Violin

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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)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * 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
• 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    , ..., 232.79999, 235.5    ,
           238.59999],
          [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
           239.29999],
          [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
           241.7    ],
          ...,
          [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
           294.69998],
          [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
           295.19998],
          [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
           296.6    ]],
  
         [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
           235.79999],
          [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
           235.7    ],
          [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
           238.5    ],
  ...
          [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
           294.29   ],
          [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
           294.38998],
          [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
           295.19   ]],
  
         [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
           241.79   ],
          [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
           241.68999],
          [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
           246.29   ],
          ...,
          [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
           294.69   ],
          [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
           295.19   ],
          [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
           295.69   ]]], dtype=float32)

• 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(Float64Index([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='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([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='float64', 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")

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