Practice PivotTables and Crosstab with Student Grades data
Practice PivotTables and Crosstab with Student Grades data Data Science Project
Data Wrangling with Pandas

Practice PivotTables and Crosstab with Student Grades data

In this lab activity, we will be working with the Student Grade dataset, exploring and analyzing the data using pivot tables and crosstabs. We will examine factors that influence student grades and discover patterns and trends. By utilizing these powerful tools, we will summarize and calculate statistics based on different variables. This will enable us to gain insights into student performance and understand how various factors contribute to their grades. Through hands-on exercises, you will learn how to effectively analyze and interpret data using pivot tables and crosstabs. By the end of this lab, you will have a solid understanding of these techniques and be able to apply them to other datasets as well. Get ready to enhance your data analysis skills and uncover valuable insights into student performance!
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Practice PivotTables and Crosstab with Student Grades dataPractice PivotTables and Crosstab with Student Grades data
Project Created by

Mohamed Rawash

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Calculate the average G1 grade by moother job (`Mjob`).

Store the resulting dataframe in the variable pivot_table_g1_by_Mjob.

Your result should look similar to this dataframe: activity1-answer

codevalidated

Calculate the average G2 grade by school and sex.

Store the resulting dataframe in the variable pivot_table_avg_g2_by_school_sex.

Your result should look similar to this dataframe: activity2-answer

codevalidated

Calculate the maximum G3 grade by age.

Store the resulting dataframe in the variable pivot_table_max_g3_by_age.

Your result should look similar to this dataframe: activity3-answer

codevalidated

Calculate the average health (`health`) by family size (`famsize`) and fill missing values with 0.

Store the resulting dataframe in the variable pivot_table_avg_g1_by_famsize.

Your result should look similar to this dataframe: activity4-answer

codevalidated

Calculate the average G2 grade by school and include row and column totals.

Store the resulting dataframe in the variable pivot_table_avg_g2_by_school.

Your result should look similar to this dataframe: activity5-answer

codevalidated

Calculate the average free time (`freetime`) grade by school, and include sex and age as columns (fill missing values with 0).

Store the resulting dataframe in the variable pivot_table_avg_freetime_by_school_sex_age.

Your result should look similar to this dataframe: activity6-answer

codevalidated

Calculate the average absences by school and address.

Store the resulting dataframe in the variable pivot_table_avg_absences_by_school_address.

Your result should look similar to this dataframe: activity7-answer

codevalidated

Calculate the average freetime and number of absences by school.

Store the resulting dataframe in the variable pivot_table_avg_freetime_absences_by_school.

Your result should look similar to this dataframe: activity8-answer

codevalidated

Calculate the sum of absences by guardian.

Store the resulting dataframe in the variable pivot_table_sum_absences_by_guardian.

Your result should look similar to this dataframe: activity9-answer

codevalidated

Calculate the average G1 grade by school and fill missing values with 'N/A'.

Store the resulting dataframe in the variable pivot_table_avg_g1_by_school.

Your result should look similar to this dataframe: activity10-answer

codevalidated

Count the number of students by guardian and age and include sex as column.

Store the resulting dataframe in the variable cross_tab_students_by_guardian_age_sex.

Your result should look similar to this dataframe: activity11-answer

codevalidated

Count the number of students by school, and include sex and age as columns and include row and column totals.

Store the resulting dataframe in the variable cross_tab_students_by_school_sex_age.

Your result should look similar to this dataframe: activity12-answer

codevalidated

Calculate the average travel time (`traveltime`) by address, sex, and age.

Store the resulting dataframe in the variable crosstab_avg_traveltime_by_school_sex_age.

Your result should look similar to this dataframe: activity13-answer

codevalidated

Count the number of students who have participated in extracurricular activities by school and sex.

Store the resulting dataframe in the variable crosstab_activities_by_school_sex.

Your result should look similar to this dataframe: activity14-answer

codevalidated

Calculate the percentage of students who have internet access, categorized by school and sex.

Store the resulting dataframe in the variable crosstab_internet_access_by_school_sex.

Your result should look similar to this dataframe: activity15-answer

Practice PivotTables and Crosstab with Student Grades dataPractice PivotTables and Crosstab with Student Grades data
Project Created by

Mohamed Rawash

This project is part of

Data Wrangling with Pandas

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