pandas Sorting Operations: An Introduction and Practical Guide to the sort_values Function

This article introduces the sorting method of the `sort_values` function in pandas, which is applicable to sorting DataFrame/Series data. Core parameters: `by` specifies the column(s) to sort by (required), `ascending` controls ascending/descending order (default is ascending True), and `inplace` determines whether to modify the original data (default is False, returning a new dataset). Basic usage: Single-column sorting, e.g., ascending order by "Chinese" (default) or descending order by "Math"; multi-column sorting can pass a list of column names and corresponding ascending/descending directions (e.g., first by "Chinese" ascending, then by "Math" descending). Setting `inplace=True` directly modifies the original data; it is recommended to prioritize preserving the original data (default False). Practical examples: After adding a "Total Score" column, sort by total score in descending order to clearly display the ranking of comprehensive scores. Notes: For multi-column sorting, ensure the lengths of the `by` and `ascending` lists are consistent; prioritize data safety to avoid accidental overwriting of original data. By mastering core parameters and common scenarios through examples, sorting serves as a foundational step in data processing, becoming more critical when combined with subsequent analyses (e.g., TopN).