gistfile1.txt

import pandas as pd

raw_df = pd.read_csv('reddit.r.scotch.tsv', sep='\t')

# Rid ourselves of data we don't need for the recommendation
raw_df.drop('Timestamp', axis=1, inplace=True)
raw_df.drop('Link To reddit Review', axis=1, inplace=True)
raw_df.drop('Region', axis=1, inplace=True)
raw_df.drop('Price', axis=1, inplace=True)
raw_df.drop('Date', axis=1, inplace=True)

# single word column names
raw_df.rename(columns={'Whisky Name': 'whisky', 'Reviewer Username': 'user', 'Rating': 'rating'}, inplace=True)

# Drop all rows that have NaN for the rating
raw_df = raw_df.dropna(subset=['rating'])

# what are the unique names for both whisky, and users
whisky_names = raw_df['whisky'].unique()
user_names = raw_df['user'].unique()

whisky_df = pd.DataFrame(columns={'index', 'name'})
user_df = pd.DataFrame(columns={'index', 'name'})

for index, name in enumerate(whisky_names):
	raw_df.replace(name, index, inplace=True)
	whisky_df.loc[len(whisky_df)+1] = [index, name]

for index, name in enumerate(user_names):
	raw_df.replace(name, index, inplace=True)
	user_df.loc[len(user_df)+1] = [index, name]

raw_df.to_csv('raw.tsv', sep='\t')
whisky_df.to_csv('whisky_names.tsv', sep='\t')
user_df.to_csv('user_names.tsv', sep='\t')