Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.reindex_like() function return an object with matching indices as other object. It conform the object to the same index on all axes.
Syntax: Series.reindex_like(other, method=None, copy=True, limit=None, tolerance=None)
Parameter :
other : Its row and column indices are used to define the new indices of this object.
method : Method to use for filling holes in reindexed DataFrame.
copy : Return a new object, even if the passed indexes are the same.
limit : Maximum number of consecutive labels to fill for inexact matches.
tolerance : Maximum distance between original and new labels for inexact matches.Returns : Series or DataFrame
Example #1: Use Series.reindex_like() function to reindex the given series object based on the other object.
# importing pandas as pd import pandas as pd # Creating the first Series sr1 = pd.Series([10, 25, 3, 11, 24, 6]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr1.index = index_ # Print the series print(sr1) # Creating the second Series sr2 = pd.Series([10, 25, 3, 11, 24, 6, 25, 45]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp', 'Mirinda', 'Appy'] # set the index sr2.index = index_ # Print the series print(sr2) |
Output :
Now we will use Series.reindex_like() function to reindex the sr2 series object based on sr1.
# reindex sr2 using sr1 result = sr2.reindex_like(sr1) # Print the result print(result) |
Output :
As we can see in the output, the Series.reindex_like() function has successfully reindexed sr2 object using sr1. Notice for the extra labels has been dropped.
Example #2 : Use Series.reindex_like() function to reindex the given series object based on the other object.
# importing pandas as pd import pandas as pd # Creating the first Series sr1 = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Create the Index index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index sr1.index = index_ # Print the series print(sr1) # Creating the second Series sr2 = pd.Series(['New York', 'Toronto', 'Lisbon', 'Rio']) # Create the Index index_ = ['City 1', 'City 3', 'City 4', 'City 5'] # set the index sr2.index = index_ # Print the series print(sr2) |
Output :
Now we will use Series.reindex_like() function to reindex the sr2 series object based on sr1.
# reindex sr2 using sr1 result = sr2.reindex_like(sr1) # Print the result print(result) |
Output :
As we can see in the output, the Series.reindex_like() function has successfully reindexed sr2 object using sr1. Notice for the newer additions NaN values has been used.
