drop_duplicates() for removal in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove duplicate rows from the DataFrame df.

Pandas

df_clean = df.[1]()

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

Adrop_duplicates

Bsort_values

Cfillna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() which removes missing values, not duplicates.

Using fillna() which fills missing values.

Using sort_values() which sorts rows but does not remove duplicates.

The drop_duplicates() method removes duplicate rows from a DataFrame.

2fill in blank

medium

Complete the code to remove duplicates based only on the 'Name' column.

Pandas

df_unique = df.drop_duplicates(subset=[1])

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A'City'

B'Salary'

C'Name'

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column unrelated to duplicates like 'Age' or 'City'.

Not passing the column name as a string.

The subset parameter specifies columns to consider for identifying duplicates. Here, only 'Name' is used.

3fill in blank

hard

Fix the error in the code to remove duplicates and keep the last occurrence.

Pandas

df_last = df.drop_duplicates(keep=[1])

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

A'first'

B'last'

C'none'

D'all'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'first' which keeps the first occurrence instead.

Using invalid options like 'none' or 'all'.

The keep parameter controls which duplicate to keep. 'last' keeps the last occurrence.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using '<' instead of '>' in the condition.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than zero.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase.

Using wrong comparison operators like '<'.

The dictionary comprehension uses uppercase keys, original values, and filters values greater than zero.