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Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more. 💻 Code: 🤍 🎥 Tutorial from Keith Galli. Check out his YouTube channel: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (01:15) What is NumPy ⌨️ (01:35) NumPy vs Lists (speed, functionality) ⌨️ (09:17) Applications of NumPy ⌨️ (11:08) The Basics (creating arrays, shape, size, data type) ⌨️ (16:08) Accessing/Changing Specific Elements, Rows, Columns, etc (slicing) ⌨️ (23:14) Initializing Different Arrays (1s, 0s, full, random, etc...) ⌨️ (31:34) Problem #1 (How do you initialize this array?) ⌨️ (33:42) Be careful when copying variables! ⌨️ (35:45) Basic Mathematics (arithmetic, trigonometry, etc.) ⌨️ (38:20) Linear Algebra ⌨️ (42:19) Statistics ⌨️ (43:57) Reorganizing Arrays (reshape, vstack, hstack) ⌨️ (47:29) Load data in from a file ⌨️ (50:20) Advanced Indexing and Boolean Masking ⌨️ (55:59) Problem #2 (How do you index these values?) ⭐️ Links with more info ⭐️ 🔗 NumPy vs Lists: 🤍 🔗 Indexing: 🤍 🔗 Array Creation Routines: 🤍 🔗 Math Routines Docs: 🤍 🔗 Linear Algebra Docs: 🤍 Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Learn Numpy in 5 minutes! A brief introduction to the great python library - Numpy. I cover Numpy Arrays and slicing amongst other topics. 3 Data Science Learning Platforms I would recommend 1. Data Quest - 🤍 (my favourite) 2 Data Camp - 🤍 3 365 Data Science - 🤍 2 Recommended Python Courses 1. Exploratory Data Analysis with Python and Pandas - 🤍 2. Complete Python Programmer Bootcamp - 🤍 (These contain affiliate links, which means I receive a percentage of any sales made. There is no additional cost for anybody clicking on them) ►Subscribe to my YouTube Channel 🤍 Free book for Statistical Learning (One of the best I have found) Introduction to Statistical Learning 🤍 Music by bensound.com
Learn all essential numpy functions in this tutorial. Get my Free NumPy Handbook: 🤍 ✅ Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: 🤍 * ⭐ Join Our Discord : 🤍 📓 ML Notebooks available on Patreon: 🤍 If you enjoyed this video, please subscribe to the channel: ▶️ : 🤍 NumPy 1 Hour Crash Course: 🤍 ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: 🤍 🐦 Twitter - 🤍 ✉️ Newsletter - 🤍 📸 Instagram - 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 ~~~~~~~~~~~~~~ SUPPORT ME ~~~~~~~~~~~~~~ 🅿 Patreon - 🤍 #Python Timeline: 00:00 - Introduction 00:50- Installation and Basics 02:11 - Array vs List 05:00 - Dot Product 06:00 - Speed Test array vs list 06:55 - Multidimensional (nd) arrays 11:12 - Indexing/Slicing/Boolean Indexing 15:25 - Reshaping 17:52 - Concatenation 19:40 - Broadcasting 21:10 - Functions and Axis 22:58 - Datatypes 24:12 - Copying 25:02 - Generating arrays 26:30 - Random numbers 29:06 - Linear Algebra (Eigenvalues / Solving Linear Systems) 30:48 - Loading CSV files * This is an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏
In this first Python Numpy Tutorial For Beginners video, I am going to give you the brief Introduction about numpy. I will explain what is numpy. why do we use numpy, NumPy is suited to what applications. at last i am going to show How to install numpy on windows using pip install and how to add numpy to your pycharm IDE. How to transpose() NumPy Array in Python? The fundamental library needed for scientific computing with Python is called NumPy. NumPy is a Python library for array-oriented computing. This Open Source library contains: a powerful N-dimensional array object advanced array slicing methods (to select array elements) convenient array reshaping methods ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ 🤍 ⚫️ 🤍 Java Programming Course ➡️ 🤍 ⚫️ 🤍 Bash Shell Scripting Course ➡️ 🤍 ⚫️ 🤍 Linux Command Line Tutorials ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 PHP Programming Course ➡️ 🤍 ⚫️ 🤍 Android Development Course ➡️ 🤍 ⚫️ 🤍 C# Programming Course ➡️ 🤍 ⚫️ 🤍 JavaFx Programming Course ➡️ 🤍 ⚫️ 🤍 NodeJs Programming Course ➡️ 🤍 ⚫️ 🤍 Jenkins Course For Developers and DevOps ➡️ 🤍 ⚫️ 🤍 Scala Programming Tutorial Course ➡️ 🤍 ⚫️ 🤍 Bootstrap Responsive Web Design Tutorial ➡️ 🤍 ⚫️ 🤍 MongoDB Tutorial Course ➡️ 🤍 ⚫️ 🤍 QT C GUI Tutorial For Beginners ➡️ 🤍 ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - 🤍 Data Science - 🤍 | 🤍 Machine Learning - 🤍 | 🤍 Artificial Intelligence - 🤍 | 🤍 MERN Stack E-Degree Program - 🤍 | 🤍 DevOps E-degree - 🤍 | 🤍 Data Analytics with R - 🤍 | 🤍 AWS Certification Training - 🤍 | 🤍 Projects in Java - 🤍 | 🤍 Machine Learning With TensorFlow - 🤍 | 🤍 Angular 8 - Complete Essential Guide - 🤍 Kotlin Android Development Masterclass - 🤍 Learn iOS Programming Building Advance Projects - 🤍 ★★★ Follow ★★★ My Website - 🤍 DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
In this tutorial, we will learn about NumPy arrays in great detail! 🤓 NumPy is one of the most popular Python libraries and just as it sounds - it deals with numbers! An array, on the other hand, is a collection of items of the same type and it's the data structure that NumPy uses. This video will give you the tools and demos to understand how NumPy manages data and how it approaches memory consumption. We will explore different functions and methods to: ⭐ create N-dimensional arrays ⭐ reshape arrays ⭐ sort arrays with different algorithms ⭐ filter and manipulate array values The goal of this Ultimate Guide (and all the future Ultimate Guides I'll be filming for other libraries) - is to cover a HUGE part of the NumPy documentation. It's meant for all the folks who learn by example rather than by reading dry text and hopefully this will take your NumPy skills to an expert level! 🥼🥼🥼 You can find PART 2 of this tutorial here: 🤍 If you don't feel like coding along with me, you can always clone my code from Wayscript! It's the IDE I'm using in this video, and if you like what you see - you can sign up for free!! 😀 ⭐ get complete tutorial code: 🤍 ⭐ what is Wayscript? 🤍 🛠️ Related Tutorials of Mine 🛠️ ⭐ PART 2 of NumPy The Ultimate Guide: 🤍 ⭐ Convert Binary to Decimal Like a Pro: 🤍 ⭐ Train Basic Neural Network with NumPy and Pandas: 🤍 ⭐ Python Learning Roadmap: 🤍 ⭐ Anaconda Guide for Beginners: 🤍 ⏲️ TIME STAMPS ⏲️ 00:00 - intro 00:43 - install NumPy 01:02 - import NumPy 01:09 - create an array with NumPy arange 04:09 - create an array with NumPy array 04:27 - why arrays are better than lists 07:08 - NumPy data types 08:06 - 2 dimensional arrays 09:47 - shape attribute 10:38 - reshape arrays 12:33 - create empty arrays 14:14 - NumPy eye function 15:10 - change array values 18:41 - sort NumPy arrays 21:13 - difference between copy and view 23:50 - next NumPy tutorial and thank you for watching! 🐍 Install with Anaconda 🐍 conda install -c anaconda numpy * more details here: 🤍 🔗 Important Links 🔗 ⭐ NumPy Documentation: 🤍 💳 CREDITS 💳 ⭐ Icons by: flaticon.com ⭐ Text Animations by: mixkit.co THANK YOU SO MUCH FOR WATCHING! 💗
Check out 🤍 to practice your Python Pandas data science skills! This video overviews the NumPy library. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. A full video timeline can be found in the comments. Link to code used in video: 🤍 Feel free to watch at 1.5x to learn more quickly! If you enjoyed this video, please consider subscribing :). Let me know your feedback and what I should make a video on next. Videos of mine that use NumPy - Creating Connect 4 Game: 🤍 - Plotting (with some use of NumPy): 🤍 - Generating Mock Data: 🤍 Links with more information! NumPy vs Lists: 🤍 Indexing: 🤍 Array Creation Routines: 🤍 Math Routines Docs: 🤍 Linear Algebra Docs: 🤍 Video Timeline! 0:00 - Introduction 1:15 - What is NumPy 1:35 - NumPy vs Lists (speed, functionality) 9:17 - Applications of NumPy 11:08 - The Basics (creating arrays, shape, size, data type) 16:08 - Accessing/Changing Specific Elements, Rows, Columns, etc (slicing) 23:14 - Initializing Different Arrays (1s, 0s, full, random, etc...) 31:34 - Problem #1 (How do you initialize this array?) 33:42 - Be careful when copying variables! 35:45 - Basic Mathematics (arithmetic, trigonometry, etc.) 38:20 - Linear Algebra 42:19 - Statistics 43:57 - Reorganizing Arrays (reshape, vstack, hstack) 47:29 - Load data in from a file 50:20 - Advanced Indexing and Boolean Masking 55:59 - Problem #2 (How do you index these values?) - If you are curious to learn how I make my tutorials, check out this video: 🤍 Join the Python Army to get access to perks! YouTube - 🤍 Patreon - 🤍 *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Python Certification Training: 🤍 This Edureka Python Numpy tutorial (Python Tutorial Blog: 🤍 explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples. Check out our Python Training Playlist: 🤍 This tutorial helps you to learn following topics: 1. What is Numpy? 2. Numpy v/s Lists 3. Numpy Operations 4. Numpy Special Functions 🔥Edureka Elevate Program. Learn now, Pay Later: 🤍 Subscribe to our channel to get video updates. Hit the subscribe button above. PG in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 Post Graduate Certification in Data Science with IIT Guwahati - 🤍 (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonNumpy Introducing Edureka Elevate, a one of its kind software development program where you only pay the program fees once you get a top tech job. If you are a 4th year engineering student or a fresh graduate, this program is open to you! Learn more: 🤍 How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍
Get my Free NumPy Handbook: 🤍 Learn NumPy in this complete 60 minutes Crash Course! I show you all the essential functions of NumPy, and some tricks and useful methods. NumPy is the core library for scientific computing in Python. It is essential for any data science or machine learning algorithms. ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ ✅ Write cleaner code with Sourcery: 🤍 * 📚 Get my FREE NumPy Handbook: 🤍 📓 Notebooks available on Patreon: 🤍 ⭐ Join Our Discord : 🤍 If you enjoyed this video, please subscribe to the channel! Timestamps: 00:00 - Overview 01:59 - NumPy Introduction 03:30 - Installation and Basics 08:00 - Array vs List 12:06 - Dot Product 15:52 - Speed Test array vs list 17:54 - Multidimensional (nd) arrays 22:09 - Indexing/Slicing/Boolean Indexing 29:37 - Reshaping 32:40 - Concatenation 36:16 - Broadcasting 38:26 - Functions and Axis 41:50 - Datatypes 44:03 - Copying 45:15 - Generating arrays 48:05 - Random numbers 51:29 - Linear Algebra (Eigenvalues / Solving Linear Systems) 01:00:04 - Loading CSV files You can play around with the notebook here: 🤍 Data Loading with NumPy: 🤍 My Machine Learning Tutorials with NumPy: 🤍 NumPy Official site: 🤍 You can find me here: Website: 🤍 Twitter: 🤍 GitHub: 🤍 #Python * This is a sponsored link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏
This video is a full crash course for NumPy in Python. We learn everything from scratch. ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾ 📚 Programming Books & Merch 📚 🐍 The Python Bible Book: 🤍 💻 The Algorithm Bible Book: 🤍 👕 Programming Merch: 🤍 🌐 Social Media & Contact 🌐 📱 Website: 🤍 📷 Instagram: 🤍 🐦 Twitter: 🤍 🤵 LinkedIn: 🤍 📁 GitHub: 🤍 🎙 Discord: 🤍 🎵 Outro Music From: 🤍 Timestamps: (0:00) Intro (1:21) Installation (2:19) NumPy Arrays (5:26) Array Attributes (8:14) Numpy Data Types (15:09) Filling Arrays (20:16) NaN & Inf (22:34) Mathematical Operations (28:31) Array Methods (32:50) Structuring Methods (40:25) Concatenating, Stacking, Splitting (45:35) Aggregate Functions (46:43) NumPy Random (49:38) Exporting & Importing (51:46) Outro
NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in NumPy, and discuss how to write your own routines. Along the way, we'll learn just enough about matplotlib to display results from our examples. See tutorial materials here: 🤍 Connect with us! * 🤍 🤍 🤍
In this video, learn Python Numpy Full Tutorial For Beginners | Numpy Full Course in 4 Hours 🔥. Timestamps: 00:00:00 | What is a NumPy 00:12:48 | Python Lists vs. NumPy Arrays 00:22:40 | NumPy Array Creation 00:38:28 | Special Types of Arrays ( filled with specific value) 00:53:44 | Creating Random Valued Arrays 01:05:59 | NumPy - Data Types 01:22:57 | Shape and Reshape in NumPy 01:38:02 | NumPy - Arithmetic Operations 02:02:37 | Broadcasting with NumPy Arrays 02:17:04 | Indexing & Slicing 02:45:57 | NumPy Array Iterating 03:00:39 | The Difference Between Copy and View 03:06:14 | Join & Split Function 03:23:28 | Search , Sort , Search Shorted, Filter Functions 03:40:39 | Shuffle, Unique, Resize, Flatten, Ravel Functions 03:52:22 | Insert and Delete Function 04:03:23 | NumPy - Matrix 04:16:24 | Matrix Function 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-7878985501, +91-9269698122 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #NumpyTutorial #Numpy #NumpyCourse
This from-scratch tutorial on NumPy is designed specifically for those in physics, mathematics, and engineering. In the future, I will be making tutorial videos on all the essential python packages, so subscribe for more! All code can be found here: 🤍 0:00 Introduction 3:43 Array Operations 8:28 Indexing and Slicing (1 Dimension) 15:18 Calculus and Statistics 21:28 Examples 47:18 Multi-Dimensional Arrays 52:22 Functions on Multi-Dimensional Arrays 56:26 Linear Algebra: Matrix Operations 58:33 Linear Algebra: Systems of Equations 59:53 Linear Algebra: Eigenvalue Problems 1:02:02 Examples 1:28:42 Basic Datasets
In this video I explain how to implement arrays in python using the module numpy. This is a module you must download as it is not built into python. Numpy is extremely useful for using data structures and multi-dimensional lists. It has some built-in methods and properties that will save you a lot of time. If you want to learn more about numpy and some more advanced examples stay tuned for the rest of the videos and subscribe! Text-Based Tutorial: 🤍 Twitter: 🤍 Want To Support This Channel? Bitcoin: 1PbkAYLFaJBgjbKn2ptGyBz65xWN8hJgBU Ethereum: 0xdd42dbbdba60f7163fc7a840e189474b6e8bfcad Ripple: rD4arM9CVjQWqi8f1kxdpCgkCgEkqBgtud Please leave a LIKE and SUBSCRIBE for more content! Tags: - Tech - Tech With Tim - Programming - Coding - Pygame - Python Tutorials - Numpy - Numpy Arrays - Arrays in python - Numpy module - Nump tutorial - How to use an array in python
In this video on NumPy and Pandas Tutorial, you'll learn how to perform data analysis with Python libraries. You'll look at the different functions available in NumPy and Pandas and how you can use it to clean, manipulate, arrange, sort and analyze data. 🔥Free Python Course with completion certificate : 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Python tutorial videos: 🤍 #NumPyAndPandasTutorial #DataAnalysisWithPython #NumPyPython #PandasPython #DataAnalysis #LearnPython #PythonLibraries #PythonTutorialForBeginners #PythonTutorial #PythonForDataScience #Simplilearn What are NumPy and Pandas? NumPy or Numerical Python, is a Python library for numerical computation. It consists of multidimensional array objects and a collection of routines for processing those arrays. NumPy operations are divided into three main categories: Fourier Transform and Shape Manipulation, Mathematical and Logical Operations, and Linear Algebra and Random Number Generation. Pandas is a data analysis and data manipulation library in Python. Pandas provides various data structures, operations and functions for manipulating numerical and time series data. About Python Certification Course: Simplilearn’s comprehensive Python Training Course will teach you the basics of Python, data operations, conditional statements, shell scripting, and Django. This Python certification course will give you hands-on development experience and prepare you for an exciting career as a professional Python programmer. This Python Training course covers the fundamentals of Python and how to apply it to real-world applications. The modules, lesson-end projects, and assignments comprising the curriculum cover data operations in Python, strings, conditional statements, error handling, shell scripting, web scraping and the commonly used Python web framework Django. Key Features: ✅ 38 hours of Blended Learning ✅ 30 hours of instructor-led training ✅ 8 hours of online self-paced learning ✅ 20+ assisted practices on all modules ✅ Industry-recognized course completion certificate Eligibility for this Python Certification Course: Anyone interested in learning Python for software development or data science job roles will benefit from this Python certification. This Python course also is well-suited for: 1. Software developers 2. Software engineers 3. Technical leads 4. Architects 5. Programming enthusiasts Pre-requisites for this Python Certification Course: No prior programming knowledge or experience is necessary to take this online Python course. Benefits of this Python Certification Course: The StackOverflow’s developer survey of 2019 states that Python is the second most loved programming language in the world. Also, it is the most sought after programming language for Data Scientists, AI engineers, and Machine Learning engineers. Python developers earn around $115,000 per annum. Learn more at: 🤍 🔥Free Python Course with completion certificate: 🤍 For more information about Simplilearn’s courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 - Instagram: 🤍 - Telegram Mobile: 🤍 - Telegram Desktop: 🤍 Get the Simplilearn app: 🤍
This NumPy tutorial will help you learn the basics of NumPy, install and import NumPy, and deal with NumPy arrays. You will get an idea to perform NumPy arithmetic operations and understand the different NumPy functions. Finally, you will implement some practical examples and build histograms using the matplotlib library. So, let's learn about the NumPy library in detail. Below are the topics covered in this Numpy Full Course Video: 00:00:00 What is Numpy? 00:02:10 Installing and Importing Numpy 00:05:13 Numpy Array vs Python List 00:14:59 Basics Of Nympy 00:19:55 Finding size and shape of any array 00:20:23 Range and arrange functions 00:22:17 Numpy String Functions 00:30:32 Array Manipulation 00:40:58 Numpy Arthematic Operations 00:44:33 Slicing Arrays 00:46:53 Iterating Over Arrays 00:51:06 Joining Array 00:55:12 Splitting Arrays 00:57:24 Resizing an Array 01:00:00 Numpy Histogram Using Matplotlib 01:04:07 Other Useful Functions in Numpy 01:06:57 Practice Example ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Python tutorial videos: 🤍 #PythonNumPyTutorial #NumPyArray #PythonTutorialForBeginners #PythonTraining #PythonProgrammingForBeginners #LearnPythonProgramming #Simplilearn Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer. What is this course about? The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python. What are the course objectives? By the end of this online Python training course, you will be able to: 1. Internalize the concepts & constructs of Python 2. Learn to create your own Python programs 3. Master Python Django & advanced web development in Python 4. Master PyGame & game development in Python 5. Create a flappy bird game clone The Python training course is recommended for: 1. Any aspiring programmer can take up this bundle to master Python 2. Any aspiring web developer or game developer can take up this bundle to meet their training needs Learn more at: 🤍 For more information about Simplilearn courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍
This tutorial covers an introduction to numpy python module. We'll see why numpy is very popular and talk about its main feature "n dimensional array". It is memory efficient, fast and convenient compared to python native list. Topics that are covered in this Python Video: 0:00 what is numpy? 1:24 benefits of numpy array over python list 3:48 numpy using less memory (Memory presentation diagram of numpy and python array ) 5:08 why numpy is fast and convenient Do you want to learn technology from me? Check 🤍 for my affordable video courses. Next Video: numpy tutorial - basic array operations: 🤍 Website: 🤍 Facebook: 🤍 Twitter: 🤍
What is Numpy? Python for Data Science, data mining, data analysis tutorial This video is an introduction to the python package "Numpy" or numeric python. This video explains how regular python list is different from Numpy Arrays along with the examples on Pycharm. It also covers the basics of regular python list. Introduction to Python Lists : 🤍 Video by Aditya Ekawade.
Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included! NOTE: Check description for updated Notebook links. Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever. In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills. 💻 Course created by Santiago Basulto from RMOTR 🔗 Check out all Data Science courses from RMOTR: 🤍 ⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: 🤍 ⭐️ Course Contents ⭐️ ⌨️ Part 1: Introduction What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science? 🔗 Slides for this section: 🤍 ⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11) A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections 🔗 Notebooks: 🤍 ⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50) A step by step tutorial to learn how to use Juptyer Notebooks 🔗 Twitter Cheat Sheet: 🤍 🔗 Notebooks: 🤍 ⌨️ Part 4: Intro to NumPy (01:04:58) Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data. 🔗 Notebooks: 🤍 ⌨️ Part 5: Intro to Pandas (01:57:08) Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data 🔗 Notebooks: 🤍 ⌨️ Part 6: Data Cleaning (02:47:18) Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them. 🔗 Notebooks: 🤍 ⌨️ Part 7: Reading Data from other sources (03:25:15) 🔗 Notebooks: 🤍 ⌨️ Part 8: Python Recap (03:55:19) If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures. 🔗 Notebooks: 🤍 Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
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🔥 Complete the Course and get your free certificate of completion for the Data Science with Python Course, Register Now: 🤍 🔥 NumPy is a python library that is used for working with arrays. NumPy was developed by Travis Oliphant in 2005. It is an open source project and you can use it freely. NumPy stands for Numerical Python. It is helpful in solving problems related to linear algebra, fourier transform, and matrices. One of the most common Python data structures is lists that serve the purpose of arrays, but they are slow to process. NumPy provides an array object which is known as “ndarray” and the fun fact is, it’s 50x faster than lists. NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. 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. To know more about this interesting library with amazing examples, watch this tutorial till the end. Following pointers will be covered in this video: 00:00:00 - Introduction 00:00:41 Python Numpy 00:01:07 Creating NumPy Array 00:11:44 NumPy-Shape 00:14:02 Joining NumPy Arrays 00:17:34 NumPy Intersection & Difference 00:21:08 Numpy Array Mathematics 00:28:35 NumPy Matrix 00:31:47 NumPy Matrix Transpose 00:32:42 NumPy Matrix Multiplication 00:35:05 NumPy Save & Load Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. 🤍 Get the free Great Learning App for a seamless experience, enroll for free courses and watch them offline by downloading them. 🤍 About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - For more interesting tutorials, don't forget to subscribe to our channel: 🤍 - Learn More at 🤍 For more updates on courses and tips follow us on: - Telegram: 🤍 - Facebook: 🤍 - LinkedIn: 🤍 - Follow our Blog: 🤍 #NumPy#Python#PythonNumPy#GreatLearning
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Episodio 01 del corso di NumPy. Oggi vediamo cos'è NumPy, a cosa serve e perchè è importante per tutti coloro che vogliono perseguire il settore di Data Analysis, Machine Learning, Data engineering ecc.. Andremo poi ad installare numpy con il comando pip e vedremo le prime righe di codice, giusto per farci un'idea di come funziona. 📚 ► Libro ufficiale Pandas e Numpy 🤍 🌐 ►Ti serve un hosting per mettere online il tuo sito? 🤍 💻 ►Documentazione della lezione 🤍 📚 ► Consigli per libri e postazione da lavoro 🤍 💰 ► Sostieni il canale con una donazione 🤍 🤍 💬 ►Gruppo Telegram e Discord Gruppo: 🤍 Canale: 🤍 Discord: 🤍 💼 ► Linkedin 🤍
Never miss a tutorial! Subscribe to the Project Data Science channel: 🤍 Go from zero to hero with our Data Science Specialization: 🤍 Or learn about all of data science in one blog post! 🤍 ... The 20% of NumPy that you need to know to get 80% of the value. NumPy is a numeric computation library that is the foundation of many other Python data science libraries. Using pandas? It's built on NumPy. Using Scikit-Learn? Also built on NumPy. If you're going to be doing data science in Python, you're going to need to know how to use the primary object in NumPy—NumPy arrays. But, there are really only a handful of things you need to know to get up and running quickly in NumPy, and that's what we're going to cover in this video. - If you need to get your professional data science environment set up, here's a guide on exactly how to do that: 🤍 (Or you can watch this video: 🤍 If you prefer a blog version of this NumPy tutorial, you can find that here: 🤍 And finally, here's a course that walks you through a whole machine learning / data science project from start to finish in Python: 🤍 - 00:00 Introduction 01:22 What is NumPy? 02:48 Launching Jupyter notebooks and creating a notebook 05:05 Importing NumPy 06:17 Primary NumPy data structure - ndarray 09:31 Loading NumPy array data from Scikit-Learn 11:03 Looking at the data in NumPy arrays 13:02 Looking at the shape of our NumPy array 14:22 Indexing and slicing our NumPy array 21:50 Using start-stop-step indexing 25:24 Mathematical operations on NumPy arrays 32:51 Filtering NumPy data using boolean masks 40:22 If-then logic using np.where 44:11 Sorting NumPy arrays 48:04 Getting NumPy arrays from pandas DataFrames 50:09 NumPy arrays are homogeneous 52:29 Creating ranges using np.arange and np.linspace 55:31 Reshaping NumPy arrays using np.reshape and np.flatten 01:00:15 Wrap-up and thank you!
Cette Formation Python Numpy est un tutoriel français spécial machine learning: Numpy est le package python le plus important pour faire du machine learning et du data science. Numpy comprend le tableau array dit ndarray (n dimensions) qui est un objet extrêmement puissant en machine learning et data science. Numpy propose beaucoup de méthode pour le ndarray, dans cette vidéo nous voyons les différents constructeurs qui permettent d'initialiser les tableau ndarray: np.array() np.zeros() np.ones() np.full() np.random.randn() les deux attributs les plus importants à retenir sont : shape size pour développer des programmes puissants, pensez à définir le type de valeur dans le np.array() avec dtype = np.int16, np.float64 Nous voyons aussi les méthodes les plus utiles pour manipuler la forme de nos tableau numpy: np.vstack np.hstack np.concatenate np.reshape np.squeeze np.ravel Il n'y a rien de plus à retenir pour bien se lancer avec Numpy. Ignorez les autres attributs et méthodes pour le moment ! ► Timeline de la vidéo : 0:00 Intro 00:40 Le tableau Numpy, ses dimensions et sa shape 05:20 initialiser un ndarray: np.ones, np.zeros, 09:15 np.random.randn 12:04 np.linspace, np.arange 13:24 dtype=np.float16 np.float64 15:43 Assembler des tableaux: vstack hstack concatenate 18:40 np.reshape np.squeeze 22:10 np.ravel() 23:08 Exercice ► Soutenez-moi sur Tipeee pour du contenu BONUS: 🤍 ► Documentation Numpy pour ndarray: 🤍 ► Documentation Numpy pour np.random: 🤍 ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: 🤍 ► NOTRE COMMUNAUTÉ DISCORD 🤍 ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: 🤍 ► Télécharger gratuitement mes codes sur github: 🤍 ► Abonnez-vous : 🤍 ► Pour En Savoir plus : Visitez Machine Learnia : 🤍 ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: contact🤍machinelearnia.com
New Data Science / Machine Learning Video Everyday at 1 PM EST!!! [ Click Notification Bell ] NumPy is an amazing scientific computing library that is used by numerous other Python Data Science libraries. NumPy contains many mathematical, array and string functions that are extremely useful. Along with all the basic math functions you'll also find them for Linear Algebra, Statistics, Simulation, etc. I cover most everything you'll learn by reading the API here. Next I'll cover SciPy, Pandas, StatsModels, MatPlotLib, Seaborn, Plotly, Scikit-learn, TensorFlow, PyTorch, Keras, Scrapy, Linear Algebra, Calculus and more. CLICK THE NOTIFICATION BELL ❇️ LIVESTREAMS : 🤍 ❇️ DISCORD : 🤍 ( Contact Me Anytime ) MY UDEMY COURSES ARE 87.5% OFF TIL August 16th ($9.99) ➡️ Python Data Science Series for $9.99 : Highest Rated & Largest Python Udemy Course + 56 Hrs + 200 Videos + Data Science 🤍 ➡️ New C Programming Bootcamp Series for $9.99 : Over 23 Hrs + 53 Videos + Quizzes + Graded Assignments + New Videos Every Month 🤍 NumPy GitHub Cheat Sheet : 🤍 Install for Windows : 🤍 Install for MacOS : 🤍 Probability in One Video : 🤍 Statistics in One Video : 🤍 #LearnWithMe #NewVideoEveryday #NumPy The most in demand skills in the world right now are in Data Science & Machine Learning! I will teach you everything imaginable about Data Science & Machine Learning including all the math, libraries, algorithms so that rather then playing with it on an elementary level you will Master it! The journey will be hard, but those willing to put in the time and brain power will be rewarded in the end. Here is a Table of Contents that will allow you to jump around in the video and learn what ever you are interested in. TABLE OF CONTENTS 00:00 Intro 01:04 Creating Arrays 05:37 Data Types 07:10 Slicing & Indexing 12:49 Reshaping Arrays 14:23 Stacking & Splitting 18:36 Copying Arrays 20:55 Universal Math 29:43 Reading From Files 32:50 Statistics Functions 36:51 Creating Formulas 40:50 Trigonometry Functions 43:23 Linear Algebra 57:37 Saving & Loading NumPy Objects 59:25 Loading Libraries in Anaconda 1:00:13 Financial Functions 1:07:15 Comparison Functions Like the channel? Consider becoming a Patreon and get access to exclusive videos! All Patreons who contribute $1 or more get a FREE coupon code to my Python Programming Bootcamp Series!!! Check it out here: 🤍 GET FREE STUFF AND SUPPORT MY TUTORIALS 1. Get a Free Stock : 🤍 2. Get 2 Free Audiobooks : 🤍 THANK YOU TO MY PATREON SUPPORTERS LIKE : 🤍 (Calorie Counter & Weight Tracking App) ckcoder.com vsolutions.be instagram.com/lumarycodes/ github.com/metabake greedygammon.com twitter.com/mrjak318
Learn the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis in this course for beginners. This was originally presented as a live course. By the end of the course, you will be able to build an end-to-end real-world course project and earn a verified certificate of accomplishment. There are no prerequisites for this course. Learn more and register for a certificate of accomplishment here: 🤍 This full course video includes 6 lectures (all in this video): • Introduction to Programming with Python • Next Steps with Python • Numerical Computing with Numpy • Analyzing Tabular Data with Pandas • Visualization with Matplotlib and Seaborn • Exploratory Data Analysis - A Case Study 💻 Code References • First steps with Python: 🤍 • Variables and data types: 🤍 • Conditional statements and loops: 🤍 • Functions and scope: 🤍 • Working with OS & files: 🤍 • Numerical computing with Numpy: 🤍 • 100 Numpy exercises: 🤍 • Analyzing tabular data with Pandas: 🤍 • Matplotlib & Seaborn tutorial: 🤍 • Data visualization cheat sheet: 🤍 • EDA on StackOverflow Developer Survey: 🤍 • Opendatasets python package: 🤍 • EDA starter notebook: 🤍 ⭐️ Course Contents ⭐️ 0:00:00 Course Introduction Lecture 1 0:01:42 Python Programming Fundamentals 0:02:40 Course Curriculum 0:05:24 Notebook - First Steps with Python and Jupyter 0:08:30 Performing Arithmetic Operations with Python 0:11:34 Solving Multi-step problems using variables 0:20:17 Combining conditions with Logical operators 0:22:22 Adding text using Markdown 0:23:50 Saving and Uploading to Jovian 0:26:38 Variables and Datatypes in Python 0:31:28 Built-in Data types in Python 1:07:19 Further Reading Lecture 2 1:08:46 Branching Loops and Functions 1:09:02 Notebook - Branching using conditional statements and loops in Python 1:09:24 Branching with if, else, elif 1:15:25 Non Boolean conditions 1:19:00 Iteration with while loops 1:28:57 Iteration with for loops 1:36:27 Functions and scope in Python 1:36:53 Creating and using functions 1:42:24 Writing great functions in Python 1:45:38 Local variables and scope 2:08:19 Documentation functions using Docstrings 2:11:40 Exercise - Data Analysis for Vacation Planning Lecture 3 2:17:17 Numercial Computing with Numpy 2:18:00 Notebook - Numerical Computing with Numpy 2:26:09 From Python Lists to Numpy Arrays 2:29:09 Operating on Numpy Arrays 2:34:33 Multidimensional Numpy Arrays 3:03:41 Array Indexing and Slicing 3:17:49 Exercises and Further Reading 3:20:50 Assignment 2 - Numpy Array Operations 3:29:16 100 Numpy Exercises 3:31:25 Reading from and Writing to Files using Python Lecture 4 4:02:59 Analysing Tabular Data with Pandas 4:03:58 Notebook - Analyzing Tabular Data with Pandas 4:16:33 Retrieving Data from a Data Frame 4:32:00 Analyzing Data from Data Frames 4:36:27 Querying and Sorting Rows 5:01:45 Grouping and Aggregation 5:11:26 Merging Data from Multiple Sources 5:26:00 Basic Plotting with Pandas 5:38:27 Assignment 3 - Pandas Practice Lecture 5 5:52:48 Visualization with Matplotlib and Seaborn 5:54:04 Notebook - Data Visualization with Matplotlib and Seaborn 6:06:43 Line Charts 6:11:27 Improving Default Styles with Seaborn 6:16:51 Scatter Plots 6:28:14 Histogram 6:38:47 Bar Chart 6:50:00 Heatmap 6:57:08 Displaying Images with Matplotlib 7:03:37 Plotting multiple charts in a grid 7:15:42 References and further reading 7:20:17 Course Project - Exploratory Data Analysis Lecture 6 7:49:56 Exploratory Data Analysis - A Case Study 7:50:55 Notebook - Exploratory Data Analysis - A case Study 8:04:36 Data Preparation and Cleaning 8:19:37 Exploratory Analysis and Visualization 8:54:02 Asking and Answering Questions 9:22:57 Inferences and Conclusions 9:25:00 References and Future Work 9:29:41 Setting up and running Locally 9:34:21 Project Guidelines 9:45:00 Course Recap 9:48:01 What to do next? 9:49:10 Certificate of Accomplishment 9:50:11 What to do after this course? 9:52:16 Jovian Platform ✏️ This course is taught by Aakash N S, co-founder, and CEO of Jovian. Jovian's YouTube channel: 🤍
Сегодня мы изучим основы библиотеки NumPy. Научимся работать с одномерными массивами, матрицами. Рассмотрим стандартные функции, операции и объекты данной библиотеки. ✔Основы Matplotlib | Построение Графиков На Python: 🤍 ✔ Ссылка на группу ВКонтакте: 🤍 ✔ Telegram: 🤍 ✔ Канал PyLounge: 🤍 ✔ По вопросам сотрудничества и предложений: peoplesdreamer🤍gmail.ru ✔ Music: 🤍 ✔ Хочешь поддержать канал: Никнейм QIWI Кошелька - PYLOUNGE Ссылки из видео: ✔ Jupyter-файл с основами NumPy из видео: 🤍 ✔ NumPy: 🤍 ✔ NumPy Cheat Sheet — Python for Data Science: 🤍 Привет! Я долго занимаюсь программированием, в частности программирование на языке Python. Я много чего узнал за это время, и мне есть, чем поделиться со зрителями моего канала. Здесь выходят разнообразные ролики, касающиеся IT-тематики и программирования. Подписывайся, будем узнавать что-то новое и работать вместе! Погнали! #numpy #python #data_science #уроки_python #pylounge
Comprehensive course explaining Python NumPy. This course includes theory, examples, and practice problems. 0:00 - promo 0:24 - introduction 1:05 - 2.1 numpy array motivation 6:36 - 2.2 numpy array basics 12:48 - 2.3 creating numpy arrays 17:26 - 2.4 indexing 1d arrays 23:21 - 2.5 indexing multidimensional arrays 28:53 - 2.6 basic math on arrays 30:49 - 2.7 challenge: high school reunion 33:36 - 2.8 challenge: gold miner 36:31 - 2.9 challenge: chic-fil-a 41:12 - 3.1 broadcasting 46:11 - 3.2 newaxis 47:57 - 3.3 reshape() 52:33 - 3.4 boolean indexing 56:14 - 3.5 nan 57:31 - 3.6 infinity 59:04 - 3.7 random 1:10:12 - 3.8 challenge: love distance 1:13:10 - 3.9 challenge: professor prick 1:16:49 - 3.10 challenge: psycho parent 1:20:51 - 4.1 where() 1:24:11 - 4.2 math functions 1:26:35 - 4.3 all() and any() 1:27:40 - 4.4 concatenate() 1:29:00 - 4.5 stacking 1:32:59 - 4.6 sorting 1:37:17 - 4.7 unique() 1:38:50 - 4.8 challenge: movie ratings 1:41:45 - 4.9 challenge: big fish 1:44:46 - 4.10 challenge: taco truck 1:48:44 - 5.1 advanced array indexing 1:56:15 - 5.2 view vs copy 1:58:52 - 5.3 challenge: population verification 2:06:25 - 5.4 challenge: prime locations 2:10:27 - 5.5 challenge: the game of doors 2:16:22 - 5.6 challenge: peanut butter 2:22:41 - 6.1 as_strided() 2:26:24 - 6.2 einsum() 2:31:17 - 6.3 challenge: one-hot-encoding 2:35:08 - 6.4 challenge: cumulative rainfall 2:39:08 - 6.5 challenge: table tennis 2:47:34 - 6.6 challenge: wheres waldo 2:52:25 - 6.7 challenge: outer product - Code 🤍 - Vids & Playlists Google Colab - 🤍 NumPy - 🤍 Pandas - 🤍 Neural Networks - 🤍 - Subscribe To Mailing List 🤍 - Support 🤍
BASICS OF NUMPY 1. Creation of ndarray 2. array( ) Function 3. ndim 4. type( ) Function
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This Python data science course will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib. 💻 Code: 🤍 This is a hands-on course and you will practice everything you learn step-by-step. This course was created by Maxwell Armi. You can check out more of his data science videos on his YouTube channel here: 🤍 🎥 Learn more about Data Science with videos from freeCodeCamp's Data Science Playlist: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to the Course and Outline ⌨️ (0:03:53) The Basics of Programming ⌨️ (1:11:35) Why Python ⌨️ (1:33:09) How to Install Anaconda and Python ⌨️ (1:37:25) How to Launch a Jupyter Notebook ⌨️ (1:46:28) How to Code in the iPython Shell ⌨️ (1:53:33) Variables and Operators in Python ⌨️ (2:27:45) Booleans and Comparisons in Python ⌨️ (2:55:37) Other Useful Python Functions ⌨️ (3:20:04) Control Flow in Python ⌨️ (5:11:52) Functions in Python ⌨️ (6:41:47) Modules in Python ⌨️ (7:30:04) Strings in Python ⌨️ (8:23:57) Other Important Python Data Structures: Lists, Tuples, Sets, and Dictionaries ⌨️ (9:36:10) The NumPy Python Data Science Library ⌨️ (11:04:12) The Pandas Python Data Science Python Library ⌨️ (12:01:31) The Matplotlib Python Data Science Library ⌨️ (12:09:00) Example Project: A COVID19 Trend Analysis Data Analysis Tool Built with Python Libraries
Что из себя представляет пакет NumPy для языка Python. Как он устанавливается и импортируется в программы. Первое знакомство с массивами array. Способ их задания с помощью функции array и демонстрация некоторых возможностей. Инфо-сайт: 🤍 Функция array: 🤍
What does it mean to add two arrays of different shapes? In Numpy, broadcasting allows you to do just this. ― mCoding with James Murphy (🤍) Source code: 🤍 SUPPORT ME ⭐ - Sign up on Patreon to get your donor role and early access to videos! 🤍 Feeling generous but don't have a Patreon? Donate via PayPal! (No sign up needed.) 🤍 Want to donate crypto? Check out the rest of my supported donations on my website! 🤍 BE ACTIVE IN MY COMMUNITY 😄 - Discord: 🤍 Github: 🤍 Reddit: 🤍 Facebook: 🤍
An introduction tutorial to Python Numpy, a multi-dimensional numerical array library for mathematical operations. Learn basic data analysis for beginners and intermediate Python programmers. RELATED VIDEOS ► Numpy Intro: 🤍 ► Numpy Intro Jupyter nb: 🤍 ► Pandas Intro: 🤍 ► Pandas Import Data: 🤍 ► Pandas Time Series: 🤍 ► Pandas and MatPlotLib: 🤍 ► Matplotlib Intro: 🤍 ► Twitter: 🤍 ► Code on GitHub: 🤍 ► Subscribe: 🤍 ► Thank me on Patreon: 🤍 #Python #Numpy
Yarım saatte NumPy kütüphanesini anlattığım videomda numpy kurulumunu, numpy kütüphanesinin ne işe yaradığını, matris işlemlerini ve daha birçok şeyi anlattım. Kanala abone olmanız ve videoyu beğenmeniz güzel bir teşvik olur. Teşekkürler. Python Giriş Dersleri : 🤍 C Programlama Giriş Dersleri: 🤍 Web sitem: 🤍 #Numpy #Python #VeriBilimi
Kaggle notebook with all the code: 🤍 Blog article with more/clearer math explanation: 🤍
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