Selecting columns by name in Pandas - Time & Space Complexity

Time Complexity: Selecting columns by name

O(n)

Understanding Time Complexity

When we select columns by their names in pandas, we want to know how the time it takes changes as the data grows.

We ask: How does the work increase when the number of rows or columns grows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'A': range(1000),
    'B': range(1000, 2000),
    'C': range(2000, 3000)
})

selected = df[['A', 'C']]

This code creates a DataFrame with 3 columns and selects two columns by their names.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Accessing and copying the selected columns from the DataFrame.
• How many times: Once per row for each selected column.

How Execution Grows With Input

As the number of rows grows, the work to copy selected columns grows proportionally.

Input Size (n rows)	Approx. Operations
10	20 (2 columns x 10 rows)
100	200 (2 columns x 100 rows)
1000	2000 (2 columns x 1000 rows)

Pattern observation: The operations grow linearly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the time to select columns grows directly with the number of rows in the DataFrame.

Common Mistake

[X] Wrong: "Selecting columns by name is instant and does not depend on data size."

[OK] Correct: Even though column names are used, pandas must copy data for each row in those columns, so time grows with rows.

Interview Connect

Understanding how data selection scales helps you write efficient code and explain your choices clearly in real projects.

Self-Check

"What if we select all columns instead of just a few? How would the time complexity change?"

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