In [1]:

    

with open('./my_key.key', 'r') as f:
    api_key = next(f)


    

In [2]:

    

import datetime
import requests
import urllib

import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline


    

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In [4]:

    

hist_base_url = 'https://api.worldweatheronline.com/free/v2/past-weather.ashx'
base_url = 'https://api.worldweatheronline.com/free/v2/weather.ashx'
url = '{base_url}?key={api_key}&q={location}&num_of_days=3&tp=3&format=json&cc=no&extra=utcDateTime'.format(
    base_url=base_url,
    api_key=api_key,
    location=urllib.parse.quote('Köln'),
)
#dates in yyyy-MM-dd
hist_url = '{base_url}?key={api_key}&q={location}&date={start}&enddate={end}&tp=3&format=json&cc=no&extra=utcDateTime'.format(
    base_url=hist_base_url,
    api_key=api_key,
    location='Aachen',
    start='2016-02-01',
    end='2016-03-01',
)


    

In [12]:

    

resp = requests.get(url)


    

In [13]:

    

data = resp.json()


    

In [14]:

    

list(data['data'].keys())


    

    
Out[14]:





['weather', 'request']

In [20]:

    

data['data']['request']


    

    
Out[20]:





[{'query': 'Koln, Germany', 'type': 'City'}]

In [15]:

    

#data['data']['current_condition']


    

In [25]:

    

#data['data']['weather'][0]


    

In [5]:

    

hist_resp = requests.get(hist_url)


    

In [6]:

    

hist_data = hist_resp.json()


    

In [7]:

    

list(hist_data['data']['weather'][0].keys())


    

    
Out[7]:





['uvIndex',
 'mintempF',
 'maxtempC',
 'astronomy',
 'hourly',
 'maxtempF',
 'date',
 'mintempC']

In [8]:

    

dts = []
vals = []
idxs = []
weather_descritpions = set()
for element in hist_data['data']['weather']:
    for hour_element in element['hourly']:
        year, month, day = list(map(int, hour_element.get('UTCdate').split('-')))
        time = int(hour_element.get('UTCtime'))
        hour, minute = time//100, time%100
        dt = datetime.datetime(year, month, day, hour, minute, 0)
        dts.append(dt)
        weather_descritpions.add(hour_element.get('weatherDesc')[0].get('value'))
        vals.append(float(hour_element.get('tempC')))
        idxs.append(float(hour_element.get('HeatIndexC')))


    

In [9]:

    

#hist_data['data']['weather']


    

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In [11]:

    

plt.figure(figsize=(15, 8))
plt.step(dts, vals)
#plt.step(dts, idxs)


    

    
Out[11]:





[<matplotlib.lines.Line2D at 0x29066e63a58>]






    

In [66]:

    

weather_descritpions


    

    
Out[66]:





{'Clear',
 'Cloudy',
 'Light drizzle',
 'Light rain',
 'Light rain shower',
 'Light sleet',
 'Light sleet showers',
 'Light snow',
 'Light snow showers',
 'Mist',
 'Moderate or heavy rain in area with thunder',
 'Moderate rain',
 'Overcast',
 'Partly Cloudy',
 'Patchy light drizzle',
 'Patchy light rain',
 'Patchy rain nearby',
 'Patchy sleet nearby',
 'Sunny',
 'Thundery outbreaks in nearby'}

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