Selecting columns by name in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Selecting multiple columns by name

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

Pandas

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

   A  C
0  1  7
1  2  8
2  3  9

B

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

C

   B
0  4
1  5
2  6

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

💡 Hint

Remember to use double brackets to select multiple columns by name.

Using df[['A', 'C']] selects only columns 'A' and 'C' from the DataFrame, returning a new DataFrame with those columns.

❓ data_output

intermediate

Selecting a single column returns a Series

What is the type and output of selecting column 'B' from the DataFrame below?

Pandas

import pandas as pd

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

result = df['B']
print(type(result))
print(result)

A <class 'pandas.core.frame.DataFrame'>\n B\n0 30\n1 40

B <class 'pandas.core.series.Series'>\n0 30\n1 40\nName: B, dtype: int64

C <class 'list'>\n[30, 40]

D KeyError: 'B'

💡 Hint

Selecting a single column by name returns a Series, not a DataFrame.

df['B'] returns a pandas Series object representing the column 'B'. The type confirms this.

🔧 Debug

advanced

Identify the error when selecting columns

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

Pandas

import pandas as pd

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

result = df[['X', 'Y']]
print(result)

A TypeError: unhashable type: 'list'

B AttributeError: 'DataFrame' object has no attribute 'X'

C IndexError: list index out of range

D KeyError: "['X', 'Y'] not in index"

💡 Hint

Check if the columns exist in the DataFrame before selecting.

Selecting columns that do not exist in the DataFrame raises a KeyError listing the missing columns.

🚀 Application

advanced

Select columns by name using a list variable

Given a list of column names, which option correctly selects those columns from the DataFrame?

Pandas

import pandas as pd

cols = ['A', 'C']
df = pd.DataFrame({
    'A': [5, 6],
    'B': [7, 8],
    'C': [9, 10]
})

result = None  # Fill in the correct selection

A result = df[cols]

B result = df.cols

C result = df[['cols']]

D result = df.loc[:, cols]

💡 Hint

Use the list variable directly inside the brackets to select columns.

Using df[cols] selects the columns named in the list cols. df.loc[:, cols] also works but is not in options as correct here.

🧠 Conceptual

expert

Difference between selecting columns by single vs double brackets

What is the main difference between df['A'] and df[['A']] when selecting columns in pandas?

A Both return a Series but with different index labels

B df['A'] returns a DataFrame, df[['A']] returns a Series

C df['A'] returns a Series, df[['A']] returns a DataFrame

D Both return a DataFrame but with different column order

💡 Hint

Think about the data structure type returned by each selection.

Selecting a single column with single brackets returns a Series. Using double brackets returns a DataFrame with one column.

Practice

1.

Which syntax correctly selects a single column named 'Age' from a pandas DataFrame df?

easy

A. df.Age[]

B. df['Age']

C. df[['Age']]

D. df('Age')

Solution

1. Step 1: Understand single column selection syntax

   Using single brackets with the column name as a string selects one column and returns a Series.

2. Step 2: Compare options

   df['Age'] is the correct syntax. Double brackets return a DataFrame, and other options are invalid syntax.

3. Final Answer:

   df['Age'] -> Option B

4. Quick Check:

   Single brackets select one column [OK]

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

Common Mistakes:

• Using double brackets for single column returns DataFrame, not Series
• Trying to use parentheses instead of brackets
• Using dot notation with brackets incorrectly

2.

Which of the following is the correct syntax to select multiple columns 'Name' and 'Salary' from a DataFrame df?

easy

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

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

C. df['Name' & 'Salary']

D. df.Name, df.Salary

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Multiple columns must be passed as a list inside double brackets.

2. Step 2: Evaluate options

   Only df[['Name', 'Salary']] correctly uses a list inside double brackets. Others are invalid or return separate Series.

3. Final Answer:

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

4. Quick Check:

   Double brackets + list select multiple columns [OK]

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

Common Mistakes:

• Using single brackets with multiple column names separated by comma
• Using bitwise AND (&) instead of list
• Trying to select columns separately without combining

3.

Given the DataFrame df below, what is the output of df[['City', 'Age']]?

import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30], 'City': ['NY', 'LA']}
df = pd.DataFrame(data)

medium

A. City Name
0 NY Alice
1 LA Bob

B. Name Age
0 Alice 25
1 Bob 30

C. City Age
0 NY 25
1 LA 30

D. Age City
0 25 NY
1 30 LA

Solution

1. Step 1: Understand the selection

   The code selects columns 'City' and 'Age' in that order from the DataFrame.

2. Step 2: Check the output order and values

   The output DataFrame has columns 'City' then 'Age' with values from original data.

3. Final Answer:

   City Age 0 NY 25 1 LA 30 -> Option C

4. Quick Check:

   Selected columns match output order [OK]

Hint: Output columns appear in the order listed inside double brackets [OK]

Common Mistakes:

• Mixing column order in output
• Selecting wrong columns
• Confusing Series output with DataFrame output

4.

What is wrong with this code to select columns 'A' and 'B' from DataFrame df?

df['A', 'B']

medium

A. It raises a KeyError because tuple ('A', 'B') is not a column

B. It returns a DataFrame with columns 'A' and 'B'

C. It returns a Series with combined values of 'A' and 'B'

D. It raises a SyntaxError due to wrong brackets

Solution

1. Step 1: Analyze the indexing syntax

   Using single brackets with multiple comma-separated strings creates a tuple key, not a list.

2. Step 2: Understand pandas column selection behavior

   Since ('A', 'B') is a tuple, pandas looks for a column with that exact tuple name, which does not exist, causing KeyError.

3. Final Answer:

   It raises a KeyError because tuple ('A', 'B') is not a column -> Option A

4. Quick Check:

   Single brackets with comma create tuple key, causing KeyError [OK]

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

Common Mistakes:

• Using single brackets with multiple column names separated by comma
• Expecting a DataFrame but getting error
• Confusing tuple with list in indexing

5.

You have a DataFrame df with columns 'Name', 'Age', 'Salary', and 'Department'. You want to create a new DataFrame with only 'Name' and 'Salary', but also want to rename 'Salary' to 'Income'. Which code correctly does this?

hard

A. new_df = df[['Name', 'Salary']].rename(columns=['Name', 'Income'])

B. new_df = df['Name', 'Salary'].rename(columns={'Salary': 'Income'})

C. new_df = df[['Name', 'Salary']].rename({'Salary': 'Income'})

D. new_df = df[['Name', 'Salary']].rename(columns={'Salary': 'Income'})

Solution

1. Step 1: Select columns 'Name' and 'Salary'

   Use double brackets with a list: df[['Name', 'Salary']] to get a DataFrame with those columns.

2. Step 2: Rename 'Salary' to 'Income'

   Use rename(columns={'Salary': 'Income'}) to rename the column correctly.

3. Final Answer:

   new_df = df[['Name', 'Salary']].rename(columns={'Salary': 'Income'}) -> Option D

4. Quick Check:

   Select columns then rename with columns dict [OK]

Hint: Select columns first, then rename with columns dict [OK]

Common Mistakes:

• Using single brackets for multiple columns
• Passing rename argument without 'columns=' keyword
• Passing list instead of dict to rename