Improving Data Analysis with Cleaner Queries

Wanna gear up yourself with a short python and sql practice challenge. Here is a set of questions. Try a shot with best optimised solutions. #Quant #DataScience #Python #SQL #DSA #LeetCode #ProjectEuler #LearningInPublic #TechJourney

Data scientists often switch between SQL and Pandas, which can make workflows unnecessarily complex. Tools like DuckDB now allow SQL queries directly on DataFrames combining the power of SQL with the flexibility of Python. The image shows a simple way to think about which tool works best for different tasks. For a quick explanation of this approach, read here: https://lnkd.in/eZPTyP2 #DataScience #Python #SQL #Analytics

I wrote a tutorial on "Filtering Financial Data" with Python's filter() and lambda. If you're not familiar with these functions, this will give you a quick introduction on how to use them. "Filtering Financial Data" https://lnkd.in/eXs9PuQq This is part of my "Python for Finance" series https://lnkd.in/exFszkjG #Python #Finance #Data

Python moment that aged me 10 years: Spent 2 hours debugging why my Pandas merge was returning empty. merged = df1.merge(df2, on='user_id', how='inner') The problem? Column was actually named 'User_ID' (capital U and I). Python is case-sensitive. I am not emotionally stable. Now I triple-check column names like my life depends on it. #Python #Pandas #DataEngineering #Debugging #TechLife #Humour #WomenInTech #MomInTech #DataAnalyst #LearningEveryday

Stop writing "clunky" Python. 🐍💻 I used to rely heavily on standard for loops for every data transformation. They work, but they can get messy fast. Today, I’m focusing on List Comprehensions to streamline my workflow. The Challenge: Calculate a 10% tax on prices, but only for items over $20. 🔴 The "Old" Way: 5 lines of code, an empty list, and an append function. 🟢 The Optimized Way: 1 clean, readable line. Less boilerplate, more efficiency. Professional-grade Python is all about writing code that is as readable as it is functional. #Python #DataScience #CodingTips #Pythonic #DataAnalytics #ContinuousLearning

One small habit that makes data analysis easier: always check missing values early. In Python with Pandas: df.isnull().sum() This quickly shows how many missing values exist in each column. Catching this early helps you decide whether to drop, fill, or further investigate the data before building any model or analysis. Many issues in analysis come from unnoticed missing data. #Python #DataAnalytics #MachineLearning #DataScience

🚀 Day 16: Insert & Delete Functions Today I learned how to modify arrays. 👉 insert(): add elements 👉 delete(): remove elements This helps in dynamic data manipulation. 💡 Key takeaway: Data can be updated easily in NumPy. #Python #NumPy #Coding

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/etgeQqgF - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link uses 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 to visualize execution and reveals what’s actually happening.

In Python, Pandas stands out as one of the most important libraries for data analysis. Why? Because of its efficiency in handling, cleaning, and analyzing data. From simple data manipulation to complex analytical tasks, Pandas makes the workflow smoother and more intuitive. Interestingly, in today’s data world, how well you know Pandas often reflects your strength in Python-based data analysis. For many, Pandas isn’t just a library—it’s almost synonymous with data analysis in Python. Mastering it can significantly boost your ability to extract insights and work with real-world datasets effectively. #DataAnalytics #Python #Pandas #DataScience #LearningJourney