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def insertion_sort(a):
    ''' implement insertion sort'''
    for i in xrange(1, len(a)):
        key = a[i]
        j = i - 1
        while j >= 0: 
            if a[j] > key:
                a[j], a[j+1] = a[j + 1], a[j]
            j = j - 1
       

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from scipy.stats import beta

def binom_interval(success, total, confint=0.95):
    quantile = (1 - confint) / 2.
    lower = beta.ppf(quantile, success, total - success + 1)
    upper = beta.ppf(1 - quantile, success + 1, total - success)
    return (lower, upper)

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# coding: utf-8

""" Create a scatter-plot matrix using Matplotlib. """

from __future__ import division, print_function

__author__ = "adrn <[email protected]>"

# Standard library
import os, sys

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# This Python application will calculate the passer rating
# (passer efficiency or pass efficiency) given the five required variables.
#
# Variables to be used as values are defined below. Set to strings so input
# can be anything. Later the strings will be converted to numbers, given
# they pass a series of error checking tests.

COMP = "null"
ATT = "null"
YARDS = "null"

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## Warren Buffet's 1B Basketball Challenge ##

expected_value <- function(p,ngames=63,prize=1000000000){
    p^ngames * prize
}

## What is the expected value of an entry 
## given a particular level of prediction accuracy
expected_value(p=0.80)
expected_value(p=0.85)

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getBREFTeamStatTable <-
	function(season_end = 2015, table_name = 'team', date = T) {
		packages <-
			c('rvest','dplyr','pipeR','RCurl', 'XML','reshape2', 'tidyr', 'magrittr')
		lapply(packages, library, character.only = T)
		base <-
			'http://www.basketball-reference.com/leagues/'
		season <-
			(season_end - 1) %>%
			paste0("-",season_end)

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## Custom Basketball Reference Scraper Season Scraper
get_bref_player_season_stats <- 
	function(season.end, stat_type = c("Advanced","Totals","Per Minute","Per Game"), 
					 team.totals = F , league = 'NBA'){
	packages <-
		c('rvest','magrittr','dplyr','stringr','tidyr')
	lapply(packages, library, character.only = T)

	bref_team_base <-
		'http://www.basketball-reference.com/leagues/'

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source(
  "https://gist.githubusercontent.com/abresler/bc5c6d8753bac661415e/raw/a9cde8afae83777f4fa32d1ac978e843f8398ee0/nba_player_data.r"
)
devtools::install_github('cmpolis/datacomb', subdir = 'pkg')
packages <-
  c('datacomb', 'dplyr', 'htmlwidgets', 'magrittr')
lapply(packages, library, character.only = T)

data <-
  get_bref_season_player_stats(

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require(twitteR)
require(devtools)
install_github('rCharts', 'ramnathv') ## install rCharts
require(data.table)


#Register Twitter
## A real example, but using a fictitious consumerkey and consumer
## secret - you'll need to supply your own
#register access to twitter API

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library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%