DataFrame.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)
[source]
Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False
Parameters: |
labels : array-like New labels / index to conform to. Preferably an Index object to avoid duplicating data
method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional Method to use for filling holes in reindexed DataFrame:
copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level limit : int, default None Maximum number of consecutive elements to forward or backward fill tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type. New in version 0.21.0: (list-like tolerance) |
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Returns: |
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See also
>>> df.reindex_axis(['A', 'B', 'C'], axis=1)
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http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.reindex_axis.html