arviz_base.extract

arviz_base.extract(data, group='posterior', sample_dims=None, *, combined=True, var_names=None, filter_vars=None, num_samples=None, weights=None, resampling_method=None, keep_dataset=False, random_seed=None)

Extract a group or group subset from a DataTree.

Parameters:

idataDataTree-like

DataTree from which to extract the data.

groupstr, optional

Which group to extract data from.

sample_dimssequence of hashable, optional

List of dimensions that should be considered sampling dimensions. Random subsets and potential stacking if combine=True happen over these dimensions only. Defaults to rcParams["data.sample_dims"].

combinedbool, optional

Combine sample_dims dimensions into sample. Won’t work if a dimension named sample already exists. It is irrelevant and ignored when sample_dims is a single dimension.

var_namesstr or list of str, optional

Variables to be extracted. Prefix the variables by when you want to exclude them.

filter_vars{None, “like”, “regex”}, optional

If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A la pandas.filter. Like with plotting, sometimes it’s easier to subset saying what to exclude instead of what to include

num_samplesint, optional

Extract only a subset of the samples. Only valid if combined=True or sample_dims represents a single dimension.

weightsarray_like, optional

Extract a weighted subset of the samples. Only valid if num_samples is not None.

resampling_methodstr, optional

Method to use for resampling. Default is “multinomial”. Options are “multinomial” and “stratified”. For stratified resampling, weights must be provided. Default is “stratified” if weights are provided, “multinomial” otherwise.

keep_datasetbool, optional

If true, always return a DataSet. If false (default) return a DataArray when there is a single variable.

random_seedint, numpy.Generator, optional

Random number generator or seed. Only used if weights is not None or if num_samples is not None.

Returns:

xarray.DataArray or xarray.Dataset

Examples

The default behaviour is to return the posterior group after stacking the chain and draw dimensions.

import arviz_base as az
idata = az.load_arviz_data("centered_eight")
az.extract(idata)

<xarray.Dataset> Size: 209kB
Dimensions:  (sample: 2000, school: 8)
Coordinates:
  * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  * sample   (sample) object 16kB MultiIndex
  * chain    (sample) int64 16kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3
  * draw     (sample) int64 16kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
Data variables:
    mu       (sample) float64 16kB 7.872 3.385 9.1 7.304 ... 1.767 3.486 3.404
    theta    (school, sample) float64 128kB 12.32 11.29 5.709 ... 8.452 1.295
    tau      (sample) float64 16kB 4.726 3.909 4.844 1.857 ... 2.741 2.932 4.461
Attributes:
    created_at:                 2022-10-13T14:37:37.315398
    arviz_version:              0.13.0.dev0
    inference_library:          pymc
    inference_library_version:  4.2.2
    sampling_time:              7.480114936828613
    tuning_steps:               1000

xarray.Dataset

• Dimensions:
  • sample: 2000
  • school: 8
• Coordinates: (4)
  • school
    (school)
    <U16
    'Choate' ... 'Mt. Hermon'

    array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • sample
    (sample)
    object
    MultiIndex

    [2000 values with dtype=object]

  • chain
    (sample)
    int64
    0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3

    [2000 values with dtype=int64]

  • draw
    (sample)
    int64
    0 1 2 3 4 5 ... 495 496 497 498 499

    [2000 values with dtype=int64]

• Data variables: (3)
  • mu
    (sample)
    float64
    7.872 3.385 9.1 ... 3.486 3.404

    array([7.87179637, 3.38455431, 9.10047569, ..., 1.76673325, 3.48611194,
           3.40446391], shape=(2000,))

  • theta
    (school, sample)
    float64
    12.32 11.29 5.709 ... 8.452 1.295

    array([[12.32068558, 11.28562322,  5.70850575, ...,  3.53251469,
             4.1827505 ,  0.19295578],
           [ 9.90536689,  9.12932369,  5.7579324 , ...,  2.00890089,
             7.55425055,  6.4984278 ],
           [14.9516155 ,  3.13926327, 10.94458539, ...,  0.51080602,
             4.45603444, -0.89442402],
           ...,
           [16.90179529,  2.39308809,  8.14332707, ...,  4.70724881,
             1.5289581 ,  7.93646013],
           [13.19805933, 10.05522282,  7.60475341, ...,  3.07331446,
             1.09609826,  6.76245459],
           [15.06136584,  6.17672421,  8.76764689, ..., -2.62306892,
             8.45228161,  1.2950506 ]], shape=(8, 2000))

  • tau
    (sample)
    float64
    4.726 3.909 4.844 ... 2.932 4.461

    array([4.72574006, 3.90899361, 4.8440252 , ..., 2.74060666, 2.93237926,
           4.46124596], shape=(2000,))

• Indexes: (2)
  • school
    PandasIndex

    PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
          dtype='object', name='school'))

  • sample
    chain
    draw
    PandasMultiIndex

    PandasIndex(MultiIndex([(0,   0),
                (0,   1),
                (0,   2),
                (0,   3),
                (0,   4),
                (0,   5),
                (0,   6),
                (0,   7),
                (0,   8),
                (0,   9),
                ...
                (3, 490),
                (3, 491),
                (3, 492),
                (3, 493),
                (3, 494),
                (3, 495),
                (3, 496),
                (3, 497),
                (3, 498),
                (3, 499)],
               name='sample', length=2000))

• Attributes: (6)

  created_at :

  2022-10-13T14:37:37.315398

  arviz_version :

  0.13.0.dev0

  inference_library :

  pymc

  inference_library_version :

  4.2.2

  sampling_time :

  7.480114936828613

  tuning_steps :

  1000

You can also indicate a subset to be returned, but in variables and in samples:

az.extract(idata, var_names="theta", num_samples=100)

<xarray.DataArray 'theta' (school: 8, sample: 100)> Size: 6kB
array([[ 3.09387429e+01,  5.71620186e+00,  8.14716772e+00,
         1.22880450e+01, -8.12128199e+00,  6.32454995e+00,
         1.25833151e+01,  7.35127382e+00,  6.61626771e+00,
         6.97027172e+00,  3.34359524e+00,  3.21324974e-01,
         2.86064613e+00,  8.15327277e+00,  2.28772366e+00,
         2.10585946e+00,  7.80435210e+00,  9.54687800e+00,
         2.07538479e+00,  7.68522224e+00,  3.11147235e+00,
         7.03102581e+00,  7.88942950e+00,  8.64601026e+00,
         1.59722640e+01,  4.07866337e+00,  3.98511536e-01,
         8.06311068e+00,  1.26162675e+01,  9.44361810e+00,
         7.57793225e-01,  1.64329887e+01,  1.20049894e+01,
         2.97785044e+00, -1.13886614e+00,  1.04025013e+01,
         4.53814410e+00,  8.03517762e+00,  9.15852024e+00,
         5.83123935e+00,  1.00726585e+01,  8.65007191e+00,
        -3.17824163e+00,  3.54038165e+00,  6.10959984e+00,
         7.21919702e+00, -6.50164631e-01,  7.05688130e+00,
        -2.02773042e+00,  1.83440742e+01,  3.06489534e+00,
         3.33922737e+00,  8.06378285e-01,  1.69945398e+01,
         1.68496500e+01,  6.03475550e+00,  1.13370369e+01,
         8.93305013e+00,  7.03588032e+00,  1.22060355e+01,
...
        -8.11232046e+00,  5.79259069e+00,  7.63541882e+00,
         2.56752133e+00, -1.02702293e+00,  5.88696762e+00,
         3.78957862e-01, -2.76035595e+00,  4.53714710e+00,
         7.18026928e+00, -3.58286664e+00,  6.65588464e+00,
         7.86258400e+00,  5.30239912e+00,  7.84375303e+00,
         8.79079955e+00,  7.42415492e+00,  1.82571418e+00,
         3.77671414e+00,  8.77336892e+00,  5.59107632e+00,
         1.50613658e+01,  7.47430176e+00,  1.78499767e+00,
         3.41427310e+00,  2.94760112e+00, -1.16496750e-01,
         1.75683067e+01,  2.85143502e+00,  5.16522809e+00,
         1.15479225e+01,  6.57878387e+00,  3.14127906e+00,
         2.34452622e+00,  2.69293481e+00, -2.07833386e+00,
         3.93829921e+00,  7.20084454e+00, -6.25581924e+00,
         8.52437956e+00,  3.14824203e+00,  9.55063157e-01,
        -4.86167897e+00,  5.08930303e+00,  7.92068098e+00,
         4.18385037e+00,  3.35132225e+00,  6.80602415e+00,
         9.24872843e+00,  1.40735354e+01, -2.58684632e+00,
         6.99697482e+00,  4.95648908e+00,  5.24910668e+00,
         6.99697482e+00,  1.06309246e+01,  6.30089763e+00,
         3.59552609e+00]])
Coordinates:
  * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  * sample   (sample) object 800B MultiIndex
  * chain    (sample) int64 800B 3 0 2 3 1 0 0 3 1 1 2 ... 2 3 3 2 3 1 1 3 3 2 1
  * draw     (sample) int64 800B 157 462 170 3 19 151 28 ... 487 72 123 40 5 260

xarray.DataArray

'theta'

• school: 8
• sample: 100

• 30.94 5.716 8.147 12.29 -8.121 6.325 ... 5.249 6.997 10.63 6.301 3.596

  array([[ 3.09387429e+01,  5.71620186e+00,  8.14716772e+00,
           1.22880450e+01, -8.12128199e+00,  6.32454995e+00,
           1.25833151e+01,  7.35127382e+00,  6.61626771e+00,
           6.97027172e+00,  3.34359524e+00,  3.21324974e-01,
           2.86064613e+00,  8.15327277e+00,  2.28772366e+00,
           2.10585946e+00,  7.80435210e+00,  9.54687800e+00,
           2.07538479e+00,  7.68522224e+00,  3.11147235e+00,
           7.03102581e+00,  7.88942950e+00,  8.64601026e+00,
           1.59722640e+01,  4.07866337e+00,  3.98511536e-01,
           8.06311068e+00,  1.26162675e+01,  9.44361810e+00,
           7.57793225e-01,  1.64329887e+01,  1.20049894e+01,
           2.97785044e+00, -1.13886614e+00,  1.04025013e+01,
           4.53814410e+00,  8.03517762e+00,  9.15852024e+00,
           5.83123935e+00,  1.00726585e+01,  8.65007191e+00,
          -3.17824163e+00,  3.54038165e+00,  6.10959984e+00,
           7.21919702e+00, -6.50164631e-01,  7.05688130e+00,
          -2.02773042e+00,  1.83440742e+01,  3.06489534e+00,
           3.33922737e+00,  8.06378285e-01,  1.69945398e+01,
           1.68496500e+01,  6.03475550e+00,  1.13370369e+01,
           8.93305013e+00,  7.03588032e+00,  1.22060355e+01,
  ...
          -8.11232046e+00,  5.79259069e+00,  7.63541882e+00,
           2.56752133e+00, -1.02702293e+00,  5.88696762e+00,
           3.78957862e-01, -2.76035595e+00,  4.53714710e+00,
           7.18026928e+00, -3.58286664e+00,  6.65588464e+00,
           7.86258400e+00,  5.30239912e+00,  7.84375303e+00,
           8.79079955e+00,  7.42415492e+00,  1.82571418e+00,
           3.77671414e+00,  8.77336892e+00,  5.59107632e+00,
           1.50613658e+01,  7.47430176e+00,  1.78499767e+00,
           3.41427310e+00,  2.94760112e+00, -1.16496750e-01,
           1.75683067e+01,  2.85143502e+00,  5.16522809e+00,
           1.15479225e+01,  6.57878387e+00,  3.14127906e+00,
           2.34452622e+00,  2.69293481e+00, -2.07833386e+00,
           3.93829921e+00,  7.20084454e+00, -6.25581924e+00,
           8.52437956e+00,  3.14824203e+00,  9.55063157e-01,
          -4.86167897e+00,  5.08930303e+00,  7.92068098e+00,
           4.18385037e+00,  3.35132225e+00,  6.80602415e+00,
           9.24872843e+00,  1.40735354e+01, -2.58684632e+00,
           6.99697482e+00,  4.95648908e+00,  5.24910668e+00,
           6.99697482e+00,  1.06309246e+01,  6.30089763e+00,
           3.59552609e+00]])

• Coordinates: (4)
  • school
    (school)
    <U16
    'Choate' ... 'Mt. Hermon'

    array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • sample
    (sample)
    object
    MultiIndex

    [100 values with dtype=object]

  • chain
    (sample)
    int64
    3 0 2 3 1 0 0 3 ... 2 3 1 1 3 3 2 1

    [100 values with dtype=int64]

  • draw
    (sample)
    int64
    157 462 170 3 19 ... 123 40 5 260

    [100 values with dtype=int64]

• Indexes: (2)
  • school
    PandasIndex

    PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
          dtype='object', name='school'))

  • sample
    chain
    draw
    PandasMultiIndex

    PandasIndex(MultiIndex([(3, 157),
                (0, 462),
                (2, 170),
                (3,   3),
                (1,  19),
                (0, 151),
                (0,  28),
                (3, 439),
                (1, 489),
                (1, 419),
                (2, 358),
                (3, 252),
                (2, 193),
                (2,  34),
                (3, 229),
                (2, 329),
                (0, 434),
                (3, 341),
                (0, 177),
                (2, 458),
                (2, 232),
                (3, 310),
                (3, 128),
                (1, 116),
                (3,  70),
                (1,  89),
                (0, 402),
                (0, 125),
                (2, 228),
                (3,  46),
                (0,  82),
                (0, 172),
                (2,  13),
                (1, 114),
                (3, 454),
                (0, 498),
                (0, 394),
                (1, 369),
                (0, 113),
                (0, 480),
                (1, 354),
                (1, 250),
                (2, 101),
                (3, 325),
                (2, 407),
                (0, 159),
                (1, 223),
                (1, 156),
                (0, 284),
                (2, 234),
                (0, 341),
                (1, 300),
                (2,  98),
                (2, 222),
                (1, 198),
                (0, 131),
                (3, 246),
                (0,  99),
                (2, 456),
                (1, 167),
                (1,  88),
                (0,  50),
                (3, 304),
                (0,   0),
                (0, 337),
                (1, 151),
                (1,  65),
                (1,  59),
                (3, 455),
                (0, 363),
                (1, 415),
                (0, 410),
                (0, 342),
                (0,   6),
                (1, 394),
                (3, 175),
                (1, 416),
                (2,   9),
                (0, 242),
                (3,  17),
                (1, 345),
                (3,  85),
                (3, 180),
                (1,  32),
                (1, 373),
                (2, 463),
                (1, 306),
                (1, 322),
                (1, 448),
                (2, 345),
                (3,  31),
                (3, 362),
                (2, 159),
                (3, 117),
                (1, 487),
                (1,  72),
                (3, 123),
                (3,  40),
                (2,   5),
                (1, 260)],
               name='sample'))

• Attributes: (0)

To keep the chain and draw dimensions, use combined=False.

az.extract(idata, group="prior", combined=False)

<xarray.Dataset> Size: 45kB
Dimensions:  (chain: 1, draw: 500, school: 8)
Coordinates:
  * chain    (chain) int64 8B 0
  * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
  * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
Data variables:
    tau      (chain, draw) float64 4kB 1.941 3.388 4.208 ... 0.06893 2.145
    theta    (chain, draw, school) float64 32kB 4.866 4.59 ... -2.031 6.045
    mu       (chain, draw) float64 4kB 3.903 3.915 -1.751 ... 0.7908 2.869
Attributes:
    arviz_version:              0.13.0.dev0
    created_at:                 2022-10-13T14:37:26.602116
    inference_library:          pymc
    inference_library_version:  4.2.2

xarray.Dataset

• Dimensions:
  • chain: 1
  • draw: 500
  • school: 8
• Coordinates: (3)
  • chain
    (chain)
    int64
    0

    array([0])

  • draw
    (draw)
    int64
    0 1 2 3 4 5 ... 495 496 497 498 499

    array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • school
    (school)
    <U16
    'Choate' ... 'Mt. Hermon'

    array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

• Data variables: (3)
  • tau
    (chain, draw)
    float64
    1.941 3.388 4.208 ... 0.06893 2.145

    array([[1.94060202e+00, 3.38786056e+00, 4.20819481e+00, 5.68742111e+00,
            4.82244460e+00, 7.84969782e+00, 2.41850082e+00, 1.11683428e+00,
            5.10984626e-01, 1.67640274e+00, 8.37550751e+00, 3.55750200e+01,
            1.87888479e+01, 7.38007966e+01, 7.19904857e+00, 2.97111678e+00,
            2.38009906e+00, 8.61688371e-01, 1.82005508e+00, 7.05099843e-01,
            3.73935694e+00, 8.73489254e+00, 1.39668221e+00, 2.17078500e+01,
            9.59674138e+00, 3.15703268e+00, 3.33847615e+00, 3.51144898e+01,
            6.80634203e+00, 4.11871168e+00, 4.93255227e+00, 4.83826812e+00,
            2.72760625e+01, 5.24910941e+00, 2.85667668e+00, 4.28176247e+00,
            2.28124585e+01, 1.34945279e+01, 9.68820812e+00, 1.07728226e+01,
            1.20481030e+02, 3.31906378e-01, 4.63523234e-01, 1.26739022e+00,
            2.51130780e+03, 5.07713155e+00, 9.28824918e-01, 9.14991935e+00,
            4.66414362e+01, 8.84758086e+01, 3.00341165e-01, 7.30464820e-01,
            3.40976947e+01, 2.99629266e+01, 2.26765592e+00, 1.78358355e+01,
            3.30808448e+01, 3.88184972e+00, 8.84199075e-01, 1.51343747e+01,
            2.65672354e+00, 1.31077618e+00, 2.25382327e+01, 1.22813964e+02,
            7.85565515e+00, 7.41863308e-01, 9.49848204e+00, 1.41059847e+02,
            9.25655368e-01, 1.78766382e+00, 2.62553715e+00, 8.58588175e-01,
            1.74600321e+01, 4.62398480e+00, 1.01280222e+00, 5.74512287e+00,
            1.26181092e+01, 4.56117182e+00, 1.03129481e+01, 1.05807520e+02,
    ...
            4.44939392e+00, 6.07065997e+01, 3.09150476e+00, 1.07895462e+00,
            3.40577271e+01, 8.87566932e+03, 5.14275783e-01, 2.34873432e+00,
            4.28166200e+00, 6.11436072e+00, 7.02201974e+00, 9.11145054e-02,
            5.06762347e+00, 5.54001194e+00, 1.65166669e+00, 4.62314520e+00,
            2.79342562e+01, 8.20769981e+00, 1.34952820e+01, 8.67771773e-01,
            2.67941929e+00, 5.78094132e+00, 4.08853487e+01, 1.21540351e+01,
            6.21499874e+00, 2.05937430e+01, 5.18603595e+00, 5.93430042e+00,
            1.27090430e+01, 9.37798481e+00, 3.80986538e+00, 1.14897162e+03,
            6.66454695e+01, 1.68318555e+01, 6.50185117e+01, 1.77443210e+00,
            1.98821067e+01, 1.48732781e+00, 5.32400098e+01, 8.90298404e-01,
            9.74456150e+00, 3.17898046e+00, 4.64437308e+00, 1.67900077e+00,
            2.11625865e+00, 5.88562332e-01, 8.72761717e+00, 3.23788264e+01,
            2.62725682e+00, 2.48389110e+00, 5.78998327e+00, 4.86828624e+00,
            8.96244600e+00, 9.85486905e-01, 4.35475195e+00, 8.21724160e+00,
            3.67358130e+00, 3.19700555e-01, 4.32539022e+00, 7.87027890e-01,
            2.16835786e+00, 3.34426424e+00, 1.67688831e+01, 9.15246738e-01,
            5.68705452e+00, 4.86622906e+00, 2.32790878e+00, 3.16238422e+01,
            8.20289530e+00, 1.59128252e+03, 1.70394780e+00, 2.42565587e+00,
            7.62767038e+00, 2.41576900e+01, 4.25452773e+01, 1.21193875e+00,
            1.02716549e+01, 8.35319808e-01, 6.89347686e-02, 2.14488132e+00]])

  • theta
    (chain, draw, school)
    float64
    4.866 4.59 -0.7404 ... -2.031 6.045

    array([[[ 4.86620856,  4.58956728, -0.74044057, ...,  3.9610381 ,
              2.65493249,  6.01863257],
            [ 4.37914099,  5.96929909, -2.97367789, ..., 11.03055872,
              5.05284821,  2.67610001],
            [-1.19602373, -9.83468642,  2.28396607, ..., -1.73761977,
              8.42950283,  2.36634401],
            ...,
            [-2.81086618, -3.98468829, -2.08099901, ..., -2.53012815,
             -2.05930505, -3.19172734],
            [ 0.81207434,  0.79427128,  0.83181532, ...,  0.79266084,
              0.86104331,  0.7793056 ],
            [ 3.57736989,  3.53795807,  5.30449351, ...,  3.3301618 ,
             -2.03071509,  6.04538653]]], shape=(1, 500, 8))

  • mu
    (chain, draw)
    float64
    3.903 3.915 -1.751 ... 0.7908 2.869

    array([[ 3.90253102e+00,  3.91500660e+00, -1.75114302e+00,
             2.59485538e+00, -4.44296321e-01,  7.61000307e+00,
            -1.04548271e+01, -6.00723208e+00, -2.99616365e+00,
             4.75484334e+00, -7.53597289e-01, -4.49206911e+00,
             1.22966674e+00, -1.17350445e+00, -1.14598007e+00,
            -1.10634181e+01,  6.31763132e+00,  1.73772124e+00,
            -4.91208423e-01, -2.75385285e+00, -3.41248265e+00,
             1.42264349e+00,  4.11819735e-01,  1.73585996e-01,
            -5.07312782e+00, -9.99257696e+00, -6.56537987e+00,
            -6.24410186e+00,  1.30989418e+00,  1.92730225e+00,
             9.19697020e+00,  8.53506427e+00,  3.39755738e+00,
            -4.93707846e+00, -7.33345648e+00,  2.98252256e+00,
            -3.18159106e-01,  2.27959142e+00, -4.62958545e+00,
            -9.70160468e-02, -1.60798088e+00, -6.00729427e+00,
             8.37985516e+00,  9.52705402e-01,  1.47243794e+00,
            -4.65377145e+00,  7.28348474e-01, -3.06384730e+00,
            -6.15183994e+00, -1.41063529e+00,  5.31880456e+00,
             4.28382637e-01, -8.23432363e+00,  1.22762109e+00,
            -8.29777595e-01,  4.21780345e+00,  5.62271511e-01,
            -1.17786709e+00,  8.09037873e+00, -6.71477871e-01,
    ...
             4.19794394e+00,  3.37369715e+00,  4.68554077e+00,
             2.98601993e+00, -9.56558363e+00, -4.80824536e-01,
             2.32683687e+00,  1.84240293e+00,  4.31505480e+00,
             1.78131745e+00,  4.74830962e+00, -2.51044292e-01,
            -6.19005739e+00, -6.80007397e+00,  5.41635224e-01,
             2.53050533e+00, -1.22188016e+00,  5.05691442e+00,
             2.03910664e+00, -5.22800335e-02, -1.12511498e+00,
             6.59945576e+00, -3.58516561e+00,  7.22804145e+00,
             4.59556418e+00, -4.82300404e+00,  4.70726322e+00,
            -3.32578499e+00,  3.15320672e+00,  1.14604995e+01,
            -4.35587322e+00,  7.06014478e+00,  2.34213481e+00,
            -1.31820085e+00,  3.72632528e+00, -9.31774491e-01,
            -5.03118301e+00,  2.59806644e+00, -2.02997029e+00,
            -7.30940496e+00, -2.53759146e+00, -3.66411956e+00,
             9.38270535e+00,  3.79591101e+00, -5.59892859e-01,
             1.18231368e+01,  1.26943270e+01,  5.77152605e+00,
            -7.33882918e+00,  2.08163637e+00, -4.50230861e+00,
            -1.39653308e-01, -1.04822114e-01,  2.08910055e+00,
            -6.44041200e+00, -6.60626614e-01, -2.29356629e+00,
             7.90828077e-01,  2.86879752e+00]])

• Indexes: (3)
  • chain
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='chain'))

  • draw
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
          dtype='int64', name='draw', length=500))

  • school
    PandasIndex

    PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
           'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
          dtype='object', name='school'))

• Attributes: (4)

  arviz_version :

  0.13.0.dev0

  created_at :

  2022-10-13T14:37:26.602116

  inference_library :

  pymc

  inference_library_version :

  4.2.2