Cleaning Netflix-style recommendation engine datasets I am working on a portfolio project that deals with creating a Netflix-style recommendation engine for movies. This code is currently ran locally but I will need to upload it into Jupyter Notebooks and implement it with Amazon SageMaker. The .tsv files are imported from IMDb Datasets.
The code below is working code created by myself. Is there any room for improvement from an efficiency/cleanliness aspect?
import pandas as pd 
import numpy as np

basics = pd.read_table('basics.tsv')
origin = pd.read_table('akas.tsv')
ratings = pd.read_table('ratings.tsv')

#setting 'basics' columns
basics.drop(basics.columns[[3,4,6,7]], axis=1, inplace=True)
basics.columns = ['tconst','type','title','year','genre']

#removing all non-movies from index
cond1 = basics['type'].isin(['movie','tvMovie'])

#setting new 'basics' variable
basics2 = basics[cond1]

#setting 'origin' columns
origin.drop(origin.columns[[1,2,5,6,7]], axis = 1, inplace = True)
origin.columns = ['tconst','region','language']

#removing non-english movies from index
cond2 = origin['region'].isin(['US','GB'])
cond3 = origin['language'] == 'en'

#setting new 'origin' variable
origin2 = origin[cond2 | cond3]

#setting 'ratings' columns
ratings.columns = ['tconst','rating','votecount']

#converting strings to integers
ratings.rating = pd.to_numeric(ratings.rating, errors='coerce').astype(np.int64)
ratings.votecount = pd.to_numeric(ratings.votecount, errors='coerce').astype(np.int64)

#setting new 'ratings' variable
ratings2 = ratings[(ratings['rating'] &gt;= 5) &amp; (ratings['votecount'] &gt;= 50)]

#finalizing movie recommendation list
rcmd = basics2.merge(origin2,on='tconst').merge(ratings2,on='tconst')
rcmd.drop_duplicates(subset = 'tconst', keep =&quot;first&quot;, inplace = True) 
print(rcmd)

Thanks in advance.
The usual
Use a PEP8 linter; move global code into functions; use type hints; use a __main__ guard.
Column use
basics.drop(basics.columns[[3,4,6,7]], axis=1, inplace=True)
basics.columns = ['tconst','type','title','year','genre']

really shouldn't be necessary. Use the usecols kwarg of read_table instead; the col names are a loose guess from the IMDB documentation:
basics = pd.read_table(
    'basics.tsv',
    usecols=(
        'tconst', 'titleType', 'primaryTitle', 'startYear', 'genres',
    ),
)