![]() ![]() Let’s look into some examples of using Pandas rename() function. We can rename single column or multiple columns with this function, depending on the values in the dictionary.It’s recommended to use keyword arguments to clearly specify the intent.Provided by Data Interview Questions, a mailing list for coding. Some important points about rename() function. A step-by-step Python code example that shows how to rename columns in a Pandas DataFrame. If ‘ignore’, existing keys will be renamed and extra keys will be ignored. If specified as ‘raise’ then KeyError is raised when a dict-like ‘mapper’, ‘index’, or ‘columns’ contains labels that are not present in the Index being transformed. errors: possible values are (‘ignore’, ‘raise’), default is ‘ignore’.It’s used in case of a MultiIndex, only rename labels in the specified level. Otherwise, a new DataFrame is returned and the current DataFrame remains unchanged. inplace: if True, the DataFrame is changed.The allowed values are (‘index’, ‘columns’) or number (0, 1). It’s used with ‘mapper’ parameter to define the target axis. columns: must be a dictionary or function to change the column names.index: must be a dictionary or function to change the index names.The ‘axis’ parameter determines the target axis - columns or indexes. mapper: dictionary or a function to apply on the columns and indexes. ![]() We can use pandas DataFrame rename() function to rename columns and indexes. Sometimes we want to rename columns and indexes in the Pandas DataFrame object. ![]()
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