arviz.InferenceData.extend

InferenceData.extend(other, join='left')

Extend InferenceData with groups from another InferenceData.

Parameters

otherInferenceData

InferenceData to be added

join{‘left’, ‘right’}, default ‘left’

Defines how the two decide which group to keep when the same group is present in both objects. ‘left’ will discard the group in other whereas ‘right’ will keep the group in other and discard the one in self.

See also

add_groups

Add new groups to InferenceData object.

concat

Concatenate InferenceData objects.

Examples

Take two InferenceData objects, and extend the first with the groups it doesn’t have but are present in the 2nd InferenceData object.

First InferenceData:

import arviz as az
idata = az.load_arviz_data("rugby")

Second InferenceData:

other_idata = az.load_arviz_data("radon")

Call the extend method:

idata.extend(other_idata)
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(Index([0, 1, 2, 3], 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))

    • 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'
  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: (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])

    • 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: (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(Index([0, 1, 2, 3], 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))

    • 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

  
  
• 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(Index([0, 1, 2, 3], 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))

    • obs_id
      PandasIndex

      PandasIndex(Index([  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:
      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(Index([0, 1, 2, 3], 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))

  • 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(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))

    • 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

  
  
• 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(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))

    • obs_id
      PandasIndex

      PandasIndex(Index([  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:      (match: 60)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland 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: (1)
    • 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: (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

  
  
• 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(Index([  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

  
  

See how now the first InferenceData has more groups, with the data from the second one, but the groups it originally had have not been modified, even if also present in the second InferenceData.