Pandas Grouping and Aggregation MCQ Challenge

multiplechoice

Which method in Pandas allows you to apply custom functions to grouped data?

multiplechoice

What does the `as_index` parameter in `.groupby()` method control?

multiplechoice

How can you handle missing values when creating a `pivot table` in Pandas?

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How can you include `row` and `column` totals in a pivot table?

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What is the purpose of the `margins_name` parameter in the `pivot_table` function?

multiplechoice

How can you specify multiple columns to group by in the groupby function?

multiplechoice

When using the `groupby` function in pandas, which parameter is used to specify whether to include observed values for categorical groupers?

multiplechoice

What does the `group_keys` parameter control in the groupby function?

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In the `groupby` function, which parameter is used to specify the level or levels to group by when dealing with a `MultiIndex`?

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Which parameter in the `pivot_table` function allows you to control whether to drop rows or columns with missing values before creating the pivot table?

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Which option of the `crosstab` function allows you to perform normalization over each column?

multiplechoice

You want to group the data by the `Country` column and calculate the `average age` for each country, but you want to exclude missing values. Which code snippet achieves this?

You have a DataFrame called customer_data with the following structure:

	Customer_ID	Age	Country	Category
0	1	25	USA	A
1	2	40	Canada	B
2	3	32	USA	C
3	4	50	Canada	A
4	5	28	USA	B

multiplechoice

Which of the following code snippets correctly groups the DataFrame by `Student` and calculates the average score for each student, taking into account the observed parameter?

Consider a DataFrame df that contains information about students and their test scores in different subjects.

The DataFrame has the following structure:

	Student	Subject	Score
0	John	Math	80
1	Jane	Science	90
2	John	Science	95
3	Jane	Math	85
4	John	Math	75

You want to group the DataFrame by the Student column and calculate the average score for each student. However, you also want to include students who have not appeared in all subjects.

multiplechoice

Which of the following code snippets correctly generates the desired `crosstab`?

You have a dataset containing information about students' test scores in different subjects.

The dataset has the following structure:

	Student_ID	Subject	Score
0	1	Math	85
1	2	Science	92
2	3	Math	78
3	4	English	88
4	5	Science	90

Your task is to create a crosstab that displays the count of students who scored above 80 in each subject. The crosstab should have the subjects as columns and the count of students as values.

multiplechoice

Which of the following code snippets correctly creates the desired pivot table using the given DataFrame and all the available parameters in the `pivot_table` function?

Consider a DataFrame df that contains information about students, their test scores, and the subject they belong to.

The DataFrame has the following structure:

	Student	Subject	Score	Grade
0	John	Math	80	A
1	Jane	Science	90	A
2	John	Science	95	A
3	Jane	Math	85	B
4	John	Math	75	B

You want to create a pivot table that shows the average score for each student based on their subject, and you want the columns of the pivot table to represent the different grades. Additionally, you want to include all students and subjects in the resulting pivot table, even if there are missing values.

Vidhi Shah

Project Activities

Which method in Pandas allows you to apply custom functions to grouped data?

What does the `as_index` parameter in `.groupby()` method control?

How can you handle missing values when creating a `pivot table` in Pandas?

How can you include `row` and `column` totals in a pivot table?

What is the purpose of the `margins_name` parameter in the `pivot_table` function?

How can you specify multiple columns to group by in the groupby function?

When using the `groupby` function in pandas, which parameter is used to specify whether to include observed values for categorical groupers?

What does the `group_keys` parameter control in the groupby function?

In the `groupby` function, which parameter is used to specify the level or levels to group by when dealing with a `MultiIndex`?

Which parameter in the `pivot_table` function allows you to control whether to drop rows or columns with missing values before creating the pivot table?

Which option of the `crosstab` function allows you to perform normalization over each column?

You want to group the data by the `Country` column and calculate the `average age` for each country, but you want to exclude missing values. Which code snippet achieves this?

Which of the following code snippets correctly groups the DataFrame by `Student` and calculates the average score for each student, taking into account the observed parameter?

Which of the following code snippets correctly generates the desired `crosstab`?

Which of the following code snippets correctly creates the desired pivot table using the given DataFrame and all the available parameters in the `pivot_table` function?

Vidhi Shah

Data Wrangling with Pandas

Set Operations using Sakila

LIKE Operator using World

Membership and Range Operators with World Database