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Course Notes: Joining Data with pandas

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Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

# Import any packages you want to use here

Take Notes

Add notes here about the concepts you've learned and code cells with code you want to keep.

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# Add your code snippets here

right join

# Merge action_movies to the scifi_movies with right join
action_scifi = action_movies.merge(scifi_movies, on='movie_id', how='right',
                                   suffixes=('_act','_sci'))

# From action_scifi, select only the rows where the genre_act column is null
scifi_only = action_scifi[action_scifi['genre_act'].isnull()]

# Merge the movies and scifi_only tables with an inner join
movies_and_scifi_only = movies.merge(scifi_only, left_on='id', right_on='movie_id')

# Print the first few rows and shape of movies_and_scifi_only
print(movies_and_scifi_only.head())
print(movies_and_scifi_only.shape)
# Use right join to merge the movie_to_genres and pop_movies tables
genres_movies = movie_to_genres.merge(pop_movies, how='right', 
                                      left_on='movie_id', 
                                      right_on='id')

# Count the number of genres
genre_count = genres_movies.groupby('genre').agg({'id':'count'})

# Plot a bar chart of the genre_count
genre_count.plot(kind='bar')
plt.show()

outer join

# Merge iron_1_actors to iron_2_actors on id with outer join using suffixes
iron_1_and_2 = iron_1_actors.merge(iron_2_actors, 
                                     how='outer',
                                     on='id',
                                     suffixes=('_1','_2'))

# Create an index that returns true if name_1 or name_2 are null
m = ((iron_1_and_2['name_1'].isnull()) | 
     (iron_1_and_2['name_2'].isnull()))

# Print the first few rows of iron_1_and_2
print(iron_1_and_2[m].head())
# Merge the crews table to itself
crews_self_merged = crews.merge(crews, on='id', how='inner',
                                suffixes=('_dir','_crew'))

# Create a boolean index to select the appropriate rows
boolean_filter = ((crews_self_merged['job_dir'] == 'Director') & 
                  (crews_self_merged['job_crew'] != 'Director'))
direct_crews = crews_self_merged[boolean_filter]

# Print the first few rows of direct_crews
print(direct_crews.head())

a = pd.merge(

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