![]() Let's take a look at a DataFrame df = pd. There is also a new DataFrame method rename_axis available to change the index level names. Index.rename (name: UnionAny, TupleAny,, ListUnionAny, TupleAny,, inplace: bool False) Optional .Index source Alter Index or MultiIndex name. Pandas has some quirkiness when it comes to renaming the levels of the index. The currently selected answer does not mention the rename_axis method which can be used to rename the index and column levels. This article describes the following contents. You can also rename index names (labels) of pandas.Series in the same way. Note: this attribute is just a list, and you could do the renaming as a list comprehension/map. You can rename (change) column/index names of pandas.DataFrame by using rename (), addprefix (), addsuffix (), setaxis () methods or updating the columns / index attributes. ![]() Extra labels listed don’t throw an error. Labels not contained in a dict / Series will be left as-is. Function / dict values must be unique (1-to-1). Whilst renaming the level names: In : = rename (mapper None,, index None, columns None, axis None, copy None, inplace False, level None, errors 'ignore') source Alter axes labels. You can see the rename on the index, which can change the value 1: In : df1.rename(index=) By renaming a Pandas dataframe index, you’re changing the name of the index column. In this tutorial, you’ll learn how to use Pandas to rename an index, including how to rename a Pandas dataframe index and a Pandas multi-index dataframe. This is a little bit confusing since the index names have a similar meaning to columns, so here are some more examples: In : df = pd.DataFrame(, ], columns=list('ABC')) Pandas Rename Index: How to Rename a Pandas Dataframe Index. You want to rename to index level's name: df.index.names = Ī good way to think about this is that columns and index are the same type of object ( Index or MultiIndex), and you can interchange the two via transpose. The following examples show how to use this sytnax in practice. inplace: Specifying True allows pandas to replace the index in the original DataFrame instead of creating a copy of the DataFrame. Keep in mind that this produces a copy of the dataframe with renamed index values and should be assigned to a variable name in order to make it persist. ![]() The rename method takes a dictionary for the index which applies to index values. drop: Specifying True prevents pandas from saving the original index as a column in the DataFrame.
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