n

    Important points to remember:<\/p>\n\n\n\n