Using `prodb`

1. Requests to metaweather open weather API

get_json_from_query[source]

get_json_from_query(location)

Search for a city and return metadata from API

l = get_json_from_query('London')
l

{'title': 'London',
 'location_type': 'City',
 'woeid': 44418,
 'latt_long': '51.506321,-0.12714'}

Check woeid(Where On Earth ID) is correct for London

assert l['woeid'] == 44418

Get weather data for the London woeid

utc = arrow.utcnow().format('YYYY/MM/DD')
utc

'2021/12/30'

get_current_weather[source]

get_current_weather(location)

inputs: location str "London" to find woeid i.e. 44418

Today's forecast 🌤️

%%time
res = get_current_weather('London')
res['consolidated_weather'][0]

CPU times: user 31.2 ms, sys: 0 ns, total: 31.2 ms
Wall time: 1.48 s

{'id': 6292426705600512,
 'weather_state_name': 'Heavy Cloud',
 'weather_state_abbr': 'hc',
 'wind_direction_compass': 'SW',
 'created': '2021-12-30T12:59:01.380456Z',
 'applicable_date': '2021-12-30',
 'min_temp': 12.665,
 'max_temp': 14.465,
 'the_temp': 14.274999999999999,
 'wind_speed': 9.857931369964739,
 'wind_direction': 225.0,
 'air_pressure': 1013.0,
 'humidity': 89,
 'visibility': 7.299868766404199,
 'predictability': 71}

There are a number of metrological properties available to us from the API.

The ones we are most interested in are:

  • the_temp 🌡️ current temperature
  • weather_state_name ⛅ current sky condition
  • max_temp 🥵 daily max temp
  • min_temp 🥶 daily min temp
res = get_current_weather('London')
df =  pd.DataFrame.from_records(res['consolidated_weather'][:1])
df['readable_time'] = df.created.apply(lambda x: arrow.get(x).humanize())
df
id weather_state_name weather_state_abbr wind_direction_compass created applicable_date min_temp max_temp the_temp wind_speed wind_direction air_pressure humidity visibility predictability readable_time
0 6292426705600512 Heavy Cloud hc SW 2021-12-30T12:59:01.380456Z 2021-12-30 12.665 14.465 14.275 9.857931 225.0 1013.0 89 7.299869 71 2 hours ago

df_from_loc[source]

df_from_loc(location, days_ahead=1, keep_cols=['location', 'the_temp', 'readable_time', 'created', 'applicable_date', 'local_time', 'latt_long', 'weather_state_name', 'min_temp', 'max_temp'])

df = df_from_loc('London', days_ahead=2)
df
location the_temp readable_time created applicable_date local_time latt_long weather_state_name min_temp max_temp
0 London 14.275 2 hours ago 2021-12-30T12:59:01.380456Z 2021-12-30 15:39 51.506321,-0.12714 Heavy Cloud 12.665 14.465
1 London 14.210 2 hours ago 2021-12-30T12:59:01.869683Z 2021-12-31 15:39 51.506321,-0.12714 Light Rain 11.015 14.810

Example output showing location and current weather situation

df = df_from_loc('Auckland')
df
location the_temp readable_time created applicable_date local_time latt_long weather_state_name min_temp max_temp
0 Auckland 21.535 2 hours ago 2021-12-30T13:37:04.460044Z 2021-12-31 04:39 -36.884109,174.770416 Heavy Cloud 16.885 22.74

🦄 Pro db 

import sys
sys.path.append('../')

from prodb.core import generate_db, insert_row

Initialise empty db 

dbpath = 'weather_db.csv'
cols = 'location temp high low weather_state'.split()

generate_db(cols=cols, dbpath=dbpath)
df = pd.read_csv(dbpath)
df.head()

✓💾 weather_db.csv (37 kb)
location temp high low weather_state

Add row 

dx = df_from_loc('Auckland')
dx
location the_temp readable_time created applicable_date local_time latt_long weather_state_name min_temp max_temp
0 Auckland 21.535 2 hours ago 2021-12-30T13:37:04.460044Z 2021-12-31 04:41 -36.884109,174.770416 Heavy Cloud 16.885 22.74

visit_city[source]

visit_city(df, cities, dbpath)

df = visit_city(df, ['Christchurch', 'Wellington', 'Lagos', 'Zagreb'], dbpath)
display(df)
location temp high low weather_state latlong local_time time_utc
0 Christchurch 17.2 19.0 12.8 Heavy Rain -43.527519,172.635422 04:41 2021-12-30 15:41:55
1 Wellington 19.0 20.8 14.6 Heavy Cloud -41.283779,174.787979 04:41 2021-12-30 15:41:55
2 Lagos 32.3 34.2 24.1 Light Cloud 6.439180,3.423480 16:41 2021-12-30 15:41:57
3 Zagreb 7.4 8.9 4.3 Light Rain 45.807259,15.967600 16:41 2021-12-30 15:41:58 
df = visit_city(df, 'Yangon', dbpath)
display(df)
location temp high low weather_state latlong local_time time_utc
0 Christchurch 17.2 19.0 12.8 Heavy Rain -43.527519,172.635422 04:41 2021-12-30 15:41:55
1 Wellington 19.0 20.8 14.6 Heavy Cloud -41.283779,174.787979 04:41 2021-12-30 15:41:55
2 Lagos 32.3 34.2 24.1 Light Cloud 6.439180,3.423480 16:41 2021-12-30 15:41:57
3 Zagreb 7.4 8.9 4.3 Light Rain 45.807259,15.967600 16:41 2021-12-30 15:41:58
4 Yangon 30.5 32.0 20.9 Light Cloud 16.803890,96.154694 22:12 2021-12-30 15:42:02 
df = visit_city(df, ['Singapore', 'Alexandria', 'Bangkok'], dbpath)
display(df)
location temp high low weather_state latlong local_time time_utc
0 Christchurch 17.2 19.0 12.8 Heavy Rain -43.527519,172.635422 04:41 2021-12-30 15:41:55
1 Wellington 19.0 20.8 14.6 Heavy Cloud -41.283779,174.787979 04:41 2021-12-30 15:41:55
2 Lagos 32.3 34.2 24.1 Light Cloud 6.439180,3.423480 16:41 2021-12-30 15:41:57
3 Zagreb 7.4 8.9 4.3 Light Rain 45.807259,15.967600 16:41 2021-12-30 15:41:58
4 Yangon 30.5 32.0 20.9 Light Cloud 16.803890,96.154694 22:12 2021-12-30 15:42:02
5 Singapore 28.9 30.1 24.9 Heavy Rain 1.293780,103.853256 23:42 2021-12-30 15:42:13
6 Alexandria 16.5 17.7 13.3 Light Rain 31.210489, 29.912430 17:42 2021-12-30 15:42:14
7 Bangkok 32.4 32.1 25.0 Heavy Cloud 13.753330,100.504822 22:42 2021-12-30 15:42:15