Selecting columns in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of selecting multiple columns from a DataFrame

What is the output of this code snippet when selecting columns 'A' and 'C' from the DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

result = df[['A', 'C']]
print(result)

A

   B  C
0  4  7
1  5  8
2  6  9

B

   A  C
0  1  7
1  2  8
2  3  9

C

   A  B
0  1  4
1  2  5
2  3  6

D KeyError: "['A', 'C'] not found in axis"

💡 Hint

Remember to use double brackets to select multiple columns in pandas.

Selecting multiple columns from a DataFrame requires passing a list of column names inside double brackets. This returns a new DataFrame with only those columns.

❓ data_output

intermediate

Number of columns after selection

How many columns does the resulting DataFrame have after selecting columns 'X' and 'Y' from this DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20],
    'Y': [30, 40],
    'Z': [50, 60]
})

selected = df[['X', 'Y']]
print(len(selected.columns))

A KeyError

B 1

C 2

D 3

💡 Hint

Count the columns you selected explicitly.

Selecting columns 'X' and 'Y' results in a DataFrame with exactly those two columns, so the count is 2.

🔧 Debug

advanced

Identify the error when selecting columns

What error does this code raise when trying to select columns 'A' and 'D' from the DataFrame?

Data Analysis Python

import pandas as pd

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

result = df[['A', 'D']]
print(result)

A AttributeError: 'DataFrame' object has no attribute 'D'

B IndexError: single positional indexer is out-of-bounds

C TypeError: unhashable type: 'list'

D KeyError: "['A', 'D'] not found in axis"

💡 Hint

Check if all column names exist in the DataFrame.

Trying to select a column that does not exist ('D') raises a KeyError indicating the missing column.

🚀 Application

advanced

Selecting columns based on data type

Given a DataFrame with mixed data types, which code snippet correctly selects only the numeric columns?

Data Analysis Python

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'num1': [1, 2, 3],
    'num2': [4.5, 5.5, 6.5],
    'text': ['a', 'b', 'c'],
    'bool': [True, False, True]
})

A numeric_df = df.select_dtypes(include=['number'])

B numeric_df = df[['num1', 'num2']]

C numeric_df = df.select_dtypes(include=['object'])

D numeric_df = df.loc[:, df.dtypes == 'bool']

💡 Hint

Use pandas method to select columns by data type.

The select_dtypes method with include=['number'] selects all numeric columns regardless of their names.

🧠 Conceptual

expert

Effect of chained indexing on column selection

What is the main risk of using chained indexing like df['A']['B'] when selecting columns in pandas?

A It may return a copy instead of a view, leading to unexpected behavior when modifying data.

B It always raises a KeyError because pandas does not allow chained indexing.

C It improves performance by reducing memory usage.

D It automatically converts columns to numeric types.

💡 Hint

Think about how pandas handles views and copies.

Chained indexing can return a copy, so changes may not affect the original DataFrame, causing bugs.