In [1]:
from __future__ import division
from scipy import stats
import random, pymongo
import sklearn.metrics as metrics
import matplotlib.pyplot as plt
import pandas as pd
import rpy2.robjects as robjects
%matplotlib inline
In [2]:
r = robjects.r
In [3]:
connection = pymongo.MongoClient('localhost', 27017)
results_db = connection['results']['question_3']
cursor = results_db.find({}, {u'_id': False, u'community':True,
u'frequency_pvalue':True})
stats_df = pd.DataFrame(list(cursor))
In [5]:
for index, row in stats_df.iterrows():
community = row['community']
community_db = connection[community]['statistics']
cursor = community_db.find({'contributions_total': {'$gt':0}},
{u'_id': False, u'activity_freq':True, u'gender':True})
df = pd.DataFrame(list(cursor))
females = df.query("gender == 'Female'")
males = df.query("gender == 'Male'")
frequency = r['wilcox.test'](robjects.FloatVector(list(females['activity_freq'])),
robjects.FloatVector(list(males['activity_freq'])),
alternative="g", correct=True, exact=False)[2][0]
results_db.update({'community': community}, {'$set': {'frequency_pvalue_greater': frequency}})
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