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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) | |