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 Index.get_values() function returns the Index data as an numpy.ndarray. It returns one dimensional array for multi-index array.
Syntax: Index.get_values()
Returns : A one-dimensional numpy array of the Index values
Example #1: Use Index.get_values() function to return the Index value as a numpy array.
# importing pandas as pd import pandas as pd   # Creating the Index idx = pd.Index(['Labrador', 'Beagle', 'Labrador',                      'Lhasa', 'Husky', 'Beagle'])   # Print the Index idx |
Output :
let’s use the Index.get_values() function to return the Index data as numpy array.
# Returns the labels of Index as numpy array idx.get_values() |
Output :
As we can see in the output, the Index.get_values() function has returned the index labels as numpy array.
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Example #2: Use Index.get_values() function on multiindex array.
# importing pandas as pd import pandas as pd   # Creating the MultiIndex object midx = pd.MultiIndex.from_arrays([['Mon', 'Tue', 'Wed', 'Thr'],                    [10, 20, 30, 40]], names =('Days', 'Target'))   # Print the MultiIndex object midx |
Output :
Let’s return the Index labels into one dimensional numpy array format.
# Convert the multi-index into one # dimensional numpy array form. midx.get_values() |
Output :
As we can see in the output, even the multi-index has been converted into a one-dimensional array form.
