Pandas Series: reindex_like() function

Last update on September 15 2022 12:57:58 (UTC/GMT +8 hours)

Object with matching indices as other object in Pandas

The reindex_like() function is used to get an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing 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.

Syntax:

Series.reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None)

Parameters:

Name	Description	Type/Default Value	Required / Optional
other	Its row and column indices are used to define the new indices of this object.	Object of the same data type	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	Required
limit	Maximum number of consecutive labels to fill for inexact matches.	bool Default Value: None	Required
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.		optional

Returns: Series or DataFrame
Same type as caller, but with changed indices on each axis.

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

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
df1 = pd.DataFrame([[34.3, 77.7, 'high'],
                    [32, 88.8, 'high'],
                    [24, 72.6, 'medium'],
                    [31, 95, 'medium']],
    columns=['temp_celsius', 'temp_fahrenheit', 'windspeed'],
    index=pd.date_range(start='2019-04-12',
                        end='2019-04-15', freq='D'))
df1

Output:

            temp_celsius	temp_fahrenheit	windspeed
2019-04-12	     34.3	    77.7	        high
2019-04-13	     32.0	    88.8	        high
2019-04-14	     24.0	    72.6	       medium
2019-04-15	     31.0	    95.0	       medium

Python-Pandas Code:

import numpy as np
import pandas as pd
df2 = pd.DataFrame([[27, 'low'],
                    [28, 'low'],
                    [29.2, 'medium']],
    columns=['temp_celsius', 'windspeed'],
    index=pd.DatetimeIndex(['2019-04-12', '2019-04-13',
                            '2019-04-15']))
df2

Output:

            temp_celsius	windspeed
2019-04-12	     27.0	       low
2019-04-13	     28.0	       low
2019-04-15	     29.2	     medium

Python-Pandas Code:

import numpy as np
import pandas as pd
df2 = pd.DataFrame([[27, 'low'],
                    [28, 'low'],
                    [29.2, 'medium']],
    columns=['temp_celsius', 'windspeed'],
    index=pd.DatetimeIndex(['2019-04-12', '2019-04-13',
                            '2019-04-15']))
df2.reindex_like(df1)

Output:

            temp_celsius	temp_fahrenheit	windspeed
2019-04-12	      27.0	        NaN	         low
2019-04-13	      28.0	        NaN	         low
2019-04-14	      NaN	        NaN	         NaN
2019-04-15	      29.2	        NaN	       medium

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