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For example, if integer values are mixed with floating point values, all of them will be converted to floating point
            values, as shown below:
             >>> import pandas as pd
             >>> marks = [90.5, 70, 95, 45, 100] #Creating Pandas Series using list
             >>> marksSeries = pd.Series(marks)
             >>> print(marksSeries) #all values typecasted to float type
                 0     90.5
                 1     70.0
                 2     95.0
                 3     45.0
                 4    100.0
                 dtype: float64
            Let's consider a scenario where the fifth student was absent for the exam. In such a case, the absence can be represented
            in the list using the value None. However, when we create a series object, Pandas replaces None by NaN (short for not
            a number).
             >>> marks = [90.5, 70, 95, 45, None] #Creating Pandas Series using list
             >>> marksSeries = pd.Series(marks)
             >>> print(marksSeries) #all values typecasted to float type
                 0    90.5
                 1    70.0
                 2    95.0
                 3    45.0
                 4     NaN
                 dtype: float64
            As illustrated below explicitly, NaN represents a null object of type float64:
             >>> type(marksSeries[4])
            output:
                 numpy.float64
            In  Python,  all  types  (classes)  are  considered  as  subclasses  of  the  common  class  object.  So,  objects  of  seemingly
            unrelated types are cast to this common type object. For example, let us consider a series object representing roll
            number, name, and marks in three subjects:

             >>> import pandas as pd
             >>> student = [23, 'Radhe Shyam', [90, 80, 91]]
             >>> studentSeries = pd.Series(student)
             >>> print(studentSeries)
            output:
                 0              23
                 1     Radhe Shyam
                 2    [90, 80, 91]
                 dtype: object
            Note that all values are typecast to object type.

            1.2 Indexing and Slicing

            Now, that you are comfortable with Pandas series, we will drop the suffix series from the names of series. So, we create
            a series, called students, denoting the marks of students.

             >>> import pandas as pd
             >>> marks = [90, 75, 95, 45, 100] #Creating Pandas Series using list
             >>> students = pd.Series(marks)
             >>> print(students)
            output:
                 0     90
                 1     75
                 2     95
                 3     45
                                                                                      Data Handling using Pandas  3
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