pandas.Series.reindex¶
- Series.reindex(index=None, **kwargs)¶
Conform Series 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 : 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 : {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
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 : Series
Examples
>>> df.reindex(index=[date1, date2, date3], columns=['A', 'B', 'C'])