Selecting multiple columns in Pandas - 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?

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  B
0  1  4
1  2  5
2  3  6

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

C

   B  C
0  4  7
1  5  8
2  6  9

D

   A  C
0  1  7
1  2  8
2  3  9

💡 Hint

Selecting multiple columns requires passing a list of column names inside double brackets.

The code selects columns 'A' and 'C' from the DataFrame. The output shows only these two columns with their values.

❓ data_output

intermediate

Number of columns selected

How many columns does the resulting DataFrame have after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20],
    'Y': [30, 40],
    'Z': [50, 60],
    'W': [70, 80]
})

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

A 2

B 3

C 4

D 1

💡 Hint

Count the number of columns inside the list used for selection.

The code selects columns 'Y' and 'W', so the resulting DataFrame has exactly 2 columns.

🔧 Debug

advanced

Identify the error when selecting multiple columns

What error will this code raise?

Pandas

import pandas as pd

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

result = df['a', 'b']
print(result)

A TypeError: unhashable type: 'list'

B SyntaxError: invalid syntax

C KeyError: "('a', 'b')"

D AttributeError: 'DataFrame' object has no attribute 'a, b'

💡 Hint

Check how multiple columns should be selected using brackets.

Using df['a', 'b'] tries to access a single key which is a tuple ('a', 'b'), causing a KeyError because such a column does not exist.

🚀 Application

advanced

Selecting columns dynamically based on a condition

Given a DataFrame with columns 'temp', 'humidity', 'pressure', and 'wind', which code snippet selects only columns whose names contain the letter 'p'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'temp': [22, 23],
    'humidity': [30, 45],
    'pressure': [1012, 1013],
    'wind': [5, 7]
})

A selected = df[[col for col in df.columns if 'p' in col]]

B selected = df[df.columns.str.contains('p')]

C selected = df.loc[:, df.columns == 'p']

D selected = df[df.columns.filter('p')]

💡 Hint

Use a list comprehension to filter column names containing 'p'.

Option A correctly uses a list comprehension to select columns with 'p' in their names. Other options either use invalid syntax or methods incorrectly.

🧠 Conceptual

expert

Understanding the difference between single and multiple column selection

Which statement correctly explains the difference between selecting a single column and multiple columns in pandas DataFrame?

A Selecting a single column with df['col'] returns a DataFrame, while selecting multiple columns with df[['col1', 'col2']] returns a Series.

B Selecting a single column with df['col'] returns a Series, while selecting multiple columns with df[['col1', 'col2']] returns a DataFrame.

C Both df['col'] and df[['col1', 'col2']] return a Series.

D Both df['col'] and df[['col1', 'col2']] return a DataFrame.

💡 Hint

Think about the data structure type returned by each selection method.

Selecting a single column returns a pandas Series (one-dimensional), while selecting multiple columns returns a DataFrame (two-dimensional).

Practice

1. What is the correct way to select multiple columns 'A' and 'B' from a pandas DataFrame df?

easy

A. df.loc['A', 'B']

B. df['A', 'B']

C. df[['A', 'B']]

D. df['A' & 'B']

Solution

1. Step 1: Understand pandas column selection syntax

   To select multiple columns, pandas requires a list of column names inside double brackets.

2. Step 2: Apply correct syntax for multiple columns

   Using df[['A', 'B']] correctly selects columns 'A' and 'B'. Other options are invalid syntax or select rows instead.

3. Final Answer:

   df[['A', 'B']] -> Option C

4. Quick Check:

   Multiple columns = list inside double brackets [OK]

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

Common Mistakes:

• Using single brackets with comma-separated names
• Using bitwise operators like & between column names
• Using .loc incorrectly for column selection

2. Which of the following is the correct syntax to select columns 'X' and 'Y' from DataFrame df?

easy

A. df.loc[:, 'X', 'Y']

B. df.loc['X', 'Y']

C. df['X', 'Y']

D. df[['X', 'Y']]

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Using double brackets with a list of column names is the standard way: df[['X', 'Y']].

2. Step 2: Check other options for correctness

   df.loc[:, 'X', 'Y'] causes TypeError (invalid arguments to loc); df['X', 'Y'] raises KeyError (tuple not a column); df.loc['X', 'Y'] is incorrect syntax.

3. Final Answer:

   df[['X', 'Y']] -> Option D

4. Quick Check:

   Double brackets with list = correct syntax [OK]

Hint: Remember: double brackets with list select multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names
• Confusing .loc row and column selection syntax
• Using tuple instead of list inside brackets

3. Given the DataFrame df below:

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

What is the output of df[['B', 'C']]?

medium

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

B. A B
0 1 4
1 2 5
2 3 6

C. A C
0 1 7
1 2 8
2 3 9

D. KeyError

Solution

1. Step 1: Identify columns requested

   The code selects columns 'B' and 'C' from the DataFrame.

2. Step 2: Extract columns 'B' and 'C' values

   Columns 'B' and 'C' have values [[4,7],[5,8],[6,9]] for rows 0,1,2 respectively.

3. Final Answer:

   B C 0 4 7 1 5 8 2 6 9 -> Option A

4. Quick Check:

   Selected columns 'B' and 'C' output matches

      B  C
   0  4  7
   1  5  8
   2  6  9

   [OK]

Hint: Selected columns show only those columns with all rows [OK]

Common Mistakes:

• Confusing columns selected and expecting all columns
• Getting KeyError by misspelling column names
• Selecting rows instead of columns

4. What error will occur with this code?

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

medium

A. TypeError

B. KeyError

C. SyntaxError

D. No error, returns both columns

Solution

1. Step 1: Analyze the indexing syntax

   The code uses single brackets with a comma, creating a tuple key ('X', 'Y').

2. Step 2: Understand pandas error type

   This raises KeyError because pandas looks for a column named ('X', 'Y'), which doesn't exist.

3. Final Answer:

   KeyError -> Option B

4. Quick Check:

   Tuple key in single brackets causes KeyError [OK]

Hint: Use list inside double brackets, not comma in single brackets [OK]

Common Mistakes:

• Using comma inside single brackets for multiple columns
• Expecting no error with wrong syntax
• Confusing TypeError with KeyError

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to create a new DataFrame df_selected with only 'Name' and 'Salary'. Which code correctly does this and also resets the index?

hard

A. df_selected = df[['Name', 'Salary']].reset_index(drop=True)

B. df_selected = df.loc[:, ['Name', 'Salary']].reset_index()

C. df_selected = df[['Name', 'Salary']].reset_index()

D. df_selected = df[['Name', 'Salary']].reset_index(drop=False)

Solution

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

   Use double brackets with list: df[['Name', 'Salary']] to select these columns.

2. Step 2: Reset index without adding old index as column

   Use reset_index(drop=True) to reset index and drop old index column.

3. Step 3: Check other options

   df.loc[:, ['Name', 'Salary']].reset_index() resets index but keeps old index as column. Using reset_index() without drop=True (default behavior) keeps the old index as a new column.

4. Final Answer:

   df_selected = df[['Name', 'Salary']].reset_index(drop=True) -> Option A

5. Quick Check:

   Select columns + reset index drop old index = df_selected = df[['Name', 'Salary']].reset_index(drop=True) [OK]

Hint: Select columns with double brackets, reset index with drop=True [OK]

Common Mistakes:

• Forgetting to use double brackets for multiple columns
• Not using drop=True in reset_index to avoid old index column
• Using .loc without understanding index reset effect