CODEWITHSUNDEEP
pythonpandas
Feras
Feras

Reputation: 11

How to compare Pandas dataframe values with a list, and put the result in a new column

I have the following dataset for Manhattan neighborhoods with the most common venues in each neighborhood:

df

I made a list of venues:

fit_venues = ['Coffee Shop', 'Café', 'Park', 'Hotel', 'Sandwich Place', 'Pizza Place', 'Gym / Fitness Center', 'Exhibit', 'Gym', 'Supermarket', 'Nightclub', 'Concert Hall', 'Jazz Club']

and I want to add a column to the dataframe (let's call it "Fit Neighborhood" for example), and compare the most common venues of each neighborhood (5 columns) with the list "fit_venues". Then we assign the result to the column "Fit Neighborhood" (Yes/No or True/False). For example, the first two rows should return Yes/True and the third row should return No/False.

Any help?

Upvotes: 1

Views: 6325

Answers (2)

soundstripe
soundstripe

Reputation: 1474

Have you tried using DataFrame.isin()?

You didn't give me the names of your most common venue columns, so I'll assume they are the only columns in the DataFrame (df):

fit_venues = ['Coffee Shop', 'Café', 'Park', 'Hotel', 'Sandwich Place', 'Pizza Place', 'Gym / Fitness Center', 'Exhibit', 'Gym', 'Supermarket', 'Nightclub', 'Concert Hall', 'Jazz Club']

df['Fit Neighborhood'] = df.isin(fit_venues).any()

Upvotes: 1

pizza lover
pizza lover

Reputation: 523

See if this works: 

 fit_venues = ['Coffee Shop', 'Café', 'Park', 'Hotel', 'Sandwich Place', 'Pizza Place', 'Gym / Fitness Center', 'Exhibit', 'Gym', 'Supermarket', 'Nightclub', 'Concert Hall', 'Jazz Club']

df["binary_check"] = df[df["5th Most Common Venue"].isin(fit_venues)]

Upvotes: 1

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