Scientist
import pathlib, hvplot.pandas, notebook; from tonyfast.poser import λ
%reload_ext tonyfast.literacy
iframe = λ.partial('IPython.display.IFrame', width='100%', height=800)
pictures = λ[pathlib.Path].methodcaller('glob', '*.jpg').drop(1)[list].sorted(key=lambda x: x.stat().st_ctime).map(str)[list]
λ['requests_cache.install_cache']('calligrams')
calligram = "C@𝔏𝔏👁𝓖ⓇⒶ𝔐"
An observable-ish pattern with dask’s pub sub model.
We know nothing about Github OH MY!
python-anyconfig is a MIT licensed python library provides common APIs to load and dump configuration files in various formats with some useful features such as contents merge, templates, query, schema validation and generation support.
import tonyfast
I want improve the criteria for identifying workers of a hotel vs guests. I was thinking about identifying devices that were seen in the hotel for 3 consecutive days, more than 3 times a month. Is it possible to do that efficiently and stay inside of pandas? I was thinking resample/groupby ‘1D’ and ‘device_id’, but I need an efficient way to calculate consecutive days
import toolz, abc, inspect, functools, typing, importlib, urllib, builtins, json, pathlib, operator, itertools, fnmatch
from toolz.curried import *
import toolz, abc, inspect, functools, typing, importlib, urllib, builtins, json, pathlib, operator, itertools, fnmatch
from toolz.curried import *
```python import dataclasses, typing, collections, functools
λ is an object for fluent function composition in "python" based on the toolz library.
My white 🐳 is literate markdown programming in the notebook.
import pandas, requests_cache, requests, uritemplate
from toolz.curried import *
requests_cache.install_cache('qs')
https://developer.github.com/v3/repos/statistics/
%reload_ext tonyfast.literacy
import graphviz
import xonsh.main, xonsh.execer, IPython, ast
execer = xonsh.execer.Execer()
class CachingCompiler(IPython.core.compilerop.CachingCompiler):
def ast_parse(self, source, filename='<unknown>', symbol='exec'):
return globals()['execer']._parse_ctx_free(source + "\n", symbol, filename)[0] or ast.Module([])
Allowing code cells to accept Markdown requires different display behavior. An important feature of computational documents is the real data can be included within a narrative. In this document, we add the ability to template Markdown with jinja2. We’ll observe value changes and update the display with traitlets.
All code should compute, code for typographic decoration should require extra effort to suppress. As a rule, code in a document should work.
A common use for notebooks is to “test an idea”. Designing pathways to mature informal notebook to formal testing tools like doctest. This notebook implements the ability to doctest when a cell is run.
Most canonical examples for fastapi use fastapi.FastAPI to instantiate an application. This notebook envisions a situation where an author has written several useful notebooks and they want to reuse as API’s. This is acheived with fastapi and importnb.
Astronomy Picture of the Day is one the 
%reload_ext pidgin
import graphviz
I was 🚢🛳⛴ through twitter & saw that @shannon_crabill posted about
%reload_ext tonyfast
__import__('requests_cache').install_cache('importnb'); import inspect, sys, IPython, nbformat, nbconvert, pathlib, json, pytest