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.