Practical Data Science using Python - Pandas DataFrame 3

The video tutorial covers various operations on Pandas DataFrames, including adding records using series objects, direct cell manipulation with indexers, updating column values, creating new columns, and concatenating DataFrames both horizontally and vertically. It also explains the use of deep and shallow copies in DataFrames.

What is the first step in adding a new record to an existing DataFrame?

• Use the merge function

• Use the drop function

• Create a Series object

• Create a DataFrame object

Which function is used to append a Series to a DataFrame?

• join

• append

• concat

• merge

How can you directly change a cell value in a DataFrame?

• Using the merge function

• Using the at indexer

• Using the copy function

• Using the drop function

What operation is performed to increase each value in a column by a factor?

• Multiplication

• Addition

• Subtraction

• Division

How do you create a new column in a DataFrame?

• Use the drop function

• Assign a Series to a new column name

• Use the merge function

• Use the append function

What is the purpose of the append function in DataFrames?

• To delete rows

• To filter rows

• To add rows

• To sort rows

What does the concat function do when axis=1?

• Deletes rows

• Deletes columns

• Performs a column-wise operation

• Performs a row-wise operation

What is a deep copy of a DataFrame?

• A reference to the original DataFrame

• A physical copy of the DataFrame

• A sorted version of the DataFrame

• A filtered version of the DataFrame

What happens when you set deep=False in the copy function?

• A new DataFrame is created

• A reference to the original DataFrame is created

• The DataFrame is filtered

• The DataFrame is sorted

What is the result of a vertical concatenation of two DataFrames?

• Columns are added one after the other

• Columns are duplicated

• Rows are duplicated

• Rows are added one after the other