Folks, I'm starting my post-#PhD job search low-key on the side while I write up my #thesis.
I have an odd collection of skills - #Linux, #Python, #Jupyter, #pandas, #DevRel, and I've done a lot of work in team leadership and management, and have led a multi-million $ not for profit in the past. Keynote speaker.
I'm looking for something that harnesses all of these skills - and it will be a senior role with senior pay, given my experience, qualifications and proven capability. I have time and will be discerning about my next step.
Job titles that might fit here would be Senior Research Engineer, Engineering Lead, Lead AI Engineer or similar.
Looking for fully remote work, with one day a fortnight max in #Melbourne, AU. If you don't believe in #RemoteWork or #WFH, we're not a good fit.
Super keen on something full time rather than splitting my attention over multiple part-time roles.
Looking to start around August, so a fair amount of lead time.
Keen on organisations that have strong values alignment - #FAIR and #CARE data use, #EthicalAI, AI for social good.
Chris-Anne: What do you mean they might be bringing more of those fat, black and white bears to our zoo? We're the big attraction around here now. #RedPandas#Pandas#Wildlife#NaturePhotography
We're happy to announce the release of #pandas 2.2.1. You can install it with pip install pandas or mamba install -c conda-forge pandas. Thanks to all contributors and sponsors who made this release possible! The release notes can be found at: https://pandas.pydata.org/docs/whatsnew/v2.2.1.html
Also, does anyone with any experience using both (especially for large data frames) want to weigh in on how much better Polars is re: speed and memory?
Please do not reply with something that does not answer either of these questions. Even if you think it's really helpful. Bear in mind you have no idea what I am doing or why and I have asked these specific questions for a reason.
(1/2) Tables in Python 🐍👇🏼
Great Tables is a fairly new Python library for creating a styled table. This package is the mirror of the gt R package, and it comes with similar functionality. The package supports both Pandas 🐼 and Polar 🐻❄️ DataFrames and enables the creation of highly customized tables. 🧵👇🏼
The second edition of the Effective Pandas 🐼🐍 by Matt Harrison is out. The new edition incorporates the major changes in Pandas 2.x covers topics such as:
✅ New chapters on testing and refactoring Pandas
✅ PyArrow types
✅ Using Cython and Numba
We're happy to announce the release of #pandas 2.2.0.
You can install it with pip install pandas or mamba install -c conda-forge pandas. Thanks to all contributors and sponsors who made this release possible!
Python Pandas Tips 🚀 by Kimberly Fessel is a list of short pandas tutorials that focus on basic pandas 🐼 operations such as:
✅ Reading flat files (e.g., CSV, Excel, etc.)
✅ Columns and rows manipulation
✅ Handle missing and duplicate values
✅ Changing columns attributes
“Local Functions: Define functions anywhere in scripts and live scripts”
”Python Interface: Convert between MATLAB tables and #Python#Pandas DataFrames”
Question for #datascience and #dataanalytics folks of Mastodon - how do you deal with time-series data in #Python#Pandas and what would you prefer to use instead?
I‘m starting to get fed up with how half-baked the implementation is and it‘s feeling like time drain
I have a #python problem. A list of lists like [[1,2,3], [4,5,6], [7,8,9]] should be turned into a flat list. And every last value (position -1) of a list should be summed with the first value of the next list (position 0). The result of the example would be [1,2,7,5,13,8,9]. Any suggestions how to solve this in an elegant and pythonic way? Solutions using #pandas or #numpy are welcome, too!
TIL pprint.pprint(data, sort_dicts=False) to keep the original order of dictionary keys. Very handy for pretty-printing dataframe.to_dict() for deeper inspection!