from matplotlib import pyplot as plt
from matplotlib import pylab
import seaborn as sns
# this code
from passing_vs_notpassing import print_columns
from passing_vs_notpassing import Statistics
from utilities import list_to_account_dict
from utilities import read_pickle_3
from utilities import total_engagement_attribute
#pylab.rcParams['figure.figsize'] = (10.0, 8.0)
# plt.style.use('ggplot')
# these are total_minutes_visited
passed = read_pickle_3('passed')
distinctive = read_pickle_3('distinctive')
non_passing = read_pickle_3('non_passing')
paid_engagements = read_pickle_3('paid_engagements')
paid_submissions = read_pickle_3('paid_submissions')
passing_submissions = read_pickle_3('passing_submissions')
passing_engagement = read_pickle_3('passing_engagement')
passed_engagement = read_pickle_3('passed_engagement')
distinctive_engagement = read_pickle_3('distinctive_engagement')
non_passing_engagement = read_pickle_3('non_passing_engagement')
subway_submissions = read_pickle_3('subway_submissions')
passed = pandas.DataFrame({'passed': passed})
# python standard library
from collections import namedtuple
import csv
import pickle
# third-party
import numpy
# this code
from utilities import read_pickle_3, write_pickle
PWEAVE = __name__ in ('builtins', '__builtin__')
passed_engagement = read_pickle_3('passed_engagement')
passing_engagement = read_pickle_3('passing_engagement')
distinctive_engagement = read_pickle_3('distinctive_engagement')
non_passing_engagement = read_pickle_3('non_passing_engagement')
for collection in (passed_engagement, passing_engagement, distinctive_engagement, non_passing_engagement):
assert b'total_minutes_visited' in collection[0],\
"'total_minutes_visited' not in {0}".format(collection[0])
def get_columns(engagements, column):
return numpy.array([engagement[column] for engagement in engagements])
Quartiles = namedtuple("Quartiles", 'q1 median q3 iqr'.split())
def quartiles(array):
q1 = numpy.percentile(array, 25)
median = numpy.median(array)
q3 = numpy.percentile(array, 75)
