loc vs iloc mental model in Pandas - Performance Comparison

Time Complexity: loc vs iloc mental model

O(k)

Understanding Time Complexity

We want to understand how the time it takes to select data with loc and iloc changes as the data grows.

How does the size of the data affect the speed of these selection methods?

Scenario Under Consideration

Analyze the time complexity of selecting rows and columns using loc and iloc.

import pandas as pd

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

# Using loc to select rows by label
subset_loc = df.loc[100:199, ['A', 'B']]

# Using iloc to select rows by position
subset_iloc = df.iloc[100:200, 0:2]

This code selects 100 rows and 2 columns from a DataFrame of 1000 rows and 3 columns using both label-based and position-based indexing.

Identify Repeating Operations

Look at what repeats when selecting data.

• Primary operation: Accessing each requested row and column in the DataFrame.
• How many times: For 100 rows and 2 columns, 200 data points are accessed.

How Execution Grows With Input

As the number of rows or columns you select grows, the work grows roughly in the same way.

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

Pattern observation: The number of operations grows proportionally with the number of selected rows and columns.

Final Time Complexity

Time Complexity: O(k)

This means the time to select data grows linearly with the size of the selection, not the whole DataFrame.

Common Mistake

[X] Wrong: "Selecting data with loc or iloc always scans the entire DataFrame, so it takes the same time no matter what."

[OK] Correct: Both methods access only the requested rows and columns, so the time depends on the size of the selection, not the full data.

Interview Connect

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

Self-Check

What if we changed the selection to include all rows but only one column? How would the time complexity change?

Practice

1. What is the main difference between loc and iloc in pandas?

easy

A. loc uses labels, iloc uses integer positions

B. loc uses integer positions, iloc uses labels

C. loc selects columns only, iloc selects rows only

D. loc is faster than iloc

Solution

1. Step 1: Understand loc selection method

   loc selects data by row and column labels, meaning it uses the actual names of rows and columns.

2. Step 2: Understand iloc selection method

   iloc selects data by integer positions, starting from 0, ignoring labels.

3. Final Answer:

   loc uses labels, iloc uses integer positions -> Option A

4. Quick Check:

   loc = labels, iloc = positions [OK]

Hint: Remember: loc = labels, iloc = integer positions [OK]

Common Mistakes:

• Confusing labels with positions
• Thinking loc uses positions
• Assuming iloc uses labels

2. Which of the following is the correct syntax to select rows with labels 'a' to 'c' and columns 'x' to 'z' using loc?

easy

A. df.loc['a':'c', 'x':'z']

B. df.iloc['a':'c', 'x':'z']

C. df.loc[0:2, 0:2]

D. df.iloc[0:2, 'x':'z']

Solution

1. Step 1: Identify loc syntax

   loc uses labels, so slicing with labels like 'a':'c' and 'x':'z' is valid.

2. Step 2: Check other options

   Selections using iloc with label slices are invalid because iloc requires integer positions. Using integer slices with loc is incorrect when the labels are strings like 'a', 'c', etc.

3. Final Answer:

   df.loc['a':'c', 'x':'z'] -> Option A

4. Quick Check:

   loc uses labels in slices [OK]

Hint: Use labels inside loc brackets, positions inside iloc [OK]

Common Mistakes:

• Using integer positions with loc
• Using labels with iloc
• Mixing loc and iloc syntax

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A':[10,20,30], 'B':[40,50,60]}, index=['x','y','z'])

What is the output of df.iloc[1, 0]?

medium

A. 30

B. 40

C. 50

D. 20

Solution

1. Step 1: Understand DataFrame structure

   Rows are labeled 'x','y','z' with values in column 'A' as [10,20,30]. Positions start at 0, so row 1 is 'y', column 0 is 'A'.

2. Step 2: Apply iloc selection

   df.iloc[1, 0] selects row at position 1 ('y') and column at position 0 ('A'), value is 20.

3. Final Answer:

   20 -> Option D

4. Quick Check:

   iloc uses positions: row 1, col 0 = 20 [OK]

Hint: iloc uses zero-based positions, count rows and columns from 0 [OK]

Common Mistakes:

• Using labels instead of positions
• Confusing row and column indices
• Selecting wrong cell by mixing loc and iloc

4. Consider this code:

import pandas as pd
df = pd.DataFrame({'A':[1,2,3]}, index=[10,20,30])
print(df.loc[1])

What error will occur and why?

medium

A. No error, prints 2

B. KeyError because label 1 does not exist in index

C. IndexError because position 1 is out of range

D. TypeError because loc expects string labels

Solution

1. Step 1: Check DataFrame index labels

   Index labels are [10, 20, 30], so label 1 is not present.

2. Step 2: Understand loc behavior

   loc looks for label 1, which does not exist, causing a KeyError.

3. Final Answer:

   KeyError because label 1 does not exist in index -> Option B

4. Quick Check:

   loc needs existing labels, else KeyError [OK]

Hint: loc needs exact labels; missing label causes KeyError [OK]

Common Mistakes:

• Thinking loc uses positions
• Expecting no error when label missing
• Confusing KeyError with IndexError

5. You have a DataFrame with non-numeric row labels:

import pandas as pd
df = pd.DataFrame({'Score':[88, 92, 95]}, index=['John', 'Jane', 'Doe'])

You want to select the second row using loc and iloc. Which code will correctly select the second row and what will be the difference in output?

hard

A. df.loc['Jane'] and df.iloc['Jane'] both select by label 'Jane'; output differs

B. df.loc[1] selects by position 1; df.iloc['Jane'] selects by label 'Jane'; both return the same row

C. df.loc['Jane'] selects by label 'Jane'; df.iloc[1] selects by position 1; both return the same row

D. df.loc[1] and df.iloc[1] both select by position 1; output differs

Solution

1. Step 1: Understand row labels and positions

   Rows are labeled 'John', 'Jane', 'Doe' at positions 0, 1, 2 respectively.

2. Step 2: Use loc and iloc correctly

   df.loc['Jane'] selects row with label 'Jane'. df.iloc[1] selects row at position 1, which is also 'Jane'. Both return the same row.

3. Final Answer:

   df.loc['Jane'] selects by label 'Jane'; df.iloc[1] selects by position 1; both return the same row -> Option C

4. Quick Check:

   loc uses label, iloc uses position; same row here [OK]

Hint: loc needs label, iloc needs position; same row if label matches position [OK]

Common Mistakes:

• Using integer with loc
• Using label with iloc
• Assuming loc and iloc always differ