You can run this notebook in Binder, in Colab, in Deepnote or in Kaggle.

[13]:

!pip install --quiet climetlab

[14]:

import climetlab as cml

[15]:

r = {
    "class": "e2",
    "date": "1662-10-01/to/1663-12-31",
    "dataset": "icoads",
    "expver": "1608",
    "groupid": "17",
    "reportype": "16008",
    "format": "ascii",
    "stream": "oper",
    "time": "all",
    "type": "ofb",
}


source = cml.load_source("mars", **r)

[16]:

pd = source.to_pandas()

[17]:

pd

[17]:
expver@desc type@desc class@desc stream@desc andate@desc antime@desc seqno@hdr reportype@hdr bufrtype@hdr subtype@hdr ... entryno@body obsvalue@body varno@body vertco_type@body vertco_reference_1@body datum_status@body bias_volatility@body an_depar@body fg_depar@body ppcode@conv_body
0 '1608' 263 22 1025 16621015 120000 1 16008 1 11 ... 1 158.000000 111 1 0.0 1 NaN NaN NaN 0
1 '1608' 263 22 1025 16621015 120000 1 16008 1 11 ... 2 2.600000 112 1 0.0 1 NaN NaN NaN 0
2 '1608' 263 22 1025 16621015 120000 1 16008 1 11 ... 3 -0.973977 41 1 0.0 1 NaN NaN NaN 0
3 '1608' 263 22 1025 16621015 120000 1 16008 1 11 ... 4 2.410678 42 1 0.0 1 NaN NaN NaN 0
4 '1608' 263 22 1025 16621016 120000 2 16008 1 11 ... 1 158.000000 111 1 0.0 1 NaN NaN NaN 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
673 '1608' 263 22 1025 16630425 60000 25 16008 1 11 ... 4 2.155498 42 1 0.0 1 NaN NaN NaN 0
674 '1608' 263 22 1025 16630426 60000 26 16008 1 11 ... 1 124.000000 111 1 0.0 1 NaN NaN NaN 0
675 '1608' 263 22 1025 16630426 60000 26 16008 1 11 ... 2 6.700000 112 1 0.0 1 NaN NaN NaN 0
676 '1608' 263 22 1025 16630426 60000 26 16008 1 11 ... 3 -5.554552 41 1 0.0 1 NaN NaN NaN 0
677 '1608' 263 22 1025 16630426 60000 26 16008 1 11 ... 4 3.746593 42 1 0.0 1 NaN NaN NaN 0

678 rows × 44 columns

[12]:

cml.plot_map(pd, margins=2)
../_images/examples_13-icoads_5_0.png