Experimenting with NOAA NDBC Buoy Data from ERDDAP

Exploring use of Python to formulate ERDDAP image and data REST requests and process the responses.



In [1]:

    
from IPython.display import IFrame
IFrame('http://www.neracoos.org/erddap/tabledap/A01_met_all.graph', width='100%', height=450)









    Out[1]:



In [2]:

    
##Initialize



In [3]:

    
import urllib2
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

Obtain data from ERDDAP



In [4]:

    
start='2014-07-01T00:00:00Z'
stop='2014-07-11T00:00:00Z'



In [5]:

    
# Construct URL for CSV data:
url='http://www.neracoos.org/erddap/tabledap/A01_met_all\
.csv?station,time,\
air_temperature,wind_speed,wind_direction,\
longitude,latitude\
&time>=%s&time<=%s' % (start,stop)



In [6]:

    
# Load the CSV data directly into Pandas
df = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row



In [7]:

    
# List last ten records
df.tail(10)









    Out[7]:






  
    
      
      station
      air_temperature
      wind_speed
      wind_direction
      longitude
      latitude
    
    
      time
      
      
      
      
      
      
    
  
  
    
      2014-07-10 22:30:00
       A01
       21.04
       1.151
       159.7
      -70.56606
       42.518253
    
    
      2014-07-10 22:40:00
       A01
       21.51
       0.796
       167.0
      -70.56606
       42.518253
    
    
      2014-07-10 22:50:00
       A01
       21.51
       1.826
       195.3
      -70.56606
       42.518253
    
    
      2014-07-10 23:00:00
       A01
       21.82
       1.493
       215.3
      -70.56606
       42.518253
    
    
      2014-07-10 23:10:00
       A01
       22.10
       1.455
       197.6
      -70.56606
       42.518253
    
    
      2014-07-10 23:20:00
       A01
       20.45
       2.547
       162.3
      -70.56606
       42.518253
    
    
      2014-07-10 23:30:00
       A01
       19.97
       3.316
       168.3
      -70.56606
       42.518253
    
    
      2014-07-10 23:40:00
       A01
       19.81
       3.094
       179.3
      -70.56606
       42.518253
    
    
      2014-07-10 23:50:00
       A01
       19.51
       2.593
       179.5
      -70.56606
       42.518253
    
    
      2014-07-11 00:00:00
       A01
       19.21
       2.446
       177.6
      -70.56606
       42.518253



In [8]:

    
df[['wind_speed','air_temperature']].plot(figsize=(12,4));



In [8]:

	station	air_temperature	wind_speed	wind_direction	longitude	latitude
time
2014-07-10 22:30:00	A01	21.04	1.151	159.7	-70.56606	42.518253
2014-07-10 22:40:00	A01	21.51	0.796	167.0	-70.56606	42.518253
2014-07-10 22:50:00	A01	21.51	1.826	195.3	-70.56606	42.518253
2014-07-10 23:00:00	A01	21.82	1.493	215.3	-70.56606	42.518253
2014-07-10 23:10:00	A01	22.10	1.455	197.6	-70.56606	42.518253
2014-07-10 23:20:00	A01	20.45	2.547	162.3	-70.56606	42.518253
2014-07-10 23:30:00	A01	19.97	3.316	168.3	-70.56606	42.518253
2014-07-10 23:40:00	A01	19.81	3.094	179.3	-70.56606	42.518253
2014-07-10 23:50:00	A01	19.51	2.593	179.5	-70.56606	42.518253
2014-07-11 00:00:00	A01	19.21	2.446	177.6	-70.56606	42.518253