Selecting rows (loc, iloc) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of iloc row selection

What is the output of this code snippet selecting rows using iloc?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30, 40],
    'B': [5, 15, 25, 35]
})

result = df.iloc[1:3]
print(result)

A

    A   B
1  20  15
3  40  35

B

    A   B
1  20  15
2  30  25

C

    A   B
2  30  25
3  40  35

D

    A   B
0  10   5
1  20  15

💡 Hint

Remember that iloc uses integer position and the end index is exclusive.

The iloc[1:3] selects rows at positions 1 and 2, excluding 3. So rows with index 1 and 2 are returned.

❓ Predict Output

intermediate

Output of loc row selection with label slicing

What will be printed by this code using loc to select rows by label?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'X': [100, 200, 300, 400],
    'Y': [1, 2, 3, 4]
}, index=['a', 'b', 'c', 'd'])

result = df.loc['b':'c']
print(result)

A

    X  Y
c  300  3
d  400  4

B

    X  Y
b  200  2

C

    X  Y
b  200  2
c  300  3

D

    X  Y
a  100  1
b  200  2
c  300  3

💡 Hint

loc includes the end label in slicing.

Using loc with label slicing 'b':'c' includes both 'b' and 'c' rows.

🔧 Debug

advanced

Identify the error in iloc row selection

What error will this code raise when selecting rows with iloc?

Data Analysis Python

import pandas as pd

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

result = df.iloc['1']
print(result)

A TypeError: cannot do positional indexing on a non-integer key

B KeyError: '1'

C IndexError: single positional indexer is out-of-bounds

D No error, outputs the second row

💡 Hint

iloc expects integer positions, not strings.

Passing a string '1' to iloc causes a TypeError because iloc only accepts integers or integer lists.

❓ data_output

advanced

Result of mixed loc and iloc selection

Given this DataFrame, what is the output of selecting rows using loc and columns using iloc?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12]
}, index=['w', 'x', 'y', 'z'])

result = df.loc['x':'z'].iloc[:, 1:3]
print(result)

A

    B   C
x  6  10
y  7  11
z  8  12

B

    A   B
x  2   6
y  3   7
z  4   8

C

    B   C
w  5   9
x  6  10
y  7  11

D

    A   C
x  2  10
y  3  11
z  4  12

💡 Hint

loc selects rows by label, iloc selects columns by position.

loc['x':'z'] selects rows with labels x, y, z. iloc[:, 1:3] selects columns at positions 1 and 2, which are 'B' and 'C'.

🧠 Conceptual

expert

Difference between loc and iloc in row selection

Which statement correctly describes the difference between loc and iloc when selecting rows in pandas?

A <code>loc</code> selects rows by integer position and includes the end index; <code>iloc</code> selects rows by label and excludes the end label.

B <code>loc</code> selects rows by integer position and excludes the end index; <code>iloc</code> selects rows by label and includes the end label.

C <code>loc</code> and <code>iloc</code> both select rows by label but differ in column selection methods.

D <code>loc</code> selects rows by label and includes the end label in slices; <code>iloc</code> selects rows by integer position and excludes the end index.

💡 Hint

Think about how slicing works differently for labels vs positions.

loc uses labels and includes the end label in slices, while iloc uses integer positions and excludes the end index.