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analyzer.py
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analyzer.py
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from collections import OrderedDict
from pip._vendor.requests.packages.urllib3.packages import ordered_dict
__author__ = 'aoden'
import pandas as pd
from pandas import DataFrame
import matplotlib
import matplotlib.pyplot as plt
matplotlib.matplotlib_fname()
matplotlib.get_backend()
import threading
import numpy as np
df_2008_pollster = pd.read_csv('2008-polls.csv')
df_2012_pollster = pd.read_csv('2012-polls.csv')
df_2008_result = pd.read_csv('2008-results.csv')
df_2012_result = pd.read_csv('2012-results.csv')
df_2008_pollster = DataFrame(df_2008_pollster)
df_2012_pollster = DataFrame(df_2012_pollster)
pollsters_2008 = df_2008_pollster['Pollster'].tolist()
states_2008 = df_2008_pollster['State'].tolist()
# print(df_2008_pollster[(df_2008_pollster.Pollster == 'Rasmussen') & (df_2008_pollster.State == 'AL')])
unique_2008_pollster = pd.unique(df_2008_pollster.Pollster.ravel())
unique_2008_state = pd.unique(df_2008_pollster.State.ravel())
unique_2012_state = pd.unique(df_2012_pollster.State.ravel())
unique_2012_pollster = pd.unique(df_2012_pollster.Pollster.ravel())
# print unique_2008_pollster
dict_pollster_2008 = OrderedDict()
dict_pollster_state_2008 = OrderedDict()
dict_pollster_per_state_2008 = OrderedDict()
dict_pollster_change = OrderedDict()
dict_pollster_per_state_percentage_change = OrderedDict()
dict_pollster_result_est_2008 = OrderedDict()
dict_pollster_result_est_2012 = OrderedDict()
dict_pollster_2012 = OrderedDict()
dict_pollster_state_2012 = OrderedDict()
dict_pollster_per_state_2012 = OrderedDict()
for state in unique_2008_state:
dict_pollster_per_state_2008[state] = len(pd.unique(df_2008_pollster[df_2008_pollster['State'] == state].Pollster))
for state in unique_2012_state:
dict_pollster_per_state_2012[state] = len(pd.unique(df_2012_pollster[df_2012_pollster['State'] == state].Pollster))
for state in unique_2008_state:
default = 0
original = dict_pollster_per_state_2008[state]
dict_pollster_per_state_percentage_change[state] = (dict_pollster_per_state_2012.get(state, default) - original) * 100 / original
for state in unique_2012_state:
dict_pollster_per_state_2012[state] = len(pd.unique(df_2012_pollster[df_2012_pollster['State'] == state].Pollster))
# print dict_pollster_per_state_2008
# print dict_pollster_per_state_2012
for pollster in unique_2008_pollster:
dict_pollster_2008[pollster] = df_2008_pollster[(df_2008_pollster.Pollster == pollster)]
dict_pollster_state_2008[pollster] = len(df_2008_pollster[df_2008_pollster['Pollster'] == pollster])
for pollster in unique_2012_pollster:
dict_pollster_2008[pollster] = df_2012_pollster[(df_2012_pollster.Pollster == pollster)]
dict_pollster_state_2012[pollster] = len(df_2012_pollster[df_2012_pollster['Pollster'] == pollster])
for pollster in unique_2008_pollster:
min = 100
for state in unique_2008_state:
results_temp = df_2008_pollster[(df_2008_pollster.Pollster == pollster) & (df_2008_pollster.State == state)]
actual_result_temp = df_2008_result[(df_2008_result.State == state)]
if not results_temp.empty:
avg = results_temp.Dem.ravel().mean()
actual = actual_result_temp.Dem.ravel().mean()
if (abs(actual - avg) < min):
min = abs(actual - avg)
dict_pollster_result_est_2008[state] = pollster
for pollster in unique_2012_pollster:
min = 100
for state in unique_2012_state:
results_temp = df_2012_pollster[(df_2012_pollster.Pollster == pollster) & (df_2012_pollster.State == state)]
actual_result_temp = df_2012_result[(df_2012_result.State == state)]
if not results_temp.empty:
avg = results_temp.Dem.ravel().mean()
actual = actual_result_temp.Dem.ravel().mean()
if (abs(actual - avg) < min):
min = abs(actual - avg)
dict_pollster_result_est_2012[state] = pollster
print(dict_pollster_result_est_2008)
print(dict_pollster_result_est_2012)
# # average = results_temp.mean()
# dict_pollster_result_est_2008[pollster] = results_temp
for pollster in unique_2012_pollster:
for state in unique_2012_state:
results_temp = df_2012_pollster[(df_2008_pollster.Pollster == pollster) & (df_2012_pollster.State == state)]
# average = sum(results_temp) / float(len(results_temp))
dict_pollster_result_est_2012[pollster] = results_temp
# print(dict_pollster_result_est_2008)
# print(dict_pollster_result_est_2012)
for pollster in dict_pollster_state_2008:
default = 0
original = dict_pollster_state_2008.get(pollster, default)
change = dict_pollster_state_2008.get(pollster, default) - dict_pollster_state_2012.get(pollster, default)
dict_pollster_change[pollster] = (change * 100 / original)
for pollster in dict_pollster_state_2012:
default = 0
original = dict_pollster_state_2012.get(pollster, default)
if dict_pollster_state_2012.get(pollster, default) > dict_pollster_state_2008.get(pollster, default):
change = dict_pollster_state_2012.get(pollster, default) - dict_pollster_state_2008.get(pollster, default)
else:
change = dict_pollster_state_2008.get(pollster, default) - dict_pollster_state_2012.get(pollster, default)
dict_pollster_change[pollster] = (change * 100 / original)
def plot(data_dict, title, ylabel, xlabel):
width = 0.3
plt.bar(np.arange(len(data_dict)), data_dict.values(), align='center')
plt.xticks(np.arange(len(data_dict) + width / 2), data_dict.keys(), rotation=60, size= 1)
plt.title(title, y=1.08)
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.grid(True)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
fig.savefig('test2png.png', dpi=1000)
plt.show()
# threading.Thread(target= plot(dict_pollster_state_2008, "Pollster occurences " + "2008", "Number of occurences", "Polsters")).start()
# threading.Thread(target= plot(dict_pollster_state_2012, "Pollster occurences " + "2012", "Number occurences", "Pollsters")).start()
# threading.Thread(target= plot(dict_pollster_change, "Pollster occurences" + " change", "Changes", "Pollsters")).start()
# threading.Thread(target= plot(dict_pollster_per_state_percentage_change, "Pollster number per state " + " change", "Changes(%)", "Pollsters")).start()
# threading.Thread(target= plot(dict_pollster_per_state_2008, "Number of pollster per state " + "2008", "Number of pollster", "States")).start()
# threading.Thread(target= plot(dict_pollster_per_state_2012, "Number of pollster per state " + "2012", "Number of pollster", "States")).start()
# print(dict_pollster_state_2008)
# print(dict_pollster_state_2012)
# print pd.unique(df_2012_pollster.Pollster.ravel())