[25% Off] Python NumPy Programming and Project Development using NumPy Course Coupon – Free learning guide

[25% Off] Python NumPy Programming and Project Development using NumPy Course Coupon

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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