print '\nCENTRAL TENDENCY'
print 'geometricmean:', stats.geometricmean(l), stats.geometricmean(
lf), stats.geometricmean(a), stats.geometricmean(af)
print 'harmonicmean:', stats.harmonicmean(l), stats.harmonicmean(
lf), stats.harmonicmean(a), stats.harmonicmean(af)
print 'mean:', stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af)
print 'median:', stats.median(l), stats.median(lf), stats.median(
a), stats.median(af)
print 'medianscore:', stats.medianscore(l), stats.medianscore(
lf), stats.medianscore(a), stats.medianscore(af)
print 'mode:', stats.mode(l), stats.mode(a)
print '\nMOMENTS'
print 'moment:', stats.moment(l), stats.moment(lf), stats.moment(
a), stats.moment(af)
print 'variation:', stats.variation(l), stats.variation(a), stats.variation(
lf), stats.variation(af)
print 'skew:', stats.skew(l), stats.skew(lf), stats.skew(a), stats.skew(af)
print 'kurtosis:', stats.kurtosis(l), stats.kurtosis(lf), stats.kurtosis(
a), stats.kurtosis(af)
print 'mean:', stats.mean(a), stats.mean(af)
print 'var:', stats.var(a), stats.var(af)
print 'stdev:', stats.stdev(a), stats.stdev(af)
print 'sem:', stats.sem(a), stats.sem(af)
print 'describe:'
print stats.describe(l)
print stats.describe(lf)
print stats.describe(a)
print stats.describe(af)
print '\nFREQUENCY'
lf[2] = 3.0
a = N.array(l)
af = N.array(lf)
ll = [l]*5
aa = N.array(ll)
print('\nCENTRAL TENDENCY')
print('geometricmean:',stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af))
print('harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af))
print('mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af))
print('median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af))
print('medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af))
print('mode:',stats.mode(l),stats.mode(a))
print('\nMOMENTS')
print('moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af))
print('variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af))
print('skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af))
print('kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af))
print('mean:',stats.mean(a),stats.mean(af))
print('var:',stats.var(a),stats.var(af))
print('stdev:',stats.stdev(a),stats.stdev(af))
print('sem:',stats.sem(a),stats.sem(af))
print('describe:')
print(stats.describe(l))
print(stats.describe(lf))
print(stats.describe(a))
print(stats.describe(af))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(stats.itemfreq(l))
quit()
v1 = [1, 0, 1, 0, 1, 0]
v2 = [1, 0, 1, 0, 1, 1]
print(euclidean_distance(v1, v2))
print(euclidean_distance1(v1, v2))
print(euclidean_distance_array(v1, v2))
print(euclidean_distance_array_norm(v1, v2))
import scipy.stats
table = [1, 2, 3, 4, 5, 6]
chi2, prob, df, expected = scipy.stats.chi2_contingency(table)
output = "test Statistics: {}\ndegrees of freedom: {}\np-value: {}\nexpected:{}"
print(output.format(chi2, df, prob, expected))
print(stats.chi2(table))
from stats import variation
import scipy.stats
samples = [1, 2, 3, 4, 5]
print(scipy.stats.variation(samples))
print(variation(samples))
#0.47140452079103173
#0.47140452079103173
