Python Quiz for Pandas – 02

Python Quiz for Pandas

The pandas library stands as a cornerstone of the Python ecosystem. It empowers data scientists, analysts, and developers to efficiently work with structured data, perform complex operations, and extract valuable insights. Welcome to our “Python Quiz for Pandas”, and Test your knowledge of pandas with this engaging and interactive quiz! Challenge yourself and learn fascinating python package. Python quiz for pandas includes questions from basic to advance level to evaluate the skills of the developers.

Prerequisite: Knowledge of python package – pandas

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6

Pandas – 02

Level: Intermediate

1 / 10

1. How do you apply a function to each element in a Series?

a) Series.map()

b) Series.apply()

c) Series.foreach()

d) Series.function()

2 / 10

2. How do you rename a column ‘old_name’ to ‘new_name’?

a) df.update_column('old_name', 'new_name')

b) df.rename(columns={'old_name': 'new_name'})

c) df.change_name('old_name', 'new_name')

d) df.rename_column('old_name', 'new_name')

3 / 10

3. How do you drop a column from a DataFrame?

a) df.remove('col')

b) df.drop(columns=['col'])

c) df.pop('col')

d) df.delete('col')

4 / 10

4. How do you get the median of column ‘A’?

a) df.median('A')

b) df.middle('A')

c) df['A'].midpoint()

d) df['A'].median()

5 / 10

5. How do you drop duplicates based on a specific column ‘A’?

a) df.unique('A')

b) df.remove_duplicates('A')

c) df.drop_duplicates(subset=['A'])

d) df.delete_duplicates('A')

6 / 10

6. Which method returns unique values in a Series?

a) uniques()

b) distinct()

c) unique()

d) one_of_a_kind()

7 / 10

7. How do you filter rows where column ‘A’ is greater than 10?

a) df.filter('A > 10')

b) df.select('A > 10')

c) df[df['A'] > 10]

d) df.query('A > 10')

8 / 10

8. How do you select rows where column ‘A’ is null?

a) df[df['A'].isnull()]

b) df[df['A'].isna()]

c) df[df['A'] == None]

d) df.query('A == null')

9 / 10

9. How do you sort a DataFrame by multiple columns?

a) df.sort_values(['col1', 'col2'])

b) df.multi_sort(['col1', 'col2'])

c) df.sort(['col1', 'col2'])

d) df.sort_by_columns(['col1', 'col2'])

10 / 10

10. How do you get the standard deviation of column ‘A’?

a) df['A'].standard_deviation()

b) df.std('A')

c) df.stdev('A')

d) df['A'].std()

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