Joomi K

%matplotlib inline 
import numpy as np
import pandas as pd
from collections import defaultdict
import csv
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.plotly as py
import plotly.tools as tls
from ggplot import *

def make_int(i):
    """
    Make strings into integers
    """
    if i == '':
        return None
    else:
        return int(i)
    
def group_data(data, key_name):
    """
    Building a dict of lists
    """
    grouped_data = defaultdict(list)
    for data_point in data:
        key = data_point[key_name]
        grouped_data[key].append(data_point)
    return grouped_data

def sum_grouped_items(grouped_data, field_name):
    summed_data = {}
    for key, data_points in grouped_data.items():
        total = 0
        for data_point in data_points:
            total += data_point[field_name]
        summed_data[key] = total
    return summed_data

def cleanup(dic):
    """
    Make string years into integers and sort (by year)
    """
    year, total = zip(*(sorted(dic.items())))
    return list(map(int,year)), total

def describe_data(data):
    print('Mean:', np.mean(data))
    print('Std:', np.std(data))
    print('Min:', np.min(data))
    print('Max:', np.max(data))
    
def percent_of_total(specific,total):
    """Get percent of total"""
    return (specific/total)*100

with open('fatality_monthly.csv') as f:
    reader = csv.DictReader(f)
    records = list(reader)

for record in records:
    record['Fatalities'] = make_int(record['Fatalities'])
    record['PedFatalit'] = make_int(record['PedFatalit'])
    record['BikeFatali'] = make_int(record['BikeFatali'])
    record['MVOFatalit'] = make_int(record['MVOFatalit'])

# Group by year
accidents_by_year = group_data(records, 'YR') 

# Deleting 2016 because we don't have complete records.
del accidents_by_year['2016']

total_by_year = sum_grouped_items(accidents_by_year, 'Fatalities')
total_bike_by_year = sum_grouped_items(accidents_by_year, 'BikeFatali')
total_ped_by_year = sum_grouped_items(accidents_by_year, 'PedFatalit')
total_mvo_by_year = sum_grouped_items(accidents_by_year, 'MVOFatalit')

total_fatalities = total_by_year.values()
describe_data(list(total_fatalities))

total_by_year_list = cleanup(total_by_year)

plt.bar(range(len(total_by_year_list[0])),total_by_year_list[1],align='center')
plt.title('Total fatalities by year')
plt.ylabel('fatalities')
plt.xlabel('year')
plt.xticks(range(len(total_by_year_list[0])),total_by_year_list[0])

total_bike_by_year_list = cleanup(total_bike_by_year)

plt.bar(range(len(total_bike_by_year_list[0])),total_bike_by_year_list[1],align='center')
plt.xticks(range(len(total_bike_by_year_list[0])),total_bike_by_year_list[0])
plt.title('Total bike fatalities by year')
plt.xlabel('year')
plt.ylabel('bike fatalities')

total_ped_by_year_list = cleanup(total_ped_by_year)

plt.bar(range(len(total_ped_by_year_list[0])),total_ped_by_year_list[1],align='center')
plt.xticks(range(len(total_ped_by_year_list[0])),total_ped_by_year_list[0])

plt.title('Total pedestrianfatalities by year')
plt.xlabel('year')
plt.ylabel('pedestrian fatalities')

total_mvo_by_year_list = cleanup(total_mvo_by_year)
plt.bar(range(len(total_mvo_by_year_list[0])),total_mvo_by_year_list[1],align='center')
plt.xticks(range(len(total_mvo_by_year_list[0])),total_mvo_by_year_list[0])

plt.title('Total pedestrian fatalities by year')
plt.xlabel('year')
plt.ylabel('pedestrian fatalities')

total_year_s = pd.Series(total_by_year)
total_bike_year_s = pd.Series(total_bike_by_year)
total_ped_year_s = pd.Series(total_ped_by_year)
total_mvo_year_s = pd.Series(total_mvo_by_year)

bike_percent = percent_of_total(total_bike_year_s, total_year_s)
ped_percent = percent_of_total(total_ped_year_s, total_year_s)
mvo_percent = percent_of_total(total_mvo_year_s, total_year_s)

print(bike)
print(ped)
print(mvo)

# Check that the percentanges add to 100
bike_pct = pd.Series(bike_percent)
ped_pct = pd.Series(ped_percent)
mvo_pct = pd.Series(mvo_percent)

bike_pct + ped_pct + mvo_pct

multiple_bars=plt.figure()
x=[2009,2010,2011,2012,2013,2014,2015]
ind=np.arange(len(x))
bike=bike_percent
ped=ped_percent
mvo=mvo_percent

ax.set_title('Percentage of fatalities by type')

ax=plt.subplot(111)
ax.bar(ind-.2, ped, width=.2, color='r', align='center', label='ped')
ax.bar(ind, mvo, width=.2, color='g', align='center', label='MV')
ax.bar(ind+.2, bike, width=.2, color='b', align='center', label='bike')

ax.set_xticks(ind)
ax.set_xticklabels(x)
ax.set_ylabel('Percentage of total')
ax.set_xlabel('Year')
ax.legend(loc='best')

fatalities = pd.read_csv('fatality_monthly.csv')

# Delete all 2016 entries because they're not complete 
fatalities_subset = fatalities[fatalities.YR !=2016]

# Group by year
grouped_year = fatalities_subset.groupby('YR')

df = pd.DataFrame({'nodeX':fatalities_subset.loc[:,'nodeX'],
                   'nodeY':fatalities_subset.loc[:,'nodeY'],
                   'YR':fatalities_subset.loc[:,'YR']})

# NYC map of fatalitites from 2009-2015
print(ggplot(aes(x='nodeX', y='nodeY', color='factor(YR)'), data=df) + geom_point())