pandas.DataFrame.reindex_axis

DataFrame.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)

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

axis : {0 or ‘index’, 1 or ‘columns’}

method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

Method to use for filling holes in reindexed DataFrame:

• default: don’t fill gaps
• pad / ffill: propagate last valid observation forward to next valid
• backfill / bfill: use next valid observation to fill gap
• nearest: use nearest valid observations to fill gap

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 abs(index[indexer] - target) <= tolerance.

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)

Returns:

reindexed : DataFrame

See also

reindex, reindex_like

Examples

>>> df.reindex_axis(['A', 'B', 'C'], axis=1)