import pandas as pd
businesses = pd.read_json(businesses_filepath, lines=True, encoding='utf_8')
restaurantes = businesses['Restaurants' in businesses['categories']]
I would like to remove the lines that do not have Restaurants in the categories column, and this column has lists, however gave the error 'KeyError: False' and I would like to understand why and how to solve.
If you find that your data contains spelling variations or alternative restaurant related terms, the following may be of benefit. Essentially you put your restaurant related terms in restuarant_lst. The lambda function returns true if any of the items in restaurant_lst are contained within each row of the business series. The .loc indexer filters out rows which return false for the lambda function.
restaurant_lst = ['Restaurant','restaurantes','diner','bistro']
restaurant = businesses.loc[businesses.apply(lambda x: any(restaurant_str in x for restaurant_str in restaurant_lst))]
The reason for this is that the Series class implements a custom in operator that doesn't return an iterable like the == does, here's a workaround
businesses[['Restaurants' in c for c in list(businesses['categories'])]]
hopefully this helps someone where you're looking for a substring in the column and not a full match.
None of the answers here actually worked for me,
businesses[businesses['categories'] == 'Restaurants']
obviously won't work since the value in 'categories' is not a string, it's a list, meaning the comparison will always fail.
What does, however, work, is converting the column into tuples instead of strings:
businesses['categories'] = businesses['categories'].apply(tuple)
That allows you to use the standard .loc thing:
businesses.loc[businesses['categories'] == ('Restaurants',)]