pandas.Series.reindex

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.

param index

array-like, optional

New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data

param method

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

Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

• None (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

param copy

bool, default True

Return a new object, even if the passed indexes are the same.

param level

int or name

Broadcast across a level, matching Index values on the passed MultiIndex level.

param fill_value

scalar, default np.NaN

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

param limit

int, default None

Maximum number of consecutive elements to forward or backward fill.

param 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)

return

Series with changed index.

Warning

This feature is currently unsupported by Intel Scalable Dataframe Compiler