Hist

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import hvplot.xarray  # noqa
import xarray as xr

hist is often a good way to start looking at data to get a sense of the distribution. Similar methods include kde (also available as density).

ds = xr.tutorial.open_dataset('air_temperature').load()
air = ds.air
air1d = air.sel(lon=285.,lat=40.)
air1d

<xarray.DataArray 'air' (time: 2920)>
array([276.  , 276.79, 276.  , ..., 271.09, 270.29, 270.49], dtype=float32)
Coordinates:
    lat      float32 40.0
    lon      float32 285.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

xarray.DataArray

'air'

• time: 2920

• 276.0 276.8 276.0 276.9 275.0 271.5 ... 273.1 272.3 271.1 270.3 270.5

  array([276.  , 276.79, 276.  , ..., 271.09, 270.29, 270.49], dtype=float32)

• Coordinates: (3)
  • lat
    ()
    float32
    40.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array(40., dtype=float32)

  • lon
    ()
    float32
    285.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array(285., dtype=float32)

  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2014-12-31T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',
           '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2014-12-29 12:00:00', '2014-12-29 18:00:00',
                   '2014-12-30 00:00:00', '2014-12-30 06:00:00',
                   '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                   '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                   '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=2920, freq=None))

• Attributes: (11)

  long_name :

  4xDaily Air temperature at sigma level 995

  units :

  degK

  precision :

  2

  GRIB_id :

  11

  GRIB_name :

  TMP

  var_desc :

  Air temperature

  dataset :

  NMC Reanalysis

  level_desc :

  Surface

  statistic :

  Individual Obs

  parent_stat :

  Other

  actual_range :

  [185.16 322.1 ]

air1d.hvplot.hist()

Customize the plot by changing the title and bar color.

air1d.hvplot.hist(title="Air Temperature over time at lat=40,lon285", color='gray')

This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.

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Download this notebook from GitHub (right-click to download).