xiaolai

How to run Jupyterlab as a desktop app on Mac OSX

In Sep, 2021, Jupyterlab Desktop App (electron) was released by Mehmet Bektas (github repo).

brew install --cask jupyterlab

ulope

def implicit_chaining():
    """
    This is the Python 2 style. Just raising another exception from within an except block.
    In Python 3 this causes the exceptions to be chained with the message:
    
        During handling of the above exception, another exception occurred:
    
    Which is usually not correct when just re-raising with a more appropriate type.
    """
    try:

gvolpe

Dependency Injection in Functional Programming

There exist several DI frameworks / libraries in the Scala ecosystem. But the more functional code you write the more you'll realize there's no need to use any of them.

A few of the most claimed benefits are the following:

• Dependency Injection.
• Life cycle management.
• Dependency graph rewriting.

aparrish

toch

• https://blog.r3bl.me/en/yubikey-usefulness/
• https://b3n.org/yubikey-two-factor-authentication/
• https://www.ahmadnassri.com/blog/github-gpg-keybase-pgp/
• https://www.yubico.com/2016/04/github-verify-strengthens-yubikey-value/

bmabey

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drorata

#+BEGIN_SRC python :results raw
import pandas as pd
import numpy as np
from tabulate import tabulate

df = pd.DataFrame(np.random.random((4,3)), columns=['A','B','C'])
print("foo")
return(tabulate(df, headers="keys", tablefmt="orgtbl"))
#+END_SRC

davidadamojr

from flask import Flask
from flask.ext import restful
from flask.ext.sqlalchemy import SQLAlchemy

app = Flask(__name__)
app.config.from_object('config')

#flask-sqlalchemy
db = SQLAlchemy(app)

AtlasPilotPuppy

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql import Row, StructField, StructType, StringType, IntegerType

sc = SparkContext('spark://master:7077', 'Spark SQL Intro')
sqlContext = SQLContext(sc)
dividends = sc.textFile("hdfs://master:9000/user/hdfs/NYSE_dividends_A.csv")
dividends_parsed = dividends.filter(lambda r: not r.startswith('exchange')).map(lambda r: r.split(',')).map(
  lambda row: {'exchange': row[0], 'stock_symbol': row[1], 'date': row[2], 'dividends': float(row[3])})

Superbil

#+TITLE: [PyHUG] July: Use Python in Org-mode
#+DATE: <2013-07-01 Mon>
#+AUTHOR: Superbil
#+EMAIL: superbil@gmail.com
#+EXCLUDE_TAGS: noexport
#+KEYWORDS: emacs org-mode python pyhug
#+OPTIONS: html-postamble:auto html-preamble:t tex:t ^:nil
#+CREATOR: <a href="http://www.gnu.org/software/emacs/">Emacs</a> 24.3.1 (<a href="http://orgmode.org">Org</a> mode 8.0.3)

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