DataFrame.reindex(index=None, columns=None, **kwargs)

Conform DataFrame 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 :

index, columns : array-like, optional (can be specified in order, or as

keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data

method : {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed DataFrame pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation 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

fill_value : scalar, default np.NaN

Value to use for missing values. Defaults to NaN, but can be any “compatible” value

limit : int, default None

Maximum size gap to forward or backward fill

Returns :

reindexed : DataFrame


>>> df.reindex(index=[date1, date2, date3], columns=['A', 'B', 'C'])