In [3]:

    

from __future__ import print_function, division
%matplotlib inline

import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

import json
import numpy as np
import os


    

In [4]:

    

with open(os.path.join("reviews.json")) as f:
    reviews = json.load(f)


    

In [5]:

    

hours_played_v_helpfulness = list()
for app in reviews:
    for review in reviews[app]:
        if review["total"] > 0 and review["on_record"] > 1:
            hours_played_v_helpfulness.append((review["num_reviews"], review["helpful"] / review["total"]))
hours, helpfulness = np.array(hours_played_v_helpfulness).T

_, bins = np.histogram(hours, bins=20)
score = np.zeros(bins.shape)
for i in xrange(len(bins) - 1):
    score[i] = np.mean(helpfulness[np.logical_and(bins[i] <= hours, hours < bins[i+1])])
score[-1] = np.mean(helpfulness[bins[-1] <= hours])

with PdfPages('num_reviews_v_helpfulness.pdf') as pdf:
    fig = plt.figure(figsize=(8,4))
    plt.bar(bins, score, width=np.max(bins)/len(bins))
    plt.xlabel("Number of reviews by user")
    plt.ylabel("% found helpful")
    pdf.savefig(fig)


    

    




C:\Program Files\Anaconda2\envs\cs4300\lib\site-packages\numpy\core\_methods.py:59: RuntimeWarning: Mean of empty slice.
  warnings.warn("Mean of empty slice.", RuntimeWarning)
C:\Program Files\Anaconda2\envs\cs4300\lib\site-packages\numpy\core\_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)






    

In [7]:

    

hours_played_v_helpfulness = list()
for app in reviews:
    for review in reviews[app]:
        if review["total"] > 0 and review["on_record"] > 1:
            hours_played_v_helpfulness.append((review["on_record"], review["helpful"] / review["total"]))
hours, helpfulness = np.array(hours_played_v_helpfulness).T

_, bins = np.histogram(hours, bins=20)
score = np.zeros(bins.shape)
for i in xrange(len(bins) - 1):
    score[i] = np.mean(helpfulness[np.logical_and(bins[i] <= hours, hours < bins[i+1])])
score[-1] = np.mean(helpfulness[bins[-1] <= hours])

with PdfPages('hours_played_v_helpfulness.pdf') as pdf:
    fig = plt.figure(figsize=(8,4))
    plt.bar(bins, score, width=np.max(bins)/len(bins))
    plt.xlabel("Hours played")
    plt.ylabel("% found helpful")
    pdf.savefig(fig)


    

In [ ]: