def datelist(start_date, end_date) :
from datetime import date, timedelta as td
import datetime
import daily_weather as dw
import pandas as pd
import requests
d1 = end_date #End date
d2 = start_date #Start date
#A DarkSky.net api key is needed to be input.
status = False
while status == False:
apikey= str(input('Enter your Dark Sky APIkey:'))
url = 'https://api.forecast.io/forecast/'+apikey+'/41.878311,-87.616342,'+datetime.date.today().isoformat()+'T00:00:00'
r = requests.get(url)
status = r.status_code == requests.codes.ok
#Determine how many days are between the end date and start date
delta= d1-d2
d = pd.DataFrame() #Create a data fame d
'''
This function goes into each day in the date range, uses daily_weather.py
to grab the information that is needed to run the analyses in the future,
and places it in the 'd' data frame.
'''
for i in range(delta.days+1) :
day =d2+td(days=i) #Getting the specific day
#create the datetime format needed for the darksky.net api.
da =day.isoformat()+'T00:00:00'
#run the daily_weather function that returns the
#weather for a specific day.
data = dw.daily_weather(lat= '41.878311', long='-87.616342',date=da,apikey=apikey)
d.loc[i,'Date'] = day.isoformat() #Add the date to the d data frame.
#Put all the weather information from dark sky onto the d dataframe
for j in data :
d.loc[i,j] = data.loc[0,j]
#Put data frame d into 2016_weather.csv
d.to_csv(d2.isoformat()+'_to_'+d1.isoformat()+'.csv',index=False)
return d
#Grab the Latitude of the beach we are going to be pulling the weather for
lattitude = str(df.loc[i,'lat'])
#Grab the Latitude of the beach we are going to be pulling the weather for
longitude = str(df.loc[i,'long'])
for day in range(5) :#for each of the past 5 days at the beach/row.
if day == 0:
#Find the day we are grabbing the weather for
d = pd.to_datetime(df.loc[i,'Full_date'],format = '%Y-%m-%d')+datetime.timedelta(days=day+1)
else :
#Find the day we are grabbing the weather for
d = pd.to_datetime(df.loc[i,'Full_date'],format = '%Y-%m-%d')-datetime.timedelta(days=day-1)
#Strip the date and time we found for d
d = datetime.datetime.strptime(str(d),'%Y-%m-%d %H:%M:%S')
#Grab the weather for the day
weather = dw.daily_weather(lat= lattitude, long=longitude,date=d.strftime("%Y-%m-%dT%H:%M:%S"),apikey=apikey)
#Fill in the weather variables needed for the day that you are predicting
if day == 0 :
for item in newcols_today :
if item =='humidity_hour_4' :
df.loc[i,item]= weather.loc[0,'4.humidity.hourly']
elif item == 'pressure_hour_0' :
df.loc[i,item]= weather.loc[0,'0.pressure.hourly']
elif item == 'temperature_hour_0' :
df.loc[i,item]= weather.loc[0,'0.temperature.hourly']
elif item == 'temperature_hour_1' :
df.loc[i,item]= weather.loc[0,'1.temperature.hourly']
elif item == 'temperature_hour_2' :
df.loc[i,item]= weather.loc[0,'2.temperature.hourly']
elif item == 'temperature_hour_3' :