static-frame
pdbpp
static-frame | pdbpp | |
---|---|---|
8 | 9 | |
406 | 1,249 | |
1.0% | 0.8% | |
9.9 | 0.0 | |
about 19 hours ago | 22 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
static-frame
- Static-frame: Immutable/statically-typed DataFrames with runtime type validation
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Type-Hinting DataFrames for Static Analysis and Runtime Validation
This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:
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Memoizing DataFrame Functions: Using Hashable DataFrames and Message Digests to Optimize Repeated Calculations
StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.
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The Performance Advantage of No-Copy DataFrame Operations
A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.
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Which not so well known Python packages do you like to use on a regular basis and why?
static-frame. An immutable alternative to pandas.
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One Fill Value Is Not Enough: Preserving Columnar Types When Reindexing DataFrames
StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.
- static-frame: Immutable and grow-only Pandas-like DataFrames with a more explicit and consistent interface.
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Bug Sur 11.4 stuttering issues on RX 6800
For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?
pdbpp
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The new pdbp (Pdb+) Python debugger!
Why not just use Python’s built-in pdb debugger or another existing one like ipdb or pdbpp?
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Show HN: Clamshell- an experimental Python based shell
I like pdbpp. Make sure to install from source as there hasn’t been a release in a while.
https://github.com/pdbpp/pdbpp
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Useful Python Modules for us
pdbpp: Improved pdb boltons: assorted python addtions twisted: event driven networking framework sorcery: Dark magic in python, things know where+how they are being called, helps reducing boilerplate sh: Better alternative for subprocess module, much more pythonic taskipy: npm run scipt_name like functionality snoop: pdb lite, record+replay function steps birdseye: graphical debugger remote-pdb: easy pdb from inside containers typer: wrapper around click for simpler code for CLIs arrow: Always TZ aware datetimes, plus more features more-itertools: more functions for iterators pydantic: data validation + dataclasses loguru: better logging notifiers: sending notifications from python
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For whose use Emacs and VS Code, when and why you use VSCode? #emacs #vscode
If you want to use pdbpp, install it into your Python environment you're using the debugger from and it'll automatically hook itself into pdb with no additional setup.
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What Python debugger do you use?
I love pdbpp
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Which not so well known Python packages do you like to use on a regular basis and why?
pdbpp feels like getting super powers over using pdb
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What dev tools do you use in your python projects?
Most of the tools and libraries I use have been mentioned, but I haven’t seen pdb++ brought up. It’s like ipython for debugging!
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Debug in VIM
Improved version of built-in debugger: https://github.com/pdbpp/pdbpp
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Icecream: Never use print() to debug again in Python
I like to use PDB++ which is a drop in replacement for PDB
https://github.com/pdbpp/pdbpp
What are some alternatives?
pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
ipdb - Integration of IPython pdb
pandastable - Table analysis in Tkinter using pandas DataFrames.
pudb - Full-screen console debugger for Python
python-lenses - A python lens library for manipulating deeply nested immutable structures
pdbr - pdb + Rich library
bidict - The bidirectional mapping library for Python.
PySnooper - Never use print for debugging again
bambi - BAyesian Model-Building Interface (Bambi) in Python.
python-devtools - Dev tools for python
rubygems - Library packaging and distribution for Ruby.
snoop - A powerful set of Python debugging tools, based on PySnooper