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Series: One-dimensional data structure of Pandas module that can accommodate objects of various types.




        1.3 Typecasting
        The method astype() is used for typecasting the type of objects in a series. For example, while marks of some students
        may appear as integers, those of others may appear as fractions. We can transform them all as integers as shown below:

          01 import pandas as pd
          02 marks = [90.5, 70, 95, 45, 100]
          03 rollNumbers = [101, 102 , 103, 105, 106]
          04 students = pd.Series(marks, index = rollNumbers)
          05 print('Before Typecasting:')
          06 print(students)
          07 students = students.astype(int)
          08 print('After Typecasting:')
          09 print(students)
        output:
              Before Typecasting:
              101     90.5
              102     70.0
              103     95.0
              105     45.0
              106    100.0
              dtype: float64
              After Typecasting:
              101     90
              102     70
              103     95
              105     45
              106    100
              dtype: int32

          C T  02     Write a Python code to transform the salaries of employees stored in empSalary to floating

                      point value.








               astype(): Typecasting the type of objects in a series.



        1.4 Using Dictionary to Create Series Object

        Suppose, you want to create a series of national sports of some countries, indexed by the name of the country. As
        Pandas allows us to transform a dictionary to a series, a dictionary becomes the natural choice for such situations, as
        illustrated below:
         >>> import pandas as pd
         >>> sportsDict = {'Bhutan': 'Archery',
         ...               'Scotland': 'Golf',
         ...               'Japan': 'Sumo',
         ...               'India': 'Hockey' }          #Creating Series from Dictionary


           6   Touchpad Informatics Practices-XII
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