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from datetime import datetime
import requests
import os
import joblib
import pandas as pd
import json
def get_weather_by_date(date):
return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{date}?unitGroup=metric&include=days&key=J7TT2WGMUNNHD8JBEDXAJJXB2&contentType=json').json()
def get_weather_df(data):
col_names = [
'name',
'datetime',
'tempmax',
'tempmin',
'temp',
'feelslikemax',
'feelslikemin',
'feelslike',
'dew',
'humidity',
'precip',
'precipprob',
'precipcover',
'snow',
'snowdepth',
'windgust',
'windspeed',
'winddir',
'sealevelpressure',
'cloudcover',
'visibility',
'solarradiation',
'solarenergy',
'uvindex',
'conditions'
]
new_data = pd.DataFrame(
data,
columns=col_names
)
new_data.datetime = new_data.datetime.apply(timestamp_2_time1)
#new_data.rename(columes={'pressure':'sealevelpressure'})
return new_data
def timestamp_2_time1(x):
dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def timestamp_2_time(x):
dt_obj = datetime.strptime(str(x), '%m/%d/%Y')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
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