Keeping first vs last vs none in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to drop duplicate rows keeping the first occurrence.

Pandas

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

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

ANone

B"first"

CFalse

D"last"

Attempts:

3 left

💡 Hint

Common Mistakes

Using keep=None causes an error.

Using keep=False drops all duplicates.

Using keep='last' keeps the last duplicates, not the first.

Using keep="first" keeps the first occurrence of duplicates and drops the rest.

2fill in blank

medium

Complete the code to drop duplicate rows keeping the last occurrence.

Pandas

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

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

A"first"

BTrue

CNone

D"last"

Attempts:

3 left

💡 Hint

Common Mistakes

Using keep='first' keeps the first duplicates, not the last.

Using keep=None causes an error.

Using keep=True is invalid.

Using keep="last" keeps the last occurrence of duplicates and drops the earlier ones.

3fill in blank

hard

Fix the error in the code to drop all duplicates (keep none).

Pandas

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

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

A"first"

B"last"

CFalse

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using keep=None causes an error.

Using keep='first' or 'last' keeps some duplicates.

Using keep=True is invalid.

Using keep=False drops all duplicates, keeping none.

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

Using 'word' instead of 'len(word)' for the value.

Using 'word' in the condition instead of length.

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 with 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 'k' instead of 'k.upper()' for keys.

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

Using 'k' instead of 'v' for values.

This dictionary comprehension creates keys as uppercase strings and includes only items where the value is greater than zero.