GitHub - langchain-ai/langchain: 🦜🔗 Build context-aware reasoning applications 🦜🔗You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
tqdm documentation
Home Library Reference Developer Reference Release History Contributing Merch Store! Ports to Other Languages Presentation (PyData) External Links Licence Video tqdm# tqdm means "progress" in Arabic (taqadum, تقدّم) and is an abbreviation for "I love you so much" in Spanish (te quiero demasiado). Instantly make your loops show a smart progress meter - just wrap any iterable with tqdm(iterable), an
Manim Community
class ContinuousMotion(Scene): def construct(self): func = lambda pos: np.sin(pos[0] / 2) * UR + np.cos(pos[1] / 2) * LEFT stream_lines = StreamLines(func, stroke_width=2, max_anchors_per_line=30) self.add(stream_lines) stream_lines.start_animation(warm_up=False, flow_speed=1.5) self.wait(stream_lines.virtual_time / stream_lines.flow_speed) class OpeningManim(Scene): def construct(self): title = T
cutlet: a Japanese to Romaji Converter in Python
A few months ago I released cutlet, a Python library and application for converting arbitrary Japanese text to romaji. Katsu curry illustrated by Irasutoya Update: Check out the online demo for cutlet! You can check the results in your browser. Compared to other libraries cutlet has several advantages: it uses fugashi, so you can re-use your existing dictionary words of foreign origin optionally u
Welcome to Bokeh — Bokeh documentation
Bokeh documentation# Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. Finding the right documentation resources# Bokeh’s documentation consists
Pattern | CLiPS
Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization. The module is free, well-document
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
GitHub - Parquery/icontract: Design-by-contract in Python3 with informative violation messages and inheritance
tqdm/tqdm: A fast, extensible progress bar for Python and CLI
4 Python Web Scraping Libraries To Mining News Data | NewsCatcher
GitHub - ranahaani/GNews: A Happy and lightweight Python Package that Provides an API to search for articles on Google News and returns a JSON response.
GitHub - michalc/sqlite-s3-query: Python functions to query SQLite files stored on S3
GitHub - bsdz/calcengine: Simple Python Calculation Engine
GitHub - rapidfuzz/RapidFuzz: Rapid fuzzy string matching in Python using various string metrics
GitHub - dry-python/returns: Make your functions return something meaningful, typed, and safe!