🚀 Day 7 of #100DaysOfDataEngineering Topic: Python Advance - NumPy Basics Tags: #Python #NumPy #DataEngineering #DataScience Today marks the start of our journey into Python for numerical computing. Meet NumPy (Numerical Python), the core library that powers data transformations, mathematical operations, and many popular tools like Pandas and Scikit-learn. 💡 What is NumPy? NumPy provides multi-dimensional arrays (ndarray) and efficient functions to work with them. It is built for speed, allowing you to process large datasets much faster than standard Python lists. Install NumPy with: pip install numpy Import the library: import numpy as np 🧱 Creating Arrays You can create NumPy arrays directly from Python lists or nested lists: import numpy as np # 1D array arr1 = np.array([10, 20, 30]) # 2D array arr2 = np.array([[5, 10, 15], [20, 25, 30]]) print(arr1.shape) # (3,) print(arr2.shape) # (2, 3) print(arr1.dtype) # int64 ⚙️ Common Attributes AttributeDescriptionndarray.shapeDimensions of the array (rows, columns)ndarray.dtypeType of data stored (int, float, etc.)ndarray.ndimNumber of dimensionsndarray.reshape()Change array shape without changing data 📊 Built-in Methods NumPy includes several helpful functions for creating arrays quickly: # Evenly spaced values (like range) np.arange(0, 10, 2) # [0 2 4 6 8] # Arrays of zeros and ones np.zeros((2, 3)) # 2x3 array of zeros np.ones((3, 2)) # 3x2 array of ones # Equally spaced numbers np.linspace(0, 10, 5) # [0. 2.5 5. 7.5 10.] 🎲 Generating Random Numbers NumPy makes it simple to generate test data for experiments and simulations: # Random values (uniform distribution) np.random.rand(3, 4) # Random values (normal distribution) np.random.randn(2, 3) # Random integers between 4 and 40 np.random.randint(4, 40, 10) # 4x4 matrix of random integers up to 50 np.random.randint(50, size=(4,4)) ✅ Key Takeaway NumPy is all about speed and simplicity. It lets you handle large datasets and perform calculations efficiently. These array operations form the foundation of every scalable data pipeline.

As data volumes grow, efficiency in data processing becomes critical. Our latest blog explores whether Polars could be the next big step beyond Pandas for Python developers and data scientists. Read the full insight here: https://lnkd.in/g38X4uS9 #DataScience #Python #PolarsvsPandas #polarspythonlibrary #pandasinpython #DataMites

🚀 Day 42 of My Python Full Stack Journey 💡 Topic: NumPy Methods — The Powerhouse of Data Handling in Python! Today, I explored one of the most powerful libraries in Python — NumPy (Numerical Python). It’s the backbone of data science, AI, and scientific computing. Here’s what I learned and practiced 👇 🧩 1️⃣ Array Creation Methods np.array() → Creates arrays from Python lists np.zeros() → Creates arrays filled with zeros np.ones() → Creates arrays filled with ones np.arange() → Creates ranges of evenly spaced values np.linspace() → Generates evenly spaced numbers between a range np.full() → Fills an array with a constant value np.eye() → Creates an identity matrix np.random.rand() → Generates random floats between 0 and 1 np.random.randint() → Generates random integers np.empty() → Creates an uninitialized array (faster but filled with random memory values) 🧮 2️⃣ Array Attributes Every NumPy array comes with helpful attributes: .shape → Rows & columns .ndim → Number of dimensions .size → Total number of elements .dtype → Data type of elements 🔁 3️⃣ Reshaping & Flattening Transforming arrays is easy with NumPy: reshape() → Change array shape flatten() → Convert multi-dimensional to 1D ravel() → Similar to flatten, returns a view resize() → Change size (can repeat/truncate data) ➕ 4️⃣ Array Math Operations NumPy supports element-wise and matrix operations: np.add(), np.subtract(), np.multiply(), np.divide() np.dot() → Dot product np.power() → Exponentiation np.mod() → Modulus operation ⚙️ Code Example import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print(np.add(a, b)) print(np.reshape(np.arange(1, 7), (2, 3))) print(np.eye(3)) 💬 Key Takeaway NumPy makes data handling in Python blazing fast, memory-efficient, and easy to manipulate. It’s the foundation of Pandas, TensorFlow, and Scikit-learn — mastering it is essential for every developer! #Python #NumPy #PythonFullStack #10000Coders #LearningJourney #DataScience #MachineLearning #CodingCommunity #PythonDeveloper 10000 Coders Harish M Bhagavathula Srividya Spandana Chowdary

🧮 NumPy Logic Building: Practice Session 🙋♀️ Since I am started with NumPy, it is crucial to understand its concepts manually. Therefore, I have started NumPy practice sessions from today where i will be building logic and practically solving NumPy problems in each session. 👉 The best way to practice a new skill, is not directly making projects. You rather start with: 1️⃣ Quizzes 2️⃣ Exercises/Pactice Questions 3️⃣ Logic Building Exercises 4️⃣ Syntax Practice 🤔 What's Next? 👉 Once ur comfortable with the concepts, pick a dataset and apply NumPy that u have learnt on it 🤔 This is how u can not only recognize the patterns but also work with real-world data in form of NumPy projects (for instance, data cleaning/preprocessing, data analyzing, applying arithmetical ops on data, and much more) 🤔 Should u be ashamed of ERRORS? 👉 The answer is NO! Be as shameless as u can if u want to learn and unlock a new skill. Be consistent on making errors so that u can recognize the patterns on the way! ‼️I won't be showcasing practice sessions in my captions instead, i would be dropping them in the comments, so that anyone who wants to practice NumPy concepts can use my practice sessions easily 🫡 Until we meet again, my fellow coders! ------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- 📊 Data Science For Beginners 🤓 NumPy (Beginner To Intermediate): 🧮 1. Arrays: https://lnkd.in/ebghYRYE 💻 2. Coders Of Delhi Project: https://lnkd.in/eRYc9j63 ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #numpy #python #pythonfordatascience #numpylogic #pythonforbeginners #numpyforbeginners #datascience #datacleaning #datanalyzing

Feel like you're writing complex loops for simple data cleaning tasks in Python? Ibrahim Salami's debut TDS article shares 7 lesser-known NumPy functions that can help you write cleaner, more efficient code for data analysis.

Python Data Visualization Using MatplotLib & Seaborn With Numpy 📊🧮 While working with random numbers in NumPy today, i bumped into subtle Data Visualization with MatplotLib and Seaborn! 📊MatplotLib: It helps seaborn to make displots 📊Seaborn: It uses help of matplotlib to create histograms for data visualization ‼️Let's just say we can visualize data and data behavior with MatplotLib and Seaborn that has been obtained from NumPy ------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- 🤓 NumPy (Beginner To Intermediate): 🧮Arrays: https://lnkd.in/ebghYRYE ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #matplotlib #seaborn #numpy #randomnumbers #pythonforbeginners #pythonfordatascience

🚀 Day 6 of my 30 Days Python Challenge: Mastering Python starts with mastering lists! Here’s your ready reckoner with real code + outputs and use cases—just for you! A Python list is an ordered, dynamic, and mutable collection that can hold any type of data—like numbers, strings, or even other lists. 💡 Why use lists? Flexible for all data types Store, access, edit, or remove elements easily Used everywhere—from automation to data science! 🎯 Let’s see real list magic in Python: # 1️⃣ Create & check type my_list = [1, 2, 3, 4, 5] print(type(my_list)) # Output: <class 'list'> ###################################### # 2️⃣ List with multiple data types mixed = [10, "Krishna", 2.5, True] print(mixed) # Output: [10, 'Krishna', 2.5, True] ###################################### # 3️⃣ Access by index fruits = ['Apple', 'Banana', 'Mango'] print(fruits[0]) # Output: Apple print(fruits[2]) # Output: Mango ###################################### # 4️⃣ Slicing numbers = [1, 2, 3, 4, 5] print(numbers[1:4]) # Output: [2, 3, 4] ###################################### # 5️⃣ Change value numbers = [10, 20, 30, 40] numbers[2] = 99 print(numbers) # Output: [10, 20, 99, 40] ###################################### # 6️⃣ Add elements (append, insert, extend) nums = [1, 2, 3] nums.append(4) print(nums) # Output: [1, 2, 3, 4] nums.insert(1, 99) print(nums) # Output: [1, 99, 2, 3, 4] nums.extend([7, 8]) print(nums) # Output: [1, 99, 2, 3, 4, 7, 8] ###################################### # 7️⃣ Remove element (pop) colors = ['red', 'green', 'blue'] removed = colors.pop() print(removed) # Output: blue print(colors) # Output: ['red', 'green'] ###################################### # 8️⃣ Nested lists matrix = [ [1, 2, 3], [4, 5, 6] ] print(matrix[1][2]) # Output: 6 ###################################### # 9️⃣ List functions (len, max, min) data = [7, 12, 4, 9, 21] print(len(data)) # Output: 5 print(max(data)) # Output: 21 print(min(data)) # Output: 4 ###################################### # 🔟 Methods (reverse, copy, count) nums = [5, 2, 5, 7] nums.reverse() print(nums) # Output: [7, 5, 2, 5] copy_nums = nums.copy() print(copy_nums) # Output: [7, 5, 2, 5] print(nums.count(5)) # Output: 2 ###################################### 🔥 Real-world uses: Data science: holding survey results, experiment values Web: lists of products, users ML/AI: feature sets, predictions Automation: batch rename, organize files and folders ❓ Your turn: What’s the most creative way you used a Python list? Share your favorite tip, question, or challenge below 👇 Want more Python details? Comment "YES" & save this post! #Python #DevOps #LearnPython #CodingTips #Automation #DataScience #AIBasics #Programming #LinkedInLearning Follow for more actionable DevOps and Python tips with real-world examples!

Looking to manage large datasets more efficiently in Python? Check out these 7 overlooked tricks using Pandas library: chunked loading, downcasting data types, categorical data conversion, Parquet file saving, GroupBy aggregation, query

🚀 New Python Notebooks Uploaded! Just added two new learning resources to my GitHub repository Python for Data Analysis 📘 - https://lnkd.in/g3fbNDab 🔹 OOPs Concepts in Python – Understand classes, objects, inheritance, polymorphism, and encapsulation. 🔹 NumPy Library in Python – Learn about fast numerical computations and efficient data handling. Perfect for anyone strengthening their Python fundamentals for data analysis and automation. 💡 Check them out and star ⭐ the repo if you find it useful! #Python #DataAnalysis #Learning #NumPy #OOPs #GitHub #Coding

### 🧠 **Day 48 – Python OOPs: Private, Protected, and Class Method Concepts** Today I learned how to use **private and protected variables** in Python classes and how **name mangling** helps access them safely. #### 🧩 **Code Summary** ```python class cl_new:   def __init__(self, name, age):     self.__name = name   # Private variable     self._age = age     # Protected variable   def m_new(self):     print(f"name = {self.__name}, age = {self._age}")   @classmethod   def m_cl_ndth(cls, name, age):     return cls(name, age)   def m_2(self):     self.__m_new()     # Call private method internally # Object 1 n = cl_new("siva", 27) n._cl_new__m_new()       # Access private method via name mangling print(n._cl_new__name)     # Access private variable using mangling print(n._age) # Object 2 n2 = cl_new("krishna", 45) n2.m_new() # Class method usage prxy = cl_new.m_cl_ndth("pawan", 60) prxy.m_new() # Object 3 n3 = cl_new("kalyan", 20) n3.m_new() ``` --- ### 🧾 **Concepts Explained** #### 🔒 1. **Private Variables (`__var`)** * Declared with **double underscores (`__`)**. * Not directly accessible outside the class. * Accessed only using **name mangling** like:  ```python  object._ClassName__variable  ``` * Example: `self.__name` #### 🛡️ 2. **Protected Variables (`_var`)** * Declared with a **single underscore (`_`)**. * Accessible within the class and subclasses, but **should not** be accessed directly from outside (by convention). * Example: `self._age` #### 🧩 3. **Private Methods** * Methods declared with `__` prefix. * Example: `__m_new()` * Can be accessed inside the class or externally using name mangling. #### 🧠 4. **Class Method** * Declared using `@classmethod` decorator. * Takes `cls` as the first parameter. * Used to create new objects using alternative constructors. --- ### 🖥️ **Output Explanation** ``` name = siva, age = 27 name = siva, age = 27 siva 27 name = krishna, age = 45 name = pawan, age = 60 name = kalyan, age = 20 ``` ✅ Demonstrates how to define and access private/protected members. ✅ Shows **class method** usage for creating objects dynamically. ✅ Explains **encapsulation**, one of the main OOPs principles. --- ### 💡 **Key Takeaway** Encapsulation in Python ensures data hiding and protects object integrity. Using **private, protected, and class methods**, we can control access to class data efficiently. --- ✨ Every day brings a deeper understanding of Object-Oriented Programming with Python! #Python #OOPs #Encapsulation #Day48 #LearningJourney #Programming #LinkedInLearning #ClassMethod #PrivateVariables