Visualizing Time-Series Data with Python: Line, Area, Stacked Charts

In my latest article on the Towards Data Science  blogging platform, “Think Your Python Code Is Slow? Stop Guessing and Start Measuring”, I walk through how to use cProfile and SnakeViz to identify real bottlenecks and optimise with confidence. If you care about the performance of your Python code, this is essential reading. Read the full article for free using the link below. https://lnkd.in/eycxySZ5

"Think Your Python Code Is Slow? Stop Guessing and Start Measuring" Performance tuning without profiling is just educated guesswork. In my latest article on Towards Data Science, I show how you can use two great tools, cProfile and SnakeViz, to reveal exactly where your Python code is slowing you down—and how to fix it efficiently. Read the article for FREE using the link below. https://lnkd.in/eycxySZ5

🚀 Python Polars: A Lightning-Fast DataFrame Library Recently explored a tutorial on Polars, and it’s a great reminder of how modern data tools are evolving beyond traditional pandas workflows. Polars stands out for its speed, efficient memory usage, and lazy execution model—making it especially useful for large datasets, ETL pipelines, and analytics engineering use cases. Definitely worth exploring if performance and scalability are becoming bottlenecks in data workflows.

🐍📰 NumPy Tutorial: Your First Steps Into Data Science in Python In this tutorial, you'll learn everything you need to know to get up and running with NumPy, Python's de facto standard for multidimensional data arrays. #python

Reduce the complexity in your data cleaning scripts. Eirik Berge shares this practical guide to using Python's structural pattern matching for parsing API responses.

Frustrated with a Python script that feels sluggish? Instead of guessing where the slowdown is, you can measure it. Tom Reid shares a guide on using cProfile and snakeviz to find and fix the exact parts of your code that are causing delays.

🐍📰 The Pandas DataFrame: Make Working With Data Delightful In this tutorial, you'll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a Pandas DataFrame #python

Python is the go-to language for time-series analysis. The ecosystem is mature: pandas for data loading and manipulation, NumPy for numerical operations, statsmodels for ARIMA and forecasting, Matplotlib for visualization. This Tiger Data (creators of TimescaleDB) guide covers the fundamentals: identifying trends and patterns, testing for stationarity and seasonality, building forecasting models, and handling the challenges of large-scale time-series data. Includes code examples throughout. https://lnkd.in/eAyh2s6X

Data Problems Often Start with Poor Output Structure Many data projects fail quietly due to unmanaged outputs, not bad logic. Data Softout4.v6 Python explains how structured, version-aware outputs improve reliability and scalability in Python workflows. What You’ll Learn: • Why output discipline matters • How versioning improves trust • Common mistakes in data pipelines • Long-term benefits for teams • Practical relevance for real systems 🔗 Read the full guide: https://lnkd.in/dddQ57qX #DataEngineering #PythonProgramming #Analytics #TechStrategy #ScalableSystems #DeveloperBestPractices #DataReliability #TrendivaMedia

Ever waste time optimizing a piece of Python code only to see no real speed improvement? Tom Reid's new article explains how to avoid this by profiling your code first.