Data Science with Python Certification Data Science with Python Pandas DataFrame Operations 4

Question 1

When using df.loc[], what types of labels are used for row selection?

Accepted Answer

Index labels (strings, integers as labels, etc.)

Answer

df.loc[] is label-based, using the actual index labels rather than integer positions.

Question 2

What does pd.concat([df1, df2], axis=1) do?

Accepted Answer

Joins df1 and df2 side-by-side (column-wise)

Answer

axis=1 concatenates along columns, placing DataFrames side by side.

Question 3

How do you select all rows where the DataFrame index is in a list [10, 20, 30]?

Accepted Answer

df.loc[[10,20,30]]

Answer

df.loc[[10,20,30]] selects rows by their index labels from a list.

Question 4

What is the purpose of the inplace=True parameter in methods like df.drop()?

Accepted Answer

Modifies the DataFrame in memory without requiring reassignment

Answer

inplace=True modifies the existing DataFrame object directly instead of returning a new one.

Question 5

Which method would you use to rank values in a column, handling ties with average rank?

Accepted Answer

df['col'].rank(method='average')

Answer

rank(method='average') assigns tied values the average of the ranks they would occupy.

Data Science with Python Certification Practice Test