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update.py
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# Created by Era Iyer
# June 2020
# update.py file
# parses through csv NYT data, creates json file with state, dates, total cases,
# new cases, and rolling average cases
# also, determines appropriate color representation based on data
# regarding daily new coronavirus cases
#
# winning = green
# nearly there = orange
# needs action = red
#
# methods to determine color representation:
# 1. green --> current average < 10 OR current average < 20 and current avrage < 0.5*peak
# 2. orange --> current average < 1.5*20 and current average < peak*0.5
# OR current average < peak*0.2
# 3. red --> all other cases
import csv
import json
import requests
from datetime import datetime
import numpy as np
import pandas as pd
us_states = ["Alaska", "Alabama", "Arkansas", "American Samoa", "Arizona", "California", "Colorado", "Connecticut",
"District of Columbia", "Delaware", "Florida", "Georgia", "Guam", "Hawaii", "Iowa", "Idaho", "Illinois",
"Indiana", "Kansas", "Kentucky", "Louisiana", "Massachusetts", "Maryland", "Maine", "Michigan", "Minnesota",
"Missouri", "Mississippi", "Montana", "North Carolina", "North Dakota", "Nebraska", "New Hampshire",
"New Jersey", "New Mexico", "Nevada", "New York", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Puerto Rico",
"Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Virginia", "Virgin Islands",
"Vermont", "Washington", "Wisconsin", "West Virginia", "Wyoming"]
us_states_codes = ["AK", "AL", "AR", "AS", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", "GA", "GU", "HI", "IA",
"ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", "MO", "MS", "MT", "NC",
"ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", "PR", "RI", "SC", "SD",
"TN", "TX", "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"]
#new_hospitalizations = []
stateIDs = []
#datesHospitalizations = []
# url = "https://covidtracking.com/api/v1/states/daily.csv"
# data = pd.read_csv(url)
# for index, row in data.iterrows():
# currDate = datetime.strptime(str(row['date']), '%Y%m%d').date().isoformat()
# datesHospitalizations.append(currDate)
# stateIDs.append(row['state'])
# new_hospitalizations.append(row['hospitalizedCurrently'])
#print(datesHospitalizations)
dates = []
states = []
total_cases = []
total_deaths = []
url = "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"
data = pd.read_csv(url)
for index, row in data.iterrows():
dates.append(row['date'])
states.append(row['state'])
total_cases.append(row['cases'])
total_deaths.append(row['deaths'])
arr = []
for idx, val in enumerate(us_states):
total_cases_state = []
total_deaths_state = []
#state_dict = {"state":val, "dates": [], "new_cases": [], "avg_cases": [], "new_deaths": [], "avg_deaths": [], "hospDates": [], "new_hospitalizations": [], "avg_hospitalizations": []}
state_dict = {"state":val, "dates": [], "new_cases": [], "avg_cases": [], "new_deaths": [], "avg_deaths": []}
for i, states_name in enumerate(states):
if states[i] == val:
state_dict["dates"].append(dates[i])
# state_dict["total_cases"].append(total_cases[i])
# state_dict["total_deaths"].append(total_deaths[i])
total_cases_state.append(total_cases[i])
total_deaths_state.append(total_deaths[i])
# for i, state_id in enumerate(stateIDs):
# if(state_id == us_states_codes[idx] and not(np.isnan(new_hospitalizations[i]))):
# state_dict["hospDates"].append(datesHospitalizations[i])
# state_hospitalization = 0
# if(new_hospitalizations[i] < 0):
# state_dict["new_hospitalizations"].append(state_hospitalization)
# else:
# state_dict["new_hospitalizations"].append(new_hospitalizations[i])
for i, case_val in enumerate(total_cases_state):
new_cases = 0
if i > 0:
new_cases = total_cases_state[i] - total_cases_state[i-1]
if new_cases < 0:
new_cases = 0
#special case, fixing error from Michigan data on 6/5
if state_dict["state"] == 'Michigan' and state_dict["dates"][i] == '2020-06-05':
new_cases = 284
if state_dict["state"] == 'North Carolina' and state_dict["dates"][i] == '2020-09-25':
new_cases = 1693
state_dict["new_cases"].append(new_cases)
# for i, death_val in enumerate(state_dict["total_deaths"]):
for i, death_val in enumerate(total_deaths_state):
new_deaths = 0
if i > 0:
# new_deaths = state_dict["total_deaths"][i] - state_dict["total_deaths"][i-1]
new_deaths = total_deaths_state[i] - total_deaths_state[i-1]
if new_deaths < 0:
new_deaths = 0
state_dict["new_deaths"].append(new_deaths)
window = 7
numbers_series = pd.Series(state_dict["new_cases"], dtype="float64")
avg_series = numbers_series.rolling(window=window, min_periods=1, center=True).mean()
moving_averages_list = avg_series.tolist()
state_dict["avg_cases"] = moving_averages_list
numbers_series = pd.Series(state_dict["new_deaths"], dtype="float64")
avgDeath_series = numbers_series.rolling(window=window, min_periods=1, center=True).mean()
moving_averages_list = avgDeath_series.tolist()
state_dict["avg_deaths"] = moving_averages_list
# numbers_series = pd.Series(state_dict["new_hospitalizations"], dtype="float64")
# avgHosp_series = numbers_series.rolling(window=window, min_periods=1, center=True).mean()
# moving_averages_list = avgHosp_series.tolist()
# state_dict["avg_hospitalizations"] = moving_averages_list
arr.append(state_dict)
#result.json stores the states, dates, and corresponding values
with open('result.json', 'w') as fp:
json.dump(arr, fp)
#parallel arrays to store json information
province = []
values = []
countries = []
setColors = []
allWeekValues = []
peakCases = []
all_moving_averages = []
F0 = 0.5
F1 = 0.2
N0 = 20
colors = ["green", "orange", "red"]
daysToAverage = 7
with open('./result.json') as f:
data = json.load(f)
for i in data:
#filling parallel arrays with province, values, country
province.append(i['state'])
values.append(i['new_cases'])
all_moving_averages.append(i['avg_cases'])
#another parallel array that stores each province's max number of cases
for i in range(len(all_moving_averages)):
max = 0
for j in range(len(all_moving_averages[i])):
if(all_moving_averages[i][j]>max):
max = all_moving_averages[i][j]
peakCases.append(max)
#simplifies values array to hold just values from the past week (per province/state)
for k in range(len(values)):
#gets values from last 7 days
start = len(values[k])-daysToAverage
end = len(values[k])
stateWeekVals = []
if(start < 0): #edge case, if data has less than 7 values
start = 0
for i in range(start, end):
stateWeekVals.append(values[k][i])
#array of array of new cases per week per state
allWeekValues.append(stateWeekVals)
for i in range(len(allWeekValues)):
sum = 0
for k in range(len(allWeekValues[i])):
sum += allWeekValues[i][k]
average = sum/daysToAverage
# if((average < (N0 * F0)) or ((average < N0) and (average < (peakCases[i]*F0)))):
if(average< N0):
setColors.append(colors[0])
elif(((average < (1.5*N0)) and (average < (peakCases[i]*F0))) or (average < (peakCases[i]*F1))):
setColors.append(colors[1])
else:
setColors.append(colors[2])
with open('USStateColors.csv', 'w', newline='') as file:
fieldnames = ['state', 'color']
writer = csv.DictWriter(file, fieldnames=fieldnames)
writer.writeheader()
for i in range(len(province)):
writer.writerow({'state': province[i], 'color': setColors[i]})