kennethreitz

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import urllib2

gh_url = 'https://api.github.com'

req = urllib2.Request(gh_url)

password_manager = urllib2.HTTPPasswordMgrWithDefaultRealm()

brentp

from sklearn import linear_model
from scipy import stats
import numpy as np


class LinearRegression(linear_model.LinearRegression):
    """
    LinearRegression class after sklearn's, but calculate t-statistics
    and p-values for model coefficients (betas).
    Additional attributes available after .fit()

ThiagoAnunciacao

# Retorna dados sobre o pregão
http://www.bmfbovespa.com.br/Pregao-Online/ExecutaAcaoCarregarDados.asp?CodDado=IBOV,ticker&CA=undefined

# Retorna ações e seus últimos valores negociados
http://www.bmfbovespa.com.br/Pregao-OnLine/ExecutaAcaoCarregarDados.asp?CodDado=Ticker

# Retorna o histórico do dia de um papel
http://www.bmfbovespa.com.br/Pregao-Online/ExecutaAcaoCarregarDadosPapeis.asp?CodDado=petr4

# Retorna dados de um papel

dhitinair

Can anyone convert this algorithm to java implementation?
Python implementation of k prototype


"""
	K-prototypes clustering
	"""
	
	# Author: 'Nico de Vos' <njdevos@gmail.com>
	# License: MIT

rodrigok

[
	"Aracajú",
	"Belém",
	"Belo Horizonte",
	"Boa Vista",
	"Brasília",
	"Campo Grande",
	"Cuiabá",
	"Curitiba",
	"Florianópolis",

acwoss

# Respondido em: http://pt.stackoverflow.com/questions/184192

tipo_jogo = 0

def computador_escolhe_jogada(n, m):
    
    # Vez do computador:
    print("Vez do computador!")
    
    # Pode retirar todas as peças?

richard512

Using variables

=ACOS(COS(RADIANS(90-Lat1)) *COS(RADIANS(90-Lat2)) +SIN(RADIANS(90-Lat1)) *SIN(RADIANS(90-Lat2)) *COS(RADIANS(Long1-Long2))) *6371

Using Cell References

=ACOS(COS(RADIANS(90-A2)) *COS(RADIANS(90-A3)) +SIN(RADIANS(90-A2)) *SIN(RADIANS(90-A3)) *COS(RADIANS(B2-B3))) *6371

That'll work for a table set up like this:

Name	Lat	Long

pierdom

enzoftware

from PIL import Image
import math
path = "peru.jpeg" # Your image path 
img = Image.open(path)
newimg = Image.new("RGB", (width, height), "white")
for x in range(1, width-1):  # ignore the edge pixels for simplicity (1 to width-1)
    for y in range(1, height-1): # ignore edge pixels for simplicity (1 to height-1)

        # initialise Gx to 0 and Gy to 0 for every pixel
        Gx = 0

Akash-Ansari

########################################## Importing Data in Python (Part 2) #############################################

########################################## Importing flat files from the web: your turn! #################################
# You are about to import your first file from the web! The flat file you will import will be 'winequality-red.csv' from the University # of California, Irvine's Machine Learning repository. The flat file contains tabular data of physiochemical properties of red wine, 
# such as pH, alcohol content and citric acid content, along with wine quality rating.

# The URL of the file is