iloc for position-based selection in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does iloc do in pandas?

iloc selects rows and columns by their integer position in the DataFrame, not by label.

beginner

How do you select the first row of a DataFrame using iloc?

Use df.iloc[0] to select the first row by position.

intermediate

How to select the first 3 rows and first 2 columns using iloc?

Use df.iloc[0:3, 0:2] to select rows 0 to 2 and columns 0 to 1.

intermediate

What happens if you use a negative index with iloc?

Negative indices count from the end. For example, df.iloc[-1] selects the last row.

intermediate

Can iloc accept lists or arrays for selection?

Yes, you can pass lists or arrays of integer positions to select specific rows or columns, e.g., df.iloc[[0,2,4]].

What does df.iloc[2, 3] select?

A Rows 2 to 3

B Row with label 2 and column with label 3

C Second row and third column by position

D Third row and fourth column by position

iloc uses zero-based integer positions. So 2 means third row, 3 means fourth column.

How to select all rows but only the first column using iloc?

A df.iloc[:, 0]

B df.iloc[0, :]

C df.iloc[0]

D df.iloc[:, 1]

: means all rows, 0 means first column by position.

What will df.iloc[1:4] return?

A Rows at positions 1, 2, and 3

B Rows at positions 1 to 4 inclusive

C Rows with labels 1 to 4

D Columns 1 to 4

Slicing with iloc excludes the stop index, so rows at positions 1, 2, and 3 are selected.

Which of these is NOT a valid iloc indexer?

A An integer

B A string label

C A boolean mask

D A list of integers

iloc only accepts integer positions, lists of integers, slices, but NOT string labels.

What does df.iloc[-2] select?

A Second row

B Second column

C Second last row

D Last row

Negative indices count from the end. -2 means second last row.

Explain how to use iloc to select specific rows and columns by position in a DataFrame.

Think about selecting by counting rows and columns from zero.

Describe the difference between loc and iloc in pandas.

One uses names, the other uses counting.