Violin

import hvplot.xarray  # noqa
import xarray as xr

violin plots are similar to box plots, but provide a better sense of the distribution of data. Note that violin plots depend on the scipy library.

ds = xr.tutorial.open_dataset('air_temperature').load()
air = ds.air
air

<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)> Size: 31MB
array([[[241.2 , 242.5 , 243.5 , ..., 232.8 , 235.5 , 238.6 ],
        [243.8 , 244.5 , 244.7 , ..., 232.8 , 235.3 , 239.3 ],
        [250.  , 249.8 , 248.89, ..., 233.2 , 236.39, 241.7 ],
        ...,
        [296.6 , 296.2 , 296.4 , ..., 295.4 , 295.1 , 294.7 ],
        [295.9 , 296.2 , 296.79, ..., 295.9 , 295.9 , 295.2 ],
        [296.29, 296.79, 297.1 , ..., 296.9 , 296.79, 296.6 ]],

       [[242.1 , 242.7 , 243.1 , ..., 232.  , 233.6 , 235.8 ],
        [243.6 , 244.1 , 244.2 , ..., 231.  , 232.5 , 235.7 ],
        [253.2 , 252.89, 252.1 , ..., 230.8 , 233.39, 238.5 ],
        ...,
        [296.4 , 295.9 , 296.2 , ..., 295.4 , 295.1 , 294.79],
        [296.2 , 296.7 , 296.79, ..., 295.6 , 295.5 , 295.1 ],
        [296.29, 297.2 , 297.4 , ..., 296.4 , 296.4 , 296.6 ]],

       [[242.3 , 242.2 , 242.3 , ..., 234.3 , 236.1 , 238.7 ],
        [244.6 , 244.39, 244.  , ..., 230.3 , 232.  , 235.7 ],
        [256.2 , 255.5 , 254.2 , ..., 231.2 , 233.2 , 238.2 ],
        ...,
...
        ...,
        [294.79, 295.29, 297.49, ..., 295.49, 295.39, 294.69],
        [296.79, 297.89, 298.29, ..., 295.49, 295.49, 294.79],
        [298.19, 299.19, 298.79, ..., 296.09, 295.79, 295.79]],

       [[245.79, 244.79, 243.49, ..., 243.29, 243.99, 244.79],
        [249.89, 249.29, 248.49, ..., 241.29, 242.49, 244.29],
        [262.39, 261.79, 261.29, ..., 240.49, 243.09, 246.89],
        ...,
        [293.69, 293.89, 295.39, ..., 295.09, 294.69, 294.29],
        [296.29, 297.19, 297.59, ..., 295.29, 295.09, 294.39],
        [297.79, 298.39, 298.49, ..., 295.69, 295.49, 295.19]],

       [[245.09, 244.29, 243.29, ..., 241.69, 241.49, 241.79],
        [249.89, 249.29, 248.39, ..., 239.59, 240.29, 241.69],
        [262.99, 262.19, 261.39, ..., 239.89, 242.59, 246.29],
        ...,
        [293.79, 293.69, 295.09, ..., 295.29, 295.09, 294.69],
        [296.09, 296.89, 297.19, ..., 295.69, 295.69, 295.19],
        [297.69, 298.09, 298.09, ..., 296.49, 296.19, 295.69]]])
Coordinates:
  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
  * time     (time) datetime64[ns] 23kB 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
• lat: 25
• lon: 53

• 241.2 242.5 243.5 244.0 244.1 243.9 ... 297.4 297.2 296.5 296.2 295.7

  array([[[241.2 , 242.5 , 243.5 , ..., 232.8 , 235.5 , 238.6 ],
          [243.8 , 244.5 , 244.7 , ..., 232.8 , 235.3 , 239.3 ],
          [250.  , 249.8 , 248.89, ..., 233.2 , 236.39, 241.7 ],
          ...,
          [296.6 , 296.2 , 296.4 , ..., 295.4 , 295.1 , 294.7 ],
          [295.9 , 296.2 , 296.79, ..., 295.9 , 295.9 , 295.2 ],
          [296.29, 296.79, 297.1 , ..., 296.9 , 296.79, 296.6 ]],
  
         [[242.1 , 242.7 , 243.1 , ..., 232.  , 233.6 , 235.8 ],
          [243.6 , 244.1 , 244.2 , ..., 231.  , 232.5 , 235.7 ],
          [253.2 , 252.89, 252.1 , ..., 230.8 , 233.39, 238.5 ],
          ...,
          [296.4 , 295.9 , 296.2 , ..., 295.4 , 295.1 , 294.79],
          [296.2 , 296.7 , 296.79, ..., 295.6 , 295.5 , 295.1 ],
          [296.29, 297.2 , 297.4 , ..., 296.4 , 296.4 , 296.6 ]],
  
         [[242.3 , 242.2 , 242.3 , ..., 234.3 , 236.1 , 238.7 ],
          [244.6 , 244.39, 244.  , ..., 230.3 , 232.  , 235.7 ],
          [256.2 , 255.5 , 254.2 , ..., 231.2 , 233.2 , 238.2 ],
          ...,
  ...
          ...,
          [294.79, 295.29, 297.49, ..., 295.49, 295.39, 294.69],
          [296.79, 297.89, 298.29, ..., 295.49, 295.49, 294.79],
          [298.19, 299.19, 298.79, ..., 296.09, 295.79, 295.79]],
  
         [[245.79, 244.79, 243.49, ..., 243.29, 243.99, 244.79],
          [249.89, 249.29, 248.49, ..., 241.29, 242.49, 244.29],
          [262.39, 261.79, 261.29, ..., 240.49, 243.09, 246.89],
          ...,
          [293.69, 293.89, 295.39, ..., 295.09, 294.69, 294.29],
          [296.29, 297.19, 297.59, ..., 295.29, 295.09, 294.39],
          [297.79, 298.39, 298.49, ..., 295.69, 295.49, 295.19]],
  
         [[245.09, 244.29, 243.29, ..., 241.69, 241.49, 241.79],
          [249.89, 249.29, 248.39, ..., 239.59, 240.29, 241.69],
          [262.99, 262.19, 261.39, ..., 239.89, 242.59, 246.29],
          ...,
          [293.79, 293.69, 295.09, ..., 295.29, 295.09, 294.69],
          [296.09, 296.89, 297.19, ..., 295.69, 295.69, 295.19],
          [297.69, 298.09, 298.09, ..., 296.49, 296.19, 295.69]]])

• Coordinates: (3)
  • lat
    (lat)
    float32
    75.0 72.5 70.0 ... 20.0 17.5 15.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,
           45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,
           15. ], dtype=float32)

  • lon
    (lon)
    float32
    200.0 202.5 205.0 ... 327.5 330.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. ], 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: (3)
  • lat
    PandasIndex

    PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,
           45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,
           15.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,
           225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,
           250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,
           275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,
           300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,
           325.0, 327.5, 330.0],
          dtype='float32', name='lon'))

  • 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 ]

air.hvplot.violin(y='air', by='lat', color='lat', cmap='Category20', title="Air Temperature vs. latitude")