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

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# Creating the Series

sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])

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# Creating the row axis labels

sr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] 

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# 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.