Pandas GroupBy | 28/100 Days of Python Algo Trading

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Day 28 :Pandas GroupBy

1. What does the GroupBy operation in Pandas allow you to do?

A) Perform operations on subsets of the data

B) Filter data based on conditions

C) Both A and B

D) None of the above

2. Which of the following is the correct way to group a DataFrame by a column named 'Sector'?

A) df.groupby('Sector')

B) df.GroupBy('Sector')

C) df.group_by('Sector')

D) df.groupby(Sector)

3. What will the following code return: df.groupby('Category')['Sales'].sum()?

A) The sum of Sales for each Category

B) The mean of Sales for each Category

C) A DataFrame with grouped Sales data

D) A list of unique Categories

4. How can you apply a custom function to each group in a GroupBy object?

A) Using the .apply() method

B) Using the .filter() method

C) Using the .groupby() method

D) Using the .transform() method

5. What is the purpose of the .agg() method in GroupBy?

A) To apply multiple aggregation functions at once

B) To sort data within groups

C) To iterate over groups

D) To group data by column values

6. Which of the following statements is true about GroupBy operations?

A) GroupBy is used to perform aggregations

B) GroupBy is used to sort data

C) GroupBy is used to filter data

D) GroupBy is used to transform data

7. How do you reset the index after a GroupBy operation?

A) Using the .reset_index() method

B) Using the .set_index() method

C) Using the .groupby() method

D) Using the .sort_index() method

8. Which of the following methods can be used to transform group data into another DataFrame?

A) Using the .transform() method

B) Using the .agg() method

C) Using the .size() method

D) Using the .filter() method

9. What does the .size() method return when used on a GroupBy object?

A) The number of elements in each group

B) The sum of elements in each group

C) The mean of elements in each group

D) The unique values in each group

10. Which of the following can be passed to the .agg() method?

A) A list of strings representing function names

B) A dictionary mapping column names to functions

C) A list of functions

D) None of the above

11. What is the correct syntax to group by multiple columns?

A) df.groupby(['Column1', 'Column2'])

B) df.groupby('Column1', 'Column2')

C) df.group_by(['Column1', 'Column2'])

D) None of the above

12. How can you group data by a specific column and calculate the mean of another column?

A) df.groupby('Column')['AnotherColumn'].mean()

B) df.groupby(['Column'])['AnotherColumn'].mean()

C) df['AnotherColumn'].groupby(df['Column']).mean()

D) df['AnotherColumn'].groupby('Column').mean()

13. What will the following code do: df.groupby('Category').filter(lambda x: x['Sales'].mean() > 200)?

A) It filters groups where the mean of Sales is greater than 200

B) It filters groups where the sum of Sales is greater than 200

C) It filters groups where the count of Sales is greater than 200

D) It filters groups where the median of Sales is greater than 200

14. Which method would you use to apply multiple aggregation functions at once?

A) The .agg() method

B) The .apply() method

C) The .size() method

D) The .transform() method

15. How can you iterate over groups in a GroupBy object?

A) Using the .iterrows() method

B) Using the .iteritems() method

C) Using the .groups property

D) Using the .group property

16. What will the following code do: df.groupby('Category').ngroup()?

A) It assigns a unique group number to each group

B) It assigns a counter to each row within its group

C) It groups data based on Category

D) None of the above

17. Which of the following methods is used to get the first value in each group?

A) Using the .first() method

B) Using the .last() method

C) Using the .nth() method

D) Using the .idxmax() method

18. What does the .cumcount() method do in a GroupBy operation?

A) It assigns a counter to each row within its group

B) It counts the number of unique values in each group

C) It returns the cumulative sum within each group

D) It ranks the values within each group

19. How can you use GroupBy to rank data within each group?

A) Using the .rank() method

B) Using the .apply() method

C) Using the .ngroup() method

D) Using the .transform() method

20. Which of the following statements best describes the use of the .apply() method with GroupBy?

A) It allows applying a custom function to each group

B) It allows applying a single aggregation function to each group

C) It allows applying a predefined function to each group

D) It allows applying a built-in aggregation function to each group

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