'HR', 'BA', 'HR', 'HR', 'Gen', 'Gen', 'BA', 'Gen', 'Gen', 'HR', 'Gen', 'BA', 'Gen' Create a Pandas series with the above data Use a Pandas function create a Pandas series of frequency distribution for the major’s data Permanently sort the frequency distribution from (b) above in alphabetic order of the major codes Create a dictionary with the 'HR', 'BA' and ‘Gen’ as keys and 'Human Resource', 'Business Analytics' and 'General Management' as the three respective values in the dictionary Iterate over the Pandas series of the frequency distribution and print the sorted series of frequency distribution. Use the dictionary to print the major
'HR', 'BA', 'HR', 'HR', 'Gen', 'Gen', 'BA', 'Gen', 'Gen', 'HR', 'Gen', 'BA', 'Gen'
Create a Pandas series with the above data
Use a Pandas function create a Pandas series of frequency distribution for the major’s data
Permanently sort the frequency distribution from (b) above in alphabetic order of the major codes
Create a dictionary with the 'HR', 'BA' and ‘Gen’ as keys and 'Human Resource', 'Business Analytics' and 'General Management' as the three respective values in the dictionary
Iterate over the Pandas series of the frequency distribution and print the sorted series of frequency distribution. Use the dictionary to print the major
We are given a list of major codes to use in this Python data manipulation exercise that represents various academic majors. This data will be used to create a Pandas Series, which will be sorted alphabetically, provide a frequency distribution of the majors, and produce a dictionary that maps major codes to their complete names. The majors will be printed using the dictionary once we iterate over the sorted frequency distribution.
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