NDBC buoys (tabledap -> DataFrame)¶

A tabledap dataset returns record tables as a pandas.DataFrame. Here we pull a day of NDBC standard-meteorological buoy water-temperature observations off the U.S. west coast.

Setup + request¶

OUTPUT_KIND is tabular, so download() returns a frame (also written to CSV). aggregate= would be rejected for a table.

In [1]:

import tempfile
from pathlib import Path

from earthlens import EarthLens

out_dir = Path(tempfile.mkdtemp(prefix='earthlens-erddap-ndbc-'))
df = EarthLens(
    data_source='erddap',
    dataset='cwwcNDBCMet',
    variables=['station', 'time', 'wtmp'],
    start='2023-01-01',
    end='2023-01-01',
    lat_lim=[36.0, 38.0],
    lon_lim=[-124.0, -122.0],
    path=out_dir,
).download()

print('shape  :', df.shape)
print('columns:', list(df.columns))

import tempfile from pathlib import Path from earthlens import EarthLens out_dir = Path(tempfile.mkdtemp(prefix='earthlens-erddap-ndbc-')) df = EarthLens( data_source='erddap', dataset='cwwcNDBCMet', variables=['station', 'time', 'wtmp'], start='2023-01-01', end='2023-01-01', lat_lim=[36.0, 38.0], lon_lim=[-124.0, -122.0], path=out_dir, ).download() print('shape :', df.shape) print('columns:', list(df.columns))

2026-06-25 19:51:13.301 | INFO     | earthlens.erddap.backend:download:491 - ERDDAP cwwcNDBCMet: 17 row(s) written to C:\Users\main\AppData\Local\Temp\earthlens-erddap-ndbc-0pk_is6v\cwwcNDBCMet.csv

shape  : (17, 3)
columns: ['station', 'time (UTC)', 'wtmp (degree_C)']

Peek at the records¶

ERDDAP appends units to the column names (e.g. wtmp (degree_C)).

In [2]:

df.head()

df.head()

Out[2]:

	station	time (UTC)	wtmp (degree_C)
0	46012	2023-01-01T00:00:00Z	NaN
1	46026	2023-01-01T00:00:00Z	12.3
2	46042	2023-01-01T00:00:00Z	NaN
3	46237	2023-01-01T00:00:00Z	11.7
4	46269	2023-01-01T00:00:00Z	12.5

Per-station water temperature¶

In [3]:

wtmp_col = [c for c in df.columns if c.startswith('wtmp')][0]
summary = (
    df.dropna(subset=[wtmp_col])
    .groupby('station')[wtmp_col]
    .agg(['count', 'mean', 'min', 'max'])
    .round(2)
)
print(f'{len(summary)} station(s) reported water temperature:')
summary

wtmp_col = [c for c in df.columns if c.startswith('wtmp')][0] summary = ( df.dropna(subset=[wtmp_col]) .groupby('station')[wtmp_col] .agg(['count', 'mean', 'min', 'max']) .round(2) ) print(f'{len(summary)} station(s) reported water temperature:') summary

8 station(s) reported water temperature:

Out[3]:

	count	mean	min	max
station
46026	1	12.3	12.3	12.3
46237	1	11.7	11.7	11.7
46269	1	12.5	12.5	12.5
AAMC1	1	11.4	11.4	11.4
FTPC1	1	12.8	12.8	12.8
PRYC1	1	11.9	11.9	11.9
RCMC1	1	11.9	11.9	11.9
RTYC1	1	13.3	13.3	13.3