import sys
from scipy.stats.mstats import f_oneway
import pandas as pd
data = pd.read_csv(sys.argv[1])
numbers = data.dtypes[data.dtypes == float].index
groups = data.dtypes[data.dtypes != float].index
pd.set_printoptions(max_columns=100)
for group in groups:
grouped = data.groupby(group)
ave = grouped.mean()
ave['#'] = data[group].value_counts()
for number in numbers:
F, prob = f_oneway(*grouped[number].values)
ave = ave[ave[number].notnull() & (ave['#'] > 10)]
if len(ave):
print '\n%s by %s: %0.3f' % (number, group, prob)
print ave.sort(number, ascending=False).head(10)
def test_result_attributes(self):
a = np.array([655, 788], dtype=np.uint16)
b = np.array([789, 772], dtype=np.uint16)
res = mstats.f_oneway(a, b)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
a = np.array([655, 788], dtype=np.uint16)
b = np.array([789, 772], dtype=np.uint16)
res = mstats.f_oneway(a, b)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
import sys
from scipy.stats.mstats import f_oneway
import pandas as pd
data = pd.read_csv(sys.argv[1])
numbers = data.dtypes[data.dtypes == float].index
groups = data.dtypes[data.dtypes != float].index
pd.set_printoptions(max_columns=100)
results = []
for group in groups:
grouped = data.groupby(group)
ave = grouped.mean()
ave['#'] = data[group].value_counts()
for number in numbers:
F, prob = f_oneway(*grouped[number].values)
improvement = ave[number].max() / data[number].mean() - 1
if prob < .05:
ave = ave[ave[number].notnull() & (ave['#'] > 10)]
results.append([group, number, improvement, prob,
ave.sort(number, ascending=False)])
for group, number, improvement, prob, ave in sorted(results, key=lambda v: v[2], reverse=True):
print '\n%s by %s: %0.1f%% (%0.3f)' % (number, group, 100 * improvement, prob)
# print '-' * 80
# print ave.head(10)
# if len(ave) > 20:
# print ave.tail(10)