Selecting columns by name in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the column 'Age' from the DataFrame df.

Pandas

age_column = df[[1]]

Drag options to blanks, or click blank then click option'

A'age'

Bdf['Age']

CAge

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to use quotes around the column name.

Using the variable name without quotes.

Trying to select the column without brackets.

To select a column by name in pandas, you use the column name as a string inside square brackets.

2fill in blank

medium

Complete the code to select multiple columns 'Name' and 'Salary' from the DataFrame df.

Pandas

selected_columns = df[[1]]

Drag options to blanks, or click blank then click option'

A('Name', 'Salary')

B['Name', 'Salary']

C'Name', 'Salary'

D{'Name', 'Salary'}

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple or set instead of a list.

Not using brackets around the list.

Passing column names as a single string.

To select multiple columns, pass a list of column names inside the brackets.

3fill in blank

hard

Fix the error in the code to select the 'City' column from df.

Pandas

city = df.[1]

Drag options to blanks, or click blank then click option'

ACity

B'City'

Ccity

D['City']

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes inside dot notation.

Using brackets with dot notation.

Using lowercase when column name is capitalized.

When using dot notation to select a column, use the exact column name without quotes.

4fill in blank

hard

Fill both blanks to select columns 'Height' and 'Weight' from df and assign to new_df.

Pandas

new_df = df[[1]]
print(new_df.columns[[2]])

Drag options to blanks, or click blank then click option'

A['Height', 'Weight']

B['Height', 'Weight', 'Age']

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple or set instead of a list for columns.

Using wrong index to access columns.

Including extra columns in the list.

Use a list of column names to select multiple columns. Index 0 accesses the first column name.

5fill in blank

hard

Fill all three blanks to create a dictionary with column names as keys and their data types as values.

Pandas

col_types = [1]: df[[2]].dtype for [3] in df.columns}

Drag options to blanks, or click blank then click option'

Acol

Dcolumn

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names in different blanks.

Not using the variable to select the column.

Using df.columns directly as key.

Use a dictionary comprehension with a variable iterating over df.columns. The variable is used as key and to select the 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