pandas.DataFrame.reindex

DataFrame.reindex(index=None, columns=None, method=None, level=None, fill_value=nan, limit=None, copy=True)

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

index : array-like, optional
New labels / index to conform to. Preferably an Index object to avoid duplicating data
columns : array-like, optional
Same usage as index argument
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
>>> df.reindex(index=[date1, date2, date3], columns=['A', 'B', 'C'])

reindexed : same type as calling instance