rgommers

# -*- coding: utf-8 -*-
# <nbformat>2</nbformat>

# <markdowncell>

# Kernel Density Estimation with SciPy
# ====================================

# <codecell>

christophermanning

<style type="text/css">p {text-align:center;width: auto}</style>

Created by Christopher Manning

Gallery

[![Star]

yusugomori

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

'''
 Logistic Regression
 
 References :
    - Jason Rennie: Logistic Regression,
   http://qwone.com/~jason/writing/lr.pdf
 

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

1st

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

#
# Test that I passed on codility.com for TopTal company
#


# Task #1
def binary_gap(N):

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()

jdmonaco

from collections import namedtuple

import numpy as np
import scipy.stats as st

TtestResults = namedtuple("Ttest", "T p")

def t_welch(x, y, tails=2):
    """
    Welch's t-test for two unequal-size samples, not assuming equal variances

mango314

Census Tracts of New York City

Here at PyData NYC, I heard a tutorial of how to use numpy and iPython notebooks. In a previous gist, I wrote drew all the zip codes of the Bronx in d3.js

This would be great for reproducing inforgraphics like Educational Attainment in New York City -- Brooklyn which looks a bit like a jigsaw puzzle:

Where to Obtain the Data

thigm85

require(MASS)
require(ggplot2)
require(scales)
require(gridExtra)

pca <- prcomp(iris[,-5],
              center = TRUE,
              scale. = TRUE) 

prop.pca = pca$sdev^2/sum(pca$sdev^2)

tboggs