Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
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.where() function replace values where the input condition is False for the given Series object. It takes another object as an input which will be used to replace the value from the original object.
Syntax: Series.where(cond, other=nan, inplace=False, axis=None, level=None, errors=’raise’, try_cast=False, raise_on_error=None)
Parameters :
cond : boolean NDFrame, array-like, or callable
other : scalar, NDFrame, or callable
inplace : boolean, default False
axis : int, default None
level : int, default None
errors : str, {‘raise’, ‘ignore’}, default raise
try_cast : boolean, default FalseReturns : wh : same type as caller
Example #1: Use Series.where() function to replace values in the given Series object with some other value when the passed condition is not satisfied.
# importing pandas as pd import pandas as pd # Creating the First Series sr1 = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Creating the row axis labels sr1.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # Print the series print(sr1) # Creating the second Series sr2 = pd.Series(['New York', 'Bangkok', 'London', 'Lisbon', 'Brisbane']) # Creating the row axis labels sr2.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # Print the series print(sr2) |
Output :
Now we will use Series.where() function to replace those values which does not satisfy the passed condition.
# replace the values sr1.where(sr1 == 'Rio', sr2) |
Output :
As we can see in the output, the Series.where() function has replaced the names of all cities except the ‘Rio’ city.
Example #2 : Use Series.where() function to replace values in the given Series object with some other value when the passed condition is not satisfied.
# importing pandas as pd import pandas as pd # Creating the First Series sr1 = pd.Series([22, 18, 19, 20, 21]) # Creating the row axis labels sr1.index = ['Student 1', 'Student 2', 'Student 3', 'Student 4', 'Student 5'] # Print the series print(sr1) # Creating the second Series sr2 = pd.Series([19, 16, 22, 20, 18]) # Creating the row axis labels sr2.index = ['Student 1', 'Student 2', 'Student 3', 'Student 4', 'Student 5'] # Print the series print(sr2) |
Output :
Now we will use Series.where() function to replace those values which does not satisfy the passed condition.
# replace the values sr1.where(sr1 >20, sr2) |
Output :
As we can see in the output, the Series.where() function has replaced all the values which did not satisfy the passed condition.
