How I used Window Functions to speed up my SQL project

🚀 New Project: Data Analysis in SQL using Pandas I’m excited to share my latest project where I performed data analysis using SQL-style queries within Python. For this project, I used a synthetic NHS dataset containing 100,000 records, which I cleaned earlier using Pandas to make it ready for analysis. This project is a continuation of my previous work on Exploratory Data Analysis (EDA) in Pandas — but this time, I focused more on the analytical and SQL aspects. Here’s what I did: 🔹 Used Pandas to run SQL-like queries in Python 🔹 Solved multiple real-world, scenario-based queries (like identifying trends, insights, and optimization cases) 🔹 Showcased how large datasets can be efficiently analyzed using SQL logic in Python 📺 YouTube Video: https://lnkd.in/dDYhV3_T I’ve also uploaded the complete code and dataset on my GitHub so anyone can try it out. 📂 GitHub: https://lnkd.in/dhyjBThH Always open to feedback, ideas, and collaborations! #Python #SQL #Pandas #DataAnalysis #NHSData #SyntheticData #DataScience #MachineLearning #PythonProjects #GitHub #LinkedIn #Analytics #Coding

Today I revised some of my SQL concepts and practiced a few Python loops to strengthen my logic-building skills. Here’s a quick glimpse - SQL Practice: Created a View (vw_Category_Profit) using CTE + Subquery to calculate total revenue and total cost per category. Then built another query using two CTEs to calculate Total Profit and extract the Top 3 categories by profit. It’s amazing how much clarity comes when you connect concepts like CTEs, Views, and Joins together! - Python Practice: Nested for loops to print pattern combinations Practiced looping through two lists (Colors & Sizes) Wrote a while loop and a limited-attempt for loop to build simple user input validation logic Each day I try to connect what I know with what I learn new. SQL builds structure; Python builds logic — together, they’re the backbone of Data Analytics. #SQL #Python #DataAnalytics #LearningJourney #ContinuousLearning #CareerGrowth #LinkedInLearning #PracticeMakesPerfect #CTE #View #Loops

𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹 𝗚𝗿𝗲𝗮𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗛𝗮𝘃𝗲 Most analysts chase technical mastery. They learn SQL, Python, Tableau. But great analysts master something rarer - 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. They ask: “𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝘁𝗿𝗶𝗰 𝗺𝗮𝘁𝘁𝗲𝗿?” before writing a query. Because without understanding the why, even perfect analysis becomes useless. Data is only powerful when it solves the right problem. #DataAnalytics #BusinessIntelligence #DataStorytelling

Ever noticed how Python Pandas and SQL speak almost the same language just in different accents? In SQL, we go - “SELECT name, salary FROM employees WHERE salary > 50000;” But in Pandas, it’s like - df[df['salary'] > 50000][['name', 'salary']] Same work Just one sounds like it’s wearing a suit and the other in a hoodie, chilling with coffee At the end, both just want the same thing clean data and peace of mind #Python #SQL #Pandas #DataAnalytics #DataScience

**STOP making boring charts!** 🚫📊 I've grouped my 3 most popular Matplotlib videos into one **FREE, Complete Course Playlist** for Python Data Visualization. This series takes you from basic line plots to advanced techniques like: ✅ Creating **Bubble Charts** and multi-dimensional **Colormaps**. ✅ Analyzing **Stock Prices** using real financial data. ✅ Solving data overplotting with the **Alpha** parameter. ✅ Applying professional styles like **ggplot** and **Fivethirtyeight**. If you're serious about Data Science or Financial Modeling, this is a must-watch. Master Matplotlib and make your data visualizations stand out! 🔗 **Watch the Full Matplotlib Course Here:** https://lnkd.in/ekt_yj24 #Python #Matplotlib #DataScience #DataVisualization #FinancialModeling #PythonForFinance

....some cool numpy: # Suppose we have a text file "data.txt" with rows of numbers like: # 1.0 2.0 3.0 # 4.0 5.0 6.0 # 7.0 8.0 9.0 # Python code: import numpy as np def main():   data = np.loadtxt("data.txt")   print("data loaded from file:")   print(data)   print("shape: ", data.shape)   print("mean of all values:", np.mean(data))   print("column sums:", np.sum(data, axis=0))   col2 = data[:, 1]   print("second column: ", col2) if __name__ == "__main__":   main() -------------------------- # output: data loaded from file: [[1. 2. 3.] [4. 5. 6.] [7. 8. 9.]] shape: (3, 3) mean of all values: 5.0 column sums: [12. 15. 18.] second column: [2. 5. 8.]

SQL vs Pandas: Both are powerful for data analysis — just used differently 👇 🔹 SQL → Works best for querying large databases. 🔹 Pandas → Great for data manipulation in Python. Example: SQL: SELECT AVG(salary) FROM employees; Pandas: df['salary'].mean() Different tools, same goal — turning data into insights! 📊 #SQL #Pandas #Python #DataAnalytics #LearningEveryday

df = pd.read_csv('data.csv') 👉 This reads a CSV file named data.csv and loads it into a DataFrame called df. Think of a DataFrame as a super-powered table—like an Excel sheet but in Python! df[df['column_name'] == 123] 👉 This filters the data to show only rows where the value in the column 'column_name' is equal to 123. Great for zooming in on specific data. df.groupby('column_name').mean() 👉 This groups the data by values in 'column_name' and calculates the average (mean) for each group. Perfect for summarizing and finding trends.

This week, I’ve been working on SQL queries for different analytical use cases. I’ve focused on data filtering, grouping, joins, and query optimization — essential steps to prepare clean and meaningful data for dashboards and automated reporting systems. SQL becomes truly powerful when combined with Python and automation tools. #SQL #Python #DataAnalytics #Automation #DataEngineer

1️⃣ Data Acquisition using Pandas Caption: 🚀 Exploring Data Acquisition with Pandas! Under the guidance of Prof. Ashish Sawant, I explored how to import and manage datasets efficiently using Python’s Pandas library. Data acquisition is the foundation of every data-driven project. I practiced reading data from various sources like CSV, Excel, JSON, and SQL. Also learned to inspect data using .head(), .info(), and .describe(). Clean and structured data is the first step toward meaningful analysis. This practical gave me a clear understanding of how data flows into the analytics pipeline. For more info,you can visit :- GitHub :-https://lnkd.in/edWY72Hg G drive:https://lnkd.in/ewkPtNtH #DataScience #Pandas #Python #DataAcquisition #LearningByDoing