Test your knowledge on pandas groupby with this quiz
Quiz on Group By
Refer to this Dataset to solve Quiz
Fill in the blanks by drag and drop operation in right spot
pandas
pd
“Name”
[20, 21, 19, 15, 25, 35, 20]
[‘Tom’, ‘Nick’,’Tom’, ‘John’,’Nick’,’Tom’, ‘John’]
Python
import as
df = pd.DataFrame({'Name': , 'Bill': })
df_grouped = df.groupby( ).sum()
df_grouped
Output:
Bill
Name
John 35
Nick 46
Tom 74Aggregate
“City, Age”
mean
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH']})
df.groupby([ ])['Age']. ().sum()
Output:
60Pivot
Age
Name
Year
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH'], 'Year': [2020, 2021, 2022]})
df.pivot_table(values=" ", index=" ", columns=" ")
Output:
Name John Nick Tom
Year
2020 NaN NaN 20.0
2021 NaN 21.0 NaN
2022 19.0 NaN NaNGroup By Multi Columns
“Name”
“City”
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH'], 'Sales': [100, 200, 300]})
df.groupby([ , ]).sum()
Output:
Age Sales
Name City
John CH 19 300
Nick NY 21 200
Tom NY 20 100Filter Group By
Name
mean
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 17, 19]})
df.groupby(" ").filter(lambda x: x["Age"]. () > 18)
Output:
Name Age
0 Tom 20
2 John 19ANCOVA: Analysis of Covariance with python
ANCOVA is an extension of ANOVA (Analysis of Variance) that combines blocks of regression analysis and ANOVA. Which makes it Analysis of Covariance.
Learn Python The Fun Way
What if we learn topics in a desirable way!! What if we learn to write Python codes from gamers data !!
Meet the most efficient and intelligent AI assistant : NotebookLM
Start using NotebookLM today and embark on a smarter, more efficient learning journey!
Break the ice
This can be a super guide for you to start and excel in your data science career.
Important SQL functions
This article will introduce important functions in SQL rank, denserank, over, partition.
Important queries in SQL
In SQL you can make queries in number of ways ,though we can break complex codes into small readable and calculated parts.
SQL for data science
SQL offers several powerful analytical functions that can provide valuable insights
SQL Analytic Functions
SQL’s analytic functions allow for complex calculations and deeper data insights
SQL’s window function
SQL’s window functions are a potent tool that enables you to perform
SQL’s Recursive Common Table Expressions
SQL has a powerful feature called Recursive Common Table Expressions (CTEs), enabling you to work with hierarchical or recursive data. When handling data structures such as organisational hierarchies, bills of materials, family trees, and other similar structures, they can prove extremely valuable. 1. What is a Recursive CTE? 2. Syntax of a Recursive CTE 3.…
SQL stats and maths functions
Statistical and mathematical functions in SQL
Efficient Python 1: Play with Numpy, loops, Lists, Arrays
solve these Efficient python code quizzes
Efficient Python 2
This is the second segment of simple to advanced codes
SQL Exercise : Basics
Improve your analytical skills by practicing the following tasks
2 responses to “Quiz on Group By”
solved the problems
Nice
Points You Earned
0 distinction_points
python_points 0
0 Solver points
Leave a Reply
You must be logged in to post a comment.