Tej Patel’s Post

5 Python one-liners every data analyst should know Here are 5 Python one-liners that changed how I work: Each of these saved me time on real projects at Lambton College and in my analytics work. The best part? They work on any dataset — from 100 rows to 1 million. Save this post for your next Python project. 📌 Which one do you use most? Let me know below 👇 #Python #DataAnalytics #Pandas #DataScience #Analytics #LearningInPublic

A quick refresher on Statistics in Python! From basics like mean & median to advanced topics like hypothesis testing and distributions, this guide neatly covers the key functions every data analyst should know. Definitely a handy reference for real-world data analysis 💡 #DataAnalytics #Python #Statistics

I improved my first Python project. Initially, it only calculated averages and grades. Now I added: - Class statistics - Ranking system - Subject-wise toppers This helped me understand how to work with structured data and apply logic step-by-step. Small improvements, but real progress. Code: https://lnkd.in/dRwGrnhh #Python #DataScience #LearningInPublic #BeginnerProjects

SQL 🤝 Python The essential commands for: ✅ Finding and replacing Nulls ✅ Handling Duplicates ✅ Reformatting Dates and Strings ✅ Calculating Outliers (IQR) #DataAnalysis #SQL #Python #CheatSheet #DataScience

🚀 Escape Sequences & Raw Strings in Python (Beginner Friendly!) 🐍 Understanding strings is one of the first steps to writing clean Python code. 🔹 Escape Sequences Special characters used inside strings: - "\n" → New line - "\t" → Tab space - "\\" → Backslash 🔹 Raw Strings (r"") Treat backslash as normal text (no special meaning) 👉 Example: print("Hello\nWorld") print(r"C:\Users\Name\Documents") 🎥 I’ve explained this clearly with examples in my latest video 👇 👉 [https://lnkd.in/gv45tifv] 💡 This is very useful when working with: ✔ File paths ✔ Regular expressions ✔ Clean string formatting If you're starting Python or Data Science, this is a must-know concept! #Python #CodingForBeginners #DataScience #LearnPython #YouTubeLearning

🐍 Python Dictionary Challenge! What will be the output of this code? 👇 data = {"a": 1, "b": 2, "c": 3} data["a"] = 10 data["d"] = 4 print(data) 💡 What changes happened in the dictionary? Drop your answer in the comments 👇 #Python #CodingChallenge #LearningInPublic #Beginners

🐍📈 Data Visualization With Python In this learning path, you'll see how you can use Python to turn your data into clear and useful visualizations so that you can share your findings more effectively #python #learnpython

🔁 Python Program: Reverse a String ```python text = "cloud" reversed_text = text[::-1] print("Reversed:", reversed_text) ``` 💡 Why this matters? ✔ Tests string understanding ✔ Common interview question ✔ Useful in data processing #Python #CodingInterview

One of the biggest mistakes beginners make in Python… is ignoring data types. You might write correct code, But if you don’t understand the type of data you’re working with, Your results can be completely wrong. In Python, everything has a type, from numbers to text to collections of data. Understanding this is what separates someone who copies code from someone who actually understands it. I’ll be breaking down Python data types in a simple way in my next article. 💬 Which one confuses you the most: Booleans, strings, tuples, lists, or dictionaries? #Python #Programming #DataScience #AI #Beginners #LearnToCode #Tech

📊 Day 5 of #100DaysOfBusinessAnalytics Today I explored descriptive statistics of my dataset using Python (Pandas). Using the "describe()" function, I was able to quickly understand key metrics such as: • Mean • Minimum and Maximum values • Standard deviation • Count of data points 👉 This helps in getting a quick overview of the dataset and identifying patterns or anomalies. Understanding these basic statistics is an important step before performing deeper analysis. Looking forward to extracting more insights from the data! 🚀 #100DaysOfBusinessAnalytics #BusinessAnalytics #DataAnalytics #Python #Pandas #PowerBI