Published 5/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.10 GB | Duration: 1h 42m
Learn Complete NumPy from Basic to Advance topic like Indexing, Slicing, Broadcasting, Joining, Splitting, Searching …
What you’ll learn
Understanding NumPy Library from zero to advanced
Why We Use NumPy Array Over Python List
Implementing array operations using NumPy module in python
How to work in High dimension Datasets with NumPy array
Requirements
Basic syntax of Python
Description
NumPy (short for “Numerical Python”) is a Python library used for scientific computing and data analysis. It provides a powerful set of tools for working with arrays and matrices of numerical data. NumPy is particularly useful for numerical calculations involving large amounts of data, as it is designed to be efficient and fast.One of the main features of NumPy is its ability to handle multi-dimensional arrays of data. These arrays can be used to represent vectors, matrices, or any other kind of numerical data. NumPy provides a large number of built-in functions for performing operations on these arrays, such as mathematical functions (like sin, cos, and exp), statistical functions (like mean and variance), and linear algebra functions (like matrix multiplication and eigendecomposition).NumPy also provides a number of tools for working with structured data, such as CSV files or other tabular data. These tools allow you to easily import and manipulate data, and to perform complex calculations and analyses on it.In addition to its core functionality, NumPy is often used as a foundation for other Python libraries that are used in scientific computing and data analysis, such as Pandas and SciPy. This makes NumPy an essential tool for anyone working in these fields.Overall, NumPy is a powerful and versatile library that is an essential tool for anyone working in scientific computing, data analysis, or related fields.This course introduce with all majority of concept of NumPy – numerical python library.You will learn following topics :1) Creating Arrays using NumPy in Python2) Accessing Arrays using NumPy in Python3) Finding Dimension of the Array using NumPy in Python4)Finding the Shape of the Array using NumPy5) Checking Datatype of an Array using NumPy in Python6) Changing Datatype of an Array using NumPy in Python7) Reshaping of an arrays using NumPy in Python8) Iterating through arrays using NumPy in Python9) Indexing on (1D, 2D, 3D) Arrays using NumPy in Python10) Slicing an (1D, 2D, 3D) Array using NumPy in Python11) Operation (Scalar , Relational , Vector ) of NumPy Array12) Joining Arrays using NumPy in Python13) Splitting Array using NumPy in Python14) Sorting an Array using NumPy in Python15) Searching in Array using NumPy in Python16) Filtering an Array using NumPy in Python17) Generating a Random Array using NumPy in Python18) Dot Product of NumPy Array19) Converting N D to 1 D NumPy Array20) Plotting Graphs
Overview
Section 1: Introduction
Lecture 1 Introduction of Numpy
Lecture 2 Why we Use Numpy Over Python List ?
Lecture 3 Applications of Numpy
Lecture 4 Installation of Anaconda
Section 2: How to Create Numpy Array
Lecture 5 Create 1D, 2D, 3D Numpy array
Lecture 6 Create numpy array with custom data type
Lecture 7 Create numpy array with arange function
Lecture 8 Reshape the numpy array
Lecture 9 Create Zeros, Ones , Random array
Lecture 10 Create array Using linspace
Section 3: Numpy Array Attributes
Lecture 11 Find the dimension of array
Lecture 12 Find the Shape and Size of the array
Lecture 13 Find the Itemsize of the array
Lecture 14 Find the datatype and change the datatype of an array
Section 4: Numpy Array Operation
Lecture 15 Scalar Operation of numpy array
Lecture 16 Relational Operation of numpy array
Lecture 17 Vector Operation of numpy array
Section 5: Numpy Array Function
Lecture 18 Understanding the Axis and Sum , Min, Max
Lecture 19 Finding Mean , Median , Variance of Numpy Array
Lecture 20 Dot product of an Numpy array
Lecture 21 Finding Log and exponent of an array
Lecture 22 Understanding round , floor ,ceil in numpy
Section 6: Indexing and Slicing in Numpy array
Lecture 23 What is the Indexing and Slicing
Lecture 24 Indexing on 2D array and 3D numpy array
Lecture 25 Slicing in 1D and 2D Numpy array
Lecture 26 Slicing in 3D Numpy array
Section 7: Iteration of Numpy Array
Lecture 27 Iterating 1D array
Lecture 28 Iterating 2D array
Lecture 29 Iterating 3D array
Section 8: Reshaping of Numpy Array
Lecture 30 Reshaping of numpy array
Lecture 31 Transpose the numpy array
Lecture 32 Converting N D array to 1 D array using ravel
Section 9: Joining Two Array
Lecture 33 How to Join to array
Lecture 34 Code for Joining two array using stacking
Section 10: Splitting Two Array
Lecture 35 Splitting an array
Lecture 36 Code for Splitting an array
Section 11: Advance Level
Lecture 37 Searching in Numpy Array
Lecture 38 Sorting a Numpy Array
Engineering Students, Software Developers And Aspiring Data Scientists,Beginner in Python, Who wants to learn Pandas Library
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