Abhishek Kumar’s Post

Writing a for-loop in Python to process a list of data? You might be adding hours to your script's runtime without even knowing it. I see this all the time: analysts use loops for data transformations that could be done in a fraction of the time. The bottleneck isn't your computer's speed—it's how you're talking to it. The secret to faster data processing in Python is vectorization. Instead of processing each element one-by-one in a loop, vectorized operations apply a function to an entire dataset simultaneously, leveraging optimized, pre-compiled C code under the hood. Let's take a common task: calculating the square of every number in a list. The Slow Way (Loop): python import pandas as pd data = pd.Series(range(1, 1000001)) squared_list = [] for num in data: squared_list.append(num ** 2) The Fast Way (Vectorized): python import pandas as pd data = pd.Series(range(1, 1000001)) squared_list = data ** 2 The vectorized approach isn't just cleaner—it's dramatically faster. For a million rows, the loop might take ~150ms, while the vectorized operation can finish in ~2ms. That's a 98.7% reduction in processing time! This principle applies across pandas and NumPy: Use df['column'].str.upper() instead of looping with .upper() Use df['column'].apply(function) instead of a for-loop (.apply is optimized) Use NumPy's universal functions (np.log, np.sqrt) on arrays Adopting a vectorized mindset is a game-changer for efficiency. Have you ever refactored a slow loop into a vectorized operation? What was the performance boost like? Share your story below! #Python #DataAnalysis #Pandas #CodingTips #DataScience

⚡ Handling Missing Values in Python Here’s a simple breakdown of the different methods used in Python 1️⃣ Identify Missing Values df.isnull() # Shows True/False for missing values df.isnull(). sum() # Counts missing values per column You can also check the percentage of missing data: (df.isnull(). sum() / len(df)) * 100 2️⃣ Remove Missing Values If the missing values are few or not significant: df.dropna() # Removes rows with missing values df.dropna(axis=1) # Removes columns with missing values Use this when deleting data doesn’t affect the dataset’s overall quality. 3️⃣ Fill Missing Values When you can’t afford to drop data, fill the missing values instead. 🔹 Constant value df['Name']. fillna('Unknown', inplace=True) 🔹 Mean / Median / Mode (for numerical columns) df['Age']. fillna (df['Age']. mean(), inplace=True) df['Salary'].fillna (df['Salary'].median(), inplace=True) 🔹Forward or Backward Fill (for time series) df.fillna(method='ffill', inplace=True) # Forward fill df.fillna(method='bfill', inplace=True) # Backward fill 4️⃣ Advanced Imputation Using Models For large datasets or when data is missing in patterns: from sklearn.impute import SimpleImputer imputer = SimpleImputer(strategy='mean') df[['Age', 'Salary']] = imputer.fit_transform(df[['Age', 'Salary']]) Other strategies: 'median,' 'most_frequent,' and 'constant.' 🔹 Best Practices Use mean/median for numerical data. Use mode or “Unknown” for categorical data. Drop columns if more than 40–50% of the data is missing. Always analyze the pattern of missingness before deciding. #Python #DataCleaning #Pandas #DataAnalytics

Lambda functions aren’t just for one-liners They can make your Python data workflows cleaner and faster. Here are 5 Python lambda tricks every data scientist should master: 1 → Writing concise one-off functions instead of full def blocks 2 → Using lambdas with map(), filter(), sort() for clean transformations 3 → Capturing variables in closures for pipeline convenience 4 → Combining lambdas with pandas and NumPy for inline operations 5 → Choosing when not to use lambdas (for readability & debugging) Read it here: https://lnkd.in/djGG3rfW

My first Jupyter Notebook For Python Variables!⚡ Variables are simple yet powerful since I’m diving deeper into Python for AI & ML, here’s what I practiced today 👇 🔹 Purpose: → Variables store and manage data in your programs. → Python’s dynamic typing makes it flexible and beginner-friendly — perfect for AI, ML, and data science. 🔹 Syntax Simplicity: Python is readable and beginner-friendly: name = "Sidraa" age = 20 is_learning = True JavaScript is more structured but similar in logic: let name = "Sidraa"; let age = 20; let isLearning = true; 🔹 Use Cases: Python variables → Store user input, model parameters, temporary calculations, flags for program flow. 🔹 Reassigning & Type Casting: Python allows easy updates and conversions: score = 10 score = 15 # updated value num_str = "100" num_int = int(num_str) # converts string to integer Quick Question: How do you usually organize and name your Python variables? Let me know in the comments! --------------------------- ☺️ Here is my Python Variables Exercise (Beginner to Intermediate) GitHub Repo for you: Python Variables: https://lnkd.in/e9rjz-_D ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- #python #variables #machinelearning #artificialintelligence #deeplearning #codingjourney #AI #ML #PythonBasics

𝗦𝘁𝗼𝗽 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗹𝗼𝗿𝗶𝗻𝗴 𝗰𝗲𝗹𝗹𝘀 𝗶𝗻 𝗚𝗼𝗼𝗴𝗹𝗲 𝗦𝗵𝗲𝗲𝘁𝘀! 🎨 If you're still spending time clicking Format -> Conditional Formatting on your reports, there's a better way. By leveraging Python, you can transform your data reporting from a manual chore into a fully automated workflow. 𝗪𝗵𝘆 𝘂𝘀𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗚𝗼𝗼𝗴𝗹𝗲 𝗦𝗵𝗲𝗲𝘁𝘀 𝗳𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴? ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Format 100 columns as easily as you format 1. ✅ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Eliminate "oops" moments. Get pixel-perfect reports every time. ✅ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝘂𝗹𝗲𝘀: Implement complex logic that the UI can't handle. 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲? 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝗶𝗺𝗽𝗹𝗲, 𝟯-𝘀𝘁𝗲𝗽 𝗹𝗼𝗴𝗶𝗰: 𝗦𝘁𝗲𝗽 𝟭: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗦𝗵𝗲𝗲𝘁 Use the gspread library to securely authenticate and open your target Google Sheet, all from your Python script. 𝗦𝘁𝗲𝗽 𝟮: 𝗗𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗥𝘂𝗹𝗲 𝗶𝗻 𝗖𝗼𝗱𝗲 This is the magic. You create a "rule" object that specifies three things: 𝗧𝗵𝗲 𝗥𝗮𝗻𝗴𝗲: Which cells do you want to format? (e.g., 'C2:C100') 𝗧𝗵𝗲 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻: What logic should trigger the format? This is written as a standard Google Sheets formula. (e.g., =C2 < B2 to check if the value went down). 𝗧𝗵𝗲 𝗙𝗼𝗿𝗺𝗮𝘁: What should the cell look like? (e.g., a red background). 𝗦𝘁𝗲𝗽 𝟯: 𝗔𝗽𝗽𝗹𝘆 𝘁𝗵𝗲 𝗥𝘂𝗹𝗲 Your script sends these instructions to the Google Sheets API, and your sheet is formatted instantly. Here's a simplified code example of a rule that colors a cell red if its value is less than the cell to its left: 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 # 𝙸𝚖𝚙𝚘𝚛𝚝 𝚝𝚑𝚎 𝚗𝚎𝚌𝚎𝚜𝚜𝚊𝚛𝚢 𝚏𝚘𝚛𝚖𝚊𝚝𝚝𝚒𝚗𝚐 𝚝𝚘𝚘𝚕𝚜 𝚏𝚛𝚘𝚖 𝚐𝚜𝚙𝚛𝚎𝚊𝚍_𝚏𝚘𝚛𝚖𝚊𝚝𝚝𝚒𝚗𝚐 𝚒𝚖𝚙𝚘𝚛𝚝 * # 𝟷. 𝙳𝚎𝚏𝚒𝚗𝚎 𝚝𝚑𝚎 𝚛𝚞𝚕𝚎 𝚛𝚞𝚕𝚎 = 𝙲𝚘𝚗𝚍𝚒𝚝𝚒𝚘𝚗𝚊𝚕𝙵𝚘𝚛𝚖𝚊𝚝𝚁𝚞𝚕𝚎(    𝚛𝚊𝚗𝚐𝚎𝚜=['𝙲𝟸:𝙲𝟷𝟶𝟶'], # 𝚃𝚑𝚎 𝚛𝚊𝚗𝚐𝚎 𝚝𝚘 𝚊𝚙𝚙𝚕𝚢 𝚏𝚘𝚛𝚖𝚊𝚝𝚝𝚒𝚗𝚐 𝚝𝚘    𝚋𝚘𝚘𝚕𝚎𝚊𝚗𝚁𝚞𝚕𝚎=𝙱𝚘𝚘𝚕𝚎𝚊𝚗𝚁𝚞𝚕𝚎(        𝚌𝚘𝚗𝚍𝚒𝚝𝚒𝚘𝚗=𝙱𝚘𝚘𝚕𝚎𝚊𝚗𝙲𝚘𝚗𝚍𝚒𝚝𝚒𝚘𝚗('𝙲𝚄𝚂𝚃𝙾𝙼_𝙵𝙾𝚁𝙼𝚄𝙻𝙰', ['=𝙲𝟸 < 𝙱𝟸']),        𝚏𝚘𝚛𝚖𝚊𝚝=𝙲𝚎𝚕𝚕𝙵𝚘𝚛𝚖𝚊𝚝(𝚋𝚊𝚌𝚔𝚐𝚛𝚘𝚞𝚗𝚍𝙲𝚘𝚕𝚘𝚛=𝙲𝚘𝚕𝚘𝚛(𝟷, 𝟶, 𝟶)) # 𝚁𝚎𝚍 𝚋𝚊𝚌𝚔𝚐𝚛𝚘𝚞𝚗𝚍    ) ) # 𝟸. 𝙰𝚍𝚍 𝚝𝚑𝚎 𝚛𝚞𝚕𝚎 𝚝𝚘 𝚢𝚘𝚞𝚛 𝚠𝚘𝚛𝚔𝚜𝚑𝚎𝚎𝚝 𝚊𝚗𝚍 𝚜𝚊𝚟𝚎 𝚛𝚞𝚕𝚎𝚜 = 𝚐𝚎𝚝_𝚌𝚘𝚗𝚍𝚒𝚝𝚒𝚘𝚗𝚊𝚕_𝚏𝚘𝚛𝚖𝚊𝚝_𝚛𝚞𝚕𝚎𝚜(𝚠𝚘𝚛𝚔𝚜𝚑𝚎𝚎𝚝) 𝚛𝚞𝚕𝚎𝚜.𝚊𝚙𝚙𝚎𝚗𝚍(𝚛𝚞𝚕𝚎) 𝚛𝚞𝚕𝚎𝚜.𝚜𝚊𝚟𝚎() By placing this logic inside a loop, you can apply similar rules across an entire report in seconds. A little bit of code saves hours in the long run. It's a true "set it and forget it" solution. What's the most tedious task you've automated in your workflow? Share below! 👇 #Python #GoogleSheets #DataAutomation #Automation #DataAnalytics #Reporting #BusinessIntelligence #Gspread

Exploring Pandas — The Heart of Data Analysis in Python! 🐼 If you’re working with data in Python, Pandas is one of the most essential libraries you’ll ever use. It allows you to analyze, clean, and transform data with just a few lines of code. A core structure in Pandas is the Series — a one-dimensional labeled array that holds any type of data (integers, strings, floats, etc.). Here are some powerful attributes and methods that make Pandas Series so versatile: 🔹 values – Returns data as a NumPy array 🔹 index – Returns index (labels) of the Series 🔹 shape – Shows the dimensions of the Series 🔹 size – Number of elements in the Series 🔹 mean(), sum(), min(), max() – Perform quick statistical analysis 🔹 unique(), nunique() – Find unique values or count them 🔹 sort_values(), sort_index() – Sort by values or labels 🔹 isnull(), notnull() – Detect missing data 🔹 apply() – Apply custom functions to each element Whether you’re handling financial data, healthcare analytics, or AI model preprocessing — Pandas helps you turn raw data into actionable insights efficiently. #Python #DataScience #Pandas #MachineLearning #Analytics #AI

🧠 Just tried out a really cool Python library — toon_format — and it’s a hidden gem for anyone working with LLMs or large data payloads. It’s a compact, human-readable serialization format that reduces context size by 30–60% vs JSON, while staying super easy to read and use. What makes it awesome: • YAML-like indentation • CSV-style tabular arrays • Minimal syntax, array validation • Python 3.8+ and battle-tested • Fully compatible with the official TOON spec ⚙️ Install it: pip install toon_format (or uv add toon_format) Quick example 👇 from toon_format import encode, decode encode({"name": "Alice", "age": 30}) # name: Alice # age: 30 encode([{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]) # [2,]{id,name}: # 1,Alice # 2,Bob We have been using it to trim LLM context payloads — super efficient and still human-friendly. 🚀 If you deal with JSON or token limits, give toon_format a try ! I have shared repository link in first comment. #Python #OpenSource #LLM #Serialization #AI #Developers #MachineLearning #GenAI

🐍 How Python Makes Daily Scraping Feel Effortless 💻 Let’s be real — once you start using Python for scraping, there’s no going back. From extracting business directories to cleaning messy data — it’s like having an assistant who never sleeps. Every day, I use Python to: ⚡ Automate repetitive scraping tasks 📊 Collect and structure large datasets 🔍 Extract hidden info from websites 💾 Export everything neatly into Excel or JSON What used to take hours manually, now runs in minutes with a few lines of code. That’s the power of Python + automation mindset. If your daily grind involves collecting data, leads, or insights — Python isn’t just a tool. It’s your superpower. #Python #WebScraping #Automation #DataExtraction #DataScience #LeadGeneration #FreelancerLife #ProductivityHack #DataAnalyst #Freelanar

How Machine Learning works using python ? 1. Create a model 2. Fit it 3. Train on the data 4. Test it 5. Check accuracy Using Python + scikit-learn with a basic train/test split and a classification model (Logistic Regression example). Machine Learning Workflow 1. Import Required Libraries from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score import pandas as pd 2. Load or Create Your Dataset Example dummy dataset: # Example dataset data = { "feature1": [1,2,3,4,5,6,7,8], "feature2": [5,4,3,2,1,6,7,8], "label": [0,0,0,1,1,1,1,1] } df = pd.DataFrame(data) 3. Split into Features and Labels X = df[["feature1", "feature2"]] y = df["label"] 4. Train–Test Split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) 5. Create the Model model = LogisticRegression() 6. Fit (Train) the Model model.fit(X_train, y_train) 7. Predict on Test Data y_pred = model.predict(X_test) 8. Check Accuracy accuracy = accuracy_score(y_test, y_pred) print("Model Accuracy:", accuracy) Output Example You may see something like: Model Accuracy: 0.75 #ml

🚀 Just built my own Python data type using OOP & magic methods! We all know Python gives us built-in types like int, float, and list... But what if we could design our own — that behaves just like them? 🤯 That’s exactly what I did with PyMatrixEngine 🧠 I built a custom Matrix data type that supports operations such as: ➕ Addition (A + B) ➖ Subtraction (A - B) ✖️ Multiplication (A * B) 🔁 Transpose & Determinant All powered by Python’s magic methods (__add__, __mul__, __str__, and friends) 🪄 And here’s the cool part — If you input something that doesn’t form a valid matrix, this datatype automatically checks it and raises a clean, readable error. No more silent shape mismatches or confusing bugs ✅ You can simply drop the file in your project and start using it: from matrix import Matrix A = Matrix([[1,2],[3,4]]) B = Matrix([[5,6],[7,8]]) print(A + B) print(A * B) print(A.determinant()) It’s a fun deep-dive into Object-Oriented Programming (OOP) and Python’s hidden superpowers: magic methods ✨ 🧩 GitHub Repo → https://lnkd.in/gkrheMQS Would love to hear — what’s the coolest custom data type you’ve ever built in Python? #Python #OOP #MagicMethods #Coding #Matrix #Learning #PythonProjects #Developers #PythonTips