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.values attribute return Series as ndarray or ndarray-like depending on the dtype.
Syntax:Series.values
Parameter : None
Returns : ndarray
Example #1: Use Series.values attribute to return the values in the given series object as an ndarray.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Creating the row axis labels sr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # Print the series print(sr) |
Output :
Now we will use Series.values attribute to return the values of the given Series object as an ndarray.
# return an ndarray sr.values |
Output :
As we can see in the output, the Series.values attribute has returned an ndarray object containing the values of the given Series object.
Example #2 : Use Series.values attribute to return the values in the given series object as an ndarray.
# 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.values attribute to return the values of the given Series object as an ndarray.
# return an ndarray sr.values |
Output :
As we can see in the output, the Series.values attribute has returned an ndarray object containing the values of the given Series object.
