class_docstr: This analyzer class analyzes numerical data and generates key stats for numerical values fed into it
class_docstr_tok: ["This","analyzer","class","analyzes","numerical","data","and","generates","key","stats","for","numerical","values","fed","into","it"]
docstr: Feed the value into analyzer
  
  Args:
     value: value that fed for frequency count update
docstr_tok: ["Feed","the","value","into","analyzer","Args",":","value",":","value","that","fed","for","frequency","count","update"]
code: def feed(self, value: int or str):
      """
      Feed the value into analyzer
  
      Args:
         value: value that fed for frequency count update
  
      """
      if type(value) not in NumericDataAnalyzer.SUPPORTED_TYPES:
          raise ItemDataTypeNotSupported(
              type(value),
              type(self),
              NumericDataAnalyzer.SUPPORTED_TYPES,
          )
      if np.isnan(value) or value is None or math.isnan(value):
          self._nan_counter += 1
          return
      self._values.append(value)
  
file_path: contextual-ai/xai/data/explorer/numerical/numerical_analyzer.py
imports: import math
  from typing import Optional, List, Tuple
  import numpy
  from sklearn.neighbors import KernelDensity
  from xai.data.constants import STATSCONSTANTS
  from xai.data.exceptions import ItemDataTypeNotSupported, NoItemsError
  from xai.data.explorer.abstract_analyzer import AbstractDataAnalyzer
  from xai.data.explorer.numerical.numerical_stats import NumericalStats
label_desc: Use image node_id 2 for calling the NumericDataAnalyzer obj's underlying member method code with example usage: obj.feed(value) without return types