pandas.Series.reindex_like#

Series.reindex_like(other,method=None,copy=None,limit=None,tolerance=None)[source]#

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optionalfilling logic, placing NaN in locations having no valuein the previous index. A new object is produced unless thenew index is equivalent to the current one and copy=False.

Parameters:

otherObject of the same data type

Its row and column indices are used to define the new indicesof this object.

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 amonotonically increasing/decreasing index.

• None (default): don’t fill gaps

• pad / ffill: propagate last valid observation forward to nextvalid

• backfill / bfill: use next valid observation to fill gap

• nearest: use nearest valid observations to fill gap.

copybool, default True

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

Note

Thecopy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy andignore thecopy keyword. Thecopy keyword will be removed in afuture version of pandas.

You can already get the future behavior and improvements throughenabling copy on writepd.options.mode.copy_on_write=True

limitint, default None

Maximum number of consecutive labels to fill for inexact matches.

toleranceoptional

Maximum distance between original and new labels for inexactmatches. The values of the index at the matching locations mustsatisfy the equationabs(index[indexer]-target)<=tolerance.

Tolerance may be a scalar value, which applies the same toleranceto all values, or list-like, which applies variable tolerance perelement. List-like includes list, tuple, array, Series, and must bethe same size as the index and its dtype must exactly match theindex’s type.

Returns:

Series or DataFrame

Same type as caller, but with changed indices on each axis.

See also

DataFrame.set_index

Set row labels.

DataFrame.reset_index

Remove row labels or move them to new columns.

DataFrame.reindex

Change to new indices or expand indices.

Notes

Same as calling.reindex(index=other.index,columns=other.columns,...).

Examples

>>>df1=pd.DataFrame([[24.3,75.7,'high'],...[31,87.8,'high'],...[22,71.6,'medium'],...[35,95,'medium']],...columns=['temp_celsius','temp_fahrenheit',...'windspeed'],...index=pd.date_range(start='2014-02-12',...end='2014-02-15',freq='D'))

>>>df1            temp_celsius  temp_fahrenheit windspeed2014-02-12          24.3             75.7      high2014-02-13          31.0             87.8      high2014-02-14          22.0             71.6    medium2014-02-15          35.0             95.0    medium

>>>df2=pd.DataFrame([[28,'low'],...[30,'low'],...[35.1,'medium']],...columns=['temp_celsius','windspeed'],...index=pd.DatetimeIndex(['2014-02-12','2014-02-13',...'2014-02-15']))

>>>df2            temp_celsius windspeed2014-02-12          28.0       low2014-02-13          30.0       low2014-02-15          35.1    medium

>>>df2.reindex_like(df1)            temp_celsius  temp_fahrenheit windspeed2014-02-12          28.0              NaN       low2014-02-13          30.0              NaN       low2014-02-14           NaN              NaN       NaN2014-02-15          35.1              NaN    medium