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Numpy Tutorial

Numpy Tutorial

A practical handbook for learning NumPy and performing efficient numerical computations in Python. This guide covers NumPy arrays, indexing, slicing, vectorized operations, mathematical functions, and basic linear algebra, helping learners work with data faster and more effectively.

9 Modules

77 Lessons

English

1.5 Hrs

Reading Plan

MODULE 1

Basics of Numpy

14 mins

NumPy Introduction

1 min

Installing NumPy Library

1 min

Python NumPy Arrays

1 min

NumPy Data Types

1 min

Array Creation - Empty, Zeroes and Ones

1 min

Create ndarray with Data

1 min

ndarray with Numerical Range

1 min

Indexing and Slicing

1 min

Advance indexing of ndarray

1 min

Concept of Broadcasting

1 min

ndarray Sorting

1 min

ndarray Copy and View

1 min

Accessing Array Elements

1 min

NumPy Matrix Multiplication

1 min

MODULE 2

Numpy Basic Functions

7 mins

NumPy arange() function

1 min

NumPy reshape() function

1 min

NumPy append() function

1 min

NumPy concatenate() function

1 min

NumPy array() function

1 min

NumPy transpose() function

1 min

NumPy partition() function

1 min

MODULE 3

Numpy Mathematical Functions

4 mins

NumPy ceil() function

1 min

NumPy floor() function

1 min

NumPy trunc() function

1 min

NumPy fix() function

1 min

MODULE 4

Numpy String Functions

28 mins

NumPy islower() function

1 min

NumPy find() function

1 min

NumPy count() function

1 min

NumPy istitle() function

1 min

NumPy decode() function

1 min

NumPy encode() function

1 min

NumPy replace() function

1 min

NumPy splitlines() function

1 min

NumPy add() function

1 min

NumPy multiply() function

1 min

NumPy center() function

1 min

NumPy join() function

1 min

NumPy split() function

1 min

NumPy title() function

1 min

NumPy lower() function

1 min

NumPy isupper() function

1 min

NumPy isnumeric() function

1 min

NumPy isalpha() function

1 min

NumPy index() function

1 min

NumPy startswith() function

1 min

NumPy isspace() function

1 min

NumPy isdigit() function

1 min

NumPy isdecimal() function

1 min

NumPy upper() function

1 min

NumPy capitalize() function

1 min

NumPy swapcase() function

1 min

NumPy strip() function

1 min

Numpy char.partition() function

1 min

MODULE 5

Numpy Statistical Functions

6 mins

NumPy amin() Function

1 min

NumPy amax() function

1 min

NumPy ptp() function

1 min

NumPy median() function

1 min

NumPy mean() function

1 min

NumPy std() function

1 min

MODULE 6

Matrix Library

6 mins

NumPy eye() function

1 min

NumPy identity() function

1 min

NumPy rand() function

1 min

NumPy empty() function

1 min

NumPy ones() function

1 min

NumPy zeros() function

1 min

MODULE 7

Binary Operations

6 mins

NumPy left shift() function

1 min

NumPy right shift() function

1 min

NumPy bitwise xor() function

1 min

NumPy invert() function

1 min

NumPy bitwise or() function

1 min

Numpy bitwise and() function

1 min

MODULE 8

Linear Algebra

1 min

NumPy dot() function

1 min

MODULE 9

Miscellaneous

5 mins

NumPy asarray() function

1 min

NumPy frombuffer() function

1 min

NumPy logspace() function

1 min

NumPy linspace() function

1 min

NumPy fromiter() function

1 min

Contributors

Numpy Tutorial

This handbook introduces NumPy step by step, starting with array creation and basic operations. You’ll learn how NumPy handles data efficiently, perform computations without loops, and work with multidimensional arrays. The focus stays on practical usage for data processing, scientific computing, and machine learning foundations.

Why This Handbook Matters

NumPy is the foundation of Python’s data and scientific ecosystem. Understanding NumPy allows developers and data practitioners to handle large datasets efficiently and build faster, more reliable numerical applications.

Ideal Learners for This Handbook

This handbook is ideal for Python learners moving into data-related fields, students exploring data science or machine learning, and developers who want to perform efficient numerical computations. It’s also useful for anyone working with large datasets in Python.

Prerequisites

This course is suitable for:

Basic understanding of Python programming
Familiarity with variables, loops, and functions
Basic knowledge of lists and data types
Willingness to work with numerical data

Run & Test your Code with our very own IDE that supports 16 languages

Open IDE

9 Modules

77 Lessons

English

1.5 Hrs

Contributors

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