Linking them together!

In the last lesson, you've finished the bulk of the work on your effort to link restaurants and restaurants_new. You've generated the different pairs of potentially matching rows, searched for exact matches between the cuisine_type and city columns, but compared for similar strings in the rest_name column. You stored the DataFrame containing the scores in potential_matches.

Now it's finally time to link both DataFrames. You will do so by first extracting all row indices of restaurants_new that are matching across the columns mentioned above from potential_matches. Then you will subset restaurants_new on these indices, and finally concatenate the non-duplicate values with restaurants. All DataFrames are in your environment, alongside pandas imported as pd.

This exercise is part of the course

Cleaning Data in Python

Exercise instructions

Isolate instances of potential_matches where the row sum is above or equal to 3 by using the .sum() method.
Extract the second column index from matches, which represents row indices of matching record from restaurants_new by using the .get_level_values() method.
Subset restaurants_new for rows that are not in matching_indices.
Concatenate restaurants and non_dup.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Isolate potential matches with row sum >=3
matches = ____[____.___(____) >= ____]

# Get values of second column index of matches
matching_indices = matches.____.____(____)

# Subset restaurants_new based on non-duplicate values
non_dup = ____[~restaurants_new.index.____(____)]

# Concatenate restaurants and non_dup
full_restaurants = pd.____([____, ____])
print(full_restaurants)

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.8+

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