pyncei

This module provides tools to access data from NOAA’s Climate Data Online Web Services v2 API provided by NOAA’s National Centers for Environmental information (formerly the National Center for Climate Data).

Documentation for this project is available on Read The Docs.

Install

Install using pip:

pip install pyncei

Alternatively, you can use the environment.yml file included in the GitHub repository to build a conda environment and install pyncei there:

conda env create -f environment.yml
conda activate pyncei
pip install pyncei

This method includes geopandas, which is absent from the pip installation but if installed allows the to_dataframe() method to return a GeoDataFrame when coordinates are provided by NCEI.

Getting started

To use the NCEI web services, you’ll need a token. The token is a 32-character string provided by NCEI; users can request one here. Pass the token to NCEIBot to get started:

from pyncei import NCEIBot

ncei = NCEIBot("ExampleNCEIAPIToken")

You can cache queries by using the cache_name parameter when creating an NCEIBot object:

ncei = NCEIBot("ExampleNCEIAPIToken", cache_name="ncei_cache")

The cache uses CachedSession from the requests-cache module. Caching behavior can be modified by passing keyword arguments accepted by that class to NCEIBot. For example, successful requests are cached indefinitely by default if the cache is being used. Users can change this behavior using the expire_after keyword argument when initializing an NCEIBot object.

NCEIBot includes methods corresponding to each of the endpoints described on the CDO website. Query parameters specified by CDO can be passed as arguments:

response = ncei.get_data(
      datasetid="GHCND",
      stationid=["GHCND:USC00186350"],
      datatypeid=["TMIN", "TMAX"],
      startdate="2015-12-01",
      enddate="2015-12-02",
  )

Each method call may make multiple requests to the API, for example, if more than 1,000 daily records are requested. Responses are combined in an NCEIResponse object, which extends the list class. Individual responses can be accessed using list methods, for example, by iterating through the object or accessing a single item using its index. Data from all responses can be accessed using the values() method, which returns an iterator of dicts, each of which is a single result:

for val in response.values():
    print(val)

The response object includes a to_csv() method to write results to a file:

response.to_csv("station_data.csv")

As well as a to_dataframe() method to write results to a pandas DataFrame (or a geopandas GeoDataFrame if that module is installed and the results include coordinates):

df = response.to_dataframe()

The table below provides an overview of the available endpoints and their corresponding methods:

CDO Endpoint	CDO Query Parameter	NCEIBot Method	Values
datasets	datasetid	get_datasets()	datasets.csv
datacategories	datacategoryid	get_data_categories()	datatypes.csv
datatypes	datatypeid	get_data_types()	datacategories.csv
locationcategories	locationcategoryid	get_location_categories()	locationcategories.csv
locations	locationid	get_locations()	locations.csv
stations	stationid	get_stations()	–
data	–	get_data()	–

Each of the NCEIBot get methods accepts either a single positional argument (used to return data for a single entity) or a series of keyword arguments (used to search for and retrieve all matching entities). Unlike CDO, which accepts only ids, NCEIBot will try to work with either ids or name strings. If names are provided, NCEIBot attempts to map the name strings to valid ids using find_ids():

ncei.find_ids("District of Columbia", "locations")

If a unique match cannot be found, find_ids() returns all identifiers that contain the search term. If you have no idea what data is available or where to look, you can search across all endpoints by omitting the endpoint argument:

ncei.find_ids("temperature")

Or you can browse the source files in the Values column of the table above. The data in these files shouldn’t change much, but they can be updated using refresh_lookups() if necessary:

ncei.refresh_lookups()

Example: Find and return data from a station

from datetime import date

from pyncei import NCEIBot, NCEIResponse


# Initialize NCEIBot object using your token string
ncei = NCEIBot("ExampleNCEIAPIToken", cache_name="ncei")

# Set the date range
mindate = date(2016, 1, 1)  # either yyyy-mm-dd or a datetime object
maxdate = date(2019, 12, 31)

# Get all DC stations operating between mindate and maxdate
stations = ncei.get_stations(
    datasetid="GHCND",
    datatypeid=["TMIN", "TMAX"],
    locationid="FIPS:11",
    startdate=mindate,
    enddate=maxdate,
)

# Select the station with the best data coverage
station = sorted(stations.values(), key=lambda s: -int(s["datacoverage"]))[0]

# Get temperature data for the given dates. Note that for the
# data endpoint, you can't request more than one year's worth of daily
# data at a time.
year = maxdate.year
response = NCEIResponse()
while year >= mindate.year:
    response.extend(
        ncei.get_data(
            datasetid=datasetid,
            stationid=station["id"],
            datatypeid=datatypeids,
            startdate=date(year, 1, 1),
            enddate=date(year, 12, 31),
        )
    )
    year -= 1

# Save values to CSV using the to_csv method
response.to_csv(station["id"].replace(":", "") + ".csv")

# Alternatively, merge observation and station data together in a pandas
# DataFrame. If geopandas is installed and coordinates are given, this
# method will return a GeoDataFrame instead.
df_stations = stations.to_dataframe()
df_response = response.to_dataframe()
df_merged = df_stations.merge(df_response, left_on="id", right_on="station")