Data Science
Marimo Is a .py File. Jupyter Still Has Hidden State
KDnuggets walked marimo as a Git-friendly notebook. CoreWeave put it in Forge on Oct 1. The graph is the product, not the banner.
Data Science
Master data analysis and visualization with NumPy, Pandas, Matplotlib and Seaborn. Explore statistical methods, data preprocessing, and insights extraction techniques. Browse 32 curated articles covering data science with practical implementation detail.
32 Articles
What you will learn in Data Science
Pandas, Polars, visualization, and preprocessing workflows
Performance, memory optimization, and reproducible analysis
Production-minded data pipelines beyond notebook prototypes
Data Science
The Tools List Still Starts With pandas. DuckDB Is Reading One Month
Analytics Insight’s Sep 27 stack still opens on Python, SQL, NumPy, and pandas. DuckDB Lab’s Sep 26 note is partition pruning on v1.5.x. Those are not the same article.
Data Science
dbt v2 Ships DuckDB. Your pip Adapter Is Optional
DuckDB's Sep 22 post: dbt 2.0.0 bundles a pinned DuckDB over ADBC. catalogs.yml is v2-only. The bundled driver still will not load httpfs.
Data Science
DuckLake Puts the Catalog in SQL. Iceberg Can Keep the JSON
Talk Python 562 is Pedro Holanda on a lakehouse whose metadata is a database. QuestDB registers Parquet without copying it. You probably do not need a new platform.
Data Science
Pandas Does Not Need to Go Extinct. Your Spark Cluster Might
A Latency Conference talk argued pandas should die. The useful part is Arrow: most tables never needed a cluster, and Polars 2.0 already made streaming the default.
Data Science
Polars 2.0 RC Makes Streaming the Default. Check Your Joins.
Polars 2.0rc1 makes LazyFrame.collect() stream. Row order is no longer free. What to pin, what raises, and what to leave.
Data Science
Python Data Validation in 2026: Pydantic vs Pandera and When to Use Which
Data validation in Python has two dominant approaches: Pydantic for API and config validation, Pandera for DataFrame testing. Here's how they work, where they overlap, and why most teams need both.
Data Science
Python Data Cleaning Libraries That Actually Make the Work Enjoyable
Data cleaning is the least glamorous part of data science, but the right libraries turn it from a tedious chore into a satisfying pipeline. Here are five Python tools that make cleaning faster, more readable, and less error-prone.
Data Science
Python Auto-EDA Tools in 2026: fg-data-profiling, Sweetviz, DataPrep, and D-Tale Compared
Four Python libraries automate exploratory data analysis. One just got renamed, one focuses on speed, one on interactivity, and one on compact reports. Here's how to pick the right one.
Data Science
Matplotlib, Plotly, or Altair: Which Python Visualization Library Should You Use in 2026?
Three libraries dominate Python data visualization, and each one excels in different scenarios. Here is a practical decision framework for choosing the right tool.
Data Science
Jupyter Just Outlived Two Big-Tech Notebook Killers — And Python Data Science Won
JetBrains and Microsoft both killed their notebook products in 2026, blaming AI for the shift. But Jupyter Notebook usage grew 75% in the same period. The real story is not about AI replacing notebooks — it's about which language owns the data science workflow.
Data Science
Apache Arrow at 10: The Invisible Standard That Runs Your Entire Python Data Stack
You probably don't think about Apache Arrow. But every time you load a Parquet file in pandas, run a query in DuckDB, or pass data to Polars, Arrow is the reason it's fast. Here's what the 10-year-old project achieved — and what's coming next.
Data Science
Choosing the Right Python Data Tool in 2026: Pandas, Polars, or DuckDB?
A practical decision guide for picking between Pandas, Polars, and DuckDB for your data workloads in 2026 — with real benchmarks, code comparisons, and honest tradeoffs.
Data Science
Data Validation in Python: Pandera vs Great Expectations — Which One Should You Use?
Dirty data breaks pipelines, corrupts models, and wastes hours of debugging. Pandera and Great Expectations both solve data validation, but they take fundamentally different approaches. Here's how to choose.
Data Science
Python Data Pipelines in 2026: From ETL to Self-Healing Lakehouse Architecture
How Python data engineers are adopting lakehouse-native patterns with DuckDB, Delta, and Iceberg to build self-healing pipelines that keep AI agents fed with live data.
Data Science
DuckDB in Python Data Pipelines: Why In-Process Analytics Is Replacing Your Local Database
DuckDB has become the go-to tool for Python data pipelines that need SQL-speed analytics without spinning up a server. Here's how to use it effectively.
Data Science
Narwhals: Write DataFrame Code Once, Run It Anywhere in Python
Narwhals is a lightweight compatibility layer that lets you write DataFrame code once and execute it on pandas, Polars, cuDF, and more. Here's how to use it in production.
Data Science
DuckDB in Python: A Complete Data Analytics Guide for 2026
DuckDB brings fast SQL analytics directly into Python without a server. Learn how to use it for data analysis, Parquet queries, and workflows that beat pandas on large datasets.
Data Science
Geospatial Analysis in Python 2026: GeoPandas, DuckDB Spatial, and the End of Shapefiles
A guide to modern geospatial analysis in Python, covering GeoPandas with Parquet, DuckDB's spatial extension, and cloud-native geospatial formats.
Data Science
Pandas 3.0 in Production: What the Arrow-Native DataFrame Means for Your Existing Code
A practical migration guide for pandas 3.0, covering breaking changes, performance improvements from Arrow-native storage, and how to update legacy codebases.
Data Science
DuckDB in Python: Why In-Process Analytics Is Replacing Your ETL Pipeline
How DuckDB's embedded OLAP engine is changing the way Python data teams handle analytical queries, eliminating the need for separate database servers in many workflows.
Data Science
Polars Lazy Evaluation: Building Data Pipelines That Only Compute What You Need
How Polars' lazy evaluation engine optimizes query plans, eliminates redundant work, and transforms data pipeline performance in Python.
Data Science
Pandas Arrow-Backed DataFrames: When PyArrow Changes Everything About Your Memory Budget
An exploration of pandas' Arrow-backed data types and how they transform memory usage, I/O speed, and cross-language compatibility for data science workflows.
Data Science
Polars 2.0: What the New DataFrame API Means for Python Data Work in 2026
Polars 2.0 brings a redesigned API, GPU acceleration via cuDF integration, and streaming improvements that push DataFrame performance further. Here's how the update changes everyday data workflows in Python.
Data Science
Pandas 3.0 Migration Guide: What Changed and How to Upgrade Safely
Complete guide to migrating from Pandas 2.x to 3.0. Learn about Copy-on-Write defaults, new string dtype, breaking changes, and step-by-step upgrade strategies.
Data Science
Polars vs Pandas: The Complete Migration Guide
A practical guide to Polars covering benchmarks, API comparisons, lazy evaluation, and when to migrate from Pandas. Includes real code examples and production patterns.
Data Science
Complete Guide to Python Data Analysis: From Pandas to Visualization
In-depth comparison of core data analysis tools like Pandas and NumPy, mastering the complete workflow from data cleaning to visualization, with practical code examples and modern best practices.
Data Science
Pandas Memory Optimization: Complete Guide to Handling Large Datasets
Master Pandas memory optimization with practical techniques. Reduce memory usage by 90%, process 10M-row datasets in seconds, and learn when to switch to Polars for massive workloads.
Data Science
Time Series Forecasting with Python and statsmodels
Learn how to build accurate time series forecasting models using Python's statsmodels library. Master ARIMA, SARIMA, and seasonal decomposition techniques.
Data Science
Python Data Preprocessing: Complete Guide to Clean Data
Master data preprocessing techniques with Python. Learn to handle missing values, encode categories, scale features, and prepare datasets for machine learning with practical examples.
Data Science
Build Interactive Dashboards with Plotly Dash: A Complete Guide
Learn how to create dynamic, web-based dashboards using Plotly Dash. This tutorial covers setup, callbacks, multi-page layouts, and deployment.
Data Science
Master Data Visualization with Matplotlib and Seaborn
Learn how to create professional statistical visualizations in Python using Matplotlib and Seaborn. Step-by-step tutorial with real code examples.