Selecting rows (loc, iloc) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the row with label 2 using loc.

Data Analysis Python

selected_row = df.loc[[1]]

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

A2

Biloc[2]

Cdf[2]

D'2'

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the number when the index is integer.

Using iloc instead of loc.

Using loc with the label 2 selects the row with index label 2.

2fill in blank

medium

Complete the code to select the third row using iloc.

Data Analysis Python

selected_row = df.iloc[[1]]

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

A3

B2

C'2'

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3 instead of 2 for the third row.

Using loc instead of iloc.

iloc uses zero-based integer positions, so the third row is at position 2.

3fill in blank

hard

Fix the code to select rows 1 to 3 (inclusive) using loc.

Data Analysis Python

subset = df.loc[[1]]

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

A1:3

B[1:3]

C1:4

D[1,3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1:4, confusing loc (inclusive end) with iloc (exclusive end).

Using list syntax instead of slice notation.

loc slicing with labels is inclusive of both start and end, so 1:3 selects rows with index labels 1, 2, and 3.

4fill in blank

hard

Fill both blanks to select rows with iloc from position 0 to 2 and columns 'A' and 'B'.

Data Analysis Python

subset = df.iloc[[1], [2]]

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

A0:3

B['A', 'B']

C[0, 1]

Dslice(0, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using column labels instead of positions with iloc.

Using list syntax for rows instead of slice.

iloc uses integer slicing for rows (0:3 selects positions 0,1,2) and list of integer positions for columns ([0,1] assuming 'A' and 'B' are the first two columns).

5fill in blank

hard

Fill all three blanks to create a dictionary of row labels and values from column 'score' for rows where score > 50 using loc.

Data Analysis Python

result = {index: row[[1]] for index, row in df.loc[df[[2]] [3] 50].iterrows()}

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

A'score'

Bscore

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using score without quotes as a string key.

Using < instead of > for the condition.

We use 'score' as the column label in both loc filtering and row access. The condition uses > to filter scores greater than 50.