class_docstr: Creates Arena object
  
  This class should be used to create Python connector for Arena dashboard. Initialized
  object can work both in static and live mode. Use `push_*` methods to add your
  models, observations and datasets.
  
  Parameters
  ----------
  precalculate : bool
      Enables precalculating plots after using each `push_*` method.
  enable_attributes : bool
      Enables attributes of observations and variables. Attributes are required to
      display details of observation in Arena, but it also increases generated
      file size.
  enable_custom_params : bool
      Enables modififying observations in dashboard. It requires attributes and works
      only in live version.
  verbose : bool
      Enables printing progresss of computations
  
  Attributes
  --------
  models : list of ModelParam objects
      List of pushed models encapsulated in ModelParam class
  observations : list of ObservationParam objects
      List of pushed observations encapsulated in ObservationParam class
  datasets : list of DatasetParam objects
      List of pushed datasets encapsulated in DatasetParam class
  variables_cache : list of VariableParam objects
      Cached list of VariableParam objects generated using pushed models and datasets
  server_thread : threading.Thread
      Thread of running server or None otherwise
  precalculate : bool
      if plots should be precalculated
  enable_attributes : bool
      if attributes are enabled
  enable_custom_params : bool
      if modifying observations is enabled
  timestamp : float
      timestamp of last modification
  mutex : _thread.lock
      Mutex for params, plots and resources cache. Common to Arena, PlotsManager and ResourcesManager class.
  options : dict
      Options for plots
  resource_manager: ResourceManager
      Object responsible for managing resources
  plots_manager: PlotsManager
      Object responsible for managing plots
  verbose : bool
      If progress of computations should be displayed
  
  Notes
  --------
  For tutorial look at https://arena.drwhy.ai/docs/guide/first-datasource
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docstr: Adds observations to Arena
  
  Pushed observations will be used to local explainations. Function
  creates ObservationParam object for each row of pushed dataset. Label
  for each observation is taken from row name. When precalculation
  is enabled triggers filling cache.
  
  Parameters
  -----------
  observations : pandas.DataFrame
      Data frame of observations to be explained using instance level plots.
      Label for each observation is taken from row name.
  precalculate : bool or None
      Overrides constructor `precalculate` parameter when it is not None.
      If true, then only plots using thease observations will be precalculated.
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code: def push_observations(self, observations, precalculate=None):
      """Adds observations to Arena
  
      Pushed observations will be used to local explainations. Function
      creates ObservationParam object for each row of pushed dataset. Label
      for each observation is taken from row name. When precalculation
      is enabled triggers filling cache.
  
      Parameters
      -----------
      observations : pandas.DataFrame
          Data frame of observations to be explained using instance level plots.
          Label for each observation is taken from row name.
      precalculate : bool or None
          Overrides constructor `precalculate` parameter when it is not None.
          If true, then only plots using thease observations will be precalculated.
      """
      if not isinstance(observations, DataFrame):
          raise Exception(
              "Observations argument is not a pandas DataFrame"
          )
      if len(observations.index.names) != 1:
          raise Exception(
              "Observations argument need to have only one index"
          )
      if not observations.index.is_unique:
          raise Exception(
              "Observations argument need to have unique indexes"
          )
      precalculate = (
          self.precalculate
          if precalculate is None
          else bool(precalculate)
      )
      old_observations = self.list_params("observation")
      observations = observations.set_index(
          observations.index.astype(str)
      )
      params_objects = []
      for x in observations.index:
          if x in old_observations:
              raise Exception(
                  "Indexes of observations need to be unique across all observations"
              )
          params_objects.append(
              ObservationParam(dataset=observations, index=x)
          )
      with self.mutex:
          self.update_timestamp()
          self.observations.extend(params_objects)
      if precalculate:
          for obs in params_objects:
              self.plots_manager.fill_cache({"observation": obs})
  
imports: import threading
  import time
  import numpy
  import webbrowser
  from datetime import datetime
  from dalex import Explainer
  from pandas.core.frame import DataFrame
  from .server import start_server
  from .params import ModelParam, DatasetParam, VariableParam, ObservationParam, Param
  from .._global_checks import global_check_import
  from .static import get_json, upload_arena, generate_token
  from ._resource_manager import ResourceManager
  from ._plots_manager import PlotsManager
label_desc: Use image node_id 7 for calling the Arena obj's underlying member method code with example usage: obj.push_observations(observations, precalculate) without return types