Public data APIs#
How do we access amazing data? The answer is data APIs.
What is an API?#
API stands for Application Programming Interface and it serves as a data transmitter between two different pieces of software. The term has historically been used to describe any sort of connectivity interface to an application.
Today, the term API typically takes an additional meaning:
Modern APIs adhere to certain standards (typically HTTP and REST)
They are treated like products
They often adhere to high security standards
We usually have two interfaces for Software:
UI (User Interface) - humans talking to computers
API - computers talking to computers
Types of APIs#
By availability#
Open and public APIs are available to everyone, allowing anyone to access or perform operations on public datasets.
Internal APIs are used in-house by developers.
Partner APIs are a form of semi-open API where access is granted under certain conditions determined by the publisher.
By structure#
APIs also differ in architecture. The most popular API architectures are:
JSON-RPC and XML-RPC RPC stands for Remote Procedure Call and is a protocol for data transmission in JSON or XML format.
REST stands for representational state transfer. It’s a software architectural style that provides a set of recommendations for web development.
SOAP stands for Simple Object Access Protocol and is a definition of API protocols and standards.
GraphQL Graph Query Language, used for accessing highly interconnected data (e.g. GitHub)
Accessing a Public Data API with Python#
In order to retrieve data from an API we will combine the request library and the JSON library.
When we want to receive data from the API we first make a request.
To do so in python, we need to install the requests library.
import requests
Example: NASA’s APOD API#
Let’s test out one of NASA’s open APIs:
demo_key = "DEMO_KEY"
url = "https://api.nasa.gov/planetary/apod"
response = requests.get(url, params={"api_key": demo_key, "date": "2023-10-16"})
response
200 https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY&date=2023-10-16
headers: Access-Control-Allow-Origin: *
Access-Control-Expose-Headers: X-RateLimit-Limit, X-RateLimit-Remaining
Age: 0
Connection: keep-alive
Content-Encoding: gzip
Content-Type: application/json
Date: Wed, 25 Oct 2023 09:47:42 GMT
Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
Transfer-Encoding: chunked
Vary: Accept-Encoding
Via: http/1.1 api-umbrella (ApacheTrafficServer [cMsSf ])
X-Api-Umbrella-Request-Id: cbcotovkqsd1bir13org
X-Cache: MISS
X-Content-Type-Options: nosniff
X-Frame-Options: DENY
X-Ratelimit-Limit: 40
X-Ratelimit-Remaining: 39
X-Vcap-Request-Id: 076d6fe2-3d57-441b-6a5d-cf6737433b3c
X-XSS-Protection: 1; mode=block
body (application/json):
{'copyright': '\nJerry Zhang (left), \nBaolong Chen (photographer) & Amber Zhang (right)\n',
'date': '2023-10-16',
'explanation': 'She knew everything but the question. She was well aware that there would be a complete annular eclipse of the Sun visible from their driving destination: Lake Abert in Oregon. She knew that the next ring-of-fire eclipse would occur in the USA only in 16 more years, making this a rare photographic opportunity. She was comfortable with the plan: that she and her boyfriend would appear in front of the eclipse in silhouette, sometimes alone, and sometimes together. She knew that the annular phase of this eclipse would last only a few minutes and she helped in the many hours of planning. She could see their friend who set up the camera about 400 meters away at the bottom of a ridge. What she didn\'t know was the question she would be asked. But she did know the answer: "yes". Album: Selected eclipse images sent in to APOD',
'hdurl': 'https://apod.nasa.gov/apod/image/2310/AnnularProposal_Zhang_3500.jpg',
'media_type': 'image',
'service_version': 'v1',
'title': 'Eclipse Rings',
'url': 'https://apod.nasa.gov/apod/image/2310/AnnularProposal_Zhang_960.jpg'}
import json
data = response.json()
from IPython.display import HTML, Image, display
display(HTML(response.json()["explanation"]))
Image(url=data["url"], width=600)
She knew everything but the question. She was well aware that there would be a complete annular eclipse of the Sun visible from their driving destination: Lake Abert in Oregon. She knew that the next ring-of-fire eclipse would occur in the USA only in 16 more years, making this a rare photographic opportunity. She was comfortable with the plan: that she and her boyfriend would appear in front of the eclipse in silhouette, sometimes alone, and sometimes together. She knew that the annular phase of this eclipse would last only a few minutes and she helped in the many hours of planning. She could see their friend who set up the camera about 400 meters away at the bottom of a ridge. What she didn't know was the question she would be asked. But she did know the answer: "yes". Album: Selected eclipse images sent in to APOD
Key concepts:#
Get and post
The two most common requests we make are get and post:
GETis used for viewing (without changing)POSTis used for changing, and sometimes viewing data
Response codes
We can check the response code to see if our request was successfull.
if 200 <= response.status_code < 300:
print("Request succeeded!")
else:
print(f"Uh oh, we got response code {response.status_code}...")
Request succeeded!
Here are some common status codes:
(defined by HTTP)
Code |
Status |
Description |
|---|---|---|
200 |
OK |
The request was successfully completed |
400 |
Bad request |
The request was invalid. |
401 |
Unauthorized |
The request did not include an authentication token or the authentication token was expired. |
403 |
Forbidden |
The client did not have permission to access the requested resource. |
404 |
Not Found |
The requested resource was not found. |
405 |
Method Not Allowed |
Often the wrong HTTP VERB, e.g. POST to a URL that only allows GET |
500 |
Internal Server Error |
The request was not completed due to an internal error on the server side. |
503 |
Service unavailable |
The server was unavailable. |
Headers
We can communicate metadata (like who we are) to the API by means of a header.
The server also communicates back to us with a header:
dict(response.headers)
{'Date': 'Wed, 25 Oct 2023 09:47:42 GMT',
'Content-Type': 'application/json',
'Transfer-Encoding': 'chunked',
'Connection': 'keep-alive',
'Access-Control-Allow-Origin': '*',
'Access-Control-Expose-Headers': 'X-RateLimit-Limit, X-RateLimit-Remaining',
'Age': '0',
'Content-Encoding': 'gzip',
'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload',
'Vary': 'Accept-Encoding',
'Via': 'http/1.1 api-umbrella (ApacheTrafficServer [cMsSf ])',
'X-Api-Umbrella-Request-Id': 'cbcotovkqsd1bir13org',
'X-Cache': 'MISS',
'X-Ratelimit-Limit': '40',
'X-Ratelimit-Remaining': '39',
'X-Vcap-Request-Id': '076d6fe2-3d57-441b-6a5d-cf6737433b3c',
'X-Frame-Options': 'DENY',
'X-Content-Type-Options': 'nosniff',
'X-XSS-Protection': '1; mode=block'}
Etiquette
When you access a public data API you are expected to follow the playing rules of the API. Therefore you should always start by checking the documentation on the website for the API.
Exercise#
Make a request to a public API
For examples:
url = "https://www.api.toys/api/dice_roll"
r = requests.get(url, params={"sides": 6, "rolls": 3})
r.json()
{'dice': 'd6', 'rolls': [5, 4, 4]}
url = "https://meowfacts.herokuapp.com/"
r = requests.get(url)
data = r.json()
data
{'data': ["Cats can get tapeworms from eating fleas. These worms live inside the cat forever, or until they are removed with medication. They reproduce by shedding a link from the end of their long bodies. This link crawls out the cat's anus, and sheds hundreds of eggs. These eggs are injested by flea larvae, and the cycles continues. Humans may get these tapeworms too, but only if they eat infected fleas. Cats with tapeworms should be dewormed by a veterinarian."]}
facts = data["data"]
for fact in facts:
print(fact)
Cats can get tapeworms from eating fleas. These worms live inside the cat forever, or until they are removed with medication. They reproduce by shedding a link from the end of their long bodies. This link crawls out the cat's anus, and sheds hundreds of eggs. These eggs are injested by flea larvae, and the cycles continues. Humans may get these tapeworms too, but only if they eat infected fleas. Cats with tapeworms should be dewormed by a veterinarian.
requests.get?
Signature: requests.get(url, params=None, **kwargs)
Docstring:
Sends a GET request.
:param url: URL for the new :class:`Request` object.
:param params: (optional) Dictionary, list of tuples or bytes to send
in the query string for the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response
File: ~/conda/envs/3110/lib/python3.11/site-packages/requests/api.py
Type: function
Data formats#
The actual data typically gets returned in a JSON, XML or CSV file.
CSV#
A Comma-Separated Values file is a delimited text file that uses a comma to separate values. It typically stores tabular data.
Example: FHI provides data about the number of confirmed COVID cases in Norway as a .csv file.
JSON#
JSON is the most common way of sending data back and forth in APIs.
A JavaScript Object Notation (JSON) file encodes data structures so that they are easy to read for machines and somewhat easy to read for humans.
JSON is a text file or string that follows the JavaScript object syntax. Most programming languages will have the ability to read (parse) and generate JSON files.
The json library in Python has two main functions:
json.dumps()Takes in a python object and converts it (dumps it) to a stringjson.loads()Takes in a JSON string and converts it (loads it) to a Python object.
which will convert from/to the following formats
JSON |
Python |
|---|---|
object |
dict |
array |
list |
string |
str |
number |
int or float |
true |
True |
false |
False |
null |
None |
Let’s look at what NASA gave us
# Load data using response.json
response.json?
Signature: response.json(**kwargs)
Docstring:
Returns the json-encoded content of a response, if any.
:param \*\*kwargs: Optional arguments that ``json.loads`` takes.
:raises requests.exceptions.JSONDecodeError: If the response body does not
contain valid json.
File: ~/conda/envs/3110/lib/python3.11/site-packages/requests/models.py
Type: method
data = response.json()
print(f"response.json() is a {type(data)}:")
data
response.json() is a <class 'dict'>:
{'copyright': '\nJerry Zhang (left), \nBaolong Chen (photographer) & Amber Zhang (right)\n',
'date': '2023-10-16',
'explanation': 'She knew everything but the question. She was well aware that there would be a complete annular eclipse of the Sun visible from their driving destination: Lake Abert in Oregon. She knew that the next ring-of-fire eclipse would occur in the USA only in 16 more years, making this a rare photographic opportunity. She was comfortable with the plan: that she and her boyfriend would appear in front of the eclipse in silhouette, sometimes alone, and sometimes together. She knew that the annular phase of this eclipse would last only a few minutes and she helped in the many hours of planning. She could see their friend who set up the camera about 400 meters away at the bottom of a ridge. What she didn\'t know was the question she would be asked. But she did know the answer: "yes". Album: Selected eclipse images sent in to APOD',
'hdurl': 'https://apod.nasa.gov/apod/image/2310/AnnularProposal_Zhang_3500.jpg',
'media_type': 'image',
'service_version': 'v1',
'title': 'Eclipse Rings',
'url': 'https://apod.nasa.gov/apod/image/2310/AnnularProposal_Zhang_960.jpg'}
Exercise#
Make a get request from the NASA Mars Rover API to get pictures from Mars.
Specifiy the camera viewpoint in parameters
# Get satellite image from Houston
parameters = {
"sol": 1500,
"api_key": "DEMO_KEY",
"page": 1,
}
url = "https://api.nasa.gov/mars-photos/api/v1/rovers/curiosity/photos"
r = requests.get(url, params=parameters)
from IPython.display import JSON
JSON(r.json())
<IPython.core.display.JSON object>
Entur API#
The NASA is a good ‘hello world’ example of APIs but it does not quite showcase the usefullness of APIs.
Entur has APIs is an example of an open API, that features both an open source code and APIs for stops, real-time data, mobility trends etc.
The following snippet is adapted from ruterstop by stigok:
stop_id = 5926
__version__ = "0.5.1"
ENTUR_CLIENT_ID = __version__
ENTUR_GRAPHQL_ENDPOINT = "https://api.entur.io/journey-planner/v2/graphql"
ENTUR_GRAPHQL_QUERY = """
{
stopPlace(id: "NSR:StopPlace:$STOP_ID") {
name
estimatedCalls(timeRange: 72100, numberOfDepartures: 20) {
expectedArrivalTime
realtime
destinationDisplay {
frontText
}
serviceJourney {
directionType
line {
publicCode
}
}
}
}
}
"""
headers = {
"Accept": "application/json",
"ET-Client-Name": "UIO:IN3110 - ingeborggjerde",
"ET-Client-Id": ENTUR_CLIENT_ID,
}
qry = ENTUR_GRAPHQL_QUERY.replace("$STOP_ID", str(stop_id))
res = requests.post(
ENTUR_GRAPHQL_ENDPOINT,
headers=headers,
timeout=5,
json=dict(query=qry, variables={}),
)
JSON(res.json())
<IPython.core.display.JSON object>
Getting public API data into pandas#
Let’s check the weather
Yr has an API: https://developer.yr.no
They ask you to identify yourself in the User-Agent header: https://developer.yr.no/doc/TermsOfService/
url = "https://api.met.no/weatherapi/locationforecast/2.0/compact?lat=51.5&lon=0"
s = requests.Session()
s.headers[
"User-Agent"
] = "uio-in3110 https://github.com/uio-in3110/uio-in3110.github.io"
r = s.get(url)
r
200 https://api.met.no/weatherapi/locationforecast/2.0/compact?lat=51.5&lon=0
headers: Accept-Ranges: bytes
Access-Control-Allow-Headers: Origin
Access-Control-Allow-Methods: GET
Access-Control-Allow-Origin: *
Age: 0
Connection: keep-alive
Content-Encoding: gzip
Content-Length: 2988
Content-Type: application/json
Date: Wed, 25 Oct 2023 09:47:45 GMT
Expires: Wed, 25 Oct 2023 10:17:49 GMT
Last-Modified: Wed, 25 Oct 2023 09:47:44 GMT
Server: nginx/1.18.0 (Ubuntu)
Vary: Accept, Accept-Encoding
Via: 1.1 varnish (Varnish/7.0)
X-Backend-Host: b_157_249_76_16_loc
X-Varnish: 99576577
body (application/json):
{'type': 'Feature',
'geometry': {'type': 'Point', 'coordinates': [0, 51.5, 4]},
'properties': {'meta': {'updated_at': '2023-10-25T09:38:21Z',
'units': {'air_pressure_at_sea_level': 'hPa',
'air_temperature': 'celsius',
'cloud_area_fraction': '%',
'precipitation_amount': 'mm',
'relative_humidity': '%',
'wind_from_direction': 'degrees',
'wind_speed': 'm/s'}},
'timeseries': [{'time': '2023-10-25T09:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.1,
'air_temperature': 10.7,
'cloud_area_fraction': 100.0,
'relative_humidity': 89.2,
'wind_from_direction': 33.3,
'wind_speed': 4.0}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-25T10:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.1,
'air_temperature': 11.3,
'cloud_area_fraction': 94.5,
'relative_humidity': 86.6,
'wind_from_direction': 33.0,
'wind_speed': 4.1}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-25T11:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.2,
'air_temperature': 11.5,
'cloud_area_fraction': 89.8,
'relative_humidity': 85.7,
'wind_from_direction': 6.9,
'wind_speed': 4.1}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.4,
'air_temperature': 12.3,
'cloud_area_fraction': 75.8,
'relative_humidity': 81.7,
'wind_from_direction': 11.4,
'wind_speed': 3.4}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T13:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.3,
'air_temperature': 13.7,
'cloud_area_fraction': 42.2,
'relative_humidity': 77.0,
'wind_from_direction': 22.9,
'wind_speed': 3.1}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T14:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.7,
'air_temperature': 13.8,
'cloud_area_fraction': 98.4,
'relative_humidity': 75.3,
'wind_from_direction': 0.8,
'wind_speed': 3.6}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T15:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.9,
'air_temperature': 13.2,
'cloud_area_fraction': 17.2,
'relative_humidity': 75.1,
'wind_from_direction': 358.6,
'wind_speed': 3.0}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_1_hours': {'summary': {'symbol_code': 'fair_day'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T16:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.2,
'air_temperature': 12.4,
'cloud_area_fraction': 6.2,
'relative_humidity': 78.6,
'wind_from_direction': 343.4,
'wind_speed': 1.8}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_1_hours': {'summary': {'symbol_code': 'clearsky_day'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T17:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.5,
'air_temperature': 9.4,
'cloud_area_fraction': 89.1,
'relative_humidity': 89.7,
'wind_from_direction': 322.6,
'wind_speed': 2.1}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.7,
'air_temperature': 8.6,
'cloud_area_fraction': 100.0,
'relative_humidity': 91.9,
'wind_from_direction': 322.2,
'wind_speed': 2.5}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T19:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.9,
'air_temperature': 8.2,
'cloud_area_fraction': 100.0,
'relative_humidity': 93.7,
'wind_from_direction': 317.6,
'wind_speed': 2.1}},
'next_12_hours': {'summary': {'symbol_code': 'lightrain'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-25T20:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.8,
'air_temperature': 9.5,
'cloud_area_fraction': 100.0,
'relative_humidity': 90.5,
'wind_from_direction': 258.4,
'wind_speed': 0.9}},
'next_12_hours': {'summary': {'symbol_code': 'lightrain'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-25T21:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.7,
'air_temperature': 8.4,
'cloud_area_fraction': 100.0,
'relative_humidity': 95.7,
'wind_from_direction': 117.6,
'wind_speed': 1.7}},
'next_12_hours': {'summary': {'symbol_code': 'lightrain'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.2}}}},
{'time': '2023-10-25T22:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.7,
'air_temperature': 8.0,
'cloud_area_fraction': 96.1,
'relative_humidity': 96.1,
'wind_from_direction': 123.6,
'wind_speed': 1.9}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.4}}}},
{'time': '2023-10-25T23:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.2,
'air_temperature': 7.8,
'cloud_area_fraction': 100.0,
'relative_humidity': 96.0,
'wind_from_direction': 112.6,
'wind_speed': 2.4}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.4}}}},
{'time': '2023-10-26T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.6,
'air_temperature': 8.4,
'cloud_area_fraction': 45.3,
'relative_humidity': 94.2,
'wind_from_direction': 113.7,
'wind_speed': 3.0}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.5}}}},
{'time': '2023-10-26T01:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.1,
'air_temperature': 8.4,
'cloud_area_fraction': 96.1,
'relative_humidity': 94.4,
'wind_from_direction': 107.0,
'wind_speed': 3.2}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
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{'time': '2023-10-27T08:00:00Z',
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{'time': '2023-10-27T14:00:00Z',
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{'time': '2023-10-27T15:00:00Z',
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'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T16:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.5,
'air_temperature': 12.1,
'cloud_area_fraction': 100.0,
'relative_humidity': 81.7,
'wind_from_direction': 219.1,
'wind_speed': 4.5}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T17:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.6,
'air_temperature': 11.2,
'cloud_area_fraction': 96.9,
'relative_humidity': 85.9,
'wind_from_direction': 214.9,
'wind_speed': 4.7}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.8,
'air_temperature': 11.0,
'cloud_area_fraction': 39.1,
'relative_humidity': 87.3,
'wind_from_direction': 215.1,
'wind_speed': 4.7}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T19:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.9,
'air_temperature': 10.5,
'cloud_area_fraction': 28.9,
'relative_humidity': 88.1,
'wind_from_direction': 208.9,
'wind_speed': 4.8}},
'next_1_hours': {'summary': {'symbol_code': 'fair_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T20:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 992.0,
'air_temperature': 10.2,
'cloud_area_fraction': 93.7,
'relative_humidity': 86.3,
'wind_from_direction': 204.5,
'wind_speed': 5.4}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T21:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.9,
'air_temperature': 10.1,
'cloud_area_fraction': 78.9,
'relative_humidity': 88.2,
'wind_from_direction': 204.7,
'wind_speed': 4.9}},
'next_1_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T22:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.9,
'air_temperature': 10.0,
'cloud_area_fraction': 100.0,
'relative_humidity': 90.8,
'wind_from_direction': 200.0,
'wind_speed': 4.8}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-27T23:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.5,
'air_temperature': 10.2,
'cloud_area_fraction': 99.2,
'relative_humidity': 90.4,
'wind_from_direction': 194.5,
'wind_speed': 4.8}},
'next_1_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-28T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.0,
'air_temperature': 10.4,
'cloud_area_fraction': 99.2,
'relative_humidity': 90.0,
'wind_from_direction': 187.2,
'wind_speed': 4.6}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-28T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 988.9,
'air_temperature': 10.7,
'cloud_area_fraction': 100.0,
'relative_humidity': 89.5,
'wind_from_direction': 208.6,
'wind_speed': 5.1}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-28T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.7,
'air_temperature': 14.8,
'cloud_area_fraction': 25.0,
'relative_humidity': 68.2,
'wind_from_direction': 217.0,
'wind_speed': 7.3}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'fair_day'},
'details': {'precipitation_amount': 0.5}}}},
{'time': '2023-10-28T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 989.2,
'air_temperature': 12.3,
'cloud_area_fraction': 100.0,
'relative_humidity': 81.6,
'wind_from_direction': 154.9,
'wind_speed': 4.7}},
'next_12_hours': {'summary': {'symbol_code': 'rain'}},
'next_6_hours': {'summary': {'symbol_code': 'heavyrain'},
'details': {'precipitation_amount': 8.9}}}},
{'time': '2023-10-29T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 983.0,
'air_temperature': 12.1,
'cloud_area_fraction': 95.3,
'relative_humidity': 90.7,
'wind_from_direction': 197.8,
'wind_speed': 6.3}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'rain'},
'details': {'precipitation_amount': 1.0}}}},
{'time': '2023-10-29T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 980.1,
'air_temperature': 11.3,
'cloud_area_fraction': 96.9,
'relative_humidity': 86.3,
'wind_from_direction': 218.0,
'wind_speed': 9.0}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-29T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 987.1,
'air_temperature': 14.7,
'cloud_area_fraction': 21.1,
'relative_humidity': 64.0,
'wind_from_direction': 228.6,
'wind_speed': 11.8}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_6_hours': {'summary': {'symbol_code': 'fair_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-29T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 990.5,
'air_temperature': 11.1,
'cloud_area_fraction': 82.0,
'relative_humidity': 77.5,
'wind_from_direction': 219.4,
'wind_speed': 6.1}},
'next_12_hours': {'summary': {'symbol_code': 'fair_night'}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-30T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 995.3,
'air_temperature': 8.9,
'cloud_area_fraction': 4.7,
'relative_humidity': 89.5,
'wind_from_direction': 233.5,
'wind_speed': 4.5}},
'next_12_hours': {'summary': {'symbol_code': 'fair_day'}},
'next_6_hours': {'summary': {'symbol_code': 'clearsky_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-30T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 997.5,
'air_temperature': 6.8,
'cloud_area_fraction': 5.5,
'relative_humidity': 92.8,
'wind_from_direction': 214.1,
'wind_speed': 2.1}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'clearsky_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-30T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 997.0,
'air_temperature': 12.5,
'cloud_area_fraction': 100.0,
'relative_humidity': 77.4,
'wind_from_direction': 83.2,
'wind_speed': 1.8}},
'next_12_hours': {'summary': {'symbol_code': 'rain'}},
'next_6_hours': {'summary': {'symbol_code': 'rain'},
'details': {'precipitation_amount': 4.2}}}},
{'time': '2023-10-30T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 993.8,
'air_temperature': 9.8,
'cloud_area_fraction': 100.0,
'relative_humidity': 91.2,
'wind_from_direction': 25.0,
'wind_speed': 4.0}},
'next_12_hours': {'summary': {'symbol_code': 'lightrain'}},
'next_6_hours': {'summary': {'symbol_code': 'heavyrain'},
'details': {'precipitation_amount': 8.2}}}},
{'time': '2023-10-31T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 997.0,
'air_temperature': 8.4,
'cloud_area_fraction': 100.0,
'relative_humidity': 91.0,
'wind_from_direction': 12.4,
'wind_speed': 3.5}},
'next_12_hours': {'summary': {'symbol_code': 'cloudy'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.1}}}},
{'time': '2023-10-31T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1000.2,
'air_temperature': 4.5,
'cloud_area_fraction': 100.0,
'relative_humidity': 91.2,
'wind_from_direction': 359.2,
'wind_speed': 2.6}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_day'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-31T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1002.8,
'air_temperature': 8.9,
'cloud_area_fraction': 45.3,
'relative_humidity': 71.0,
'wind_from_direction': 16.0,
'wind_speed': 3.9}},
'next_12_hours': {'summary': {'symbol_code': 'fair_day'}},
'next_6_hours': {'summary': {'symbol_code': 'partlycloudy_day'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-10-31T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1006.8,
'air_temperature': 4.8,
'cloud_area_fraction': 0.0,
'relative_humidity': 86.1,
'wind_from_direction': 333.6,
'wind_speed': 1.9}},
'next_12_hours': {'summary': {'symbol_code': 'partlycloudy_night'}},
'next_6_hours': {'summary': {'symbol_code': 'clearsky_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-11-01T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1010.0,
'air_temperature': 1.2,
'cloud_area_fraction': 32.8,
'relative_humidity': 98.9,
'wind_from_direction': 302.6,
'wind_speed': 1.4}},
'next_12_hours': {'summary': {'symbol_code': 'lightsleetshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'fair_night'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-11-01T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1009.0,
'air_temperature': 1.1,
'cloud_area_fraction': 100.0,
'relative_humidity': 99.4,
'wind_from_direction': 90.1,
'wind_speed': 2.4}},
'next_12_hours': {'summary': {'symbol_code': 'rain'}},
'next_6_hours': {'summary': {'symbol_code': 'sleet'},
'details': {'precipitation_amount': 3.7}}}},
{'time': '2023-11-01T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 1002.5,
'air_temperature': 8.7,
'cloud_area_fraction': 100.0,
'relative_humidity': 92.1,
'wind_from_direction': 105.5,
'wind_speed': 6.5}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'rain'},
'details': {'precipitation_amount': 3.4}}}},
{'time': '2023-11-01T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 996.4,
'air_temperature': 12.2,
'cloud_area_fraction': 100.0,
'relative_humidity': 88.8,
'wind_from_direction': 155.6,
'wind_speed': 6.0}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'rain'},
'details': {'precipitation_amount': 2.7}}}},
{'time': '2023-11-02T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 991.7,
'air_temperature': 12.1,
'cloud_area_fraction': 39.1,
'relative_humidity': 90.6,
'wind_from_direction': 185.0,
'wind_speed': 5.9}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'rainshowers_night'},
'details': {'precipitation_amount': 2.3}}}},
{'time': '2023-11-02T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 986.0,
'air_temperature': 11.5,
'cloud_area_fraction': 100.0,
'relative_humidity': 88.3,
'wind_from_direction': 148.4,
'wind_speed': 5.0}},
'next_12_hours': {'summary': {'symbol_code': 'lightrainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'lightrain'},
'details': {'precipitation_amount': 0.7}}}},
{'time': '2023-11-02T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 982.2,
'air_temperature': 13.3,
'cloud_area_fraction': 56.2,
'relative_humidity': 77.7,
'wind_from_direction': 266.0,
'wind_speed': 5.3}},
'next_12_hours': {'summary': {'symbol_code': 'lightrain'}},
'next_6_hours': {'summary': {'symbol_code': 'rainshowers_day'},
'details': {'precipitation_amount': 1.0}}}},
{'time': '2023-11-02T18:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 986.6,
'air_temperature': 10.3,
'cloud_area_fraction': 100.0,
'relative_humidity': 85.9,
'wind_from_direction': 247.2,
'wind_speed': 7.3}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'cloudy'},
'details': {'precipitation_amount': 0.0}}}},
{'time': '2023-11-03T00:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 981.3,
'air_temperature': 7.3,
'cloud_area_fraction': 100.0,
'relative_humidity': 75.7,
'wind_from_direction': 162.0,
'wind_speed': 2.7}},
'next_12_hours': {'summary': {'symbol_code': 'rainshowers_day'}},
'next_6_hours': {'summary': {'symbol_code': 'heavyrain'},
'details': {'precipitation_amount': 11.6}}}},
{'time': '2023-11-03T06:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 958.5,
'air_temperature': 10.6,
'cloud_area_fraction': 17.2,
'relative_humidity': 79.5,
'wind_from_direction': 198.8,
'wind_speed': 7.7}},
'next_6_hours': {'summary': {'symbol_code': 'rainshowers_day'},
'details': {'precipitation_amount': 3.0}}}},
{'time': '2023-11-03T12:00:00Z',
'data': {'instant': {'details': {'air_pressure_at_sea_level': 960.5,
'air_temperature': 7.9,
'cloud_area_fraction': 100.0,
'relative_humidity': 82.4,
'wind_from_direction': 288.9,
'wind_speed': 10.5}}}}]}}
data = r.json()
record = data["properties"]["timeseries"][0]
JSON(data)
<IPython.core.display.JSON object>
from IPython.display import JSON
JSON(record)
<IPython.core.display.JSON object>
Exercise#
Tidy data is easier to work with for tools like pands.
Every column is a variable.
Every row is an observation.
Every cell is a single value.
Task: tidy a record dict from Yr:
turn nested dictionary into one-level dictionary of keys, values
add prefixes for nested keys that may conflict, e.g.
next(iter(record["data"].items()))
('instant',
{'details': {'air_pressure_at_sea_level': 995.1,
'air_temperature': 10.7,
'cloud_area_fraction': 100.0,
'relative_humidity': 89.2,
'wind_from_direction': 33.3,
'wind_speed': 4.0}})
def tidy_format(record):
tidy_dict = {"time": record["time"]}
for section, sub_dict in record["data"].items():
for _, fields in sub_dict.items():
for key, value in fields.items():
if section == "instant":
tidy_key = key
else:
tidy_key = f"{section}_{key}"
tidy_dict[tidy_key] = value
return tidy_dict
tidy_format(record)
{'time': '2023-10-25T09:00:00Z',
'air_pressure_at_sea_level': 995.1,
'air_temperature': 10.7,
'cloud_area_fraction': 100.0,
'relative_humidity': 89.2,
'wind_from_direction': 33.3,
'wind_speed': 4.0,
'next_12_hours_symbol_code': 'partlycloudy_day',
'next_1_hours_symbol_code': 'cloudy',
'next_1_hours_precipitation_amount': 0.0,
'next_6_hours_symbol_code': 'partlycloudy_day',
'next_6_hours_precipitation_amount': 0.1}
tidy_records = [tidy_format(record) for record in r.json()["properties"]["timeseries"]]
import pandas as pd
df = pd.DataFrame.from_dict(tidy_records)
df
| time | air_pressure_at_sea_level | air_temperature | cloud_area_fraction | relative_humidity | wind_from_direction | wind_speed | next_12_hours_symbol_code | next_1_hours_symbol_code | next_1_hours_precipitation_amount | next_6_hours_symbol_code | next_6_hours_precipitation_amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-10-25T09:00:00Z | 995.1 | 10.7 | 100.0 | 89.2 | 33.3 | 4.0 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.1 |
| 1 | 2023-10-25T10:00:00Z | 995.1 | 11.3 | 94.5 | 86.6 | 33.0 | 4.1 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.1 |
| 2 | 2023-10-25T11:00:00Z | 995.2 | 11.5 | 89.8 | 85.7 | 6.9 | 4.1 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.0 |
| 3 | 2023-10-25T12:00:00Z | 995.4 | 12.3 | 75.8 | 81.7 | 11.4 | 3.4 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 |
| 4 | 2023-10-25T13:00:00Z | 995.3 | 13.7 | 42.2 | 77.0 | 22.9 | 3.1 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 85 | 2023-11-02T12:00:00Z | 982.2 | 13.3 | 56.2 | 77.7 | 266.0 | 5.3 | lightrain | NaN | NaN | rainshowers_day | 1.0 |
| 86 | 2023-11-02T18:00:00Z | 986.6 | 10.3 | 100.0 | 85.9 | 247.2 | 7.3 | rainshowers_day | NaN | NaN | cloudy | 0.0 |
| 87 | 2023-11-03T00:00:00Z | 981.3 | 7.3 | 100.0 | 75.7 | 162.0 | 2.7 | rainshowers_day | NaN | NaN | heavyrain | 11.6 |
| 88 | 2023-11-03T06:00:00Z | 958.5 | 10.6 | 17.2 | 79.5 | 198.8 | 7.7 | NaN | NaN | NaN | rainshowers_day | 3.0 |
| 89 | 2023-11-03T12:00:00Z | 960.5 | 7.9 | 100.0 | 82.4 | 288.9 | 10.5 | NaN | NaN | NaN | NaN | NaN |
90 rows × 12 columns
df = pd.DataFrame.from_dict(tidy_records)
df["time"] = pd.to_datetime(df["time"])
df = df.set_index("time")
df
| air_pressure_at_sea_level | air_temperature | cloud_area_fraction | relative_humidity | wind_from_direction | wind_speed | next_12_hours_symbol_code | next_1_hours_symbol_code | next_1_hours_precipitation_amount | next_6_hours_symbol_code | next_6_hours_precipitation_amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||
| 2023-10-25 09:00:00+00:00 | 995.1 | 10.7 | 100.0 | 89.2 | 33.3 | 4.0 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.1 |
| 2023-10-25 10:00:00+00:00 | 995.1 | 11.3 | 94.5 | 86.6 | 33.0 | 4.1 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.1 |
| 2023-10-25 11:00:00+00:00 | 995.2 | 11.5 | 89.8 | 85.7 | 6.9 | 4.1 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.0 |
| 2023-10-25 12:00:00+00:00 | 995.4 | 12.3 | 75.8 | 81.7 | 11.4 | 3.4 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 |
| 2023-10-25 13:00:00+00:00 | 995.3 | 13.7 | 42.2 | 77.0 | 22.9 | 3.1 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2023-11-02 12:00:00+00:00 | 982.2 | 13.3 | 56.2 | 77.7 | 266.0 | 5.3 | lightrain | NaN | NaN | rainshowers_day | 1.0 |
| 2023-11-02 18:00:00+00:00 | 986.6 | 10.3 | 100.0 | 85.9 | 247.2 | 7.3 | rainshowers_day | NaN | NaN | cloudy | 0.0 |
| 2023-11-03 00:00:00+00:00 | 981.3 | 7.3 | 100.0 | 75.7 | 162.0 | 2.7 | rainshowers_day | NaN | NaN | heavyrain | 11.6 |
| 2023-11-03 06:00:00+00:00 | 958.5 | 10.6 | 17.2 | 79.5 | 198.8 | 7.7 | NaN | NaN | NaN | rainshowers_day | 3.0 |
| 2023-11-03 12:00:00+00:00 | 960.5 | 7.9 | 100.0 | 82.4 | 288.9 | 10.5 | NaN | NaN | NaN | NaN | NaN |
90 rows × 11 columns
df.air_temperature.plot(grid=True)
<Axes: xlabel='time'>
df.cloud_area_fraction.hist()
<Axes: >
Another API:
The weather API expects latitude, longitude, but we have location names.
There’s an API for that!
r = requests.get(
"https://geocode.maps.co/search",
params={"city": "Bergen", "country": "Norway"},
)
r.json()
[{'place_id': 286402934,
'licence': 'Data © OpenStreetMap contributors, ODbL 1.0. https://osm.org/copyright',
'powered_by': 'Map Maker: https://maps.co',
'osm_type': 'relation',
'osm_id': 404159,
'boundingbox': ['60.1760905', '60.5360925', '5.1445788', '5.6867918'],
'lat': '60.3943055',
'lon': '5.3259192',
'display_name': 'Bergen, Vestland, Norway',
'class': 'boundary',
'type': 'administrative',
'importance': 0.8412391232119534},
{'place_id': 62960991,
'licence': 'Data © OpenStreetMap contributors, ODbL 1.0. https://osm.org/copyright',
'powered_by': 'Map Maker: https://maps.co',
'osm_type': 'node',
'osm_id': 5638206557,
'boundingbox': ['59.6009016', '59.6209016', '8.9104283', '8.9304283'],
'lat': '59.6109016',
'lon': '8.9204283',
'display_name': 'Bergen, Hjartdal, Vestfold og Telemark, 3692, Norway',
'class': 'place',
'type': 'farm',
'importance': 0.42}]
Write a function to locate a city, returning latitude, longitude as floats
def locate_city(city: str, country: str = "Norway") -> tuple[float, float]:
"""return (lat, lon) for a city"""
r = requests.get(
"https://geocode.maps.co/search",
params={"city": city, "country": country},
)
matches = r.json()
if len(matches) < 1:
raise ValueError(f"No match for {city}, {country}")
location = matches[0]
return (float(location["lat"]), float(location["lon"]))
locate_city("Oslo")
(59.9133301, 10.7389701)
locate_city("Bergen")
(60.3943055, 5.3259192)
Exercise:
write a function to get the city forcast, given name and country
look up lat, lon from name, country
get forecast
add ‘city’ column
def city_forecast(city: str, country: str = "Norway") -> pd.DataFrame:
"""Given a city name, return a DataFrame of the weather forecast
1. resolves gps for city
2. fetches forecast
3. tidies data
4. wraps in DataFrame
"""
lat, lon = locate_city(city, country)
url = "https://api.met.no/weatherapi/locationforecast/2.0/compact"
r = s.get(url, params={"lat": lat, "lon": lon})
tidy_records = [
tidy_format(record) for record in r.json()["properties"]["timeseries"]
]
df = pd.DataFrame.from_dict(tidy_records)
df["time"] = pd.to_datetime(df["time"])
df["city"] = city
return df
oslo = city_forecast("Oslo")
oslo
| time | air_pressure_at_sea_level | air_temperature | cloud_area_fraction | relative_humidity | wind_from_direction | wind_speed | next_12_hours_symbol_code | next_1_hours_symbol_code | next_1_hours_precipitation_amount | next_6_hours_symbol_code | next_6_hours_precipitation_amount | city | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-10-25 09:00:00+00:00 | 1015.2 | 2.7 | 91.2 | 74.1 | 46.8 | 4.7 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 1 | 2023-10-25 10:00:00+00:00 | 1015.0 | 3.5 | 79.0 | 71.3 | 48.5 | 4.5 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 2 | 2023-10-25 11:00:00+00:00 | 1014.2 | 4.3 | 68.0 | 68.1 | 47.8 | 4.8 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 3 | 2023-10-25 12:00:00+00:00 | 1013.8 | 5.0 | 73.0 | 65.7 | 52.4 | 4.8 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 4 | 2023-10-25 13:00:00+00:00 | 1013.5 | 5.3 | 68.1 | 64.7 | 52.7 | 5.2 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 83 | 2023-11-03 06:00:00+00:00 | 1002.4 | 4.0 | 100.0 | 93.2 | 56.0 | 4.1 | lightrain | NaN | NaN | cloudy | 0.0 | Oslo |
| 84 | 2023-11-03 12:00:00+00:00 | 999.3 | 3.8 | 100.0 | 89.4 | 57.0 | 4.6 | rain | NaN | NaN | rain | 2.7 | Oslo |
| 85 | 2023-11-03 18:00:00+00:00 | 996.7 | 5.1 | 100.0 | 94.9 | 61.9 | 4.4 | lightrain | NaN | NaN | rain | 2.9 | Oslo |
| 86 | 2023-11-04 00:00:00+00:00 | 995.8 | 4.4 | 100.0 | 95.2 | 54.7 | 4.1 | NaN | NaN | NaN | cloudy | 0.0 | Oslo |
| 87 | 2023-11-04 06:00:00+00:00 | 993.7 | 4.2 | 100.0 | 91.5 | 55.2 | 4.6 | NaN | NaN | NaN | NaN | NaN | Oslo |
88 rows × 13 columns
forecasts = pd.concat(
city_forecast(city) for city in ("Oslo", "Bergen", " Tromsø", "Trondheim")
)
forecasts
| time | air_pressure_at_sea_level | air_temperature | cloud_area_fraction | relative_humidity | wind_from_direction | wind_speed | next_12_hours_symbol_code | next_1_hours_symbol_code | next_1_hours_precipitation_amount | next_6_hours_symbol_code | next_6_hours_precipitation_amount | city | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-10-25 09:00:00+00:00 | 1015.2 | 2.7 | 91.2 | 74.1 | 46.8 | 4.7 | partlycloudy_day | cloudy | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 1 | 2023-10-25 10:00:00+00:00 | 1015.0 | 3.5 | 79.0 | 71.3 | 48.5 | 4.5 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 2 | 2023-10-25 11:00:00+00:00 | 1014.2 | 4.3 | 68.0 | 68.1 | 47.8 | 4.8 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 3 | 2023-10-25 12:00:00+00:00 | 1013.8 | 5.0 | 73.0 | 65.7 | 52.4 | 4.8 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| 4 | 2023-10-25 13:00:00+00:00 | 1013.5 | 5.3 | 68.1 | 64.7 | 52.7 | 5.2 | partlycloudy_day | partlycloudy_day | 0.0 | partlycloudy_day | 0.0 | Oslo |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 83 | 2023-11-03 06:00:00+00:00 | 1001.9 | 3.6 | 100.0 | 70.9 | 128.2 | 4.2 | cloudy | NaN | NaN | cloudy | 0.0 | Trondheim |
| 84 | 2023-11-03 12:00:00+00:00 | 994.9 | 5.6 | 98.8 | 67.3 | 116.6 | 4.2 | cloudy | NaN | NaN | cloudy | 0.0 | Trondheim |
| 85 | 2023-11-03 18:00:00+00:00 | 997.6 | 4.5 | 95.7 | 74.1 | 129.1 | 4.2 | cloudy | NaN | NaN | cloudy | 0.0 | Trondheim |
| 86 | 2023-11-04 00:00:00+00:00 | 995.8 | 4.3 | 100.0 | 74.1 | 135.3 | 4.1 | NaN | NaN | NaN | cloudy | 0.0 | Trondheim |
| 87 | 2023-11-04 06:00:00+00:00 | 995.0 | 3.8 | 100.0 | 76.2 | 120.3 | 4.1 | NaN | NaN | NaN | NaN | NaN | Trondheim |
352 rows × 13 columns
forecasts.groupby(["time", "city"])
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x16cd7da90>
forecasts.groupby(["time", "city"]).air_temperature
<pandas.core.groupby.generic.SeriesGroupBy object at 0x16cda3790>
forecasts.groupby(["time", "city"]).air_temperature.first()
time city
2023-10-25 09:00:00+00:00 Tromsø -0.7
Bergen 10.7
Oslo 2.7
Trondheim 2.1
2023-10-25 10:00:00+00:00 Tromsø 0.5
...
2023-11-04 00:00:00+00:00 Trondheim 4.3
2023-11-04 06:00:00+00:00 Tromsø -0.9
Bergen 6.3
Oslo 4.2
Trondheim 3.8
Name: air_temperature, Length: 352, dtype: float64
forecasts.groupby(["time", "city"]).air_temperature.first().unstack()
| city | Tromsø | Bergen | Oslo | Trondheim |
|---|---|---|---|---|
| time | ||||
| 2023-10-25 09:00:00+00:00 | -0.7 | 10.7 | 2.7 | 2.1 |
| 2023-10-25 10:00:00+00:00 | 0.5 | 11.6 | 3.5 | 3.5 |
| 2023-10-25 11:00:00+00:00 | 1.4 | 12.4 | 4.3 | 4.9 |
| 2023-10-25 12:00:00+00:00 | 1.7 | 13.2 | 5.0 | 5.9 |
| 2023-10-25 13:00:00+00:00 | 1.7 | 13.5 | 5.3 | 6.3 |
| ... | ... | ... | ... | ... |
| 2023-11-03 06:00:00+00:00 | -0.6 | 6.0 | 4.0 | 3.6 |
| 2023-11-03 12:00:00+00:00 | 0.8 | 8.3 | 3.8 | 5.6 |
| 2023-11-03 18:00:00+00:00 | -0.5 | 6.8 | 5.1 | 4.5 |
| 2023-11-04 00:00:00+00:00 | -1.3 | 6.3 | 4.4 | 4.3 |
| 2023-11-04 06:00:00+00:00 | -0.9 | 6.3 | 4.2 | 3.8 |
88 rows × 4 columns
forecasts.groupby(["time", "city"]).air_temperature.first().unstack().plot()
<Axes: xlabel='time'>
forecasts.groupby(["time", "city"]).cloud_area_fraction.first().unstack().plot()
<Axes: xlabel='time'>
forecasts.groupby(
["next_1_hours_symbol_code", "city"]
).next_1_hours_symbol_code.count().unstack().plot(kind="bar")
<Axes: xlabel='next_1_hours_symbol_code'>
I don’t really care about day/night. How do we remove that?
forecasts["next_1_hours_symbol_code"].value_counts()
next_1_hours_symbol_code
clearsky_night 54
clearsky_day 42
cloudy 40
partlycloudy_day 32
partlycloudy_night 31
fair_night 14
rain 6
fair_day 5
lightrain 4
Name: count, dtype: int64
forecasts["next_1_hours_symbol_code"].str.split("_", n=1, expand=True).value_counts()
0 1
clearsky night 54
day 42
partlycloudy day 32
night 31
fair night 14
day 5
Name: count, dtype: int64
forecasts["next_1_hours_symbol_code"].str.split("_", n=1, expand=True)
| 0 | 1 | |
|---|---|---|
| 0 | cloudy | None |
| 1 | partlycloudy | day |
| 2 | partlycloudy | day |
| 3 | partlycloudy | day |
| 4 | partlycloudy | day |
| ... | ... | ... |
| 83 | NaN | NaN |
| 84 | NaN | NaN |
| 85 | NaN | NaN |
| 86 | NaN | NaN |
| 87 | NaN | NaN |
352 rows × 2 columns
for n in (1, 6, 12):
forecasts[f"next_{n}_hours_symbol_code_clean"] = forecasts[
f"next_{n}_hours_symbol_code"
].str.split("_", n=1, expand=True)[0]
forecasts.groupby(
["next_1_hours_symbol_code_clean", "city"]
).next_1_hours_symbol_code.count().unstack().plot(kind="bar")
<Axes: xlabel='next_1_hours_symbol_code_clean'>
forecasts.groupby(
["next_6_hours_symbol_code_clean", "city"]
).next_1_hours_symbol_code.count().unstack().plot(kind="bar")
<Axes: xlabel='next_6_hours_symbol_code_clean'>
forecasts.groupby(
["time", "city"]
).next_6_hours_precipitation_amount.first().unstack().dropna().plot()
<Axes: xlabel='time'>
