arviz.InferenceData.map

InferenceData.map(fun, groups=None, filter_groups=None, inplace=False, args=None, **kwargs)

Apply a function to multiple groups.

Applies fun groupwise to the selected InferenceData groups and overwrites the group with the result of the function.

Parameters:

funcallable()

Function to be applied to each group. Assumes the function is called as fun(dataset, *args, **kwargs).

groupsstr or list of str, optional

Groups where the selection is to be applied. Can either be group names or metagroup names.

filter_groups{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.

inplacebool, optional

If True, modify the InferenceData object inplace, otherwise, return the modified copy.

argsarray_like, optional

Positional arguments passed to fun.

**kwargsmapping, optional

Keyword arguments passed to fun.

Returns:

InferenceData

A new InferenceData object by default. When inplace==True perform selection in place and return None

Examples

Shift observed_data, prior_predictive and posterior_predictive.

import arviz as az
import numpy as np
idata = az.load_arviz_data("non_centered_eight")
idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_vars")
idata_shifted_obs

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:  (chain: 4, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 0 1 2 3
    * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
    * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
  Data variables:
      mu       (chain, draw) float64 ...
      theta_t  (chain, draw, school) float64 ...
      tau      (chain, draw) float64 ...
      theta    (chain, draw, school) float64 ...
  Attributes:
      created_at:                 2022-10-13T14:37:26.351883
      arviz_version:              0.13.0.dev0
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              4.738754749298096
      tuning_steps:               1000

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • school: 8
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • school
      (school)
      object
      'Choate' ... 'Mt. Hermon'

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

  • Data variables: (4)
    • mu
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • theta_t
      (chain, draw, school)
      float64
      ...

      [16000 values with dtype=float64]

    • tau
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • theta
      (chain, draw, school)
      float64
      ...

      [16000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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: (6)

    created_at :

    2022-10-13T14:37:26.351883

    arviz_version :

    0.13.0.dev0

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    4.738754749298096

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, obs_dim_0: 8)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * obs_dim_0  (obs_dim_0) int64 0 1 2 3 4 5 6 7
  Data variables:
      obs        (chain, draw, obs_dim_0) float64 -8.912 0.4851 ... 13.55 27.95
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:34.333731
      inference_library:          pymc
      inference_library_version:  4.2.2

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • obs_dim_0: 8
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • obs_dim_0
      (obs_dim_0)
      int64
      0 1 2 3 4 5 6 7

      array([0, 1, 2, 3, 4, 5, 6, 7])

  • Data variables: (1)
    • obs
      (chain, draw, obs_dim_0)
      float64
      -8.912 0.4851 -4.71 ... 13.55 27.95

      array([[[ -8.91164858,   0.48513691,  -4.70995568, ...,  25.54688492,
                13.94575018,   9.80299148],
              [ 37.4801699 , -10.77370835,  -0.5719224 , ...,   0.43335956,
                27.32805956,  14.09089747],
              [ 17.70959145, -11.12171465, -21.98355095, ...,  19.10421895,
                 8.85410192,  -8.10962076],
              ...,
              [ 27.27407986,   7.61924923,  26.24515535, ...,  -0.3350811 ,
                11.69606961,  39.63282956],
              [  4.79252722,   0.43572688,  -9.33809594, ...,   9.92145935,
                -5.72201279,   4.97166968],
              [  8.34887428,   8.83802449,   4.87002072, ...,   9.48416992,
                -5.19603508,  -1.88720116]],
      
             [[ 14.660454  ,  14.56500642,  30.54658322, ...,   0.820571  ,
                17.58317036,  11.41523916],
              [ 12.23886235,  17.66178128,  10.9959666 , ...,  19.48767939,
                31.47333474,  14.07945178],
              [ 33.47381555,  23.8333277 ,  -0.67617714, ...,   7.95021076,
                 8.66776442,  36.76402444],
      ...
              [ 45.14657727,  19.31827266, -13.77595216, ...,  21.35397137,
                12.08007358,  19.71077765],
              [ 14.1760222 ,  -4.54869822,   8.69720246, ...,   7.5198413 ,
                -9.63781631, -15.25261963],
              [  9.0665652 , -24.09127382,  -5.36918153, ...,  27.94605585,
                -5.79860404, -35.2355531 ]],
      
             [[ 27.4155747 ,  27.81444338,  22.10513881, ...,  12.74755666,
                14.5126698 ,  14.57236846],
              [ -3.76124287, -20.82045621,  31.33247223, ...,  13.29821842,
                 1.41832433,  16.34246786],
              [ -7.00712196,  -7.93492977,   9.95458309, ..., -11.32537975,
                21.94623035,  10.70530684],
              ...,
              [ 13.80440934,  15.44637947, -10.90413704, ...,  10.01640909,
                16.47727939,  31.34665798],
              [-11.99095788,   5.75164919,  28.06463307, ...,   3.68615864,
               -12.96358662, -10.4495535 ],
              [ 12.81063159,  14.01071347,  17.28607254, ...,   7.91079619,
                13.55415258,  27.94927617]]])

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_dim_0
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:34.333731

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• log_likelihood

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, obs_dim_0: 8)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * obs_dim_0  (obs_dim_0) int64 0 1 2 3 4 5 6 7
  Data variables:
      obs        (chain, draw, obs_dim_0) float64 ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.571887
      inference_library:          pymc
      inference_library_version:  4.2.2

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • obs_dim_0: 8
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • obs_dim_0
      (obs_dim_0)
      int64
      0 1 2 3 4 5 6 7

      array([0, 1, 2, 3, 4, 5, 6, 7])

  • Data variables: (1)
    • obs
      (chain, draw, obs_dim_0)
      float64
      ...

      [16000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_dim_0
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.571887

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:              (chain: 4, draw: 500)
  Coordinates:
    * chain                (chain) int64 0 1 2 3
    * draw                 (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499
  Data variables: (12/16)
      lp                   (chain, draw) float64 ...
      largest_eigval       (chain, draw) float64 ...
      perf_counter_start   (chain, draw) float64 ...
      perf_counter_diff    (chain, draw) float64 ...
      step_size            (chain, draw) float64 ...
      diverging            (chain, draw) bool ...
      ...                   ...
      max_energy_error     (chain, draw) float64 ...
      n_steps              (chain, draw) float64 ...
      step_size_bar        (chain, draw) float64 ...
      energy_error         (chain, draw) float64 ...
      smallest_eigval      (chain, draw) float64 ...
      index_in_trajectory  (chain, draw) int64 ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.362154
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              4.738754749298096
      tuning_steps:               1000

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

  • Data variables: (16)
    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • largest_eigval
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • perf_counter_start
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • perf_counter_diff
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • process_time_diff
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • acceptance_rate
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • n_steps
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • smallest_eigval
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • index_in_trajectory
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

  • Attributes: (6)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.362154

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    4.738754749298096

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset>
  Dimensions:  (chain: 1, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 0
    * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
    * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
  Data variables:
      mu       (chain, draw) float64 ...
      theta_t  (chain, draw, school) float64 ...
      theta    (chain, draw, school) float64 ...
      tau      (chain, draw) float64 ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:18.108887
      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])

    • school
      (school)
      object
      'Choate' ... 'Mt. Hermon'

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

  • Data variables: (4)
    • mu
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • theta_t
      (chain, draw, school)
      float64
      ...

      [4000 values with dtype=float64]

    • theta
      (chain, draw, school)
      float64
      ...

      [4000 values with dtype=float64]

    • tau
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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 :

    2022-10-13T14:37:18.108887

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• prior_predictive

  <xarray.Dataset>
  Dimensions:    (chain: 1, draw: 500, obs_dim_0: 8)
  Coordinates:
    * chain      (chain) int64 0
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * obs_dim_0  (obs_dim_0) int64 0 1 2 3 4 5 6 7
  Data variables:
      obs        (chain, draw, obs_dim_0) float64 25.87 -10.84 ... -4.658 17.32
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:18.111951
      inference_library:          pymc
      inference_library_version:  4.2.2

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • obs_dim_0: 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])

    • obs_dim_0
      (obs_dim_0)
      int64
      0 1 2 3 4 5 6 7

      array([0, 1, 2, 3, 4, 5, 6, 7])

  • Data variables: (1)
    • obs
      (chain, draw, obs_dim_0)
      float64
      25.87 -10.84 64.31 ... -4.658 17.32

      array([[[  25.86573102,  -10.83562762,   64.30603396, ...,
                 34.12327354,  -31.15539197,   17.40041595],
              [  21.2308377 ,  -15.94389655,   26.47959507, ...,
                 27.48371569,   12.54871934,   12.21388191],
              [ -14.10920978,   30.7429682 ,  -13.36386214, ...,
                 17.25866094,  -20.52897454,  -14.60687912],
              ...,
              [ -26.10606168,   13.11608297,   15.12723183, ...,
                 17.87300987,    7.58948329,   12.374558  ],
              [ -38.39050122,  -45.43587181,  -47.4225284 , ...,
                  6.77697971, -168.96059997,    1.26974321],
              [  28.73909128,   20.70475211,    3.50257694, ...,
                 20.58279319,   -4.65758653,   17.32397509]]])

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_dim_0
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:18.111951

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:    (obs_dim_0: 8)
  Coordinates:
    * obs_dim_0  (obs_dim_0) int64 0 1 2 3 4 5 6 7
  Data variables:
      obs        (obs_dim_0) float64 31.0 11.0 0.0 10.0 2.0 4.0 21.0 15.0
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:18.113060
      inference_library:          pymc
      inference_library_version:  4.2.2

  xarray.Dataset

  • Dimensions:
    • obs_dim_0: 8
  • Coordinates: (1)
    • obs_dim_0
      (obs_dim_0)
      int64
      0 1 2 3 4 5 6 7

      array([0, 1, 2, 3, 4, 5, 6, 7])

  • Data variables: (1)
    • obs
      (obs_dim_0)
      float64
      31.0 11.0 0.0 ... 4.0 21.0 15.0

      array([31., 11.,  0., 10.,  2.,  4., 21., 15.])

  • Indexes: (1)
    • obs_dim_0
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:18.113060

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• constant_data

  <xarray.Dataset>
  Dimensions:  (school: 8)
  Coordinates:
    * school   (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'
  Data variables:
      scores   (school) float64 ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:18.114126
      inference_library:          pymc
      inference_library_version:  4.2.2

  xarray.Dataset

  • Dimensions:
    • school: 8
  • Coordinates: (1)
    • school
      (school)
      object
      'Choate' ... 'Mt. Hermon'

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

  • Data variables: (1)
    • scores
      (school)
      float64
      ...

      [8 values with dtype=float64]

  • Indexes: (1)
    • 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 :

    2022-10-13T14:37:18.114126

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  

Rename and update the coordinate values in both posterior and prior groups.

idata = az.load_arviz_data("radon")
idata = idata.map(
    lambda ds: ds.rename({"g_coef": "uranium_coefs"}).assign(
        uranium_coefs=["intercept", "u_slope"]
    ),
    groups=["posterior", "prior"]
)
idata

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:        (chain: 4, draw: 500, uranium_coefs: 2, County: 85)
  Coordinates:
    * chain          (chain) int64 0 1 2 3
    * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * uranium_coefs  (uranium_coefs) <U9 'intercept' 'u_slope'
    * County         (County) object 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
  Data variables:
      g              (chain, draw, uranium_coefs) float64 ...
      za_county      (chain, draw, County) float64 ...
      b              (chain, draw) float64 ...
      sigma_a        (chain, draw) float64 ...
      a              (chain, draw, County) float64 ...
      a_county       (chain, draw, County) float64 ...
      sigma          (chain, draw) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.191355
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2
      sampling_time:              18.096983432769775
      tuning_steps:               1000

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • uranium_coefs: 2
    • County: 85
  • Coordinates: (4)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • uranium_coefs
      (uranium_coefs)
      <U9
      'intercept' 'u_slope'

      array(['intercept', 'u_slope'], dtype='<U9')

    • County
      (County)
      object
      'AITKIN' ... 'YELLOW MEDICINE'

      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'], dtype=object)

  • Data variables: (7)
    • g
      (chain, draw, uranium_coefs)
      float64
      ...

      [4000 values with dtype=float64]

    • za_county
      (chain, draw, County)
      float64
      ...

      [170000 values with dtype=float64]

    • b
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • sigma_a
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • a
      (chain, draw, County)
      float64
      ...

      [170000 values with dtype=float64]

    • a_county
      (chain, draw, County)
      float64
      ...

      [170000 values with dtype=float64]

    • sigma
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • uranium_coefs
      PandasIndex

      PandasIndex(Index(['intercept', 'u_slope'], dtype='object', name='uranium_coefs'))

    • County
      PandasIndex

      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'],
            dtype='object', name='County'))

  • Attributes: (6)

    created_at :

    2020-07-24T18:15:12.191355

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

    sampling_time :

    18.096983432769775

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:  (chain: 4, draw: 500, obs_id: 919)
  Coordinates:
    * chain    (chain) int64 0 1 2 3
    * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
    * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
  Data variables:
      y        (chain, draw, obs_id) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.449843
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • obs_id: 919
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918

      array([  0,   1,   2, ..., 916, 917, 918])

  • Data variables: (1)
    • y
      (chain, draw, obs_id)
      float64
      ...

      [1838000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_id
      PandasIndex

      PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
                  ...
                  909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
                 dtype='int64', name='obs_id', length=919))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.449843

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  
• log_likelihood

  <xarray.Dataset>
  Dimensions:  (chain: 4, draw: 500, obs_id: 919)
  Coordinates:
    * chain    (chain) int64 0 1 2 3
    * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
    * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
  Data variables:
      y        (chain, draw, obs_id) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.448264
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • obs_id: 919
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918

      array([  0,   1,   2, ..., 916, 917, 918])

  • Data variables: (1)
    • y
      (chain, draw, obs_id)
      float64
      ...

      [1838000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_id
      PandasIndex

      PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
                  ...
                  909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
                 dtype='int64', name='obs_id', length=919))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.448264

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:           (chain: 4, draw: 500)
  Coordinates:
    * chain             (chain) int64 0 1 2 3
    * draw              (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
  Data variables:
      step_size_bar     (chain, draw) float64 ...
      diverging         (chain, draw) bool ...
      energy            (chain, draw) float64 ...
      tree_size         (chain, draw) float64 ...
      mean_tree_accept  (chain, draw) float64 ...
      step_size         (chain, draw) float64 ...
      depth             (chain, draw) int64 ...
      energy_error      (chain, draw) float64 ...
      lp                (chain, draw) float64 ...
      max_energy_error  (chain, draw) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.197697
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2
      sampling_time:              18.096983432769775
      tuning_steps:               1000

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

  • Data variables: (10)
    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • mean_tree_accept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

  • Attributes: (6)

    created_at :

    2020-07-24T18:15:12.197697

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

    sampling_time :

    18.096983432769775

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset>
  Dimensions:        (chain: 1, draw: 500, County: 85, uranium_coefs: 2)
  Coordinates:
    * chain          (chain) int64 0
    * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * County         (County) object 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
    * uranium_coefs  (uranium_coefs) <U9 'intercept' 'u_slope'
  Data variables:
      a_county       (chain, draw, County) float64 ...
      sigma_log__    (chain, draw) float64 ...
      sigma_a        (chain, draw) float64 ...
      a              (chain, draw, County) float64 ...
      b              (chain, draw) float64 ...
      za_county      (chain, draw, County) float64 ...
      sigma          (chain, draw) float64 ...
      g              (chain, draw, uranium_coefs) float64 ...
      sigma_a_log__  (chain, draw) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.454586
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • County: 85
    • uranium_coefs: 2
  • Coordinates: (4)
    • 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])

    • County
      (County)
      object
      'AITKIN' ... 'YELLOW MEDICINE'

      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'], dtype=object)

    • uranium_coefs
      (uranium_coefs)
      <U9
      'intercept' 'u_slope'

      array(['intercept', 'u_slope'], dtype='<U9')

  • Data variables: (9)
    • a_county
      (chain, draw, County)
      float64
      ...

      [42500 values with dtype=float64]

    • sigma_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • sigma_a
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • a
      (chain, draw, County)
      float64
      ...

      [42500 values with dtype=float64]

    • b
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • za_county
      (chain, draw, County)
      float64
      ...

      [42500 values with dtype=float64]

    • sigma
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • g
      (chain, draw, uranium_coefs)
      float64
      ...

      [1000 values with dtype=float64]

    • sigma_a_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • County
      PandasIndex

      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'],
            dtype='object', name='County'))

    • uranium_coefs
      PandasIndex

      PandasIndex(Index(['intercept', 'u_slope'], dtype='object', name='uranium_coefs'))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.454586

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  
• prior_predictive

  <xarray.Dataset>
  Dimensions:  (chain: 1, draw: 500, obs_id: 919)
  Coordinates:
    * chain    (chain) int64 0
    * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
    * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
  Data variables:
      y        (chain, draw, obs_id) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.457652
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • obs_id: 919
  • 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])

    • obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918

      array([  0,   1,   2, ..., 916, 917, 918])

  • Data variables: (1)
    • y
      (chain, draw, obs_id)
      float64
      ...

      [459500 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • obs_id
      PandasIndex

      PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
                  ...
                  909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
                 dtype='int64', name='obs_id', length=919))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.457652

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:  (obs_id: 919)
  Coordinates:
    * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
  Data variables:
      y        (obs_id) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.458415
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • obs_id: 919
  • Coordinates: (1)
    • obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918

      array([  0,   1,   2, ..., 916, 917, 918])

  • Data variables: (1)
    • y
      (obs_id)
      float64
      ...

      [919 values with dtype=float64]

  • Indexes: (1)
    • obs_id
      PandasIndex

      PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
                  ...
                  909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
                 dtype='int64', name='obs_id', length=919))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.458415

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  
• constant_data

  <xarray.Dataset>
  Dimensions:     (obs_id: 919, County: 85)
  Coordinates:
    * obs_id      (obs_id) int64 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
    * County      (County) object 'AITKIN' 'ANOKA' ... 'WRIGHT' 'YELLOW MEDICINE'
  Data variables:
      floor_idx   (obs_id) int32 ...
      county_idx  (obs_id) int32 ...
      uranium     (County) float64 ...
  Attributes:
      created_at:                 2020-07-24T18:15:12.459832
      arviz_version:              0.9.0
      inference_library:          pymc3
      inference_library_version:  3.9.2

  xarray.Dataset

  • Dimensions:
    • obs_id: 919
    • County: 85
  • Coordinates: (2)
    • obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918

      array([  0,   1,   2, ..., 916, 917, 918])

    • County
      (County)
      object
      'AITKIN' ... 'YELLOW MEDICINE'

      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'], dtype=object)

  • Data variables: (3)
    • floor_idx
      (obs_id)
      int32
      ...

      [919 values with dtype=int32]

    • county_idx
      (obs_id)
      int32
      ...

      [919 values with dtype=int32]

    • uranium
      (County)
      float64
      ...

      [85 values with dtype=float64]

  • Indexes: (2)
    • obs_id
      PandasIndex

      PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
                  ...
                  909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
                 dtype='int64', name='obs_id', length=919))

    • County
      PandasIndex

      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
             'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
             'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
             'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
             'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
             'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
             'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
             'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
             'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
             'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
             'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
             'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
             'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
             'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
             'YELLOW MEDICINE'],
            dtype='object', name='County'))

  • Attributes: (4)

    created_at :

    2020-07-24T18:15:12.459832

    arviz_version :

    0.9.0

    inference_library :

    pymc3

    inference_library_version :

    3.9.2

  
  

Add extra coordinates to all groups containing observed variables

idata = az.load_arviz_data("rugby")
home_team, away_team = np.array([
    m.split() for m in idata.observed_data.match.values
]).T
idata = idata.map(
    lambda ds, **kwargs: ds.assign_coords(**kwargs),
    groups="observed_vars",
    home_team=("match", home_team),
    away_team=("match", away_team),
)
idata

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 ...
      intercept  (chain, draw) float64 ...
      atts_star  (chain, draw, team) float64 ...
      defs_star  (chain, draw, team) float64 ...
      sd_att     (chain, draw) float64 ...
      sd_def     (chain, draw) float64 ...
      atts       (chain, draw, team) float64 ...
      defs       (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.545143
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

  • Data variables: (8)
    • home
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • sd_att
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.545143

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * match        (match) object 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 'Wales' 'France' 'Ireland' ... 'France' 'Ireland'
      away_team    (match) <U8 'Italy' 'England' 'Scotland' ... 'Wales' 'England'
  Data variables:
      home_points  (chain, draw, match) int64 ...
      away_points  (chain, draw, match) int64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.563854
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (5)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

    • home_team
      (match)
      <U8
      'Wales' 'France' ... 'Ireland'

      array(['Wales', 'France', 'Ireland', 'Ireland', 'Scotland', 'France',
             'Wales', 'Italy', 'England', 'Ireland', 'Scotland', 'England',
             'Italy', 'Wales', 'France', 'Wales', 'Italy', 'France', 'England',
             'Ireland', 'Scotland', 'Scotland', 'France', 'Ireland', 'Wales',
             'England', 'Italy', 'Italy', 'Scotland', 'England', 'France',
             'Scotland', 'Ireland', 'France', 'Wales', 'Italy', 'Wales',
             'Italy', 'England', 'Ireland', 'England', 'Scotland', 'Wales',
             'Ireland', 'France', 'Scotland', 'England', 'Italy', 'Italy',
             'Wales', 'France', 'Scotland', 'Ireland', 'England', 'Wales',
             'Italy', 'England', 'Scotland', 'France', 'Ireland'], dtype='<U8')

    • away_team
      (match)
      <U8
      'Italy' 'England' ... 'England'

      array(['Italy', 'England', 'Scotland', 'Wales', 'England', 'Italy',
             'France', 'Scotland', 'Ireland', 'Italy', 'France', 'Wales',
             'England', 'Scotland', 'Ireland', 'England', 'Ireland', 'Scotland',
             'Italy', 'France', 'Wales', 'Italy', 'Wales', 'England', 'Ireland',
             'Scotland', 'France', 'Wales', 'Ireland', 'France', 'Italy',
             'England', 'Wales', 'Ireland', 'Scotland', 'England', 'France',
             'Scotland', 'Ireland', 'Italy', 'Wales', 'France', 'Italy',
             'Scotland', 'England', 'Ireland', 'France', 'Wales', 'Ireland',
             'England', 'Scotland', 'Wales', 'France', 'Italy', 'Ireland',
             'France', 'Scotland', 'Italy', 'Wales', 'England'], dtype='<U8')

  • Data variables: (2)
    • home_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.563854

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:           (chain: 4, draw: 500)
  Coordinates:
    * chain             (chain) int64 0 1 2 3
    * draw              (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
  Data variables:
      energy_error      (chain, draw) float64 ...
      energy            (chain, draw) float64 ...
      tree_size         (chain, draw) float64 ...
      tune              (chain, draw) bool ...
      mean_tree_accept  (chain, draw) float64 ...
      lp                (chain, draw) float64 ...
      depth             (chain, draw) int64 ...
      max_energy_error  (chain, draw) float64 ...
      step_size         (chain, draw) float64 ...
      step_size_bar     (chain, draw) float64 ...
      diverging         (chain, draw) bool ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.555203
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

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

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

  • Data variables: (11)
    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tune
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • mean_tree_accept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.555203

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• prior

  <xarray.Dataset>
  Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
  Coordinates:
    * chain         (chain) int64 0
    * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
    * match         (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      sd_att_log__  (chain, draw) float64 ...
      intercept     (chain, draw) float64 ...
      atts_star     (chain, draw, team) float64 ...
      defs_star     (chain, draw, team) float64 ...
      away_points   (chain, draw, match) int64 ...
      sd_att        (chain, draw) float64 ...
      sd_def_log__  (chain, draw) float64 ...
      home          (chain, draw) float64 ...
      atts          (chain, draw, team) float64 ...
      sd_def        (chain, draw) float64 ...
      home_points   (chain, draw, match) int64 ...
      defs          (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.573731
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
    • match: 60
  • Coordinates: (4)
    • 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])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (12)
    • sd_att_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • sd_att
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • sd_def_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • defs
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Int64Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Int64Index([  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))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.573731

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 'Wales' 'France' 'Ireland' ... 'France' 'Ireland'
      away_team    (match) <U8 'Italy' 'England' 'Scotland' ... 'Wales' 'England'
  Data variables:
      home_points  (match) float64 ...
      away_points  (match) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.581293
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • match: 60
  • Coordinates: (3)
    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

    • home_team
      (match)
      <U8
      'Wales' 'France' ... 'Ireland'

      array(['Wales', 'France', 'Ireland', 'Ireland', 'Scotland', 'France',
             'Wales', 'Italy', 'England', 'Ireland', 'Scotland', 'England',
             'Italy', 'Wales', 'France', 'Wales', 'Italy', 'France', 'England',
             'Ireland', 'Scotland', 'Scotland', 'France', 'Ireland', 'Wales',
             'England', 'Italy', 'Italy', 'Scotland', 'England', 'France',
             'Scotland', 'Ireland', 'France', 'Wales', 'Italy', 'Wales',
             'Italy', 'England', 'Ireland', 'England', 'Scotland', 'Wales',
             'Ireland', 'France', 'Scotland', 'England', 'Italy', 'Italy',
             'Wales', 'France', 'Scotland', 'Ireland', 'England', 'Wales',
             'Italy', 'England', 'Scotland', 'France', 'Ireland'], dtype='<U8')

    • away_team
      (match)
      <U8
      'Italy' 'England' ... 'England'

      array(['Italy', 'England', 'Scotland', 'Wales', 'England', 'Italy',
             'France', 'Scotland', 'Ireland', 'Italy', 'France', 'Wales',
             'England', 'Scotland', 'Ireland', 'England', 'Ireland', 'Scotland',
             'Italy', 'France', 'Wales', 'Italy', 'Wales', 'England', 'Ireland',
             'Scotland', 'France', 'Wales', 'Ireland', 'France', 'Italy',
             'England', 'Wales', 'Ireland', 'Scotland', 'England', 'France',
             'Scotland', 'Ireland', 'Italy', 'Wales', 'France', 'Italy',
             'Scotland', 'England', 'Ireland', 'France', 'Wales', 'Ireland',
             'England', 'Scotland', 'Wales', 'France', 'Italy', 'Ireland',
             'France', 'Scotland', 'Italy', 'Wales', 'England'], dtype='<U8')

  • Data variables: (2)
    • home_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

    • away_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

  • Indexes: (1)
    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.581293

    inference_library :

    pymc3

    inference_library_version :

    3.7