Dropping columns and rows in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas method drop() do?

The drop() method removes specified rows or columns from a DataFrame.

beginner

How do you drop a column named 'Age' from a DataFrame df?

Use df.drop('Age', axis=1) to drop the 'Age' column.

beginner

What does the parameter axis=0 mean in drop()?

axis=0 means you want to drop rows by their labels.

beginner

How can you drop multiple rows with labels 2 and 4 from a DataFrame df?

Use df.drop([2, 4], axis=0) to drop rows with labels 2 and 4.

intermediate

What happens if you use inplace=True in drop()?

The DataFrame is changed directly without creating a new copy.

Which parameter in drop() specifies if you want to drop rows or columns?

A axis

B labels

C inplace

D columns

The axis parameter tells pandas whether to drop rows (axis=0) or columns (axis=1).

What is the default value of axis in drop()?

A 0

B 2

C None

D 1

By default, axis=0, so drop() removes rows unless specified otherwise.

How do you drop a column named 'Salary' without changing the original DataFrame?

A df.drop('Salary', axis=1, inplace=True)

B df.drop('Salary', axis=1)

C df.drop('Salary', axis=0)

D df.drop(columns='Salary', inplace=True)

Using df.drop('Salary', axis=1) returns a new DataFrame without the 'Salary' column, leaving the original unchanged.

Which of these drops rows with labels 3 and 5?

A df.drop([3, 5], axis=1)

B df.drop(columns=[3, 5])

C df.drop([3, 5], axis=0)

D df.drop(rows=[3, 5])

To drop rows by label, use axis=0 with the list of labels.

What does inplace=True do in drop()?

A Drops rows only

B Returns a new DataFrame

C Drops columns only

D Modifies the original DataFrame directly

inplace=True changes the original DataFrame without making a copy.

Explain how to drop a column and a row from a pandas DataFrame. Include the parameters you use.

Think about how axis=0 and axis=1 relate to rows and columns.

Describe the difference between dropping rows and dropping columns in pandas.

Focus on the axis parameter and what labels mean.