arviz.InferenceData.rename_vars#
- InferenceData.rename_vars(name_dict=None, groups=None, filter_groups=None, inplace=False)[source]#
Perform xarray renaming of variable or coordinate names on all groups.
Loops groups to perform Dataset.rename_vars(name_dict) for every key in name_dict if key is a variable or coordinate names of the dataset. The renaming is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See
xarray.Dataset.rename_vars()
- Parameters
- name_dictdict
Dictionary whose keys are current variable or coordinate names and whose values are the desired names.
- 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 groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A lapandas.filter
.- inplacebool, optional
If
True
, modify the InferenceData object inplace, otherwise, return the modified copy.
- Returns
- InferenceData
A new InferenceData object with renamed variables including coordinates by default. When
inplace==True
perform renaming in-place and returnNone
See also
xarray.Dataset.rename_vars
Returns a new object with renamed variables including coordinates.
rename
Perform xarray renaming of variable and dimensions on all groups of an InferenceData object.
rename_dims
Perform xarray renaming of dimensions on all groups of InferenceData object.
Examples
Use
rename_vars
to renaming of variable and coordinates on all groups of the InferenceData object. We first check the data variables of original object:import arviz as az idata = az.load_arviz_data("rugby") idata
arviz.InferenceData-
<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 0.1642 0.1162 0.09299 ... 0.148 0.2265 intercept (chain, draw) float64 2.893 2.941 2.939 ... 2.951 2.903 2.892 atts_star (chain, draw, team) float64 0.1673 0.04184 ... -0.4652 0.02878 defs_star (chain, draw, team) float64 -0.03638 -0.04109 ... 0.7136 -0.0649 sd_att (chain, draw) float64 0.4854 0.1438 0.2139 ... 0.2883 0.4591 sd_def (chain, draw) float64 0.2747 1.033 0.6363 ... 0.5574 0.2849 atts (chain, draw, team) float64 0.1063 -0.01913 ... -0.2911 0.2029 defs (chain, draw, team) float64 -0.06765 -0.07235 ... 0.5799 -0.1986 Attributes: created_at: 2019-07-12T20:31:53.545143 inference_library: pymc3 inference_library_version: 3.7
-
<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) 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 -0.07666 -0.4523 ... 0.115 -0.07691 energy (chain, draw) float64 540.2 545.3 542.3 ... 544.0 545.6 tree_size (chain, draw) float64 15.0 63.0 31.0 ... 63.0 31.0 31.0 tune (chain, draw) bool True False False ... False False False mean_tree_accept (chain, draw) float64 1.0 0.8851 0.8875 ... 0.7791 0.7539 lp (chain, draw) float64 -536.4 -536.0 ... -536.1 -536.4 depth (chain, draw) int64 4 6 5 4 4 4 5 5 5 ... 6 4 6 5 3 6 5 5 max_energy_error (chain, draw) float64 -0.5361 -0.5871 ... 0.7109 1.014 step_size (chain, draw) float64 0.2469 0.2469 ... 0.2459 0.2459 step_size_bar (chain, draw) float64 0.2313 0.2313 ... 0.2488 0.2488 diverging (chain, draw) bool False False False ... False False False Attributes: created_at: 2019-07-12T20:31:53.555203 inference_library: pymc3 inference_library_version: 3.7
-
<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 1.322 -2.014 1.588 ... -0.8585 -0.1922 intercept (chain, draw) float64 4.464 3.352 1.567 ... 4.363 4.128 1.049 atts_star (chain, draw, team) float64 -2.64 4.172 ... -0.2874 -0.8538 defs_star (chain, draw, team) float64 -0.7817 -0.1478 ... 0.1655 0.01067 away_points (chain, draw, match) int64 11308 0 11 1 0 21442 ... 11 1 2 2 0 sd_att (chain, draw) float64 3.752 0.1334 4.896 ... 0.4238 0.8251 sd_def_log__ (chain, draw) float64 -0.2662 0.2411 0.6071 ... 1.402 -1.981 home (chain, draw) float64 -1.511 -0.001582 ... -0.02416 0.2651 atts (chain, draw, team) float64 -4.667 2.145 ... -0.2702 -0.8365 sd_def (chain, draw) float64 0.7663 1.273 1.835 ... 3.922 4.063 0.138 home_points (chain, draw, match) int64 0 47 11899 3262 1 ... 3 2 1 12 13 defs (chain, draw, team) float64 -0.2517 0.3823 ... 0.089 -0.06586 Attributes: created_at: 2019-07-12T20:31:53.573731 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (match: 60) Coordinates: * match (match) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (match) float64 23.0 26.0 28.0 26.0 0.0 ... 61.0 29.0 20.0 13.0 away_points (match) float64 15.0 24.0 6.0 3.0 20.0 ... 21.0 0.0 18.0 9.0 Attributes: created_at: 2019-07-12T20:31:53.581293 inference_library: pymc3 inference_library_version: 3.7
In order to rename the data variables, we use:
idata.rename_vars({"home": "home_new"}, inplace=True) idata
arviz.InferenceData-
<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_new (chain, draw) float64 0.1642 0.1162 0.09299 ... 0.148 0.2265 intercept (chain, draw) float64 2.893 2.941 2.939 ... 2.951 2.903 2.892 atts_star (chain, draw, team) float64 0.1673 0.04184 ... -0.4652 0.02878 defs_star (chain, draw, team) float64 -0.03638 -0.04109 ... 0.7136 -0.0649 sd_att (chain, draw) float64 0.4854 0.1438 0.2139 ... 0.2883 0.4591 sd_def (chain, draw) float64 0.2747 1.033 0.6363 ... 0.5574 0.2849 atts (chain, draw, team) float64 0.1063 -0.01913 ... -0.2911 0.2029 defs (chain, draw, team) float64 -0.06765 -0.07235 ... 0.5799 -0.1986 Attributes: created_at: 2019-07-12T20:31:53.545143 inference_library: pymc3 inference_library_version: 3.7
-
<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) 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 -0.07666 -0.4523 ... 0.115 -0.07691 energy (chain, draw) float64 540.2 545.3 542.3 ... 544.0 545.6 tree_size (chain, draw) float64 15.0 63.0 31.0 ... 63.0 31.0 31.0 tune (chain, draw) bool True False False ... False False False mean_tree_accept (chain, draw) float64 1.0 0.8851 0.8875 ... 0.7791 0.7539 lp (chain, draw) float64 -536.4 -536.0 ... -536.1 -536.4 depth (chain, draw) int64 4 6 5 4 4 4 5 5 5 ... 6 4 6 5 3 6 5 5 max_energy_error (chain, draw) float64 -0.5361 -0.5871 ... 0.7109 1.014 step_size (chain, draw) float64 0.2469 0.2469 ... 0.2459 0.2459 step_size_bar (chain, draw) float64 0.2313 0.2313 ... 0.2488 0.2488 diverging (chain, draw) bool False False False ... False False False Attributes: created_at: 2019-07-12T20:31:53.555203 inference_library: pymc3 inference_library_version: 3.7
-
<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 1.322 -2.014 1.588 ... -0.8585 -0.1922 intercept (chain, draw) float64 4.464 3.352 1.567 ... 4.363 4.128 1.049 atts_star (chain, draw, team) float64 -2.64 4.172 ... -0.2874 -0.8538 defs_star (chain, draw, team) float64 -0.7817 -0.1478 ... 0.1655 0.01067 away_points (chain, draw, match) int64 11308 0 11 1 0 21442 ... 11 1 2 2 0 sd_att (chain, draw) float64 3.752 0.1334 4.896 ... 0.4238 0.8251 sd_def_log__ (chain, draw) float64 -0.2662 0.2411 0.6071 ... 1.402 -1.981 home_new (chain, draw) float64 -1.511 -0.001582 ... -0.02416 0.2651 atts (chain, draw, team) float64 -4.667 2.145 ... -0.2702 -0.8365 sd_def (chain, draw) float64 0.7663 1.273 1.835 ... 3.922 4.063 0.138 home_points (chain, draw, match) int64 0 47 11899 3262 1 ... 3 2 1 12 13 defs (chain, draw, team) float64 -0.2517 0.3823 ... 0.089 -0.06586 Attributes: created_at: 2019-07-12T20:31:53.573731 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (match: 60) Coordinates: * match (match) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (match) float64 23.0 26.0 28.0 26.0 0.0 ... 61.0 29.0 20.0 13.0 away_points (match) float64 15.0 24.0 6.0 3.0 20.0 ... 21.0 0.0 18.0 9.0 Attributes: created_at: 2019-07-12T20:31:53.581293 inference_library: pymc3 inference_library_version: 3.7