Pandas GroupBy | 27/100 Days of Python Algo Trading

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Day 27: Pandas GroupBy

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

A) It permits the division of data into separate groups to perform computations for better analysis.

B) It combines several rows into a single row in a DataFrame.

C) It sorts the DataFrame based on one or several keys.

D) It filters out rows based on a specified condition.

2. How can you create a DataFrame in Pandas with one million rows using random data?

A) pd.DataFrame(np.random.rand(1000000, 4), columns=['A', 'B', 'C', 'D'])

B) pd.generate('1000000')

C) pd.DataFrame.set_size(1000000)

D) pd.random(1000000)

3. What is a common use of GroupBy in financial analysis?

A) To calculate cumulative statistics such as cumulative returns or moving averages.

B) To group transactions by stock symbol and calculate summary statistics per stock.

C) To arrange the data chronologically.

D) To merge different financial datasets.

4. How would you group a DataFrame by the 'Year' column and then get the average of the 'Profit' column?

A) df.groupby('Year')['Profit'].mean()

B) df['Year'].groupby('Profit').average()

C) df.mean('Year', 'Profit')

D) df.aggregate('Year', 'Profit')

5. Which method is used to apply multiple aggregation functions at once after grouping?

A) agg()

B) apply()

C) combine()

D) merge()

6. What is the result of calling df.groupby('column').size()?

A) It returns the total size of the DataFrame.

B) It shows the number of groups.

C) It gives the size of each group.

D) It resizes each group to the same count.

7. How do you group by multiple columns in Pandas?

A) df.groupby(['col1', 'col2'])

B) df.group(['col1', 'col2'])

C) df.groups(['col1', 'col2'])

D) df.groupby('col1', 'col2')

8. Which parameter can you use with groupby to exclude the grouping columns from the index?

A) as_index=False

B) index=False

C) set_index=False

D) drop_index=True

9. What happens if you use the sum() function after grouping a non-numeric column?

A) A TypeError is raised.

B) It concatenates the strings within each group.

C) It counts the occurrences of each string.

D) It returns NaN for that group.

10. How can you reset the index after performing a groupby operation?

A) df.groupby('column').reset_index()

B) df.groupby('column').reset()

C) df.groupby('column').apply(lambda x: x.reset_index())

D) df.groupby('column').sum().reset_index()

11. What method would you use to find the group with the highest sum of sales?

A) df.groupby('Group')['Sales'].max()

B) df.groupby('Group')['Sales'].sum().idxmax()

C) df.groupby('Group').max('Sales')

D) df.groupby('Group').sum().max()

12. How would you add a group key as a new column in the DataFrame after grouping?

A) df['Group'] = df.groupby('Key').transform('mean')

B) df['Group'] = df.groupby('Key').key

C) df = df.assign(Group=df.groupby('Key').ngroup())

D) df = df.merge(df.groupby('Key').ngroup(), on='Key')

13. What does the filter() function do when used with a groupby object?

A) It removes rows from each group not satisfying a certain condition.

B) It filters groups based on a condition.

C) It is used to select columns during the groupby operation.

D) It acts like the SQL filter clause on grouped data.

14. Which function can you use to compute the cumulative sum of returns for each stock in a grouped DataFrame?

A) df.groupby('Stock')['Returns'].cumsum()

B) df.cumsum('Stock')

C) df['Returns'].groupby('Stock').sum()

D) df.groupby('Stock').sum('Returns')

15. What is the correct way to sort the groups in a grouped DataFrame by their computed mean values?

A) df.groupby('Category').mean().sort_values(by='Values')

B) df.sort_values(by='Category').groupby().mean()

C) df.groupby('Category').sort('Values')

D) df.groupby('Category').apply(lambda x: x.sort_values('Values'))

16. How can you group a DataFrame by one column and aggregate another column using two different functions?

A) df.groupby('A')['B'].agg(['mean', 'sum'])

B) df['A'].groupby('B').agg(['mean', 'sum'])

C) df.groupby('A').agg({'B': ['mean', 'sum']})

D) Both A and C

17. What would df.groupby('Year')['Sales'].transform('sum') return?

A) The total sales per year as a scalar.

B) A DataFrame with the sum of sales for each year.

C) A series aligned with the original DataFrame's index containing the sum of sales for each year.

D) Only the maximum sales value for each year.

18. Which of these is not a valid way to perform an operation on multiple columns after a groupby?

A) df.groupby('A')[['B', 'C']].sum()

B) df.groupby('A').agg({'B': 'sum', 'C': 'mean'})

C) df.groupby('A').sum(['B', 'C'])

D) df.groupby('A')['B', 'C'].sum()

19. How do you compute percentage change for each group in a DataFrame?

A) df.groupby('Category')['Value'].pct_change()

B) df['Value'].pct_change().groupby('Category')

C) df.groupby('Category').apply(lambda x: x['Value'].pct_change())

D) Both A and C

20. How to perform a rolling mean on a DataFrame grouped by 'Category' for a 'Sales' column?

A) df.groupby('Category')['Sales'].rolling(3).mean()

B) df['Sales'].groupby('Category').rolling(3).mean()

C) df.rolling(3, on='Category')['Sales'].mean()

D) Both A and B

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