VSCode Data Wrangler Extension for Python

Python data visualization is essential for uncovering insights from complex datasets. With its rich ecosystem of top Python libraries for data visualization, it remains the go-to platform for creating everything from simple charts to dynamic data visualization and interactive dashboards. Data Visualization using matplotlib #machinelearning #datascience #datavisualization #pythonlibrary #matplotlib Matplotlib is a powerful data visualization library in Python that specializes in creating static, animated, and interactive 2D plots. First released in 2003, it has since become the most widely-used library for plotting in the Python ecosystem. Matplotlib offers versatility, supporting a wide range of visualizations, including line plots, scatter plots, bar charts, histograms, pie charts, and many others. https://lnkd.in/gKeYghJH

Part 4: Python Programming in One Page --> pandas (Python for Data) pandas are backbone of datascience. https://lnkd.in/gK8n-GjQ This is Part 4 of the One Page Learning Series. Next: matplotlib in one page Follow Scooplist for more #python #programming #pandas #datascience

Machine Learning Data Visualization using plotnine #machinelearning #datascience #datavisualization #plotnine Plotnine is a data visualization package for Python based on the grammar of graphics, a coherent system for describing and building graphs. The syntax is similar to ggplot2, a widely successful R package. https://lnkd.in/giV_TKem

Data visualization using plotly #machinelearning #datascience #datavisualization #pythonlibrary #plotly Plotly is a versatile, open-source graphing library that allows users to create interactive, web-based visualizations. Built on top of the Plotly JavaScript library, Plotly for Python (plotly.py) integrates seamlessly with Jupyter notebooks, Dash applications, and other web frameworks. It provides a wide range of charts, including 2D and 3D plots, histograms, scatter plots, bar charts, and specialized visualizations like contour plots and dendrograms. https://lnkd.in/gBYeyn8z

𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 💡 Databricks Notebooks are more powerful than many realize. A few features that significantly improve productivity: ✔ Widgets for parameterization ✔ Markdown for documentation ✔ Visualization directly from Spark results ✔ Easy collaboration with teammates ✔ Multi-language support (Python, SQL, Scala, R) When used properly, notebooks become both code + documentation in one place. #Databricks #DataEngineering #Productivity

🔥 Day 4 – Pandas Selection & Production-Style Filtering Today I focused on strengthening my data selection and filtering skills using Pandas — but doing it the right way. Instead of just filtering rows, I practiced production-style defensive programming. Here’s what I worked on: ✅ Column & row selection using .loc and .iloc ✅ Boolean filtering with multiple conditions ✅ Cleaning messy CSV column names ✅ Safe numeric conversion using pd.to_numeric() ✅ Writing a custom function to parse "HH:MM" delay values into proper Timedelta objects ✅ Handling invalid values using pd.NaT ✅ Preventing runtime errors with defensive filtering logic Built a workflow that: • Filters orders with Miles ≤ 30 • Converts delay strings into real time objects • Filters delays ≤ 30 minutes • Ensures no invalid comparisons occur Real-world data is messy. Learning how to clean, validate, and safely filter it is what turns simple analysis into production-ready logic. 📂 GitHub Repository: https://lnkd.in/gNWeQ5KE On to Day 5 🚀 #Python #Pandas #DataEngineering #Analytics #LearningInPublic #100DaysOfCode

Level up your data stack! From Polars for speed to Great Expectations for quality, here are 8 essential Python libraries every Data Engineer needs to build faster, more resilient pipelines. What’s missing from your toolkit? Drop a comment below with the libraries you think every data engineer should be using! 👇 #DataEngineering #Python #BigData #ETL #ELT #DataStack #Pyspark #SoftwareEngineering

🚀 Streamlit Full Course: From Beginner to Advanced Want to master Streamlit and build interactive data apps with ease? I've put together a comprehensive, free course covering everything from basic widgets to advanced dashboard techniques. 📚 Explore the full course: https://lnkd.in/gTSBpuvi 🔹 What you will learn • Streamlit Installation & Setup • Core display methods (st.write, st.title, st.header) • Interactive widgets (text_input, slider, checkbox, toggle) • Data visualization with charts and tables • Layouts, sidebars, and multi-page apps • Real-world project examples Happy Learning! 🚀 #Streamlit #Python #DataScience #WebDevelopment #DataApps #MachineLearning

📊 While working on a project, one thing became very clear: 🧹 Good analysis starts with clean data Before any analysis, data usually needs: • Handling missing values • Removing duplicates • Fixing data types • Cleaning messy columns Simple Pandas examples: df.drop_duplicates() df.fillna(0) pd.to_datetime(df['date']) It sounds simple, but in reality — data cleaning takes most of the time. Clean data → Better insights → Better decisions. #DataAnalytics #Python #Pandas #LearningJourney #DataScience