Data Prep - Music Recommendation

Import pandas for the purpose of creating a pandas series and reading in some datasets

import pandas as pd

read in the jams tsv file

data = pd.read_csv('jams.tsv', sep='\t', error_bad_lines=False)

b'Skipping line 7872: expected 7 fields, saw 8\nSkipping line 11730: expected 7 fields, saw 9\nSkipping line 14131: expected 7 fields, saw 8\nSkipping line 58054: expected 7 fields, saw 8\nSkipping line 58754: expected 7 fields, saw 8\n'
b'Skipping line 847129: expected 7 fields, saw 8\n'
b'Skipping line 1091153: expected 7 fields, saw 8\nSkipping line 1175375: expected 7 fields, saw 8\n'
b'Skipping line 1225935: expected 7 fields, saw 8\nSkipping line 1255357: expected 7 fields, saw 8\nSkipping line 1279671: expected 7 fields, saw 8\n'
b'Skipping line 1330675: expected 7 fields, saw 8\n'
b'Skipping line 1448033: expected 7 fields, saw 8\nSkipping line 1543893: expected 7 fields, saw 8\n'
b'Skipping line 1579569: expected 7 fields, saw 8\nSkipping line 1612448: expected 7 fields, saw 8\n'
b'Skipping line 1784588: expected 7 fields, saw 8\n'

the jams tsv file contains data on different users favorites songs from 2011 to 2015

create a pandas series that displays the full set of user_ids in the dataset

unique_id = pd.Series(data['user_id'].unique()) unique_id

with the output of the series, one can see every user_id in the tsv file

for the purpose of time, utilizing every piece of data in the dataset may take to long. Using the sample function we can select a random subset of users to work with.

sample = unique_id.sample(1000) sample

upon output, one can notice the random subset based on the out of order indexes.

now we can create a dataframe that displays the columns relating to the user_ids in our random sample

jam = data[data['user_id'].isin(sample)] jam

upon output one can notice the user_id and the relating columns

now all we need is the user_id, artist and title coulmns in the tsv file.

order = jam[['user_id', 'artist', 'title']] order

with the output one can notice the dropping of a couple of columns

Next we are creating a new column called final_jam. This new column combines the song title and the artist of our sample.

order['final_jam'] = order['title'] + ', by ' + order['artist'] order

/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

now that our final_jam column contains both the song title and artist we do not need ether of these columns anymore.

final_order = order[['user_id', final_jam']] final_order

finally we can export this data frame to a csv file for the purpose analyzing in class.

final_order.to_csv('jam-sample.csv', index=False)

read in the new csv file just to make sure everything looks right. Index is set to false so our sample is reordered correctly.

pd.read_csv('jam-sample.csv')