Python for Data Analysis: Step-By-Step with Projects - Selecting Columns

This tutorial covers how to select specific columns from a pandas dataframe. It explains accessing single columns using square brackets or as attributes, and selecting multiple columns using lists. The video also demonstrates filtering columns by data type using the select_dtypes method, including examples for numeric and non-numeric columns. The lesson concludes with a brief mention of moving on to row selection.

What is the preferred method for selecting a single column from a DataFrame?

• Using angle brackets with the column name

• Using curly braces around the column name

• Using square brackets with the column name as a string

• Using parentheses around the column name

How can you access a DataFrame column as if it's an attribute?

• By using the column name directly after the DataFrame name

• By using parentheses with the column name

• By using square brackets with the column name

• By using a semicolon with the column name

What is the correct way to select multiple columns from a DataFrame?

• Using a tuple of column names

• Using a dictionary of column names

• Using a set of column names

• Using a list of column names within square brackets

Which method is used to select columns based on their data types?

• select_dtypes

• select_columns

• filter_columns

• filter_dtypes

How can you exclude numeric columns when selecting columns by data type?

• By using the include parameter with 'string'

• By using the exclude parameter with 'number'

• By using the exclude parameter with 'string'

• By using the include parameter with 'number'