Larivact

Serving Flask under a subpath

Your Flask app object implements the __call__ method, which means it can be called like a regular function. When your WSGI container receives a HTTP request it calls your app with the environ dict and the start_response callable. WSGI is specified in PEP 0333. The two relevant environ variables are:

SCRIPT_NAME
The initial portion of the request URL's "path" that corresponds to the application object, so that the application knows its virtual "location". This may be an empty string, if the application corresponds to the "root" of the server.

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

folkertdev

"""
A sympy-based Lagrange polynomial constructor. 

Given a set of function inputs and outputs, the lagrangePolynomial function will construct an 
expression that for every input gives the corresponding output. For intermediate values,
the polynomial interpolates (giving varying results based on the shape of your input). 
This is useful when the result needs to be used outside of Python, because the 
expression can easily be copied. To convert the expression to a python function object,
use sympy.lambdify.
"""

rakeshsingh

SELECT OBJECT_TYPE
	,OBJECT_SCHEMA
	,OBJECT_NAME
FROM (
	SELECT 'TABLE' AS OBJECT_TYPE
		,TABLE_NAME AS OBJECT_NAME
		,TABLE_SCHEMA AS OBJECT_SCHEMA
	FROM information_schema.TABLES
	
	UNION

TomAugspurger

# see also https://github.com/wrobstory/pgshift

import gzip
from io import StringIO, BytesIO
from functools import wraps

import boto
from sqlalchemy import MetaData
from pandas import DataFrame
from pandas.io.sql import SQLTable, pandasSQL_builder

ramhiser

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

searls

#!/usr/bin/env bash -e

cat <<TEXT

Git has no undo feature, but maybe these will help:
===================================================

## Unstage work

  Unstage a file

uranusjr

import os
import sys
import io
import tarfile
import urllib.request


ARCHIVE_URL = 'http://d.pr/f/YqS5+'

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

ianonavy

def memoize_to_file(func=None, **options):
    """Wraps a function definition so that if the function is ever
    called with the same arguments twice, the results are pulled from
    a cache stored in a file. The filename is specified when decorating
    the memoized function. Iterable objects like dicts, lists or
    pandas DataFrames are cast to a tuple before being hashed, and
    everything else uses the object's __repr__ definition.

    Usage: