skuttruf

"""
Python code for fractional differencing of pandas time series
illustrating the concepts of the article "Preserving Memory in Stationary Time Series" 
by Simon Kuttruf

While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code.
"""

import numpy as np 
import pandas as pd

sevamoo

narphorium

lebedov

#!/usr/bin/env python 
"""
Retrieve intraday stock data from Google Finance.
"""

import csv
import datetime
import re

import pandas as pd

alimuldal

# Copyright 2014 Alistair Muldal <alistair.muldal@pharm.ox.ac.uk>

import numpy as np
import igraph
from itertools import izip, combinations, product


def bansal_shuffle(G, target_gcc, tol=1E-3, maxiter=None, inplace=True,
                   require_connected=False, seed=None, verbose=False):
    r"""

msrose

Combining two git repositories

Use case: You have repository A with remote location rA, and repository B (which may or may not have remote location rB). You want to do one of two things:

• preserve all commits of both repositories, but replace everything from A with the contents of B, and use rA as your remote location
• actually combine the two repositories, as if they are two branches that you want to merge, using rA as the remote location

NB: Check out git subtree/git submodule and this Stack Overflow question before going through the steps below. This gist is just a record of how I solved this problem on my own one day.

Before starting, make sure your local and remote repositories are up-to-date with all changes you need. The following steps use the general idea of changing the remote origin and renaming the local master branch of one of the repos in order to combine the two master branches.

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)

mrdwab

stratified <- function(df, group, size, select = NULL, 
                       replace = FALSE, bothSets = FALSE) {
  if (is.null(select)) {
    df <- df
  } else {
    if (is.null(names(select))) stop("'select' must be a named list")
    if (!all(names(select) %in% names(df)))
      stop("Please verify your 'select' argument")
    temp <- sapply(names(select),
                   function(x) df[[x]] %in% select[[x]])

peteWT

# -*- coding: utf-8 -*-
"""
example use of pandas with oracle mysql postgresql sqlite
    lightly tested.

    to do:  
            save/restore index (how to check table existence? just do select count(*)?), 
            finish odbc, 
            add booleans?, 
            sql_server?

HeartSaVioR

# -*- encoding: utf-8 -*-

import sys

if len(sys.argv) < 3:
  print "usage: %s [start] [end]" % sys.argv[0]
  sys.exit(1)

start = int(sys.argv[1])
end = int(sys.argv[2])