Pandas Series: reindex() function

Conform series in Pandas

The reindex() function is used to 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.


Series.reindex(self, index=None, **kwargs)
Pandas Series reindex image


Name Description Type/Default Value Required / Optional
index New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data array-like optional
method 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
{None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’} Required
copy Return a new object, even if the passed indexes are the same. bool
Default Value: True
level Broadcast across a level, matching Index values on the passed MultiIndex level. int or name Required
fill_value Value to use for missing values. Defaults to NaN, but can be any “compatible” value. scalar
Default Value: np.NaN
limit Maximum number of consecutive elements to forward or backward fill. int
Default Value: None
tolerance 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.

Returns: Series with changed index.


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