Page 21 - Informatics_Practices_Fliipbook_Class12
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>>> nationalSports = pd.Series(sportsDict)
             >>> print(nationalSports)
            output:
                 Bhutan      Archery
                 Scotland       Golf
                 Japan          Sumo
                 India        Hockey
                 dtype: object
            Next, we create another series object for the countries that have a passion for cricket.

             >>> countries = ['India', 'Australia', 'Barbados', 'Pakistan', 'England']
             >>> cricketLovers = pd.Series(['Cricket']*5, index=countries)
             >>> print(cricketLovers)
            output:
                 India        Cricket
                 Australia    Cricket
                 Barbados     Cricket
                 Pakistan     Cricket
                 England      Cricket
                 dtype: object
            Finally, we combine the two series to form the series allSports. To achieve this, we use the append() method of
            the Series object, as shown below:
             >>> allSports = nationalSports.append(cricketLovers)
             >>> print(allSports)
                 Bhutan       Archery
                 Scotland        Golf
                 Japan           Sumo
                 India         Hockey
                 India        Cricket
                 Australia    Cricket
                 Barbados     Cricket
                 Pakistan     Cricket
                 England      Cricket
                 dtype: object
            Note that labels associated with values in a Pandas Series do not need to be unique. In the series, allSports, the
            index 'India' appears twice. Next, suppose, we want to list the countries where a sport is popular. A straightforward
            way to do this would be to construct a series by swapping the role of the index (allSports.index) and the values
            (allSports.values), as demonstrated below:
             >>> allSportsInverted = pd.Series(allSports.index, index = allSports.values)
             >>> print(allSportsInverted)
            output:
                 Archery       Bhutan
                 Golf        Scotland
                 Sumo           Japan
                 Hockey         India
                 Cricket        India
                 Cricket    Australia
                 Cricket     Barbados
                 Cricket     Pakistan
                 Cricket      England
                 dtype: object


                   The pandas version upto 2.xxxx stores series object as the numpy array at the backend, which are both space and
                   computationally efficient. In the latest versions of pandas let us use PandasArrow as the backend which is mainly
                   useful when dealing with time series data.



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