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.

Practice

1. Which of the following is the correct way to select a single column named 'Age' from a DataFrame df?

easy

A. df[['Age']]

B. df.select('Age')

C. df.Age()

D. df['Age']

Solution

1. Step 1: Understand single column selection syntax

   To select one column from a DataFrame, use df['column_name'].

2. Step 2: Check each option

   df['Age'] uses df['Age'], which is correct. df[['Age']] returns a DataFrame, not a Series. df.Age() uses parentheses incorrectly. df.select('Age') is not a valid pandas method.

3. Final Answer:

   df['Age'] -> Option D

4. Quick Check:

   Single column selection = df['Age'] [OK]

Hint: Use single brackets for one column, double for multiple [OK]

Common Mistakes:

• Using double brackets for single column returns DataFrame, not Series
• Using dot notation with parentheses like a function
• Trying non-existent methods like select()

2. Which syntax correctly selects multiple columns 'Name' and 'Salary' from DataFrame df?

easy

A. df['Name', 'Salary']

B. df[['Name', 'Salary']]

C. df.Name, df.Salary

D. df.select(['Name', 'Salary'])

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Use double brackets with a list of column names: df[['col1', 'col2']].

2. Step 2: Evaluate options

   df[['Name', 'Salary']] matches the correct syntax. df['Name', 'Salary'] is invalid syntax. df.Name, df.Salary selects columns separately, not together. df.select(['Name', 'Salary']) is not a pandas method.

3. Final Answer:

   df[['Name', 'Salary']] -> Option B

4. Quick Check:

   Multiple columns = df[['Name', 'Salary']] [OK]

Hint: Use double brackets with list for multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names separated by commas
• Trying to select multiple columns with dot notation
• Using non-existent select() method

3. Given the DataFrame df below:

   Name  Age  Salary
0  John   28   50000
1  Anna   22   60000
2  Mike   32   55000

What is the output of df[['Name', 'Age']]?

medium

A. Name Age
0 John 28
1 Anna 22
2 Mike 32

B. Name Age Salary
0 John 28 50000
1 Anna 22 60000
2 Mike 32 55000

C. 0 John
1 Anna
2 Mike
Name: Name, dtype: object

D. 28
22
32

Solution

1. Step 1: Understand what df[['Name', 'Age']] does

   This selects only the columns 'Name' and 'Age' from the DataFrame, returning a smaller DataFrame with those columns.

2. Step 2: Match output to options

      Name  Age
   0  John   28
   1  Anna   22
   2  Mike   32

   shows only 'Name' and 'Age' columns with all rows, which matches the expected output.

      Name  Age  Salary
   0  John   28   50000
   1  Anna   22   60000
   2  Mike   32   55000

   includes 'Salary' which is not selected.

   0    John
   1    Anna
   2    Mike
   Name: Name, dtype: object

   shows only the 'Name' column as a Series.

   28
   22
   32

   shows only ages as a list, which is incorrect.

3. Final Answer:

   DataFrame with columns 'Name' and 'Age' only -> Option A

4. Quick Check:

   Selected columns = 'Name' and 'Age' only [OK]

Hint: Double brackets return DataFrame with selected columns [OK]

Common Mistakes:

• Expecting single brackets to return multiple columns
• Confusing Series output with DataFrame output
• Including unselected columns in output

4. What is wrong with this code to select the column 'Score' from DataFrame df?

df.Score()

medium

A. Parentheses cause an error; dot notation should be without ()

B. Column names cannot have uppercase letters

C. Column name must be in double brackets

D. Dot notation cannot be used to select columns

Solution

1. Step 1: Understand dot notation for column selection

   Dot notation selects columns as attributes without parentheses, like df.Score.

2. Step 2: Identify error in code

   Using parentheses () after Score tries to call it like a function, causing an error.

3. Final Answer:

   Parentheses cause an error; dot notation should be without () -> Option A

4. Quick Check:

   Dot notation = df.Score without parentheses [OK]

Hint: Dot notation is attribute access, no parentheses [OK]

Common Mistakes:

• Adding parentheses after column name with dot notation
• Thinking dot notation works for all column names
• Using double brackets with dot notation

5. You have a DataFrame df with columns 'A', 'B', 'C', and 'D'. You want to create a new DataFrame df_new that contains only columns 'B' and 'D' but in reverse order ('D' first, then 'B'). Which code correctly does this?

hard

A. df_new = df['D', 'B']

B. df_new = df[['B', 'D']]

C. df_new = df[['D', 'B']]

D. df_new = df[['B', 'D']].reindex(columns=['B', 'D'])

Solution

1. Step 1: Understand column selection order

   The order of columns in the list inside double brackets determines the order in the new DataFrame.

2. Step 2: Check each option

   df_new = df[['D', 'B']] selects columns 'D' then 'B', which matches the requirement. df_new = df[['B', 'D']] selects 'B' then 'D', wrong order. df_new = df['D', 'B'] is invalid tuple syntax inside single brackets. df_new = df[['B', 'D']].reindex(columns=['B', 'D']) reindexes to the same order 'B' then 'D', incorrect.

3. Final Answer:

   df_new = df[['D', 'B']] -> Option C

4. Quick Check:

   Order columns by list inside double brackets [OK]

Hint: Order columns in list to set output order [OK]

Common Mistakes:

• Selecting columns in wrong order
• Using reindex but specifying the original order
• Using tuple inside single brackets for multiple columns