class_docstr: Manages issues related to null/missing values in the rows of features.
  
  Parameters
  ----------
  datalab :
      The Datalab instance that this issue manager searches for issues in.
class_docstr_tok: ["Manages","issues","related","to","null\/missing","values","in","the","rows","of","features",".","Parameters","--","--","--","--","--","datalab",":","The","Datalab","instance","that","this","issue","manager","searches","for","issues","in","."]
docstr: Tracks the number of null values in each row of a feature array,
  computes quality scores based on the fraction of null values in each row,
  and returns a boolean array indicating whether each row only has null values.
docstr_tok: ["Tracks","the","number","of","null","values","in","each","row","of","a","feature","array",",","computes","quality","scores","based","on","the","fraction","of","null","values","in","each","row",",","and","returns","a","boolean","array","indicating","whether","each","row","only","has","null","values","."]
code: def _calculate_null_issues(
      features: npt.NDArray,
  ) -> tuple[ndarray, ndarray, Any]:
      """Tracks the number of null values in each row of a feature array,
      computes quality scores based on the fraction of null values in each row,
      and returns a boolean array indicating whether each row only has null values.
      """
      cols = features.shape[1]
      null_tracker = np.isnan(features)
      non_null_count = cols - null_tracker.sum(axis=1)
      scores = non_null_count / cols
      is_null_issue = non_null_count == 0
      return is_null_issue, scores, null_tracker
  
file_path: cleanlab/cleanlab/datalab/internal/issue_manager/null.py
imports: from __future__ import annotations
  from collections import Counter
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, List
  import numpy
  import pandas
  from numpy import ndarray
  from cleanlab.datalab.internal.issue_manager import IssueManager
label_desc: Use image node_id 1 for calling the NullIssueManager obj's underlying member method code with example usage: obj._calculate_null_issues(features) and returns: is_null_issue,  scores,  null_tracker