Python | Pandas Series.loc
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.loc
attribute is used to access a group of rows and columns by label(s) or a boolean array in the given Series object.
Syntax:Series.loc
Parameter : None
Returns : series
Example #1: Use Series.loc
attribute to select some values from the given Series object based on the labels.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' ]
print (sr)
|
Output :
Now we will use Series.loc
attribute to return the values of the selected labels in the given Series object.
sr.loc[[ 'City 4' , 'City 3' , 'City 1' ]]
|
Output :
As we can see in the output, the Series.loc
attribute has returned the name of the cities whose labels were passed to it.
Example #2 : Use Series.loc
attribute to select some values from the given Series object based on the labels.
import pandas as pd
sr = pd.Series([ '1/1/2018' , '2/1/2018' , '3/1/2018' , '4/1/2018' ])
sr.index = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' ]
print (sr)
|
Output :
Now we will use Series.loc
attribute to return the values of the selected labels in the given Series object.
sr.loc[[ 'Day 4' , 'Day 3' , 'Day 1' ]]
|
Output :
As we can see in the output, the Series.loc
attribute has returned the name of the cities whose labels were passed to it.
Last Updated :
28 Jan, 2019
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