I built a tool for peer performance analysis and benchmarking, enhanced data visualization and deep insights of financials. Here is what it actually does: Takes your raw Excel data and smartly auto-maps the line items. Instantly calculates 60+ financial ratios Lets you benchmark multiple companies side-by-side. Exports everything into a fully formatted Excel sheet and an interactive HTML dashboard. On the tech side: I built this using Python and Streamlit, and I used Claude to write a massive chunk of the code. Try it out here : https://lnkd.in/d3cpmQNp #FinancialAnalysis #InvestmentAnalysis #Python #Streamlit #ClaudeAI #CorporateFinance #DataAnalytics

Combining data from multiple sources is one of the most common tasks in data analysis and data engineering and in pandas, pd.concat() is the primary tool for getting it done. But there is more to it than just passing two DataFrames and getting one back. Understanding when to use axis=0 vs axis=1, how join handles mismatched columns, why concatenating inside a loop is a performance trap, and when to use concat vs merge. These are the details that separate clean, efficient data pipelines from slow, buggy ones. Get comfortable with pd.concat() and combining data from multiple sources becomes one of the fastest steps in your workflow. Read the full post here: https://lnkd.in/es7KJ7Y9 #Python #Pandas #DataScience #DataEngineering #Analytics #ETL

Introducing PydanTable I built PydanTable to utilize Pydantic as the single source of truth for table-shaped data while seamlessly integrating with FastAPI, eliminating the need for a parallel schema. With PydanTable, you define the table once and can perform filter, join, group_by, and window-style transformations on lazy plans, backed by Rust for execution. Each row is represented as a Pydantic model, which updates dynamically as you transform the data. You can materialize the results into a list of models or a column dictionary when necessary. Additionally, pydantable.fastapi serves as optional glue for FastAPI applications. Explore more: - PyPI: https://lnkd.in/ez4NZMjT - Documentation: https://lnkd.in/eV4RTqZQ - Repository: https://lnkd.in/eVpjrcRX #Python #Pydantic #DataEngineering #OpenSource #FastAPI

Day 6/10 🚀 This is where your data starts to take shape. Collections — the backbone of every Python program. Without the right one? Slower code, messy logic. With the right one? Faster lookups, cleaner design. 📋 What I covered today: 01 → Lists — slicing & comprehensions 02 → Tuples — immutability & unpacking 03 → Dictionaries — CRUD & O(1) lookup 04 → Sets — unique values & operations 05 → Frozenset 06 → Advanced — defaultdict, Counter, namedtuple 07 → Iterators — iter() & next() 08 → Mini Project — Inventory Management System Built a simple system using dictionaries to manage stock & pricing — a real-world pattern used in inventory and data pipelines. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ 4 more to go. Drop a 🐍 if you’ve ever used a list when a set would’ve been better 😄 #Python #Collections #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #DataStructures

One of the most common sources of confusion for pandas beginners and even experienced analysts is knowing when to use apply(), map(), and applymap(). They look similar. They sometimes produce the same result. But they are designed for completely different situations. map() is for single column transformations and value substitution. apply() is for complex row-level or column-level logic across a DataFrame. DataFrame.map() is for applying the same transformation to every individual cell. And before reaching for any of them — always check if a vectorized operation can do the job faster. Getting this right means cleaner code, better performance, and fewer bugs in your data pipelines. Read the full post here: https://lnkd.in/e8sJfEgh #Python #Pandas #DataScience #DataEngineering #DataAnalysis #Analytics

🚀 Day 17/60 – Generators (Write Memory-Efficient Code ⚡) Yesterday you learned map vs filter vs reduce. Today, let’s unlock high-performance Python 👇 🧠 What is a Generator? A generator is a function that returns values one at a time instead of all at once. 👉 Uses yield instead of return 👉 Saves memory 👉 Faster for large data ❌ Normal Function def numbers(): return [1, 2, 3, 4] print(numbers()) 👉 Stores all values in memory ✅ Generator Function def numbers(): for i in range(1, 5): yield i print(list(numbers())) 👉 Generates values one by one ⚡ 🔍 Generator Expression squares = (x * x for x in range(5)) print(list(squares)) 👉 Like list comprehension, but uses () ⚡ Real Use Case def read_large_file(file): for line in file: yield line 👉 Perfect for large files & streaming data 🔥 Why Use Generators? ✅ Memory efficient ✅ Faster execution ✅ Works great with big data ❌ Common Mistake Trying to reuse a generator ❌ gen = (x for x in range(3)) print(list(gen)) print(list(gen)) # Empty! 👉 Generators are exhausted after use 🔥 Pro Tip 👉 Use generators for large datasets 👉 Use lists when you need data multiple times 🔥 Challenge for today 👉 Create a generator 👉 That yields numbers from 1 to 5 👉 Print them using a loop Comment “DONE” when finished ✅ #Python #PythonProgramming #LearnPython #Coding #Programming #Developer

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗡𝘂𝗺𝗣𝘆 – 𝗦𝘂𝗺 & 𝗣𝗿𝗼𝗱 ➕✖️ | 📅 𝗗𝗮𝘆 𝟲𝟯 🚀 Today’s task: ✅ 𝗧𝗮𝗸𝗲 a 2D array (matrix). ✅ 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 sum across rows. ✅ 𝗧𝗵𝗲𝗻 take product of the result. Core idea from the code: 𝙣𝙪𝙢𝙥𝙮.𝙨𝙪𝙢(𝙖𝙧𝙧, 𝙖𝙭𝙞𝙨=0) ➡️ Adds elements column-wise Then: 𝙣𝙪𝙢𝙥𝙮.𝙥𝙧𝙤𝙙(...) ➡️ Multiplies all resulting values Example concept: Matrix: [[1 2] [3 4]] Step 1 → Sum (axis=0) [1+3, 2+4] → [4, 6] Step 2 → Product 4 * 6 = 24 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Understanding axis is key: • axis=0 → column-wise • axis=1 → row-wise Strong candidates understand: • Reduction operations • Combining multiple NumPy functions • Data aggregation patterns Because real-world data tasks are all about: Transform → Aggregate → Compute Master these patterns — and NumPy becomes your superpower. #Python #NumPy #InterviewPrep #HackerRank #DataScience #ProblemSolving #DailyCoding #Consistency

When performing PCA, understanding each principal component's contribution is essential for data interpretation. However, analyzing more than two components can be challenging, and PCA 3D plots offer an effective solution. A 3D plot visualizes high-dimensional data reduced to three principal components, uncovering patterns, clusters, and outliers that may not be apparent in 2D plots. This visualization makes the data more interpretable and offers deeper insights into its underlying structure. Here's a brief guide to creating a 3D PCA plot in Python: 1️⃣ Prepare Data: Clean and preprocess your data using pandas, ensuring all variables are numeric by removing any categorical variables. 2️⃣ Perform PCA: Conduct PCA on the preprocessed data using PCA() from the sklearn.decomposition module and extract the principal component scores. 3️⃣ Create the 3D Plot: Use the matplotlib library to generate a 3D scatter plot, making the results visually appealing. For a detailed step-by-step tutorial, check out my guide created in collaboration with Paula Villasante Soriano: https://lnkd.in/eDUBPNdP Additionally, I have developed an extensive online course on PCA, which covers the theoretical concepts and practical applications in R programming. Further details: https://lnkd.in/eUnAqErz #rprogramminglanguage #datasciencecourse #visualanalytics #businessanalyst

Here are 5 things you should know about EDA toolkits every data scientist should know today: 1. **Polars for Python**: Polars is rapidly rising as an alternative to pandas for data manipulation and analysis. It’s **2-3x faster** in some benchmarks, especially for large datasets. Check it out: https://lnkd.in/g_fYkmaa 2. **Modernviz for Advanced Plots**: While matplotlib is solid, Modernviz (a newer library) offers advanced plotting capabilities with easier customization. It’s perfect for real-time data visualizations in 2026. Dive into it: https://lnkd.in/gZZ33ZN4 3. **Pinecone for Vector Search**: In the age of embeddings and large datasets, Pinecone is a game-changer for vector search. It handles large-scale vector databases efficiently, making nearest neighbor searches a breeze. Docs: https://docs.pinecone.io/ 4. **PyCaret for Automated ML**: PyCaret is an automated machine learning library that simplifies the process of building models. It’s **ideal** for quick iterations and real-time deployments. Give it a try: https://pycaret.org/ 5. **Zeep for SOAP APIs**: Zeep is a powerful and efficient SOAP client for Python, which is crucial for interacting with web services. It’s faster and more reliable than traditional libraries. Start using it: https://lnkd.in/g2FSRAC2 The thing is, these tools are not just buzzwords they’re here to make your workflow more efficient. So, what’s your favorite EDA toolkit, or are you still sticking with pandas and matplotlib? 🤔 #DataScience #EDA #Python #DataAnalysis

Recently, I’ve been working on a personal project to track and analyze my credit card expenses 📊 Using Python, I built a simple pipeline to: 1. Clean and structure raw transaction data 2. Categorize expenses 3. Generate monthly insights One interesting finding: small recurring expenses had a bigger impact on my budget than expected. Next step: building a dashboard in to visualize spending patterns over time. Have you ever analyzed your own financial data? #DataAnalytics #Python #SQL #PersonalFinance #DataProject