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2        Bear
              3        Lion
              dtype: object
        Note that as the class Series is part of the Package, we write pd.Series to refer to the class Series. As the names
        of the animals are already availble in the list animals, we used animals as the input argument. Further, note that
        the description of the object animalSeries includes row indexes, often called row labels. The row labels 0, 1, 2, ...
        are used to refer to the associated values animalSeries[0], animalSeries[1], animalSeries[2], ...,
        as shown below:

         >>> animalSeries[3]
        output:
              'Lion'
        We can find out all the valid indexes for a series as follows:

         >>> animalSeries.index
        output:
              RangeIndex(start=0, stop=4, step=1)
        Similarly, we can display all the values in a series as follows:
         >>> animalSeries.values
        output:
              array(['Elephant', 'Tiger', 'Bear', 'Lion'], dtype=object)
        In the absence of a list comrising the names of the animals, we could create a series by providing the names of the
        animals in the form of list or a tuple, as the argument as shown below:

         >>> animalSeries = pd.Series(['Elephant', 'Tiger', 'Bear', 'Lion'])
         >>> print(animalSeries)
              0    Elephant
              1       Tiger
              2        Bear
              3        Lion
              dtype: object
        Similarly, let us define another series object marksSeries comprising marks of five students created using a list
        marks as follows:

         >>> marks = [90, 70, 95, 45, 100]
         >>> marksSeries = pd.Series(marks)
         >>> print(marksSeries)
        output:
              0     90
              1     70
              2     95
              3     45
              4    100
              dtype: int64
        A series object such as marksSeries can also be created using a numpy array comprising marks of five students as
        follows:

         >>> import pandas as pd
         >>> import numpy as np
         >>> marks = np.array([90, 70, 95, 45, 100]) #Creating Pandas Series using numpy array
         >>> marksSeries = pd.Series(marks)
         >>> print(marksSeries)
              0     90
              1     70
              2     95
              3     45
              4    100
              dtype: int64
        When a Pandas Series contains objects of different types, Pandas makes an effort to cast all the objects to a common type.

           2   Touchpad Informatics Practices-XII
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