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.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon']) # Creating the row axis labels sr.index = ['City 1', 'City 2', 'City 3', 'City 4'] # Print the series print(sr) |
Output :
Now we will use Series.loc attribute to return the values of the selected labels in the given Series object.
# return the selected values. 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.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018']) # Creating the row axis labels sr.index = ['Day 1', 'Day 2', 'Day 3', 'Day 4'] # Print the series print(sr) |
Output :
Now we will use Series.loc attribute to return the values of the selected labels in the given Series object.
# return the selected values. 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.
