zfwf

@SETLOCAL ENABLEDELAYEDEXPANSION & CALL "%~f0" & (IF NOT ERRORLEVEL 1 (python -x "%~f0" %*) ELSE (ECHO No python environment found on path)) & PAUSE & EXIT /B !ERRORLEVEL!
#%0 is the command itself. So %~f0 expands to the full path of the batch file. %* stands for all command line parameters except %0.

#do something in python

mihow

# The initial version
if [ ! -f .env ]
then
  export $(cat .env | xargs)
fi

# My favorite from the comments. Thanks @richarddewit & others!
set -a && source .env && set +a

danielrbradley

module Isin

open System.Text.RegularExpressions

// See: http://en.wikipedia.org/wiki/International_Securities_Identification_Number

[<Literal>]
let private AsciiZero = 48
[<Literal>]
let private AsciiNine = 57

gyu-don

for i in range(100):print(i%3//2*"Fizz"+i%5//4*"Buzz"or-~i)

nazrulworld

Development Status :: 1 - Planning
Development Status :: 2 - Pre-Alpha
Development Status :: 3 - Alpha
Development Status :: 4 - Beta
Development Status :: 5 - Production/Stable
Development Status :: 6 - Mature
Development Status :: 7 - Inactive
Environment :: Console
Environment :: Console :: Curses
Environment :: Console :: Framebuffer

pylover

# Extracted using: $ unzip -p lib/pycharm.jar com/jetbrains/python/PyBundle.properties | grep -B1 INSP.NAME | grep '^#' | sed 's|Inspection||g' | sed -e 's|#\s\{,1\}|# noinspection |'

# noinspection PyPep8
# noinspection PyPep8Naming
# noinspection PyTypeChecker
# noinspection PyAbstractClass
# noinspection PyArgumentEqualDefault
# noinspection PyArgumentList
# noinspection PyAssignmentToLoopOrWithParameter
# noinspection PyAttributeOutsideInit

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

Ghost---Shadow

from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize
from random import randint
import nltk.data

# Load a text file if required
text = "Pete ate a large cake. Sam has a big mouth."
output = ""

# Load the pretrained neural net

rickowski

#!/usr/bin/python3

# Copyright © 2015 Ole Rickowski
# This work is free. You can redistribute it and/or modify it under the
# terms of the Do What The Fuck You Want To Public License, Version 2,
# as published by Sam Hocevar. See http://www.wtfpl.net/ for more details.

import os,sys,random,string

# Print help

dolph

# -*- coding: utf-8 -*-
"""Demonstrate high quality docstrings.

Module-level docstrings appear as the first "statement" in a module. Remember,
that while strings are regular Python statements, comments are not, so an
inline comment may precede the module-level docstring.

After importing a module, you can access this special string object through the
``__doc__`` attribute; yes, it's actually available as a runtime attribute,
despite not being given an explicit name! The ``__doc__`` attribute is also