Building a Simple Data Ingestion API with FastAPI

In this video, I’ve explained how to create and use APIs using FastAPI in a simple way. I covered: What FastAPI is and how it works How to create an API endpoint How to convert data into an API response How to fetch data from the API using Python How to convert that data into a pandas DataFrame This is a basic concept used in real-world data pipelines and machine learning workflows. #Python #FastAPI #APIs #DataScience

🚀 Day-74 of #100DaysOfCode 📊 NumPy Practice – Replacing Negative Values Today I worked on replacing negative values with zero using NumPy. 🔹 Concepts Practiced ✔ Boolean indexing ✔ Array filtering ✔ Data cleaning techniques 🔹 Key Learning NumPy makes it easy to modify data efficiently without loops, which is very useful in real-world data preprocessing tasks. Step by step improving my data handling and NumPy skills 🚀 #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #PythonProgramming

Data analysis often goes wrong before the analysis even begins. The ingestion step: where data is pulled from databases, web sources, and APIs: is where silent errors go undetected. Duplicates, nulls, schema mismatches. Episode 3 of the Practical Learning Series covers the patterns, the validation checklist, and the mistakes to avoid. Because reliable analysis starts with trustworthy data. Swipe through → #DataScience #Python #PracticalLearning #Analytics #DataManagement #DataScienceInstitute

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞𝐝 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬 While exploring a dataset in Python recently, I noticed how often real datasets contain missing values. At first it seems like a small issue, but it can actually affect the entire analysis. Using pandas functions like isnull() and fillna() made it easier to detect and handle those gaps before doing any calculations or visualizations. It made me realize that a big part of data analysis isn’t just analyzing the data — it’s preparing the data properly so the results actually make sense. Still learning, but these small steps are starting to make the workflow clearer. #Python #Pandas #DataAnalytics #DataCleaning

My first ML project is live on GitHub. Built a Random Forest model trained on 1,460 real house sales that predicts sale prices with a Mean Absolute Error of ~$17,000. Used SHAP values to explain which features drive predictions — turns out overall quality and living area matter most. Tech used: Python, pandas, scikit-learn, SHAP https://lnkd.in/gC4DhQbg #DataScience #MachineLearning #Python #Portfolio

🚀 Day-77 of #100DaysOfCode 📊 NumPy Practice – Finding Smallest Element Today I worked on finding the minimum value in an array using NumPy. 🔹 Concepts Practiced ✔ Array operations ✔ Using np.min() ✔ Basic data analysis 🔹 Key Learning Finding minimum values is a simple yet important operation used in data analysis, optimization problems, and real-world datasets. Small steps every day → Big progress 🚀 #Python #NumPy #DataScience #CodingPractice #100DaysOfCode #PythonDeveloper

🚀 Created my Pandas Practice Notes (PDF) 📊 Compiled everything I learned: ✅ Data loading ✅ Cleaning ✅ Filtering & sorting ✅ GroupBy analysis ✅ Exporting data 💡 Learning by doing > just watching tutorials. 🔜 Next: Real-world data analysis #Pandas #Python #DataAnalytics #LearningJourney #Coding

Day 1 — Revising Data Science fundamentals Today I revisited Python fundamentals from the very beginning: * Variables & data types * Input/Output * Type casting * Operators (arithmetic, logical, comparison) Applied these concepts by building a basic calculator program Revisiting the basics gave me more clarity than rushing ahead ever could. GitHub: https://lnkd.in/gqJkKJ36 Looking forward to staying consistent and improving every day. #DataScience #Python #LearningInPublic #Consistency

Most people default to Pandas. Works fine… until your data scales. That’s where Polars wins: > Similar syntax for most operations > Faster execution > Lazy evaluation (big performance boost) Don’t ditch Pandas. But ignoring Polars now? That’s a mistake. Learn both. Use what fits. Found Insightful? ♻️ Repost in your network and follow Sahil Alam for more. #DataEngineering #Python #Pandas #Polars #BigData #DataAnalyticsSahil Alam for more.

𝐀 𝐬𝐦𝐚𝐥𝐥 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐩𝐚𝐧𝐝𝐚𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞𝐝 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐭𝐢𝐦𝐞 While working with a dataset in Python today, I came across something simple but very useful — value_counts() in pandas. Instead of writing multiple filters or loops just to see how frequently different values appear in a column, value_counts() gives a quick frequency breakdown instantly. For example, if you want to see how many records belong to each category, city, or product type, one line can show the entire distribution. It’s a small function, but it makes exploring a new dataset much faster. Slowly realizing that data analysis is really about knowing these small but powerful tools. #Python #Pandas #DataAnalytics #LearningJourney