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output:
                 Strength of first 3 sections:
                 A    35
                 B    50
                 C    60
                 dtype: int64
             >>> nSections = 3
             >>> print("\nStrength of last", nSections, "sections:\n",sections.tail(nSections))
            output:
                 Strength of last 3 sections:
                 E    45
                 F    40
                 G    55
                 dtype: int64


             C T  04     Write Python code to display names and salary of first five employees working in the company.









                  head(): Returns first n records of the series.
                  tail(): Returns last n records of the series.


            1.5.2 Summarizing a Series

            We can obtain a statistical summary of a series (containing numerical values) by using the describe() method. This
            summary includes the count of non-null observations, mean, standard deviation, quartiles, minimum, and maximum
            values.
             >>> sections.describe()
            output:
                 count     7.000000
                 mean     48.571429
                 std       8.997354
                 min      35.000000
                 25%      42.500000
                 50%      50.000000
                 75%      55.000000
                 max      60.000000
                 dtype: float64
            Note that while the minimum and maximum values in the series are 35 and 60, repectively, the mean value is 48.57.
            Further, 25% of the values have an upper limit of 42.5, 50% of the values have an upper limit of 50, 75% of the values
            have an upper limit of 55, and the remaining 25% values take value up to the maximum value , i.e. 60. If required, the
            values in the above summary may be rounded, as shown below:
             >>> # use the default option, round up to one decimal place.
             >>> round(sections.describe())
            output:
                 count     7.0
                 mean     49.0
                 std       9.0
                 min      35.0
                 25%      42.0
                 50%      50.0

                                                                                      Data Handling using Pandas  9
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