Python NumPy Programming and Project Development using NumPy

Description

A warm welcome to the Python NumPy Programming and Project Development using NumPy course by Uplatz.

NumPy stands for Numerical Python and it is a core scientific computing library in Python. NumPy provides efficient multi-dimensional array objects and various operations to work with these array objects.

NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy is written partially in Python, but most of the parts that require fast computation are written in C or C++.

Purpose of using NumPy

In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.

NumPy is essentially a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.

NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

• Extract, Transform, Load: Pandas, Intake, PyJanitor

• Exploratory analysis: Jupyter, Seaborn, Matplotlib, Altair

• Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy

• Report in a dashboard: Dash, Panel, Voila

Features of NumPy

1. POWERFUL N-DIMENSIONAL ARRAYS

   • Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

2. NUMERICAL COMPUTING TOOLS

   • NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

3. INTEROPERABLE

   • NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

4. PERFORMANT

   • The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

5. EASY TO USE

   • NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

6. OPEN SOURCE

   • Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Using NumPy, a developer can perform the following operations −

• Mathematical and logical operations on arrays.

• Fourier transforms and routines for shape manipulation.

• Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

  
  

Uplatz provides this in-depth training on Python programming using NumPy. This NumPy course explains the concepts & structure of NumPy including its architecture and environment. The course discusses the various array functions, types of indexing, etc. and moves on to using NumPy for creating and managing multi-dimensional arrays with functions and operations. This Python NumPy course also discusses the practical implementation of NumPy to develop prediction models & projects.

NumPy Python Programming and Project Development using NumPy - Course Syllabus

1. INTRODUCTION TO NUMPY

2. NUMPY TUTORIAL BASICS

3. NUMPY ATTRIBUTES AND FUNCTIONS

4. CREATING ARRAYS FROM EXISTING DATA

5. CREATING ARRAYS FROM RANGES

6. INDEXING AND SLICING IN NUMPY

7. ADVANCED SLICING IN NUMPY

8. APPEND AND RESIZE FUNCTIONS

9. NDITER AND BROADCASTING

10. NUMPY BROADCASTING

11. NDITER FUNCTION

12. ARRAY MANIPULATION FUNCTIONS

13. NUMPY UNIQUE()

14. NUMPY DELETE()

15. NUMPY INSERT FUNCTION

16. NUMPY RAVEL AND SWAPAXES()

17. SPLIT FUNCTION

18. HSPLIT FUNCTION

19. VSPLIT FUNCTION

20. LEFTSHIFT AND RIGHTSHIFT FUNCTIONS

21. NUMPY TRIGONOMETRIC FUNCTIONS

22. NUMPY ROUND FUNCTIONS

23. NUMPY ARITHMATIC FUNCTIONS

24. NUMPY POWER AND RECIPROCAL FUNCTIONS

25. NUMPY MOD FUNCTION

26. NUMPY IMAG() AND REAL() FUNCTIONS

27. NUMPY CONCATENATE()

28. NUMPY STATISTICAL FUNCTIONS

29. STATISTICAL FUNCTIONS

30. NUMPY AVERAGE FUNCTION

31. NUMPY SEARCH SORT FUNCTIONS

32. SORT FUNCTION

33. NUMPY SORT FUNCTION

34. NUMPY ARGSORT()

35. NONZERO AND WHERE FUNCTIONS

36. EXTRACT FUNCTION

37. NUMPY ARGMAX ARGMIN()

38. BYTESWAP COPIES AND VIEWS

39. NUMPY STRING FUNCTIONS

40. NUMPY CENTER FUNCTION

41. CAPITALIZE AND CENTER()

42. NUMPY TITLE FUNCTION

43. STRING FUNCTIONS

44. NUMPY MATRIX LIBRARY

45. NUMPY JOIN ARRAYS

46. LINEAR ALGEBRA

47. RANDOM MODULE

48. SECRETS MODULE

49. RANDOM MODULE UNIFORM FUNCTION

50. RANDOM MODULE GENERATE NUMBER EXCEPT K

51. SECRETSMODULE GENERATE TOKENS

52. RANDOM MODULE GENERATE BINARY STRING

53. NUMPY MODULE REVISE

54. NUMPY INDEXING

55. NUMPY BASIC OPERATIONS

56. NUMPY UNARY OPERATORS

57. BINARY OPERATORS IN NUMPY

58. NUMPY UNIVERSAL FUNCTIONS

59. NUMPY FILTER ARRAYS

60. NUMPY MODULE PROJECTS

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