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 }

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

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 }

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

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.

Practice

1.

In pandas, what does keep='first' do when removing duplicates?

easy

A. Keeps the first occurrence of each duplicate and removes the rest

B. Keeps the last occurrence of each duplicate and removes the rest

C. Removes all duplicates, keeping only unique rows

D. Keeps all duplicates without removing any

Solution

1. Step 1: Understand the meaning of keep='first'

   This option keeps the first row of each set of duplicates and removes the later ones.

2. Step 2: Compare with other options

   Unlike keep='last' which keeps the last occurrence, keep='first' keeps the earliest one.

3. Final Answer:

   Keeps the first occurrence of each duplicate and removes the rest -> Option A

4. Quick Check:

   keep='first' = Keep first duplicate [OK]

Hint: Remember: 'first' keeps earliest duplicate row [OK]

Common Mistakes:

• Confusing keep='first' with keep='last'
• Thinking keep='first' removes all duplicates
• Assuming keep='first' keeps all duplicates

2.

Which of the following is the correct syntax to remove duplicates in a pandas DataFrame df keeping only the last occurrence?

df._____('column_name', keep='last')

easy

A. drop_dupes

B. remove_duplicates

C. drop_duplicates

D. delete_duplicates

Solution

1. Step 1: Identify the pandas method for removing duplicates

   The correct method is drop_duplicates to remove duplicate rows.

2. Step 2: Confirm syntax for keeping last occurrence

   Using keep='last' inside drop_duplicates keeps the last duplicate row.

3. Final Answer:

   drop_duplicates -> Option C

4. Quick Check:

   Method to remove duplicates = drop_duplicates [OK]

Hint: Use df.drop_duplicates() to remove duplicates [OK]

Common Mistakes:

• Using non-existent methods like remove_duplicates
• Confusing method names with similar words
• Forgetting to specify keep='last'

3.

What will be the output of the following code?

import pandas as pd

data = {'A': [1, 2, 2, 3, 3, 3], 'B': ['x', 'y', 'y', 'z', 'z', 'z']}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep=False)
print(result)

medium

A. Rows with unique values only, no duplicates

B. All rows except the first occurrence of duplicates

C. Rows with A=1 B='x', A=2 B='y', A=3 B='z' only

D. Empty DataFrame

Solution

1. Step 1: Understand keep=False behavior

   Using keep=False removes all duplicates, keeping only rows that appear once.

2. Step 2: Identify unique rows in the DataFrame

   In the data, only the row with A=1 and B='x' is unique; others appear multiple times.

3. Final Answer:

   Rows with unique values only, no duplicates -> Option A

4. Quick Check:

   keep=False = Keep only unique rows [OK]

Hint: keep=False drops all duplicates, keeps only unique rows [OK]

Common Mistakes:

• Thinking keep=False keeps first or last duplicates
• Expecting some duplicates to remain
• Confusing output with keep='first' or keep='last'

4.

Consider this code snippet:

import pandas as pd

data = {'col': [1, 1, 2, 2, 3]}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep='middle')
print(result)

What is the error in this code?

medium

A. DataFrame missing required columns

B. Invalid value for the 'keep' parameter

C. Syntax error in DataFrame creation

D. No error, code runs fine

Solution

1. Step 1: Check the 'keep' parameter values

   The valid values for 'keep' are 'first', 'last', or False. 'middle' is invalid.

2. Step 2: Identify the error raised by invalid 'keep'

   Using 'middle' causes a ValueError because pandas does not recognize it.

3. Final Answer:

   Invalid value for the 'keep' parameter -> Option B

4. Quick Check:

   keep must be 'first', 'last', or False [OK]

Hint: Use only 'first', 'last', or False for keep parameter [OK]

Common Mistakes:

• Using unsupported strings for keep
• Assuming 'middle' is valid
• Ignoring error messages

5.

You have a DataFrame df with duplicate rows based on column 'ID'. You want to remove all duplicates completely, so only rows with unique 'ID' remain. Which code snippet achieves this?

1) df.drop_duplicates(subset=['ID'], keep='first')
2) df.drop_duplicates(subset=['ID'], keep='last')
3) df.drop_duplicates(subset=['ID'], keep=False)
4) df.drop_duplicates()

hard

A. Option 1

B. Option 4

C. Option 2

D. Option 3

Solution

1. Step 1: Understand the goal

   The goal is to remove all duplicates so only unique 'ID' rows remain.

2. Step 2: Analyze each option

   Options 1 and 2 keep one duplicate (first or last). Option 4 removes duplicates but keeps first by default. Only option 3 with keep=False removes all duplicates completely.

3. Final Answer:

   Option 3 -> Option D

4. Quick Check:

   keep=False removes all duplicates [OK]

Hint: Use keep=False to remove all duplicates fully [OK]

Common Mistakes:

• Using keep='first' or 'last' when full removal needed
• Not specifying subset for duplicate check
• Assuming default drop_duplicates removes all duplicates