print(np.size(whippet_weight))
print(np.size(terrier_weight))
print(np.size(pitbull_weight))
tstat, pval = f_oneway(whippet_weight, terrier_weight, pitbull_weight)
print(pval)
v = np.concatenate([whippet_weight, terrier_weight, pitbull_weight])
labels = ['whippet_weight'] * len(whippet_weight) + ['terrier_weight'] * len(
terrier_weight) + ['pitbull_weight'] * len(pitbull_weight)
tukey_results = pairwise_tukeyhsd(v, labels, 0.05)
print(tukey_results)
"""
We want to see if "poodle"s and "shihtzu"s have significantly different color breakdowns.
"""
poodle_colors = fetchmaker.get_color("poodle")
shihtzu_colors = fetchmaker.get_color("shihtzu")
color_table = [[
np.count_nonzero(poodle_colors == "black"),
np.count_nonzero(shihtzu_colors == "black")
],
[
np.count_nonzero(poodle_colors == "brown"),
np.count_nonzero(shihtzu_colors == "brown")
],
[
np.count_nonzero(poodle_colors == "gold"),
np.count_nonzero(shihtzu_colors == "gold")
],
[
np.count_nonzero(poodle_colors == "grey"),
whippet_wg = fm.get_weight("whippet")
terrier_wg = fm.get_weight("terrier")
pitbull_wg = fm.get_weight("pitbull")
pval2 = f_oneway(whippet_wg, terrier_wg, pitbull_wg).pvalue
print(pval2)
# pval > 0.05 : Not significant
weights = np.concatenate([whippet_wg, terrier_wg, pitbull_wg])
labels = ["Whippet"] * len(whippet_wg) + ["Terrier"] * len(terrier_wg) + ["Pitbull"] * len(pitbull_wg)
tukey_results = pairwise_tukeyhsd(weights, labels, 0.05)
print(tukey_results)
# Terrier and Whippet
poodle_colors = fm.get_color("poodle")
shihtzu_colors = fm.get_color("shihtzu")
color_table = [[np.count_nonzero(poodle_colors == "black"),np.count_nonzero(shihtzu_colors == "black")],[np.count_nonzero(poodle_colors == "brown"),np.count_nonzero(shihtzu_colors == "brown")],[np.count_nonzero(poodle_colors == "gold"),np.count_nonzero(shihtzu_colors == "gold")],[np.count_nonzero(poodle_colors == "grey"),np.count_nonzero(shihtzu_colors == "grey")], [np.count_nonzero(poodle_colors == "white"),np.count_nonzero(shihtzu_colors == "white")]]
tstats, pvalue, dfr, ef = chi2_contingency(color_table)
print(pvalue)
# Significant. pvalue < 0.05
if weight_pval < 0.05:
print('There is a difference between these breeds!')
else:
print('There is no difference between these breeds.')
from statsmodels.stats.multicomp import pairwise_tukeyhsd
weight_pw = np.concatenate(
[whippet_avg_weight, terrier_avg_weight, pitbull_avg_weight])
weight_labels = ['whippet'] * len(whippet_avg_weight) + ['terrier'] * len(
terrier_avg_weight) + ['pitbull'] * len(pitbull_avg_weight)
tukey_results = pairwise_tukeyhsd(weight_pw, weight_labels, 0.05)
print(tukey_results)
poodle_colors = fetchmaker.get_color('poodle')
shihtzu_colors = fetchmaker.get_color('shihtzu')
color_table = [[
np.count_nonzero(poodle_colors == 'black'),
np.count_nonzero(shihtzu_colors == 'black')
],
[
np.count_nonzero(poodle_colors == 'brown'),
np.count_nonzero(shihtzu_colors == 'brown')
],
[
np.count_nonzero(poodle_colors == 'gold'),
np.count_nonzero(shihtzu_colors == 'gold')
],
[
np.count_nonzero(poodle_colors == 'grey'),
print pval, num_whippet_rescues, num_whippets
from scipy.stats import f_oneway
a = fetchmaker.get_weight("whippet")
b = fetchmaker.get_weight("terrier")
c = fetchmaker.get_weight("pitbull")
fstat, pval2 = f_oneway(a, b, c)
print pval2
from statsmodels.stats.multicomp import pairwise_tukeyhsd
values = np.concatenate([a, b, c])
labels = ['whippet'] * len(a) + ['terrier'] * len(b) + ['pitbull'] * len(c)
print pairwise_tukeyhsd(values, labels, .05)
poodle_colors = fetchmaker.get_color("poodle")
shihtzu_colors = fetchmaker.get_color("shihtzu")
from scipy.stats import chi2_contingency
color_table = [[
np.count_nonzero(poodle_colors == "black"),
np.count_nonzero(shihtzu_colors == "black")
],
[
np.count_nonzero(poodle_colors == "brown"),
np.count_nonzero(shihtzu_colors == "brown")
],
[
np.count_nonzero(poodle_colors == "gold"),
np.count_nonzero(shihtzu_colors == "gold")
