Python Quiz for Pandas – 03

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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Pandas – 03

Level: Intermediate

1 / 10

1. Which function is used to sort a DataFrame?

a) df.arrange()

b) df.order()

c) df.sort_values()

d) df.sort()

2 / 10

2. What does ‘df.shape’ return?

a) Total elements

b) Number of rows

c) Number of rows and columns

d) Number of columns

3 / 10

3. How do you get the sum of column ‘A’?

a) df.total('A')

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

c) df.sum('A')

d) df.add('A')

4 / 10

4. How do you set a column as the index in a DataFrame?

a) df.index_set()

b) df.setindex()

c) df.index()

d) df.set_index()

5 / 10

5. What does the ‘df.describe()’ method provide?

a) First 5 rows

b) Data types of columns

c) Statistical summary

d) Generates plots

6 / 10

6. How do you fill missing values with a specific value in column ‘A’?

a) df['A'].insert_value(value)

b) df['A'].set_na(value)

c) df['A'].fillna(value)

d) df['A'].replace_na(value)

7 / 10

7. How do you drop rows with missing values?

a) df.dropna()

b) df.drop_missing()

c) df.delete_na()

d) df.remove_nulls()

8 / 10

8. How do you replace all occurrences of a value in a DataFrame?

a) df.change(old_value, new_value)

b) df.replace(old_value, new_value)

c) df.substitute(old_value, new_value)

d) df.swap(old_value, new_value)

9 / 10

9. Which method is used to change the data type of a column?

a) df.set_type()

b) df.change_type()

c) df.type()

d) df.astype()

10 / 10

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

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

b) df.average('A')

c) df.mean('A')

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

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