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)