Overview

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Learn NumPy fundamentals through a hands-on video tutorial featuring 50 practical coding exercises ranging from basic array operations to advanced mathematical computations. Master essential skills including array creation and manipulation, random number generation, mathematical operations, date handling, and custom data types. Work through progressively challenging problems covering topics like matrix operations, array indexing, memory management, type casting, and specialized NumPy functions. Practice implementing solutions for real-world scenarios such as creating checkerboard patterns, normalizing matrices, converting coordinate systems, and constructing Cauchy matrices. Follow along with detailed explanations and step-by-step solutions while building a strong foundation in scientific computing with Python's NumPy library.

Syllabus

- Video Overview & Code Setup
- 1. Import the numpy package under the name np
- 2. Print the numpy version and the configuration
- 3. Create a null vector of size 10
- 4. How to get the memory size of any array
- 5. How to get documentation of the numpy add function from the command line
- 6. Create a null vector of size 10 but the fifth value which is 1
- 7. Create a vector with values ranging from 10 to 49
- 8. Reverse a vector first number becomes last
- 9. Create a 3x3 Matrix with values ranging from 0 to 8
- 10. Find indices of non-zero elements from array
- 11. Create a 3x3 identity matrix
- 12. Create a 3x3x3 array with random values.
- 13. Create a 10x10 array with random values and find min/max values
- 14. Create a random vector of size 30 and find the mean value
- 15. Create a 2d array with 1 on the border and 0 inside
- 16. How to add a border filled with 0’s around an existing array? np.pad
- 17. Evaluate some np.nan expressions
- 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal
- 19. Create an 8x8 matrix and fill it with a checkerboard pattern
- 20. Get the 100th element from a 6,7,8 shape array
- 21. Create a checkerboard pattern 8x8 matrix using np.tile function
- 22. Normalize a random 5x5 matrix
- 23. Create a custom dtype that describes a color as four unsigned bytes RGBA
- 24. Multiply a 5x3 matrix by a 3x2 matrix real matrix product
- 25. Given a 1D array, negate all elements which are between 3 and 8, in place
- 26. Default “range” function vs numpy “range” function
- 27. Evaluate whether expressions are legal or not
- 28. Evaluate divide by zero expressions / np.nan type casting
- 29. How to round away from zero a float array?
- 30. How to find common values between two arrays?
- 31. How to ignore all numpy warnings?
- 32. Is np.sqrt-1 == np.emath.sqrt-1 ??
- 33. Get the dates of yesterday, today, and tomorrow with numpy
- 34. How to get all the dates corresponding to the month of July 2016?
- 35. How to compute A+B*-A/2 in place without copy?
- 36. Extract the integer part of a random array of positive numbers using 4 different methods
- 37. Create a 5x5 matrix with row values ranging from 0 to 4
- 38. Use generator function that generates 10 integers and use it to build an array
- 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded.
- 40. Create a random vector of size 10 and sort it.
- 41. How to sum a small array faster than np.sum?
- 42. Check if two random arrays A & B are equal
- 43. Make an array immutable read-only
- Puppies are great
- 44. Convert cartesian coordinates to polar coordinates
- 45. Create a random vector of size 10 and replace the maximum value by 0
- 46. Create a structured array with x and y coordinates covering the [0,1]x[0,1] area
- 47. Given two arrays, X and Y, construct the Cauchy matrix C Cij = 1/xi-yj
- 48. Print the min/max values for each numpy scalar type
- 49. How to print all the values of an array?
- 50. How to find the closest value to a given scalar in a vector?