Python | Pandas Series.as_matrix()

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.as_matrix() function is used to convert the given series or dataframe object to Numpy-array representation.

Syntax: Series.as_matrix(columns=None)

Parameter :
columns : If None, return all columns, otherwise, returns specified columns.

Returns : values : ndarray



Example #1: Use Series.as_matrix() function to return the numpy-array representation of the given series object.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])
  
# Create the Index
index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

City 1    New York
City 2     Chicago
City 3     Toronto
City 4      Lisbon
City 5         Rio
dtype: object

Now we will use Series.as_matrix() function to return the numpy array representation of the given series object.

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# return numpy array representation
result = sr.as_matrix()
  
# Print the result
print(result)

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Output :

['New York' 'Chicago' 'Toronto' 'Lisbon' 'Rio']

As we can see in the output, the Series.as_matrix() function has successfully returned the numpy array representation of the given series object.
 
Example #2 : Use Series.as_matrix() function to return the numpy-array representation of the given series object.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
# apply yearly frequency
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

2010-12-31 08:45:00    11.0
2011-12-31 08:45:00    21.0
2012-12-31 08:45:00     8.0
2013-12-31 08:45:00    18.0
2014-12-31 08:45:00    65.0
2015-12-31 08:45:00    18.0
2016-12-31 08:45:00    32.0
2017-12-31 08:45:00    10.0
2018-12-31 08:45:00     5.0
2019-12-31 08:45:00    32.0
2020-12-31 08:45:00     NaN
Freq: A-DEC, dtype: float64

Now we will use Series.as_matrix() function to return the numpy array representation of the given series object.

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# return numpy array representation
result = sr.as_matrix()
  
# Print the result
print(result)

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Output :

[ 11.  21.   8.  18.  65.  18.  32.  10.   5.  32.  nan]

As we can see in the output, the Series.as_matrix() function has successfully returned the numpy array representation of the given series object.

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