# Install mrjob library. This package is for running MapReduce jobs with Python # In Jupyter notebooks, "!" runs terminal commands from inside notebooks # Exercise 1 # use the pip package manager # <your code here/> ! pip install mrjob

import random contenders = ['Kill The Love', 'Dynamite', 'Likey'] # Exercise 2: generate 10 songs chosen randomly from contenders # use random.choices() to draw samples with replacement # there is an argument 'k' that can be used to specify how many samples to draw # <your code here/> random.choices(contenders, k=10)

# Exercise 3.1: Generate a list of 2000 songs # <your code here/> song_list = random.choices(contenders, k=2000) # Exercise 3.2: write the song list to a file, each entry separated by newline # <your code here/> with open('songs.txt', 'w') as myfile: myfile.write('\n'.join(song_list))

%%file wordcount.py # %%file is an Ipython magic function that saves the code cell as a file from mrjob.job import MRJob # import the mrjob library class MRSongCount(MRJob): # the map step: each line in the txt file is read as a key, value pair # in this case, each line in the txt file only contains a value but no key # _ means that in this case, there is no key for each line def mapper(self, _, song): # output each line as a tuple of (song_names, 1) yield (song, 1) # the reduce step: combine all tuples with the same key # in this case, the key is the song name # then sum all the values of the tuple, which will give the total song plays def reducer(self, key, values): yield (key, sum(values)) if __name__ == "__main__": MRSongCount.run()

# Exercise 4: run the code as a terminal command # <your code here/> ! python wordcount.py songs.txt