Streamline Python Data Models with Dataclasses

Machine Learning Text Data using sense2vec #machinelearning #datascience #textdata #sense2vec sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. sense2vec (Trask et. al, 2015) is a twist on the word2vec family of algorithms that lets you learn more interesting word vectors. Before training the model, the text is preprocessed with linguistic annotations, to let you learn vectors for more precise concepts. Part-of-speech tags are particularly helpful: many words have very different senses depending on their part of speech, so it’s useful to be able to query for the synonyms of duck|VERB and duck|NOUN separately. Named entity annotations and noun phrases can also help, by letting you learn vectors for multi-word expressions. https://lnkd.in/gAaG2H6H

Most small businesses lose hours every week updating data manually. ⏳ I recently built a reliable Python pipeline that handles the heavy lifting: ✅ Fetches data directly from APIs ✅ Cleans data & removes duplicates ✅ Stores everything in a structured PostgreSQL database ✅ Updates automatically every day No more manual copy-paste. No more messy spreadsheets. 🚫📊 This is a game-changer if you deal with: • Growing Excel files that crash constantly • API data that needs daily manual updates • Repetitive, boring reporting tasks If this sounds familiar, I can help you automate your workflow and reclaim your time. 🚀 Check out the Demo & Code here: 👇 https://lnkd.in/dyXCXSPk #DataAutomation #Python #ETL #SmallBusiness #Automation

🚀 Day 4/20 — Python for Data Engineering Reading & Writing Files (CSV / JSON) In data engineering, data rarely comes clean. 👉 It usually comes from: files logs exports APIs So the ability to read and write data is fundamental. 🔹 Why File Handling Matters We often: ingest raw data process it store cleaned output 👉 Python helps us do all of this easily. 🔹 Reading a CSV File import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 Loads structured data into a DataFrame 🔹 Reading a JSON File import json with open("data.json") as f: data = json.load(f) print(data) 👉 Useful for API responses and semi-structured data 🔹 Writing Data to a File df.to_csv("output.csv", index=False) 👉 Save processed data for further use 🔹 Where You’ll Use This Data ingestion pipelines Data transformation workflows Exporting results Logging and backups 💡 Quick Summary Python allows you to: read data from multiple formats process it write it back efficiently 💡 Something to remember Data engineering starts with reading data… and ends with writing it in a better form. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

I made Python talk to me, and it actually responded 😅 At first, I was just writing code. No interaction. No feedback. Just, output. Then I discovered something simple but powerful: The input() function Let me explain this like I’m talking to a baby Imagine you have a small robot You ask it: “Tell me anything…” The robot pauses… waits… then listens to you. After you talk, it replies: “Hmm… what you said… Really?” That’s exactly what this code does: Python anything = input("Tell me anything...") print("Hmm...", anything, "... Really?") What is happening here? • input() → Python asks you a question • It waits for your answer • It stores what you typed • print() → Python responds to you I used to think python just runs commands Now I see python can actually interact with users. Why this matters in Data Analysis As I move deeper into: Excel, SQL, Tableau and Python I’m realizing that: • You can collect user input • Make your analysis interactive • Build smarter tools Not just static reports, but dynamic systems Python is not just a tool, it’s something you can actually “talk to.” If you're learning python, what was the first thing you made Python do for you? 😅 #Python #DataAnalytics #LearningInPublic #SQL #Excel #Tableau #Programming #TechJourney #BeginnerInTech #DataScience #CareerGrowth

🚀 Last month, I built and published my first Python package — Pristinizer I wanted to solve a simple but real problem in data science: 👉 Cleaning and understanding raw datasets takes way too much time. So I built Pristinizer, a lightweight Python package that helps streamline data cleaning + EDA in just a few lines of code. 🔍 What Pristinizer does: • Cleans messy datasets (duplicates, missing values, column formatting) • Generates structured dataset summaries • Visualizes missing data (heatmap, matrix, bar chart) ⚙️ Tech Stack: Python • pandas • matplotlib • seaborn 📦 Try it out: >> pip install pristinizer >> import pristinizer as ps df = ps.clean(df) ps.summarize(df) ps.missing_heatmap(df) 🧠 What I learned while building this: • Designing a clean and intuitive API • Structuring a real-world Python package • Publishing to PyPI • Writing proper documentation for users 📌 Next, I’m planning to add: • Outlier detection • Automated preprocessing pipelines • Advanced EDA reports Would love to hear your thoughts or feedback! #Python #DataScience #MachineLearning #OpenSource #Pandas #EDA #Projects

Episode 9: What I Can Do With Python One common challenge in data cleaning is this: How do you quickly see all the unique values across every column in a dataset? Not just one column at a time…but the entire dataset in one view. If you’ve worked with real data, you know how important this is. It helps you spot inconsistencies, compare entries with a data dictionary, and decide what needs to be cleaned or standardised. This week, I attempted to do exactly that. My first instinct was Excel. I tried combining functions, nesting formulas, and exploring different approaches to get all unique entries across columns at once. It sounded like something that should be possible, but after spending quite some time on it, I couldn’t get exactly what I wanted. And VBA wasn’t something I wanted to rely on (you would know about this from my previous post 😩). So I switched to Python. I wrote a simple function (maybe not so simple) and brought it back into Excel using Python. I called it 'uniq_row_per_col()' . The function takes a dataset (as an Excel range) and returns the unique values for each column. It assumes the first row contains headers, handles duplicate column names automatically (similar to pandas), and keeps case sensitivity so inconsistencies can be clearly identified. In practice, this makes data cleaning much easier. Instead of checking columns one by one, I can now: — View all unique entries across the dataset at once — Compare them directly with a data dictionary Identify inconsistencies quickly (typos, casing differences, variations) — Decide what needs standardisation or removal Behind the scenes, pandas handles the data manipulation, while xlwings manages the interaction between Excel and Python. This is something I’m beginning to appreciate more, not just using tools as they are, but extending them to fit the workflow I need. I’ve attached a short demo video showing how it works. Would this be useful in your data cleaning process? See you in Episode 10 🚀 #WhatICanDoWithPython #Python #DataCleaning #Excel #DataAnalysis #Automation #BuildInPublic #xlwings

🚀 Python Secret #2: The Ghost of Dictionaries 👻 Ever seen this error? data = {"a": 1} print(data["b"]) # KeyError 💀 👉 Missing key = crash. But what if… you could control what happens when a key is missing? 😈 --- 🧠 Meet the hidden method: "__missing__" Most developers don’t know this exists. If you create a custom dictionary and define "__missing__", Python will call it automatically when a key is not found. --- 🔥 Example: class MyDict(dict): def __missing__(self, key): return f"Key '{key}' not found 😏" data = MyDict({"a": 1}) print(data["a"]) # 1 print(data["b"]) # Key 'b' not found 😳 👉 No error. No crash. Full control. --- 💡 Real Power Use Cases: ✔️ Default values without "get()" ✔️ Dynamic data generation ✔️ Smart fallback systems ✔️ API response handling --- 💀 Pro Example: class SquareDict(dict): def __missing__(self, key): return key * key nums = SquareDict() print(nums[4]) # 16 🔥 print(nums[10]) # 100 🚀 👉 Missing key = calculated on the fly. --- 🧠 Insight: “Dictionaries don’t fail… unless you let them 😈” --- 💬 Did you know about "__missing__"? Follow for more Python secrets 🐍 Day 2/30 — Let’s go deeper 🚀 #Python #Coding #Programming #Developers #PythonTips #LearnToCode #Tech #AI #100DaysOfCode

Excel or Python? Which one is better? 👇 Lately, I’ve been navigating the "Great Divide" between Excel and Python while handling large-scale datasets (90,000+ rows). Here’s what my recent experience has taught me: 📉 The Excel Reality Check: Excel remains the undisputed king for quick analysis, ad-hoc reporting, and day-to-day business tasks. It’s intuitive, fast, and accessible. However, once complex operations meet massive row counts, the "spinning wheel" starts to appear or even crash. 🐍 The Python Advantage: This is where Python truly shines. For scalability, automation, and handling heavy data lifting smoothly, Python is a game-changer. It transforms a potential crash into a seamless, repeatable workflow. The Verdict? They aren't rivals; they’re complementary. I’ve found the most success using: 1️⃣Excel for speed, simplicity, and stakeholder-ready reporting. 2️⃣Python for deep analysis, data cleaning, and long-term scalability. The most important thing is to choose the right tool for the job! 🛠️ #DataAnalytics #Python #Excel #Learning #Data #TechTips

Just released #datatrusted. An open-source Python library I built to help data scientists and ML engineers audit datasets before analysis or model training. It answers one question: "Can I trust this dataset?" In one function call it checks the following: - Missing values & duplicates - Schema & type issues - Outlier detection - Target imbalance & leakage hints - Train/test drift - Join integrity - Custom rule validation And returns a trust score out of 100 with a full structured report. Install: pip install datatrusted GitHub: https://lnkd.in/dvWHx5GG Would love any feedback or contributions!

Revisiting Python dictionaries today   I have used dicts for years. But watching this video made me realise I was only using about 30% of what they can do.   Here is how I am now thinking about dictionaries in data pipelines:   As schema maps: column_map = {'CUST_ID': 'customer_id', 'ACCT_BAL': 'account_balance'} One dictionary. Update one place when the source changes. Everything downstream adapts.   As pipeline config: Every script I write now starts with a config dict at the top. Source path, target path, partition column, batch size. No hardcoded values buried inside logic.   As error collectors: Accumulate errors by rule name during validation. Write the entire dict to a log table at the end. Instant audit trail.   As lookup tables: Small reference data loaded once into a dictionary and reused hundreds of times — much faster than hitting the database on every row.   An immediate thing to note:   'Dictionaries are optimised for lookup. If you are searching through a list for a value, ask yourself if a dictionary would be better.'   I had a list-based lookup I immediately refactored after that.   How do you use dictionaries in your work?   ---- #Python #DataEngineering #LearningInPublic #CleanCode #ETL