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Complete Python NumPy Tutorial in Hindi (With Notes)
7 students

Complete Python NumPy Tutorial in Hindi (With Notes)

NumPy or Numerical Python is a Python library used for working with arrays.
Created bySharad Khare
Last updated 11/2023
Hindi

What you'll learn

  • 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.

Course content

1 section • 41 lectures • 11h 45m total length

  • Complete Python NumPy Tutorial in Hindi24:07
  • NumPy - Creating Arrays with Dimensions31:12
  • NumPy - Indexing, Accessing Array Element18:38
  • NumPy Array Slicing36:09
  • NumPy Data Types26:02
  • NumPy Array Copy and View10:47
  • NumPy Array Shape11:57
  • NumPy Array Reshaping29:00
  • NumPy Array Iterating26:49
  • NumPy Joining Array20:00
  • NumPy Splitting Array20:53
  • NumPy Searching Array21:47
  • NumPy Sorting Array9:08
  • NumPy Filter Array28:34
  • NumPy Random Numbers23:49
  • NumPy Random Data Distribution17:05
  • NumPy Random Permutation & Shuffling11:02
  • NumPy Seaborn18:59
  • NumPy Normal (Gaussian) Distribution16:51
  • NumPy Binomial Distribution21:12
  • NumPy Poisson Distribution15:04
  • NumPy Uniform Distribution7:11
  • NumPy Logistic Distribution10:30
  • NumPy Multinomial Distribution10:59
  • NumPy Exponential Distribution9:39
  • NumPy Chi Square Distribution7:21
  • NumPy Rayleigh Distribution9:32
  • NumPy Pareto Distribution7:13
  • NumPy Zipf Distribution11:18
  • NumPy ufuncs11:30
  • NumPy Create Your Own ufunc16:19
  • NumPy Arithmetic ufunc22:20
  • NumPy Rounding Decimals16:30
  • NumPy Summations & Cumulative Sum13:37
  • NumPy Products & Cumulative Product13:47
  • NumPy Differences6:46
  • NumPy LCM Lowest Common Multiple14:40
  • NumPy GCD or HCF9:30
  • NumPy Trigonometric Functions32:27
  • NumPy Hyperbolic Functions13:49
  • NumPy Tutorial ufuncs Set Operations21:23

Requirements

  • No programming experience needed. I have Started Everything from a beginners level. You will learn everything you need to know

Description

What is NumPy?

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 stands for Numerical Python.

Why Use 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.


Why is NumPy Faster Than Lists?

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.

Which Language is NumPy written in?

NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++.

Where is the NumPy Codebase?

The source code for NumPy is located at this github repository github dot com/numpy/numpy



Who this course is for:

  • Python developers curious about data science

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