Watch Solving 100 Python NumPy Problems! (From easy to difficult) Video Tutorial


Tutorial Details & Info

Tutorial Title: Solving 100 Python NumPy Problems! (From easy to difficult)
Instructor / Channel: Keith Galli
Lesson Runtime: 44:58 Minutes
Publish Date: November 02, 2024
Total Students / Views: 45,429 views

Explore this free video tutorial for Solving 100 Python NumPy Problems! (From easy to difficult) created by Keith Galli. The total lesson runtime is 44:58 minutes providing step-by-step visual instructions. Follow along to build your skills today on TutorTube.

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Course Description & Lesson Notes

Official Video Description:

NumPy is a foundational library for computation in Python. In this video we walk through exercises to learn the library in a hands-on manner. We learn skills such as array creation and manipulation, working with random numbers, performing mathematical operations, handling dates, dealing with various data types, and more. Should be a lot of fun! Link to GitHub repo: https://github.com/rougier/numpy-100 My solutions: https://github.com/KeithGalli/numpy-100 If you enjoy this video, make sure to throw it a like & subscribe if you haven't already 🫡 Here is a link to the similar video I did with the Python Pandas library! https://youtu.be/i7v2m-ebXB4?si=G3U8SRK2k2mgoQ7e Video timeline! 0:00 - Video Overview & Code Setup 4:18 - 1.) Import the numpy package under the name np 5:15 - 2.) Print the numpy version and the configuration 6:21 - 3.) Create a null vector of size 10 9:29 - 4.) How to get the memory size of any array 15:19 - 5.) How to get documentation of the numpy add function from the command line 18:51 - 6.) Create a null vector of size 10 but the fifth value which is 1 20:03 - 7.) Create a vector with values ranging from 10 to 49 21:48 - 8.) Reverse a vector (first number becomes last) 23:20 - 9.) Create a 3x3 Matrix with values ranging from 0 to 8 24:41 - 10.) Find indices of non-zero elements from array 26:24 - 11.) Create a 3x3 identity matrix 29:35 - 12.) Create a 3x3x3 array with random values. 30:48 - 13.) Create a 10x10 array with random values and find min/max values 33:17 - 14.) Create a random vector of size 30 and find the mean value 34:57 - 15.) Create a 2d array with 1 on the border and 0 inside 40:19 - 16.) How to add a border (filled with 0’s around an existing array? (np.pad) 43:41 - 17.) Evaluate some np.nan expressions 48:32 - 18.) Create a 5x5 matrix with values 1,2,3,4 just below the diagonal 56:01 - 19.) Create an 8x8 matrix and fill it with a checkerboard pattern 1:02:35 - 20.) Get the 100th element from a (6,7,8) shape array 1:07:09 - 21.) Create a checkerboard pattern 8x8 matrix using np.tile function 1:16:22 - 22.) Normalize a random 5x5 matrix 1:24:20 - 23.) Create a custom dtype that describes a color as four unsigned bytes (RGBA) 1:29:27 - 24.) Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) 1:32:54 - 25.) Given a 1D array, negate all elements which are between 3 and 8, in place 1:37:16 - 26.) Default “range” function vs numpy “range” function 1:40:25 - 27.) Evaluate whether expressions are legal or not 1:55:41 - 28.) Evaluate divide by zero expressions / np.nan type casting 1:57:48 - 29.) How to round away from zero a float array? 1:59:22 - 30.) How to find common values between two arrays? 2:00:19 - 31.) How to ignore all numpy warnings? 2:03:24 - 32.) Is np.sqrt(-1) == np.emath.sqrt(-1) ?? 2:05:22 - 33.) Get the dates of yesterday, today, and tomorrow with numpy 2:19:39 - 34.) How to get all the dates corresponding to the month of July 2016? 2:27:27 - 35.) How to compute ((A+B)*(-A/2)) in place (without copy)? 2:35:00 - 36.) Extract the integer part of a random array of positive numbers using 4 different methods 2:40:47 - 37.) Create a 5x5 matrix with row values ranging from 0 to 4 2:43:07 - 38.) Use generator function that generates 10 integers and use it to build an array 2:43:58 - 39.) Create a vector of size 10 with values ranging from 0 to 1, both excluded. 2:48:49 - 40.) Create a random vector of size 10 and sort it. 2:51:07 - 41.) How to sum a small array faster than np.sum? 2:54:37 - 42.) Check if two random arrays A & B are equal 2:58:48 - 43.) Make an array immutable (read-only) 3:02:14 - Puppies are great 3:03:06 - 44.) Convert cartesian coordinates to polar coordinates 3:20:37 - 45.) Create a random vector of size 10 and replace the maximum value by 0 3:23:58 - 46.) Create a structured array with x and y coordinates covering the [0,1]x[0,1] area 3:26:25 - 47.) Given two arrays, X and Y, construct the Cauchy matrix C (Cij = 1/(xi-yj)) 3:34:31 - 48.) Print the min/max values for each numpy scalar type 3:36:50 - 49.) How to print all the values of an array? 3:39:23 - 50.) How to find the closest value (to a given scalar) in a vector? I got a little tired at the end, so not doing all 100 problems in this video. Will release the next 50 problems soon! #python #numpy -------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli -------------------- Learn data skills with hands-on exercises & tutorials at Datacamp! https://datacamp.pxf.io/c/3588040/1012793/13294 Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.

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🎓 Lesson Overview & Learning Outcomes:

Welcome to the step-by-step video guide for Solving 100 Python NumPy Problems! (From easy to difficult) taught by Keith Galli. This tutorial provides a comprehensive walkthrough designed to take you from foundational principles to practical implementation.

💡 Key Topics Covered in This Course:

  • Core Fundamentals & Setup: Understanding the workspace, essential tools, and initial setup for Solving 100 Python NumPy Problems! (From easy to difficult).
  • Step-by-Step Practical Demonstration: Hands-on implementation guided by Keith Galli with real-world examples.
  • Best Practices & Key Shortcuts: Time-saving workflows, keyboard shortcuts, and industry-standard recommendations.
  • Troubleshooting & Common Pitfalls: How to avoid common beginner errors and optimize your workflow for peak efficiency.

📋 Recommended Prerequisites & Study Notes:

No prior advanced experience is required. Follow along with the video player above on any desktop computer, tablet, or mobile device. Pause and rewind at key steps to practice along with the instructor.

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