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