Top Tweets for #sciPy
AI can write code. So what’s your role as a developer?
@teoliphant (NumPy co-creator) breaks down what to focus on in the AI era:
1. Don’t just write code, solve real problems.
Most impactful projects (#NumPy, #SciPy) started from real needs, not ideas.
2. Talk to people before you build.
The best solutions come from conversations, not solo coding.
3. Treat open source as collaboration, not contribution.
Engage, ask questions, give feedback, not just PRs.
4. Use AI, but stay accountable.
You’re still responsible for what ships.
5. If you’re new, lean into your domain knowledge.
You don’t need to be an expert coder to create value.
6. Find mentors early.
Progress in Python (and open source) comes from learning with others.
The key takeaway: Developers who collaborate, communicate, and think will thrive.
Watch the full talk: https://t.co/X325HfoCW8
データに“ぴったり合う式”を見つける
scipy.optimize.curve_fit を使えば
データに最もフィットする関数の
パラメータを推定できます。
・指数関数
・対数関数
・ロジスティック曲線
なども簡単に近似可能
https://t.co/ZG1AKC9yJ8
#Python #SciPy #curve_fit #データ分析 #回帰分析
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2Tj3Fv v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #Simulation #NumPy #SciPy #Statistics
Pythonでt検定を実行してみよう！
scipy.statsの ttest を使えば、
2群の平均の差を
統計的に検定できます
・独立2群検定
・対応のあるt検定
・p値の読み方
サンプルコード付きでわかりやすく解説 https://t.co/3NrASdhLQ9
#Python #SciPy #scipystats #t検定 #統計解析
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2TivPX v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #Simulation #NumPy #SciPy #Statistics
SciPyで基本統計量を一発計算！
scipy.stats を使えば、
平均・分散・標準偏差だけでなく
・中央値
・モード
・分位数
・信頼区間
・検定統計量
まで簡単に一発で算出できます
Pythonで統計処理を効率化したい人に最適 https://t.co/0TqOPnkur4
#Python #SciPy #scipystats #統計解析 #データ分析
Cyclic Cellular Automata
Made with #python #mlx #matplotlib and @marimo_io
Molab notebook using #numpy and #scipy instead of #mlx: https://t.co/eHXjAsNLN9
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2Tj3Fv v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #Simulation #NumPy #SciPy #Statistics
Cyclic Cellular Automata
Made with #python #mlx #matplotlib and @marimo_io !
Molab notebook using #numpy and #scipy instead of #mlx:
https://t.co/eHXjAsNLN9
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2Tj3Fv v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #AppliedMath #Simulation #NumPy #SciPy #Statistics
🚀 February Python Meetup is here!
In collaboration with🤝 @SciPyIndia
📅 21st Feb
🎤 CFPs now open
Submit your proposal 👉https://t.co/JJACRea0hD
RSVP : https://t.co/LrvpFrkRQi
#Python #SciPy #Meetup #Community
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2TivPX v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #AppliedMath #Simulation #NumPy #SciPy #Statistics
💯 my newest algo in python is blowing my mind, I dont need charts anymore, going way more advanced then I previously knew was possible. Using #Scipy plugin, algo compares realtime data with 'session averaged percentiles', deep volatility analysis marries deep price analyss (over same periods)
“In the end, it’s all pattern recognition. And mathematics is the ultimate pattern-recognition machine.”
- Jim Simons
repost @prof_conradi on IG
Greenberg-Hastings cellular automata.
Made with #python #numpy #scipy #matplotlib
Applying Math with #Python, with 70+ practical recipes for solving real-world #ComputationalMath problems (2nd Edition): https://t.co/UMFO2Tj3Fv v/ @PacktDataML
————
#Mathematics #ComputationalScience #DataScience #AppliedMath #Simulation #NumPy #SciPy #Statistics
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