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"""This file contains code used in "Think Stats",

by Allen B. Downey, available from greenteapress.com

Copyright 2014 Allen B. Downey

License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html

"""

from __future__ import print_function

import math

import random

import brfss

import first

import thinkstats2

import thinkplot

def Summarize(data):

"""Prints summary statistics.

data: pandas Series

"""

mean = data.mean()

std = data.std()

median = thinkstats2.Median(data)

print('mean', mean)

print('std', std)

print('median', median)

print('skewness', thinkstats2.Skewness(data))

print('pearson skewness',

thinkstats2.PearsonMedianSkewness(data))

return mean, median

def ComputeSkewnesses():

"""Plots KDE of birthweight and adult weight.

"""

def VertLine(x, y):

thinkplot.Plot([x, x], [0, y], color='0.6', linewidth=1)

live, firsts, others = first.MakeFrames()

data = live.totalwgt_lb.dropna()

print('Birth weight')

mean, median = Summarize(data)

y = 0.35

VertLine(mean, y)

thinkplot.Text(mean-0.15, 0.1*y, 'mean', horizontalalignment='right')

VertLine(median, y)

thinkplot.Text(median+0.1, 0.1*y, 'median', horizontalalignment='left')

pdf = thinkstats2.EstimatedPdf(data)

thinkplot.Pdf(pdf, label='birth weight')

thinkplot.Save(root='density_totalwgt_kde',

xlabel='lbs',

ylabel='PDF')

df = brfss.ReadBrfss(nrows=None)

data = df.wtkg2.dropna()

print('Adult weight')

mean, median = Summarize(data)

y = 0.02499

VertLine(mean, y)

thinkplot.Text(mean+1, 0.1*y, 'mean', horizontalalignment='left')

VertLine(median, y)

thinkplot.Text(median-1.5, 0.1*y, 'median', horizontalalignment='right')

pdf = thinkstats2.EstimatedPdf(data)

thinkplot.Pdf(pdf, label='adult weight')

thinkplot.Save(root='density_wtkg2_kde',

xlabel='kg',

ylabel='PDF',

xlim=[0, 200])

def MakePdfExample(n=500):

"""Plots a normal density function and a KDE estimate.

n: sample size

"""

# mean and var of women's heights in cm, from the BRFSS

mean, var = 163, 52.8

std = math.sqrt(var)

# make a PDF and compute a density, FWIW

pdf = thinkstats2.NormalPdf(mean, std)

print(pdf.Density(mean + std))

# make a PMF and plot it

thinkplot.PrePlot(2)

thinkplot.Pdf(pdf, label='normal')

# make a sample, make an estimated PDF, and plot it

sample = [random.gauss(mean, std) for _ in range(n)]

sample_pdf = thinkstats2.EstimatedPdf(sample)

thinkplot.Pdf(sample_pdf, label='sample KDE')

thinkplot.Save(root='pdf_example',

xlabel='Height (cm)',

ylabel='Density')

def main():

thinkstats2.RandomSeed(17)

MakePdfExample()

ComputeSkewnesses()

if __name__ == '__main__':

main()