Adding and removing columns in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding a new column with a calculation

What is the output DataFrame after running this code that adds a new column 'C' as the sum of columns 'A' and 'B'?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df['C'] = df['A'] + df['B']
print(df)

A {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [5, 7, 9]}

B {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [4, 5, 6]}

C {'A': [1, 2, 3], 'B': [4, 5, 6]}

D SyntaxError

💡 Hint

Adding columns can be done by simple arithmetic on existing columns.

The new column 'C' is created by adding values from 'A' and 'B' element-wise, resulting in [5, 7, 9].

❓ data_output

intermediate

Result after removing a column using drop

What is the DataFrame output after dropping column 'B' from this DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [10, 20], 'B': [30, 40], 'C': [50, 60]})
df2 = df.drop('B', axis=1)
print(df2)

A KeyError

B {'B': [30, 40], 'C': [50, 60]}

C {'A': [10, 20], 'C': [50, 60]}

D {'A': [10, 20], 'B': [30, 40], 'C': [50, 60]}

💡 Hint

drop with axis=1 removes columns.

The drop method removes column 'B', leaving columns 'A' and 'C'.

🔧 Debug

advanced

Identify the error when removing a column

What error does this code raise when trying to remove a column 'D' that does not exist?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
df.drop('D', axis=1)

A KeyError

B ValueError

C AttributeError

D No error, returns original DataFrame

💡 Hint

Dropping a non-existing column without errors raises a specific exception.

Pandas raises a KeyError when trying to drop a column that does not exist by default.

🚀 Application

advanced

Add a column based on condition from another column

Which option correctly adds a new column 'Status' that is 'High' if column 'Score' is above 50, otherwise 'Low'?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Score': [45, 55, 60, 30]})

A df['Status'] = ['High' if x > 50 else 'Low' for x in df['Score']]

B df['Status'] = df['Score'].map({'High': x > 50, 'Low': x <= 50})

C df['Status'] = df['Score'] > 50

D df['Status'] = df['Score'].apply(lambda x: 'High' if x > 50 else 'Low')

💡 Hint

Use apply with a lambda for conditional assignment.

Option D uses apply with a lambda function to assign 'High' or 'Low' based on the condition.

🧠 Conceptual

expert

Understanding inplace parameter effect when removing columns

What is the effect of using inplace=True in df.drop('A', axis=1, inplace=True)?

A The column 'A' is removed and a new DataFrame without 'A' is returned.

B The column 'A' is removed from df and the method returns None.

C The column 'A' is removed but df remains unchanged.

D The method raises an error because inplace=True is invalid.

💡 Hint

inplace=True modifies the original DataFrame directly.

With inplace=True, the original DataFrame is changed and the method returns None.

Practice

1. What is the correct way to add a new column named 'age' with all values set to 30 in a pandas DataFrame df?

easy

A. df.append('age', 30)

B. df.add_column('age', 30)

C. df['age'] = 30

D. df.insert_column('age', 30)

Solution

1. Step 1: Understand how to add columns in pandas

   In pandas, you add a new column by assigning a value or list to df['column_name'].

2. Step 2: Apply the correct syntax to add a column named 'age'

   Using df['age'] = 30 adds a new column 'age' with all values set to 30.

3. Final Answer:

   df['age'] = 30 -> Option C

4. Quick Check:

   Adding column = df['new_col'] = value [OK]

Hint: Use df['col_name'] = value to add columns fast [OK]

Common Mistakes:

• Using non-existent methods like add_column or insert_column
• Trying to use append which is for rows, not columns
• Forgetting to use square brackets for column name

2. Which of the following is the correct syntax to remove a column named 'salary' from a pandas DataFrame df?

easy

A. df.drop('salary', axis=1)

B. df.remove('salary')

C. df.delete_column('salary')

D. df.drop_column('salary')

Solution

1. Step 1: Recall pandas method to remove columns

   The pandas method to remove columns is drop() with axis=1 to specify columns.

2. Step 2: Use correct syntax to drop 'salary' column

   Using df.drop('salary', axis=1) removes the 'salary' column from the DataFrame.

3. Final Answer:

   df.drop('salary', axis=1) -> Option A

4. Quick Check:

   Remove column = df.drop('col', axis=1) [OK]

Hint: Use df.drop('col', axis=1) to remove columns [OK]

Common Mistakes:

• Using methods like remove or delete_column which don't exist
• Forgetting axis=1 to specify column removal
• Using drop without parentheses or wrong parameters

3. Given the DataFrame df below:

   name  score
0  Amy     90
1  Bob     80
2  Cara    85

What will be the output of this code?

df['passed'] = df['score'] >= 85
print(df)

medium

A. name score passed
0 Amy 90 False
1 Bob 80 True
2 Cara 85 False

B. name score passed
0 Amy 90 True
1 Bob 80 False
2 Cara 85 True

C. name score
0 Amy 90
1 Bob 80
2 Cara 85

D. name score passed
0 Amy 90 90
1 Bob 80 80
2 Cara 85 85

Solution

1. Step 1: Understand the condition applied to 'score'

   The expression df['score'] >= 85 creates a boolean series: True where score is 85 or more, False otherwise.

2. Step 2: Assign boolean series to new column 'passed'

   Adding df['passed'] = ... adds this boolean series as a new column.

3. Final Answer:

   DataFrame with 'passed' column showing True for scores >= 85 -> Option B

4. Quick Check:

   Boolean condition adds True/False column [OK]

Hint: Compare column with condition to add True/False column [OK]

Common Mistakes:

• Expecting numeric values instead of booleans in new column
• Not using >= operator correctly
• Printing df before adding the new column

4. What is wrong with this code snippet to remove the column 'height' from DataFrame df?

df.drop('height')

medium

A. Missing axis=1 to specify column removal

B. The column name should be in a list like ['height']

C. The method drop does not exist in pandas

D. You must assign the result back to df like df = df.drop('height')

Solution

1. Step 1: Check the drop method parameters

   By default, drop() removes rows. To remove columns, you must specify axis=1.

2. Step 2: Identify the missing axis parameter

   The code misses axis=1, so it tries to remove a row named 'height', causing an error if no such row exists.

3. Final Answer:

   Missing axis=1 to specify column removal -> Option A

4. Quick Check:

   drop columns = drop('col', axis=1) [OK]

Hint: Always add axis=1 to drop columns, not rows [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong row removal or error
• Thinking drop modifies df inplace without assignment
• Using wrong data type for column name

5. You have a DataFrame df with columns ['A', 'B', 'C']. You want to create a new column 'D' which is the sum of 'A' and 'B', then remove column 'C'. Which code correctly does this in one step?

hard

A. df['D'] = df['A'] + df['B']
df.remove('C')

B. df['D'] = df['A'] + df['B']
df.drop('C', axis=1, inplace=True)

C. df['D'] = df['A'] + df['B']
df = df.drop('C', axis=1)

D. df = df.assign(D=df['A'] + df['B']).drop('C', axis=1)

Solution

1. Step 1: Create new column 'D' as sum of 'A' and 'B'

   Using assign() allows adding a new column in a chainable way without modifying df inplace.

2. Step 2: Remove column 'C' using drop() chained after assign()

   Chaining drop('C', axis=1) removes column 'C' and returns the modified DataFrame.

3. Step 3: Assign the result back to df

   Assigning the chained operations back to df updates the DataFrame correctly in one step.

4. Final Answer:

   df = df.assign(D=df['A'] + df['B']).drop('C', axis=1) -> Option D

5. Quick Check:

   Use assign + drop chained and assign back [OK]

Hint: Chain assign and drop, then assign back to df [OK]

Common Mistakes:

• Using drop without axis=1 or inplace causing no change
• Using non-existent remove method
• Not assigning chained result back to df