merge() for SQL-like joins in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to merge two DataFrames on the 'id' column.

Pandas

merged_df = df1.merge(df2, on=[1])

Drag options to blanks, or click blank then click option'

A'name'

B'age'

C'id'

D'score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column that does not exist in both DataFrames.

Using a column that is not unique or relevant for merging.

We merge on the 'id' column because it is the common key in both DataFrames.

2fill in blank

medium

Complete the code to perform a left join using merge() on 'user_id'.

Pandas

result = df_left.merge(df_right, on=[1], how=[2])

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A'customer_id'

B'user_id'

C'product_id'

D'order_id'

E'left'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name for the join key.

Forgetting to specify the join type for a left join.

The 'user_id' column is the key to join on. The default join type is inner, but here we want a left join, so we specify how='left'.

3fill in blank

hard

Fix the error in the merge code to join on columns with different names: 'id_left' and 'id_right'.

Pandas

merged = df1.merge(df2, left_on=[1], right_on=[2])

Drag options to blanks, or click blank then click option'

A'id_left'

B'id_right'

C'id'

D'user_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using only 'on' when column names differ.

Mixing up left_on and right_on values.

When the key columns have different names, use left_on and right_on to specify them.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong condition in the if clause.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts if count is greater than 1.

Pandas

{ [1]: [2] for [3], count in word_counts.items() if count > 1 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Bcount

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the key without converting to uppercase.

Mixing up the key and value in the comprehension.

The dictionary maps each word in uppercase to its count, but only includes words with count greater than 1.