Python | Pandas Series.ndim

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.ndim attribute returns the number of dimensions of the underlying data, by definition it is 1 for series objects.

Syntax:Series.ndim

Parameter : None



Returns : dimension

Example #1: Use Series.ndim attribute to find the dimension 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'])
  
# Creating the row axis labels
sr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'
  
# Print the series
print(sr)

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Output :

Now we will use Series.ndim attribute to find the dimension of the given Series object.

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# return the dimension
sr.ndim

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Output :

As we can see in the output, the Series.ndim attribute has returned 1 indicating that the dimension of the given series object is 1.
 
Example #2 : Use Series.ndim attribute to find the dimension of the given series object.

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# 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)

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Output :

Now we will use Series.ndim attribute to find the dimension of the given Series object.

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# return the dimension
sr.ndim

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Output :

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