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Lee Vaughan's intro to NumPy part 4 — doing math with arrays + reading and writing array data. Read now and test your knowledge 👉 #NumPy #Python

##Session 2 (NumPy) - Completed 24 June 2024 Learned Concepts: - Initializing Arrays: Different methods to create arrays (e.g., `array()`, `zeros()`, `ones()`, `arange()`, `linspace()`). - Types of Arrays: Understanding various array types like 1D, 2D, and multi-dimensional arrays. - Shape and Size: Determining the shape and size of arrays using `.shape` and `.size`. - Accessing Specific Elements: Indexing and slicing arrays to access and modify elements. - Linear Algebra: Performing linear algebra operations like dot products, matrix multiplication, and finding determinants. - Statistics in Arrays: Applying statistical functions like mean, median, standard deviation, and sum on arrays. - Additional Topics: Covered various other short topics related to NumPy's capabilities. Note: • NumPy proved to be a gripping and essential topic, offering a powerful toolset for numerical computations. •Session 3 starts from tomorrow 25th June • Resource link for NumPy - https://lnkd.in/dcyp-FGN #python #programming #numpy

NumPy: Numerical Operations and Efficiency NumPy is the unsung hero of numerical computing in Python, a library that underpins the vast majority of scientific and analytic applications with its robust handling of array-based data. The ndarray, or n-dimensional array, is the core of NumPy, enabling the efficient storage and manipulation of large datasets. This powerful structure allows you to perform vectorized operations, which are both faster and more readable than looping through elements individually.

https://lnkd.in/gdVBR72g Hello 👋 people from the future , Here I published the python library named "numbersD", now it is able to solve small math aptitude problems. But, soon it will become a library to solve more mathematical problems. Looking same minded people to contribute in this dream. #opensource #python #scientific_calculations #aptitude #math #Programming #colab

Math Operations Calculator using OOP in Python Features: - Arithmetic Operations: Addition, Subtraction, Multiplication & Division Operator - Algebra Operations: Absolute Value, Square Root, Radical, Exponent, Logarithm, Factorial & Modulus - Linear Algebra Operations: Vector Addition, Cross Product, Dot Product, Matrix Multiplication, Transpose & Determinant Operator - Set Operations: Union Operator & Intersection Operator Github link: https://lnkd.in/dvTK2-Dm

My presentation this week to the MS Excel Toronto meetup (register for free in comments) will be kicking off with a demo on the Faker package in Python 🎲🐍🎲. This package, available directly in Excel, generates synthetic data, such as names, addresses, and numerical values, that can be used to simulate real-world scenarios. With this and Python's rich suite of tools in probability, visualization and more, we can turn Excel workbooks into a user-friendly, mathematically-elegant simulation machine. I'll leave a link to register for the meetup as well as some additional resources in the comments.

Lost in the Algorithm Maze? This image shows the trade-off between time and space complexity in algorithms. The winding road on the left represents a high time complexity algorithm, potentially reaching an O(n^2) complexity. Imagine lots of comparisons and swaps as you navigate those bends - it takes time! This complex path also requires more space to wind through the mountains. On the other hand, the straight road on the right symbolizes a low time complexity algorithm, often achieving an O(n log n) complexity. Think efficient division and merging, like taking shortcuts! It gets you there much faster with fewer detours, and requires less space to travel, just like needing fewer resources for calculations. When choosing an algorithm, it's like picking a route. Do you prioritize speed (low time complexity) or a clear, direct path (low space complexity)? #algorithms #timecomplexity #spacecomplexity #programming #coding #efficiency #cslife #DataAnalytics #DataScience #BigData #Analytics #dataengineer #python

🌟 Day 4: Introduction to NumPy 🌟 Today, I explore into NumPy, one of the most fundamental libraries in Python for data manipulation and scientific computing. Here’s a summary of what I learned: 🚀 Array Basics: Created and explored 1D arrays to understand the basic structure of NumPy arrays. Played with 2D arrays and reshaped them into various forms, gaining insight into how multidimensional arrays work. 🔍 Array Attributes: Learned about key array attributes like shape, size, dtype, and itemsize to analyze the structure and data type of arrays. Used .ndim to check the number of dimensions and .reshape() to modify array shapes. ⚡ Data Type Conversions: Converted between different data types like int, float, and complex to understand how NumPy handles type casting. Worked with special data types such as datetime and timedelta for time-related operations. 🧠 Key Takeaway: Mastering NumPy arrays and their attributes is crucial for handling large datasets and performing high-performance numerical operations. NumPy's power lies in its efficiency and flexibility in working with data. 📘 I've documented my learnings in a notebook with examples and clear comments for anyone looking to get started with NumPy. Feel free to check it out and share your feedback! #Python #LearningJourney #NumPy #DataScience #MachineLearning

📝NUMPY: A Python library for efficient numerical computing. It provides multi-dimensional arrays and math functions for data analysis, machine learning, and scientific tasks. 🧮✨ 🦋single dimensional array and multiple dimensional array📈 🦋 METHODS: ♦️zeros():Intializing numpy with zeros. 🧩full(): Intializing numpy array with same number. ♦️arange(): Intializing numpy within a range 🧩randint(): Intializing numpy with random numbers. ♦️vstack():vertical stack from array. 🧩hstack(): horizontal stack from array ♦️column_stack():column stack from array 🧩 Mathematical operations: sum(),mean(),median(),mode(),transpose(),dot(). #numpy #greatlearningacademy skills developed from Great Learning 🔥