Pandas Series: reindex_like() function
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, ‘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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