Skip to content 
| 📅 Today’s Picks |
dataclass vs Pydantic Field(): Declarative Constraints
Problem:
dataclass requires manual validation in __post_init__, separating validation rules from field definitions.
As your data model grows, the __post_init__ method fills with if-else statements, becoming harder to read and maintain.
Solution:
Pydantic Field() puts constraints directly on field definitions, making your model self-documenting and easier to maintain.
What you can specify with Field():
- Numeric bounds (e.g., age must be >= 0 and <= 150)
- String length (e.g., name must have at least 1 character)
- Regex patterns (e.g., email format validation)
- Default values for optional fields
Great Tables: Transform DataFrames into Publication-Ready Reports
Problem:
Standard DataFrame output can feel clunky and unfinished. Without clean headers, readable dates, or currency formatting, even great data can look unprofessional.
Solution:
Great Tables elevates your DataFrames into polished tables built for reports, dashboards, and presentations, all through one chainable interface.
Key features:
- Number formatting: currency, dates, compact notation
- Visual enhancements: mini charts, color gradients, embedded images
- Table structure: headers, subtitles, column control
- Multi-format export: PNG, PDF, HTML
| ☕️ Weekly Finds |
doxx Python Utils
Expose the contents of .docx files without leaving your terminal. Fast, safe, and smart – no Office required!
rendercv Python Utils
Version-control CVs/resumes as source code. A Typst-based Python package with CLI that allows you to manage your CV as code.
tstables Data Processing
A Python package to store time series data in HDF5 files using PyTables. Stores data into daily partitions for efficient access.
