PyData Milton Keynes Geospatial Visualisation with Python

A small Pandas lesson I learned =========================== I learned something simple but important in Pandas: loc and iloc are not the same. loc → works with labels (names) iloc → works with index numbers At first, I kept mixing them up and getting errors... 😅 Now it’s much clearer: • If I know the row/column name → use loc • If I know the position → use iloc Small concept, but very useful. 😊 #DataScience #DataAnalytics #Python #Pandas #LearnPython #DataLearning #CodingJourney #TechLearning #BeginnerFriendly #DataScienceBasics

🚀 Diving Deep into Data with Pandas! 🚀 Just finished an insightful session exploring the lending_company_data dataset, and I'm excited to share some key takeaways for anyone working with data in Python! We covered essential Pandas operations that are crucial for initial data exploration and manipulation. DataAnalysis #Pandas #Python #DataScience #DataManipulation #LendingCompanyData #MachineLearning

A quick #geopandas cheat sheet on - and if you want to learn all of these (and a lot) more in Python, then: 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - 𝐒𝐞𝐜𝐨𝐧𝐝 𝐄𝐝𝐢𝐭𝐢𝐨𝐧 The book: https://lnkd.in/dy-7m_zz Sample: https://lnkd.in/dVP-Ty-Y GeoAI: https://lnkd.in/dDz_zgCH

Key conclusions & recommendations from my heart disease analytics project — built using Python, scipy, and pandas. Full analysis in the comments 🔗 #CodecademyDataSciwithPythonBootcamp @Codecademy

A practical piece from @GISGeography on some geospatial concepts - 𝐇𝐨𝐰 𝐆𝐈𝐒 𝐔𝐬𝐞𝐬 𝐌𝐚𝐩𝐩𝐢𝐧𝐠 𝐎𝐯𝐞𝐫𝐥𝐚𝐲𝐬 More: https://zurl.co/QGbvo And a free book sample on how to do this in Python: https://zurl.co/1RqcO

🚀 Day 2 – Setting up the foundation Today was all about structuring the project properly. ⚙️ Work done: Created project in Replit Organized folders: src/ data/ pipelines/ Installed required Python libraries 💡 Lesson: A clean structure saves hours later when pipelines get complex. #DataEngineering #Python #Replit

Working with messy real-world datasets taught me one thing: The cleaning step takes longer than the actual analysis. So I spent the last few weeks building dfdoctor - an open-source Python library that audits your DataFrame, tells you what’s wrong, and helps you fix it systematically instead of manually. It helps you quickly understand what’s broken and what to fix first. The part I'm most proud of: 5 correlation methods (including Kendall τ and Phi-k) implemented from scratch in pure numpy - no scipy dependency anywhere. 164 tests. CI passing across Python 3.9–3.12. Try it: pip install dfdoctor https://lnkd.in/e-ChV6mE #Python #OpenSource #DataEngineering #Pandas #EDA #DataScience

Day 38 at Luminar Technolab Worked with Pandas DataFrame selection using iloc and explored different ways to access rows and columns. Getting more comfortable navigating structured data #Python #Pandas #DataAnalysis #LearningJourney

Day7 of #30DayChartChallenge Theme: Multiscale Category: Distributions Tool: Python Data Source: python scikit-learn Datasets I worked with a few features from a machine learning dataset and plotted their distributions. At first, everything sits on different ranges. One stretches far, another stays tight, another somewhere in between. It looks fine, but comparing them like that is off. After scaling, they fall into the same range. Now the comparison actually makes sense. It’s a small step in most workflows, but seeing it visually makes the difference clearer. #30DayChartChallenge #python #Dataviz #Datascience

Milan Janosov : A practical piece from @GISGeography on some geospatial concepts - 𝐇𝐨𝐰 𝐆𝐈𝐒 𝐔𝐬𝐞𝐬 𝐌𝐚𝐩𝐩𝐢𝐧𝐠 𝐎𝐯𝐞𝐫𝐥𝐚𝐲𝐬 More: https://zurl.co/QGbvo And a free book sample on how to do this in Python: https://zurl.co/1RqcO