• Learn
    • Courses
    • Career Tracks
    • Projects
    • Upcoming Courses
  • Certificates
    • Career Track Certificate
    • Course Certificate
  • Resources
    • All Resources
    • Course Notes
    • Templates
    • Infographics
    • Career Guides
    • Practice Exams
    • Blog
    • Reviews
    • Success Stories
    • Flashcards
    • Calculators
    • Interview Simulator
  • Pricing
  • Corporate Training
  • Login
  • Sign Up
Log In Sign Up
Follow this topic
Share
Yong Cao
Super learner
This user is a Super Learner. To become a Super Learner, you need to reach Level 8.
Posted on:

24 Aug 2023

0

Priactice Exam 2 Question 5: why use size=(3,3)?

in Data Preprocessing with NumPy / Applications of Random Data in NumPy

In explaination of correct answer to Question 5, the solution is to use:


np.mean(array.binomial(n=80, p=0.3, size=(3,3)))


Why use size=(3,3) but not (1,1), (2,2) and other sizes? They will give different answers.

0 answers ( 0 marked as helpful)

Submit an answer

GET

World-Class

Data Science

Courses

Learn with instructors from:

4.9/5

related questions

Vandit Dubey
1
6
index 1 is out of bounds for axis 0 with size 0
Samuel Akermann
1
6
npy format size vs csv format size
Khalid Alshmmari
0
4
Why do we use intervals when forecasting events?
Jason Rehkamp
4
4
Statistics with NumPy Practice Exam Question 3
Manas Pandey
0
2
Question reg Operation of Linear lagebra
james tuta
0
2
question concerning the np.save
Codjo Abraham Charlemagne Akpovo
0
2
Why am I getting an error with printing the "_savez" file
Ramsey Alizadeh
3
2
Answer to Question 4 of 6 is wrong
Myko Dornagon
0
2
temporary fill - why do we addd 1?
Asmaa Abdulhamid
0
2
An error found in the solution of question 21!!!!! it should be axis=1
Simon Oluwole
1
2
Clarify your question on section 4 exercise in numpy course
wiem khalifa
7
0
Different Solution in Q2 Practice exam 2 for Data Preprocessing

All the Data Science Courses You Need

About

  • About Us
  • Meet the Instructors
  • Become an Instructor
  • Contact Us
  • Pricing

Learn

  • Courses
  • Career Tracks
  • Career Certificate
  • Course Certificate

Resources

  • Course Notes
  • Templates
  • Career Guides
  • Practice Exams
  • Calculators
  • Flashcards
  • Blog

Platform

  • Success stories
  • Q&A Hub
  • Help Center
  • Verify Certificate

Business

  • Corporate Training
  • Team Plan
  • Live Training

Business

  • Corporate Training
  • Team Plan
  • Live Training
© 2025 365 Data Science. All Rights Reserved.
Sitemap Terms of Use Privacy Policy Cookies