diff --git a/.github/workflows/publish-python-package.yml b/.github/workflows/publish-python-package.yml
index d2b43419f..3c593d4ed 100644
--- a/.github/workflows/publish-python-package.yml
+++ b/.github/workflows/publish-python-package.yml
@@ -7,6 +7,8 @@ name: Publish Python Package
 on:
   release:
     types: [created]
+    branches:
+      - 'release/*'
 
 jobs:
   deploy:
diff --git a/dataprofiler/data_readers/data_utils.py b/dataprofiler/data_readers/data_utils.py
index 3e433d85d..58ea61179 100644
--- a/dataprofiler/data_readers/data_utils.py
+++ b/dataprofiler/data_readers/data_utils.py
@@ -1,7 +1,5 @@
 """Contains functions for data readers."""
 import json
-import os
-import random
 import re
 import urllib
 from collections import OrderedDict
@@ -28,7 +26,7 @@
 from chardet.universaldetector import UniversalDetector
 from typing_extensions import TypeGuard
 
-from .. import dp_logging, settings
+from .. import dp_logging, rng_utils
 from .._typing import JSONType, Url
 from .filepath_or_buffer import FileOrBufferHandler, is_stream_buffer  # NOQA
 
@@ -315,11 +313,7 @@ def reservoir(file: TextIOWrapper, sample_nrows: int) -> list:
 
     kinv = 1 / sample_nrows
     W = 1.0
-    rng = random.Random(x=settings._seed)
-    if "DATAPROFILER_SEED" in os.environ and settings._seed is None:
-        seed = os.environ.get("DATAPROFILER_SEED")
-        if seed:
-            rng = random.Random(int(seed))
+    rng = rng_utils.get_random_number_generator()
 
     while True:
         W *= rng.random() ** kinv
@@ -334,7 +328,7 @@ def reservoir(file: TextIOWrapper, sample_nrows: int) -> list:
         except StopIteration:
             break
         # Append new, replace old with dummy, and keep track of order
-        remove_index = rng.randrange(sample_nrows)
+        remove_index = rng.integers(0, sample_nrows)
         values[indices[remove_index]] = str(None)
         indices[remove_index] = len(values)
         values.append(newval)
@@ -824,7 +818,6 @@ def url_to_bytes(url_as_string: Url, options: Dict) -> BytesIO:
                 "Content-length" in url.headers
                 and int(url.headers["Content-length"]) >= 1024**3
             ):
-
                 raise ValueError(
                     "The downloaded file from the url may not be " "larger than 1GB"
                 )
diff --git a/dataprofiler/labelers/base_model.py b/dataprofiler/labelers/base_model.py
index a4eb0b1d2..032c2ea38 100644
--- a/dataprofiler/labelers/base_model.py
+++ b/dataprofiler/labelers/base_model.py
@@ -32,7 +32,7 @@ def __new__(
 class BaseModel(metaclass=abc.ABCMeta):
     """For labeling data."""
 
-    _BaseModel__subclasses: dict[str, type[BaseModel]] = {}
+    __subclasses: dict[str, type[BaseModel]] = {}
     __metaclass__ = abc.ABCMeta
 
     # boolean if the label mapping requires the mapping for index 0 reserved
@@ -90,7 +90,7 @@ def __eq__(self, other: object) -> bool:
     def _register_subclass(cls) -> None:
         """Register a subclass for the class factory."""
         if not inspect.isabstract(cls):
-            cls._BaseModel__subclasses[cls.__name__.lower()] = cls
+            cls.__subclasses[cls.__name__.lower()] = cls
 
     @property
     def label_mapping(self) -> dict[str, int]:
@@ -156,7 +156,7 @@ def get_class(cls, class_name: str) -> type[BaseModel] | None:
         from .column_name_model import ColumnNameModel  # NOQA
         from .regex_model import RegexModel  # NOQA
 
-        return cls._BaseModel__subclasses.get(class_name.lower(), None)
+        return cls.__subclasses.get(class_name.lower(), None)
 
     def get_parameters(self, param_list: list[str] | None = None) -> dict:
         """
diff --git a/dataprofiler/labelers/data_processing.py b/dataprofiler/labelers/data_processing.py
index 4613d05de..be1a3fee4 100644
--- a/dataprofiler/labelers/data_processing.py
+++ b/dataprofiler/labelers/data_processing.py
@@ -49,16 +49,14 @@ def __init__(self, **parameters: Any) -> None:
     def _register_subclass(cls) -> None:
         """Register a subclass for the class factory."""
         if not inspect.isabstract(cls):
-            cls._BaseDataProcessor__subclasses[  # type: ignore
-                cls.__name__.lower()
-            ] = cls
+            cls.__subclasses[cls.__name__.lower()] = cls
 
     @classmethod
-    def get_class(cls: type[Processor], class_name: str) -> type[Processor] | None:
+    def get_class(
+        cls: type[BaseDataProcessor], class_name: str
+    ) -> type[BaseDataProcessor] | None:
         """Get class of BaseDataProcessor object."""
-        return cls._BaseDataProcessor__subclasses.get(  # type: ignore
-            class_name.lower(), None
-        )
+        return cls.__subclasses.get(class_name.lower(), None)
 
     def __eq__(self, other: object) -> bool:
         """
@@ -129,7 +127,7 @@ def set_params(self, **kwargs: Any) -> None:
             self._parameters[param] = kwargs[param]
 
     @abc.abstractmethod
-    def process(self, *args: Any) -> Any:
+    def process(self, *args: Any, **kwargs: Any) -> Any:
         """Process data."""
         raise NotImplementedError()
 
@@ -169,13 +167,15 @@ def __init__(self, **parameters: Any) -> None:
         super().__init__(**parameters)
 
     @abc.abstractmethod
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         labels: np.ndarray | None = None,
         label_mapping: dict[str, int] | None = None,
         batch_size: int = 32,
-    ) -> Generator[tuple[np.ndarray, np.ndarray] | np.ndarray, None, None]:
+    ) -> Generator[tuple[np.ndarray, np.ndarray] | np.ndarray, None, None] | tuple[
+        np.ndarray, np.ndarray
+    ] | np.ndarray:
         """Preprocess data."""
         raise NotImplementedError()
 
@@ -191,7 +191,7 @@ def __init__(self, **parameters: Any) -> None:
         super().__init__(**parameters)
 
     @abc.abstractmethod
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
@@ -240,7 +240,7 @@ def help(cls) -> None:
         )
         print(help_str)
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         labels: np.ndarray | None = None,
@@ -668,7 +668,7 @@ def gen_none() -> Generator[None, None, None]:
         if batch_data["samples"]:
             yield batch_data
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         labels: np.ndarray | None = None,
@@ -735,8 +735,8 @@ def process(  # type: ignore
             X_train = np.array(
                 [[sentence] for sentence in batch_data["samples"]], dtype=object
             )
-            if labels is not None:
-                num_classes = max(label_mapping.values()) + 1  # type: ignore
+            if labels is not None and label_mapping is not None:
+                num_classes = max(label_mapping.values()) + 1
 
                 Y_train = tf.keras.utils.to_categorical(
                     batch_data["labels"], num_classes
@@ -836,7 +836,7 @@ def _validate_parameters(self, parameters: dict) -> None:
         if errors:
             raise ValueError("\n".join(errors))
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         labels: np.ndarray | None = None,
@@ -1269,7 +1269,7 @@ def match_sentence_lengths(
 
         return results
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
@@ -1439,7 +1439,7 @@ def convert_to_unstructured_format(
 
         return text, entities
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         labels: np.ndarray | None = None,
@@ -1503,8 +1503,12 @@ def process(  # type: ignore
                 unstructured_label_set,
             ) = self.convert_to_unstructured_format(batch_data, batch_labels)
             unstructured_data[ind] = unstructured_text
-            if labels is not None:
-                unstructured_labels[ind] = unstructured_label_set  # type: ignore
+            if (
+                labels is not None
+                and unstructured_labels is not None
+                and unstructured_label_set is not None
+            ):
+                unstructured_labels[ind] = unstructured_label_set
 
         if labels is not None:
             np_unstruct_labels = np.array(unstructured_labels, dtype="object")
@@ -1800,7 +1804,7 @@ def convert_to_structured_analysis(
 
         return results
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
@@ -2022,7 +2026,7 @@ def split_prediction(results: dict) -> None:
                 pred, axis=1, ord=1, keepdims=True
             )
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
@@ -2160,7 +2164,7 @@ def _save_processor(self, dirpath: str) -> None:
         ) as fp:
             json.dump(params, fp)
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
@@ -2253,7 +2257,7 @@ def help(cls) -> None:
         )
         print(help_str)
 
-    def process(  # type: ignore
+    def process(
         self,
         data: np.ndarray,
         results: dict,
diff --git a/dataprofiler/profilers/__init__.py b/dataprofiler/profilers/__init__.py
index 64e33e384..4b068fcb0 100644
--- a/dataprofiler/profilers/__init__.py
+++ b/dataprofiler/profilers/__init__.py
@@ -28,7 +28,7 @@
     DataLabelerOptions,
     DateTimeOptions,
     FloatOptions,
-    HistogramOption,
+    HistogramAndQuantilesOption,
     HyperLogLogOptions,
     IntOptions,
     ModeOption,
@@ -66,7 +66,8 @@
 
 json_decoder._options = {
     BooleanOption.__name__: BooleanOption,
-    HistogramOption.__name__: HistogramOption,
+    "HistogramOption": HistogramAndQuantilesOption,
+    HistogramAndQuantilesOption.__name__: HistogramAndQuantilesOption,
     ModeOption.__name__: ModeOption,
     BaseInspectorOptions.__name__: BaseInspectorOptions,
     NumericalOptions.__name__: NumericalOptions,
diff --git a/dataprofiler/profilers/base_column_profilers.py b/dataprofiler/profilers/base_column_profilers.py
index d9c183c99..1ef5b75fe 100644
--- a/dataprofiler/profilers/base_column_profilers.py
+++ b/dataprofiler/profilers/base_column_profilers.py
@@ -11,7 +11,7 @@
 import numpy as np
 import pandas as pd
 
-from . import utils
+from . import profiler_utils
 from .profiler_options import BaseInspectorOptions, BaseOption
 
 BaseColumnProfilerT = TypeVar("BaseColumnProfilerT", bound="BaseColumnProfiler")
@@ -76,7 +76,7 @@ def _timeit(method: Callable = None, name: str = None) -> Callable:
         :param name: key argument for the times dictionary
         :type name: str
         """
-        return utils.method_timeit(method, name)
+        return profiler_utils.method_timeit(method, name)
 
     @staticmethod
     def _filter_properties_w_options(
@@ -173,7 +173,7 @@ def _add_helper(
         else:
             raise ValueError(f"Column names unmatched: {other1.name} != {other2.name}")
 
-        self.times = utils.add_nested_dictionaries(other1.times, other2.times)
+        self.times = profiler_utils.add_nested_dictionaries(other1.times, other2.times)
 
         self.sample_size = other1.sample_size + other2.sample_size
 
diff --git a/dataprofiler/profilers/categorical_column_profile.py b/dataprofiler/profilers/categorical_column_profile.py
index caaf3778e..1376cc38e 100644
--- a/dataprofiler/profilers/categorical_column_profile.py
+++ b/dataprofiler/profilers/categorical_column_profile.py
@@ -8,7 +8,7 @@
 import datasketches
 from pandas import DataFrame, Series
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import CategoricalOptions
 
@@ -131,7 +131,7 @@ def __add__(self, other: CategoricalColumn) -> CategoricalColumn:
         elif not self.cms and not other.cms:
             # If both profiles have not met stop condition
             if not (self._stop_condition_is_met or other._stop_condition_is_met):
-                merged_profile._categories = utils.add_nested_dictionaries(
+                merged_profile._categories = profiler_utils.add_nested_dictionaries(
                     self._categories, other._categories
                 )
 
@@ -250,7 +250,7 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
         # Make sure other_profile's type matches this class
         differences: dict = super().diff(other_profile, options)
 
-        differences["categorical"] = utils.find_diff_of_strings_and_bools(
+        differences["categorical"] = profiler_utils.find_diff_of_strings_and_bools(
             self.is_match, other_profile.is_match
         )
 
@@ -258,13 +258,13 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
             [
                 (
                     "unique_count",
-                    utils.find_diff_of_numbers(
+                    profiler_utils.find_diff_of_numbers(
                         self.unique_count, other_profile.unique_count
                     ),
                 ),
                 (
                     "unique_ratio",
-                    utils.find_diff_of_numbers(
+                    profiler_utils.find_diff_of_numbers(
                         self.unique_ratio, other_profile.unique_ratio
                     ),
                 ),
@@ -275,19 +275,25 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
         if self.is_match and other_profile.is_match:
             differences["statistics"][
                 "chi2-test"
-            ] = utils.perform_chi_squared_test_for_homogeneity(
+            ] = profiler_utils.perform_chi_squared_test_for_homogeneity(
                 self._categories,
                 self.sample_size,
                 other_profile._categories,
                 other_profile.sample_size,
             )
-            differences["statistics"]["categories"] = utils.find_diff_of_lists_and_sets(
+            differences["statistics"][
+                "categories"
+            ] = profiler_utils.find_diff_of_lists_and_sets(
                 self.categories, other_profile.categories
             )
-            differences["statistics"]["gini_impurity"] = utils.find_diff_of_numbers(
+            differences["statistics"][
+                "gini_impurity"
+            ] = profiler_utils.find_diff_of_numbers(
                 self.gini_impurity, other_profile.gini_impurity
             )
-            differences["statistics"]["unalikeability"] = utils.find_diff_of_numbers(
+            differences["statistics"][
+                "unalikeability"
+            ] = profiler_utils.find_diff_of_numbers(
                 self.unalikeability, other_profile.unalikeability
             )
             cat_count1 = dict(
@@ -299,9 +305,9 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
                 )
             )
 
-            differences["statistics"]["categorical_count"] = utils.find_diff_of_dicts(
-                cat_count1, cat_count2
-            )
+            differences["statistics"][
+                "categorical_count"
+            ] = profiler_utils.find_diff_of_dicts(cat_count1, cat_count2)
 
         return differences
 
@@ -532,7 +538,7 @@ def _merge_categories_cms(
         for k in (x for x in heavy_hitter_dict2 if x not in heavy_hitter_dict1):
             heavy_hitter_dict1[k] = cms1.get_estimate(k)
 
-        categories = utils.add_nested_dictionaries(
+        categories = profiler_utils.add_nested_dictionaries(
             heavy_hitter_dict2, heavy_hitter_dict1
         )
 
@@ -604,7 +610,7 @@ def _update_categories(
             )
         else:
             category_count = self._get_categories_full(df_series)
-            self._categories = utils.add_nested_dictionaries(
+            self._categories = profiler_utils.add_nested_dictionaries(
                 self._categories, category_count
             )
             self._update_stop_condition(df_series)
diff --git a/dataprofiler/profilers/column_profile_compilers.py b/dataprofiler/profilers/column_profile_compilers.py
index e3a8ecb16..07edf13dc 100644
--- a/dataprofiler/profilers/column_profile_compilers.py
+++ b/dataprofiler/profilers/column_profile_compilers.py
@@ -8,7 +8,7 @@
 
 from pandas import Series
 
-from . import utils
+from . import profiler_utils
 from .categorical_column_profile import CategoricalColumn
 from .data_labeler_column_profile import DataLabelerColumn
 from .datetime_column_profile import DateTimeColumn
@@ -106,7 +106,7 @@ def _create_profile(
                         df_series.name, options=profiler_options
                     )
                 except Exception as e:
-                    utils.warn_on_profile(profiler.type, e)
+                    profiler_utils.warn_on_profile(profiler.type, e)
 
         # Update profile after creation
         self.update_profile(df_series, pool)
@@ -338,7 +338,7 @@ def diff(
         if all_profiles:
             for key in all_profiles:
                 if key in self._profiles and key in other._profiles:
-                    diff = utils.find_diff_of_numbers(
+                    diff = profiler_utils.find_diff_of_numbers(
                         self._profiles[key].data_type_ratio,
                         other._profiles[key].data_type_ratio,
                     )
@@ -352,7 +352,7 @@ def diff(
         data_type1 = self.selected_data_type
         data_type2 = other.selected_data_type
         if data_type1 is not None or data_type2 is not None:
-            diff_profile["data_type"] = utils.find_diff_of_strings_and_bools(
+            diff_profile["data_type"] = profiler_utils.find_diff_of_strings_and_bools(
                 data_type1, data_type2
             )
             # Find diff of matching profile statistics
diff --git a/dataprofiler/profilers/data_labeler_column_profile.py b/dataprofiler/profilers/data_labeler_column_profile.py
index 9487278d6..d9bfe1ee9 100644
--- a/dataprofiler/profilers/data_labeler_column_profile.py
+++ b/dataprofiler/profilers/data_labeler_column_profile.py
@@ -9,7 +9,7 @@
 
 from ..labelers.base_data_labeler import BaseDataLabeler
 from ..labelers.data_labelers import DataLabeler
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import DataLabelerOptions
 
@@ -325,7 +325,7 @@ def load_from_dict(cls, data, config: dict | None = None) -> DataLabelerColumn:
 
         data_labeler_load_attr = data.pop("data_labeler")
         if data_labeler_load_attr:
-            data_labeler_object = utils.reload_labeler_from_options_or_get_new(
+            data_labeler_object = profiler_utils.reload_labeler_from_options_or_get_new(
                 data_labeler_load_attr, config
             )
             if data_labeler_object is not None:
@@ -379,9 +379,13 @@ def diff(self, other_profile: DataLabelerColumn, options: dict = None) -> dict:
         other_label_rep = other_profile.label_representation
 
         differences = {
-            "data_label": utils.find_diff_of_lists_and_sets(self_labels, other_labels),
-            "avg_predictions": utils.find_diff_of_dicts(avg_preds, other_avg_preds),
-            "label_representation": utils.find_diff_of_dicts(
+            "data_label": profiler_utils.find_diff_of_lists_and_sets(
+                self_labels, other_labels
+            ),
+            "avg_predictions": profiler_utils.find_diff_of_dicts(
+                avg_preds, other_avg_preds
+            ),
+            "label_representation": profiler_utils.find_diff_of_dicts(
                 label_rep, other_label_rep
             ),
         }
diff --git a/dataprofiler/profilers/datetime_column_profile.py b/dataprofiler/profilers/datetime_column_profile.py
index fc7801dd3..af99283a9 100644
--- a/dataprofiler/profilers/datetime_column_profile.py
+++ b/dataprofiler/profilers/datetime_column_profile.py
@@ -8,7 +8,7 @@
 import numpy as np
 import pandas as pd
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnPrimitiveTypeProfiler, BaseColumnProfiler
 from .profiler_options import DateTimeOptions
 
@@ -114,7 +114,7 @@ def __add__(self, other: DateTimeColumn) -> DateTimeColumn:
             merged_profile.max = other.max
             merged_profile._dt_obj_max = other._dt_obj_max
 
-        merged_profile.date_formats = utils._combine_unique_sets(
+        merged_profile.date_formats = profiler_utils._combine_unique_sets(
             self.date_formats, other.date_formats
         )
         return merged_profile
@@ -192,13 +192,13 @@ def diff(self, other_profile: DateTimeColumn, options: dict = None) -> dict:
         super().diff(other_profile, options)
 
         differences = {
-            "min": utils.find_diff_of_dates(
+            "min": profiler_utils.find_diff_of_dates(
                 self._dt_obj_min, other_profile._dt_obj_min
             ),
-            "max": utils.find_diff_of_dates(
+            "max": profiler_utils.find_diff_of_dates(
                 self._dt_obj_max, other_profile._dt_obj_max
             ),
-            "format": utils.find_diff_of_lists_and_sets(
+            "format": profiler_utils.find_diff_of_lists_and_sets(
                 self.date_formats, other_profile.date_formats
             ),
         }
diff --git a/dataprofiler/profilers/float_column_profile.py b/dataprofiler/profilers/float_column_profile.py
index b816f221c..bc426a447 100644
--- a/dataprofiler/profilers/float_column_profile.py
+++ b/dataprofiler/profilers/float_column_profile.py
@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnPrimitiveTypeProfiler, BaseColumnProfiler
 from .numerical_column_stats import NumericStatsMixin
 from .profiler_options import FloatOptions
@@ -137,7 +137,7 @@ def diff(self, other_profile: FloatColumn, options: dict = None) -> dict:
         other_precision = other_profile.profile["precision"]
         precision_diff = dict()
         for key in self.profile["precision"].keys():
-            precision_diff[key] = utils.find_diff_of_numbers(
+            precision_diff[key] = profiler_utils.find_diff_of_numbers(
                 self.profile["precision"][key], other_precision[key]
             )
         precision_diff.pop("confidence_level")
diff --git a/dataprofiler/profilers/graph_profiler.py b/dataprofiler/profilers/graph_profiler.py
index 123961d88..0680a29a7 100644
--- a/dataprofiler/profilers/graph_profiler.py
+++ b/dataprofiler/profilers/graph_profiler.py
@@ -1,6 +1,7 @@
 """Class and functions to calculate and profile properties of graph data."""
 from __future__ import annotations
 
+import importlib
 import pickle
 from collections import defaultdict
 from datetime import datetime
@@ -10,9 +11,10 @@
 import numpy as np
 import pandas as pd
 import scipy.stats as st
+from packaging import version
 
 from ..data_readers.graph_data import GraphData
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import ProfilerOptions
 
@@ -116,34 +118,34 @@ def diff(self, other_profile: GraphProfiler, options: dict = None) -> dict:
             )
 
         diff_profile = {
-            "num_nodes": utils.find_diff_of_numbers(
+            "num_nodes": profiler_utils.find_diff_of_numbers(
                 self._num_nodes, other_profile._num_nodes
             ),
-            "num_edges": utils.find_diff_of_numbers(
+            "num_edges": profiler_utils.find_diff_of_numbers(
                 self._num_edges, other_profile._num_edges
             ),
-            "categorical_attributes": utils.find_diff_of_lists_and_sets(
+            "categorical_attributes": profiler_utils.find_diff_of_lists_and_sets(
                 self._categorical_attributes, other_profile._categorical_attributes
             ),
-            "continuous_attributes": utils.find_diff_of_lists_and_sets(
+            "continuous_attributes": profiler_utils.find_diff_of_lists_and_sets(
                 self._continuous_attributes, other_profile._continuous_attributes
             ),
-            "avg_node_degree": utils.find_diff_of_numbers(
+            "avg_node_degree": profiler_utils.find_diff_of_numbers(
                 self._avg_node_degree, other_profile._avg_node_degree
             ),
-            "global_max_component_size": utils.find_diff_of_numbers(
+            "global_max_component_size": profiler_utils.find_diff_of_numbers(
                 self._global_max_component_size,
                 other_profile._global_max_component_size,
             ),
-            "continuous_distribution": utils.find_diff_of_dicts_with_diff_keys(
+            "continuous_distribution": profiler_utils.find_diff_of_dicts_with_diff_keys(
                 self._continuous_distribution,
                 other_profile._continuous_distribution,
             ),
-            "categorical_distribution": utils.find_diff_of_dicts_with_diff_keys(
+            "categorical_distribution": profiler_utils.find_diff_of_dicts_with_diff_keys(  # noqa: E501
                 self._categorical_distribution,
                 other_profile._categorical_distribution,
             ),
-            "times": utils.find_diff_of_dicts(self.times, other_profile.times),
+            "times": profiler_utils.find_diff_of_dicts(self.times, other_profile.times),
         }
 
         return diff_profile
@@ -391,6 +393,11 @@ def _get_continuous_distribution(
             st.lognorm,
             st.gamma,
         ]
+
+        scipy_gte_1_11_0 = version.parse(
+            importlib.metadata.version("scipy")
+        ) >= version.parse("1.11.0")
+
         for attribute in attributes:
             if attribute in continuous_attributes:
                 data_as_list = self._attribute_data_as_list(graph, attribute)
@@ -401,7 +408,14 @@ def _get_continuous_distribution(
 
                 for distribution in distribution_candidates:
                     # compute fit, mle, kolmogorov-smirnov test to test fit, and pdf
-                    fit = distribution.fit(df)
+
+                    # scipy 1.11.0 updated the way they handle
+                    # the loc parameter in fit() for lognorm
+                    if distribution == st.lognorm and scipy_gte_1_11_0:
+                        fit = distribution.fit(df, superfit=True)
+
+                    else:
+                        fit = distribution.fit(df)
                     mle = distribution.nnlf(fit, df)
 
                     if mle <= best_mle:
diff --git a/dataprofiler/profilers/json_decoder.py b/dataprofiler/profilers/json_decoder.py
index 16bc2e148..fb4ff8cb9 100644
--- a/dataprofiler/profilers/json_decoder.py
+++ b/dataprofiler/profilers/json_decoder.py
@@ -1,6 +1,7 @@
 """Contains methods to decode components of a Profiler."""
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
@@ -72,6 +73,14 @@ def get_option_class(class_name: str) -> type[BaseOption]:
     options_class: type[BaseOption] | None = _options.get(class_name)
     if options_class is None:
         raise ValueError(f"Invalid option class {class_name} " f"failed to load.")
+
+    if class_name == "HistogramOption":
+        warnings.warn(
+            f"{class_name} will be deprecated in the future. During the JSON encode"
+            " process, HistogramOption is mapped to HistogramAndQuantilesOption. "
+            "Please begin utilizing the new HistogramAndQuantilesOption class.",
+            DeprecationWarning,
+        )
     return options_class
 
 
diff --git a/dataprofiler/profilers/numerical_column_stats.py b/dataprofiler/profilers/numerical_column_stats.py
index 707e916db..2b35c8792 100644
--- a/dataprofiler/profilers/numerical_column_stats.py
+++ b/dataprofiler/profilers/numerical_column_stats.py
@@ -13,7 +13,7 @@
 import pandas as pd
 import scipy.stats
 
-from . import histogram_utils, utils
+from . import histogram_utils, profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import NumericalOptions
 
@@ -82,7 +82,7 @@ def __init__(self, options: NumericalOptions = None) -> None:
         self._mode_is_enabled: bool = True
         self.num_zeros: int | np.int64 = np.int64(0)
         self.num_negatives: int | np.int64 = np.int64(0)
-        self._num_quantiles: int = 1000  # TODO: add to options
+        self._num_quantiles: int = 1000  # By default, we use 1000 quantiles
 
         if options:
             self.bias_correction = options.bias_correction.is_enabled
@@ -90,6 +90,7 @@ def __init__(self, options: NumericalOptions = None) -> None:
             self._median_is_enabled = options.median.is_enabled
             self._median_abs_dev_is_enabled = options.median_abs_deviation.is_enabled
             self._mode_is_enabled = options.mode.is_enabled
+            self._num_quantiles = options.histogram_and_quantiles.num_quantiles
             bin_count_or_method = options.histogram_and_quantiles.bin_count_or_method
             if isinstance(bin_count_or_method, str):
                 self.histogram_bin_method_names = [bin_count_or_method]
@@ -497,20 +498,26 @@ def diff(
             )
 
         differences = {
-            "min": utils.find_diff_of_numbers(self.min, other_profile.min),
-            "max": utils.find_diff_of_numbers(self.max, other_profile.max),
-            "sum": utils.find_diff_of_numbers(self.sum, other_profile.sum),
-            "mean": utils.find_diff_of_numbers(self.mean, other_profile.mean),
-            "median": utils.find_diff_of_numbers(self.median, other_profile.median),
-            "mode": utils.find_diff_of_lists_and_sets(self.mode, other_profile.mode),
-            "median_absolute_deviation": utils.find_diff_of_numbers(
+            "min": profiler_utils.find_diff_of_numbers(self.min, other_profile.min),
+            "max": profiler_utils.find_diff_of_numbers(self.max, other_profile.max),
+            "sum": profiler_utils.find_diff_of_numbers(self.sum, other_profile.sum),
+            "mean": profiler_utils.find_diff_of_numbers(self.mean, other_profile.mean),
+            "median": profiler_utils.find_diff_of_numbers(
+                self.median, other_profile.median
+            ),
+            "mode": profiler_utils.find_diff_of_lists_and_sets(
+                self.mode, other_profile.mode
+            ),
+            "median_absolute_deviation": profiler_utils.find_diff_of_numbers(
                 self.median_abs_deviation,
                 other_profile.median_abs_deviation,
             ),
-            "variance": utils.find_diff_of_numbers(
+            "variance": profiler_utils.find_diff_of_numbers(
                 self.variance, other_profile.variance
             ),
-            "stddev": utils.find_diff_of_numbers(self.stddev, other_profile.stddev),
+            "stddev": profiler_utils.find_diff_of_numbers(
+                self.stddev, other_profile.stddev
+            ),
             "t-test": self._perform_t_test(
                 self.mean,
                 self.variance,
@@ -1844,7 +1851,7 @@ def _get_skewness(
         ):
             return
 
-        batch_biased_skewness = utils.biased_skew(df_series)
+        batch_biased_skewness = profiler_utils.biased_skew(df_series)
         subset_properties["biased_skewness"] = batch_biased_skewness
         batch_count = subset_properties["match_count"]
         batch_biased_var = subset_properties["biased_variance"]
@@ -1888,7 +1895,7 @@ def _get_kurtosis(
         ):
             return
 
-        batch_biased_kurtosis = utils.biased_kurt(df_series)
+        batch_biased_kurtosis = profiler_utils.biased_kurt(df_series)
         subset_properties["biased_kurtosis"] = batch_biased_kurtosis
         batch_count = subset_properties["match_count"]
         batch_biased_var = subset_properties["biased_variance"]
diff --git a/dataprofiler/profilers/order_column_profile.py b/dataprofiler/profilers/order_column_profile.py
index c6a369d8d..308262324 100644
--- a/dataprofiler/profilers/order_column_profile.py
+++ b/dataprofiler/profilers/order_column_profile.py
@@ -7,7 +7,7 @@
 import numpy as np
 from pandas import DataFrame, Series
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import OrderOptions
 
@@ -362,7 +362,7 @@ def diff(self, other_profile: OrderColumn, options: dict = None) -> dict:
         super().diff(other_profile, options)
 
         differences = {
-            "order": utils.find_diff_of_strings_and_bools(
+            "order": profiler_utils.find_diff_of_strings_and_bools(
                 self.order, other_profile.order
             )
         }
diff --git a/dataprofiler/profilers/profile_builder.py b/dataprofiler/profilers/profile_builder.py
index fc2a2246e..113d19ef2 100644
--- a/dataprofiler/profilers/profile_builder.py
+++ b/dataprofiler/profilers/profile_builder.py
@@ -5,7 +5,6 @@
 import copy
 import json
 import logging
-import os
 import pickle
 import random
 import re
@@ -20,11 +19,11 @@
 import pandas as pd
 from HLL import HyperLogLog
 
-from .. import data_readers, dp_logging, settings
+from .. import data_readers, dp_logging, rng_utils
 from ..data_readers.data import Data
 from ..labelers.base_data_labeler import BaseDataLabeler
 from ..labelers.data_labelers import DataLabeler
-from . import utils
+from . import profiler_utils
 from .column_profile_compilers import (
     BaseCompiler,
     ColumnDataLabelerCompiler,
@@ -271,7 +270,7 @@ def diff(self, other_profile: StructuredColProfiler, options: dict = None) -> di
                 comp_diff = self.profiles[key].diff(
                     other_profile.profiles[key], options=options
                 )
-                unordered_profile = utils.recursive_dict_update(
+                unordered_profile = profiler_utils.recursive_dict_update(
                     unordered_profile, comp_diff
                 )
 
@@ -287,16 +286,16 @@ def diff(self, other_profile: StructuredColProfiler, options: dict = None) -> di
 
         unordered_profile["statistics"].update(
             {
-                "sample_size": utils.find_diff_of_numbers(
+                "sample_size": profiler_utils.find_diff_of_numbers(
                     self.sample_size, other_profile.sample_size
                 ),
-                "null_count": utils.find_diff_of_numbers(
+                "null_count": profiler_utils.find_diff_of_numbers(
                     self.null_count, other_profile.null_count
                 ),
-                "null_types": utils.find_diff_of_lists_and_sets(
+                "null_types": profiler_utils.find_diff_of_lists_and_sets(
                     self.null_types, other_profile.null_types
                 ),
-                "null_types_index": utils.find_diff_of_dicts_with_diff_keys(
+                "null_types_index": profiler_utils.find_diff_of_dicts_with_diff_keys(
                     self.null_types_index, other_profile.null_types_index
                 ),
             }
@@ -337,7 +336,7 @@ def report(self, remove_disabled_flag: bool = False) -> OrderedDict:
         """Return profile."""
         unordered_profile: dict = dict()
         for profile in self.profiles.values():
-            unordered_profile = utils.recursive_dict_update(
+            unordered_profile = profiler_utils.recursive_dict_update(
                 unordered_profile, profile.report(remove_disabled_flag)
             )
 
@@ -429,7 +428,7 @@ def _update_base_stats(self, base_stats: dict) -> None:
         self._last_batch_size = base_stats["sample_size"]
         self.sample = base_stats["sample"]
         self.null_count += base_stats["null_count"]
-        self.null_types = utils._combine_unique_sets(
+        self.null_types = profiler_utils._combine_unique_sets(
             self.null_types, list(base_stats["null_types"].keys())
         )
 
@@ -438,7 +437,7 @@ def _update_base_stats(self, base_stats: dict) -> None:
         base_nti = base_stats["null_types"]
 
         # Check if indices overlap, if they do, adjust attributes accordingly
-        if utils.overlap(self._min_id, self._max_id, base_min, base_max):
+        if profiler_utils.overlap(self._min_id, self._max_id, base_min, base_max):
             warnings.warn(
                 f"Overlapping indices detected. To resolve, indices "
                 f"where null data present will be shifted forward "
@@ -602,11 +601,11 @@ def clean_data_and_get_base_stats(
 
         # Select generator depending if sample_ids availability
         if sample_ids is None:
-            sample_ind_generator = utils.shuffle_in_chunks(
+            sample_ind_generator = profiler_utils.shuffle_in_chunks(
                 len_df, chunk_size=sample_size
             )
         else:
-            sample_ind_generator = utils.partition(
+            sample_ind_generator = profiler_utils.partition(
                 sample_ids[0], chunk_size=sample_size
             )
 
@@ -654,14 +653,7 @@ def clean_data_and_get_base_stats(
         df_series = df_series.loc[true_sample_list]
         total_na = total_sample_size - len(true_sample_list)
 
-        rng = np.random.default_rng(settings._seed)
-
-        if "DATAPROFILER_SEED" in os.environ and settings._seed is None:
-            seed = os.environ.get("DATAPROFILER_SEED")
-            if isinstance(seed, int):
-                rng = np.random.default_rng(int(seed))
-            else:
-                warnings.warn("Seed should be an integer", RuntimeWarning)
+        rng = rng_utils.get_random_number_generator()
 
         base_stats = {
             "sample_size": total_sample_size,
@@ -755,7 +747,7 @@ def __init__(
                 self.options.set({"data_labeler.data_labeler_object": data_labeler})
 
             except Exception as e:
-                utils.warn_on_profile("data_labeler", e)
+                profiler_utils.warn_on_profile("data_labeler", e)
                 self.options.set({"data_labeler.is_enabled": False})
 
     def _add_error_checks(self, other: BaseProfiler) -> None:
@@ -801,7 +793,9 @@ def __add__(self, other: BaseProfiler) -> BaseProfiler:
 
         merged_profile.total_samples = self.total_samples + other.total_samples
 
-        merged_profile.times = utils.add_nested_dictionaries(self.times, other.times)
+        merged_profile.times = profiler_utils.add_nested_dictionaries(
+            self.times, other.times
+        )
 
         return merged_profile
 
@@ -826,10 +820,10 @@ def diff(self, other_profile: BaseProfiler, options: dict = None) -> dict:
                 (
                     "global_stats",
                     {
-                        "file_type": utils.find_diff_of_strings_and_bools(
+                        "file_type": profiler_utils.find_diff_of_strings_and_bools(
                             self.file_type, other_profile.file_type
                         ),
-                        "encoding": utils.find_diff_of_strings_and_bools(
+                        "encoding": profiler_utils.find_diff_of_strings_and_bools(
                             self.encoding, other_profile.encoding
                         ),
                     },
@@ -1080,7 +1074,7 @@ def _restore_data_labelers(self, data_labeler: BaseDataLabeler = None) -> None:
                 self.options.set({"data_labeler.data_labeler_object": data_labeler})
 
             except Exception as e:
-                utils.warn_on_profile("data_labeler", e)
+                profiler_utils.warn_on_profile("data_labeler", e)
                 self.options.set({"data_labeler.is_enabled": False})
                 self.options.set({"data_labeler.data_labeler_object": data_labeler})
 
@@ -1334,13 +1328,13 @@ def diff(  # type: ignore[override]
 
         report["global_stats"].update(
             {
-                "samples_used": utils.find_diff_of_numbers(
+                "samples_used": profiler_utils.find_diff_of_numbers(
                     self.total_samples, other_profile.total_samples
                 ),
-                "empty_line_count": utils.find_diff_of_numbers(
+                "empty_line_count": profiler_utils.find_diff_of_numbers(
                     self._empty_line_count, other_profile._empty_line_count
                 ),
-                "memory_size": utils.find_diff_of_numbers(
+                "memory_size": profiler_utils.find_diff_of_numbers(
                     self.memory_size, other_profile.memory_size
                 ),
             }
@@ -1444,7 +1438,7 @@ def load_from_dict(
         """
         raise NotImplementedError("UnstructuredProfiler deserialization not supported.")
 
-    @utils.method_timeit(name="clean_and_base_stats")
+    @profiler_utils.method_timeit(name="clean_and_base_stats")
     def _clean_data_and_get_base_stats(
         self, data: pd.Series, sample_size: int, min_true_samples: int = None
     ) -> tuple[pd.Series, dict]:
@@ -1481,10 +1475,14 @@ def _clean_data_and_get_base_stats(
         data = data.apply(str)
 
         # get memory size
-        base_stats: dict = {"memory_size": utils.get_memory_size(data, unit="M")}
+        base_stats: dict = {
+            "memory_size": profiler_utils.get_memory_size(data, unit="M")
+        }
 
         # Setup sample generator
-        sample_ind_generator = utils.shuffle_in_chunks(len_data, chunk_size=sample_size)
+        sample_ind_generator = profiler_utils.shuffle_in_chunks(
+            len_data, chunk_size=sample_size
+        )
 
         true_sample_list = set()
         total_sample_size = 0
@@ -1869,34 +1867,34 @@ def diff(  # type: ignore[override]
         report = super().diff(other_profile, options)
         report["global_stats"].update(
             {
-                "samples_used": utils.find_diff_of_numbers(
+                "samples_used": profiler_utils.find_diff_of_numbers(
                     self._max_col_samples_used, other_profile._max_col_samples_used
                 ),
-                "column_count": utils.find_diff_of_numbers(
+                "column_count": profiler_utils.find_diff_of_numbers(
                     len(self._profile), len(other_profile._profile)
                 ),
-                "row_count": utils.find_diff_of_numbers(
+                "row_count": profiler_utils.find_diff_of_numbers(
                     self.total_samples, other_profile.total_samples
                 ),
-                "row_has_null_ratio": utils.find_diff_of_numbers(
+                "row_has_null_ratio": profiler_utils.find_diff_of_numbers(
                     self._get_row_has_null_ratio(),
                     other_profile._get_row_has_null_ratio(),
                 ),
-                "row_is_null_ratio": utils.find_diff_of_numbers(
+                "row_is_null_ratio": profiler_utils.find_diff_of_numbers(
                     self._get_row_is_null_ratio(),
                     other_profile._get_row_is_null_ratio(),
                 ),
-                "unique_row_ratio": utils.find_diff_of_numbers(
+                "unique_row_ratio": profiler_utils.find_diff_of_numbers(
                     self._get_unique_row_ratio(), other_profile._get_unique_row_ratio()
                 ),
-                "duplicate_row_count": utils.find_diff_of_numbers(
+                "duplicate_row_count": profiler_utils.find_diff_of_numbers(
                     self._get_duplicate_row_count(),
                     other_profile._get_duplicate_row_count(),
                 ),
-                "correlation_matrix": utils.find_diff_of_matrices(
+                "correlation_matrix": profiler_utils.find_diff_of_matrices(
                     self.correlation_matrix, other_profile.correlation_matrix
                 ),
-                "chi2_matrix": utils.find_diff_of_matrices(
+                "chi2_matrix": profiler_utils.find_diff_of_matrices(
                     self.chi2_matrix, other_profile.chi2_matrix
                 ),
                 "profile_schema": defaultdict(list),
@@ -1916,7 +1914,7 @@ def diff(  # type: ignore[override]
 
         report["global_stats"][
             "profile_schema"
-        ] = utils.find_diff_of_dicts_with_diff_keys(
+        ] = profiler_utils.find_diff_of_dicts_with_diff_keys(
             self_profile_schema, other_profile_schema
         )
 
@@ -2193,7 +2191,7 @@ def _get_duplicate_row_count(self) -> int | None:
             )
         return 0
 
-    @utils.method_timeit(name="row_stats")
+    @profiler_utils.method_timeit(name="row_stats")
     def _update_row_statistics(
         self, data: pd.DataFrame, sample_ids: list[int] = None
     ) -> None:
@@ -2347,7 +2345,7 @@ def _get_correlation(
 
         return corr_mat
 
-    @utils.method_timeit(name="correlation")
+    @profiler_utils.method_timeit(name="correlation")
     def _update_correlation(
         self, clean_samples: dict, prev_dependent_properties: dict
     ) -> None:
@@ -2371,7 +2369,7 @@ def _update_correlation(
             batch_properties["count"],
         )
 
-    @utils.method_timeit(name="correlation")
+    @profiler_utils.method_timeit(name="correlation")
     def _merge_correlation(self, other: StructuredProfiler) -> pd.DataFrame:
         """
         Merge correlation matrix from two profiles.
@@ -2570,7 +2568,7 @@ def _update_chi2(self) -> np.ndarray:
                 if not profiler2.is_match:
                     continue
 
-                results = utils.perform_chi_squared_test_for_homogeneity(
+                results = profiler_utils.perform_chi_squared_test_for_homogeneity(
                     profiler1.categorical_counts,
                     profiler1.sample_size,
                     profiler2.categorical_counts,
@@ -2834,7 +2832,7 @@ def tqdm(level: set[int]) -> Generator[int, None, None]:
                         yield e
 
         # Shuffle indices once and share with columns
-        sample_ids = [*utils.shuffle_in_chunks(len(data), len(data))]
+        sample_ids = [*profiler_utils.shuffle_in_chunks(len(data), len(data))]
 
         # If there are no minimum true samples, you can sort to save time
         if min_true_samples in [None, 0]:
@@ -2869,12 +2867,17 @@ def tqdm(level: set[int]) -> Generator[int, None, None]:
                     )
                 )
 
+        # If options.multiprocess is enabled, auto-toggle multiprocessing
+        auto_multiprocess_toggle = False
+        if self.options.multiprocess.is_enabled:
+            auto_multiprocess_toggle = profiler_utils.auto_multiprocess_toggle(data)
+
         # Generate pool and estimate datasize
         pool = None
-        if self.options.multiprocess.is_enabled:
+        if auto_multiprocess_toggle:
             est_data_size = data[:50000].memory_usage(index=False, deep=True).sum()
             est_data_size = (est_data_size / min(50000, len(data))) * len(data)
-            pool, pool_size = utils.generate_pool(
+            pool, pool_size = profiler_utils.generate_pool(
                 max_pool_size=None, data_size=est_data_size, cols=len(data.columns)
             )
 
@@ -2992,8 +2995,8 @@ def tqdm(level: set[int]) -> Generator[int, None, None]:
         # Process and label the data
         notification_str = "Calculating the statistics... "
         pool = None
-        if self.options.multiprocess.is_enabled:
-            pool, pool_size = utils.generate_pool(4, est_data_size)
+        if auto_multiprocess_toggle:
+            pool, pool_size = profiler_utils.generate_pool(4, est_data_size)
             if pool:
                 notification_str += " (with " + str(pool_size) + " processes)"
 
diff --git a/dataprofiler/profilers/profiler_options.py b/dataprofiler/profilers/profiler_options.py
index f34876a55..e3d10696b 100644
--- a/dataprofiler/profilers/profiler_options.py
+++ b/dataprofiler/profilers/profiler_options.py
@@ -9,7 +9,7 @@
 from typing import Any, Generic, TypeVar, cast
 
 from ..labelers.base_data_labeler import BaseDataLabeler
-from . import utils
+from . import profiler_utils
 from .json_decoder import load_option
 
 BaseOptionT = TypeVar("BaseOptionT", bound="BaseOption")
@@ -210,13 +210,14 @@ def _validate_helper(self, variable_path: str = "BooleanOption") -> list[str]:
         return errors
 
 
-class HistogramOption(BooleanOption["HistogramOption"]):
+class HistogramAndQuantilesOption(BooleanOption["HistogramAndQuantilesOption"]):
     """For setting histogram options."""
 
     def __init__(
         self,
         is_enabled: bool = True,
         bin_count_or_method: str | int | list[str] = "auto",
+        num_quantiles: int = 1000,
     ) -> None:
         """
         Initialize Options for histograms.
@@ -226,11 +227,16 @@ def __init__(
         :ivar bin_count_or_method: bin count or the method with which to
             calculate histograms
         :vartype bin_count_or_method: Union[str, int, list(str)]
+        :ivar num_quantiles: number of quantiles
+        :vartype num_quantiles: int
         """
         self.bin_count_or_method = bin_count_or_method
+        self.num_quantiles = num_quantiles
         super().__init__(is_enabled=is_enabled)
 
-    def _validate_helper(self, variable_path: str = "HistogramOption") -> list[str]:
+    def _validate_helper(
+        self, variable_path: str = "HistogramAndQuantilesOption"
+    ) -> list[str]:
         """
         Validate the options do not conflict and cause errors.
 
@@ -260,6 +266,12 @@ def _validate_helper(self, variable_path: str = "HistogramOption") -> list[str]:
                     "than 1, a string, or list of strings from the "
                     "following: {}.".format(variable_path, valid_methods)
                 )
+
+        if self.num_quantiles is not None and (
+            not isinstance(self.num_quantiles, int) or self.num_quantiles < 1
+        ):
+            errors.append(f"{variable_path}.num_quantiles must be a positive integer.")
+
         return errors
 
 
@@ -396,7 +408,9 @@ def __init__(self) -> None:
         self.median_abs_deviation: BooleanOption = BooleanOption(is_enabled=True)
         self.num_zeros: BooleanOption = BooleanOption(is_enabled=True)
         self.num_negatives: BooleanOption = BooleanOption(is_enabled=True)
-        self.histogram_and_quantiles: HistogramOption = HistogramOption()
+        self.histogram_and_quantiles: HistogramAndQuantilesOption = (
+            HistogramAndQuantilesOption()
+        )
         # By default, we correct for bias
         self.bias_correction: BooleanOption = BooleanOption(is_enabled=True)
         BaseInspectorOptions.__init__(self)
@@ -1308,7 +1322,7 @@ def load_from_dict(
         data_labeler_object = None
         data_labeler_load_attr = data.pop("data_labeler_object", {})
         if data_labeler_load_attr:
-            data_labeler_object = utils.reload_labeler_from_options_or_get_new(
+            data_labeler_object = profiler_utils.reload_labeler_from_options_or_get_new(
                 data_labeler_load_attr, config
             )
             if data_labeler_object:
diff --git a/dataprofiler/profilers/utils.py b/dataprofiler/profilers/profiler_utils.py
similarity index 96%
rename from dataprofiler/profilers/utils.py
rename to dataprofiler/profilers/profiler_utils.py
index 09bfbac18..a3ed375b4 100644
--- a/dataprofiler/profilers/utils.py
+++ b/dataprofiler/profilers/profiler_utils.py
@@ -7,7 +7,6 @@
 import functools
 import math
 import multiprocessing as mp
-import os
 import time
 import warnings
 from abc import abstractmethod
@@ -31,13 +30,14 @@
 import scipy
 from pandas import DataFrame, Series
 
-from .. import settings
 from ..labelers.data_labelers import DataLabeler
 
 if TYPE_CHECKING:
     from ..labelers.base_data_labeler import BaseDataLabeler
     from . import profile_builder
 
+from .. import rng_utils
+
 
 def recursive_dict_update(d: dict, update_d: dict) -> dict:
     """
@@ -109,14 +109,7 @@ def shuffle_in_chunks(
     if not data_length or data_length == 0 or not chunk_size or chunk_size == 0:
         return []
 
-    rng = np.random.default_rng(settings._seed)
-
-    if "DATAPROFILER_SEED" in os.environ and settings._seed is None:
-        seed = os.environ.get("DATAPROFILER_SEED")
-        if isinstance(seed, int):
-            rng = np.random.default_rng(int(seed))
-        else:
-            warnings.warn("Seed should be an integer", RuntimeWarning)
+    rng = rng_utils.get_random_number_generator()
 
     indices = KeyDict()
     j = 0
@@ -184,6 +177,34 @@ def partition(data: list, chunk_size: int) -> Generator[list, None, Any]:
         yield data[idx : idx + chunk_size]
 
 
+def auto_multiprocess_toggle(
+    data: DataFrame,
+    num_rows_threshold: int = 750000,
+    num_cols_threshold: int = 20,
+) -> bool:
+    """
+    Automate multiprocessing toggle depending on dataset sizes.
+
+    :param data: a dataset
+    :type data: pandas.DataFrame
+    :param num_rows_threshold: threshold for number of rows to
+        use multiprocess
+    :type num_rows_threshold: int
+    :param num_cols_threshold: threshold for number of columns
+        to use multiprocess
+    :type num_cols_threshold: int
+    :return: recommended option.multiprocess.is_enabled value
+    :rtype: bool
+    """
+    # If the number of rows or columns exceed their respective threshold,
+    # we want to turn on multiprocessing
+    if data.shape[0] > num_rows_threshold or data.shape[1] > num_cols_threshold:
+        return True
+    # Otherwise, we do not turn on multiprocessing
+    else:
+        return False
+
+
 def suggest_pool_size(data_size: int = None, cols: int = None) -> int | None:
     """
     Suggest the pool size based on resources.
diff --git a/dataprofiler/profilers/text_column_profile.py b/dataprofiler/profilers/text_column_profile.py
index e8446dcb8..bea8dbd68 100644
--- a/dataprofiler/profilers/text_column_profile.py
+++ b/dataprofiler/profilers/text_column_profile.py
@@ -6,7 +6,7 @@
 import numpy as np
 import pandas as pd
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnPrimitiveTypeProfiler, BaseColumnProfiler
 from .numerical_column_stats import NumericStatsMixin
 from .profiler_options import TextOptions
@@ -111,7 +111,9 @@ def diff(self, other_profile: TextColumn, options: dict = None) -> dict:
         differences = NumericStatsMixin.diff(self, other_profile, options)
 
         del differences["psi"]
-        vocab_diff = utils.find_diff_of_lists_and_sets(self.vocab, other_profile.vocab)
+        vocab_diff = profiler_utils.find_diff_of_lists_and_sets(
+            self.vocab, other_profile.vocab
+        )
         differences["vocab"] = vocab_diff
         return differences
 
@@ -149,7 +151,7 @@ def _update_vocab(
         :return: None
         """
         data_flat = set(itertools.chain(*data))
-        self.vocab = utils._combine_unique_sets(self.vocab, data_flat)
+        self.vocab = profiler_utils._combine_unique_sets(self.vocab, data_flat)
 
     def _update_helper(self, df_series_clean: pd.Series, profile: dict) -> None:
         """
diff --git a/dataprofiler/profilers/unstructured_labeler_profile.py b/dataprofiler/profilers/unstructured_labeler_profile.py
index d07f2647d..1c7b16c0f 100644
--- a/dataprofiler/profilers/unstructured_labeler_profile.py
+++ b/dataprofiler/profilers/unstructured_labeler_profile.py
@@ -8,7 +8,7 @@
 from ..labelers.base_data_labeler import BaseDataLabeler
 from ..labelers.data_labelers import DataLabeler
 from ..labelers.data_processing import CharPostprocessor
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import DataLabelerOptions
 
@@ -87,14 +87,16 @@ def __add__(self, other: UnstructuredLabelerProfile) -> UnstructuredLabelerProfi
         options.data_labeler_object = self.data_labeler
 
         merged_profile = UnstructuredLabelerProfile(options=options)
-        merged_profile.entity_counts = utils.add_nested_dictionaries(
+        merged_profile.entity_counts = profiler_utils.add_nested_dictionaries(
             self.entity_counts, other.entity_counts
         )
 
         merged_profile.char_sample_size = self.char_sample_size + other.char_sample_size
         merged_profile.word_sample_size = self.word_sample_size + other.word_sample_size
 
-        merged_profile.times = utils.add_nested_dictionaries(self.times, other.times)
+        merged_profile.times = profiler_utils.add_nested_dictionaries(
+            self.times, other.times
+        )
 
         merged_profile._update_percentages()
 
@@ -133,10 +135,10 @@ def diff(
         entity_counts_diff = {}
         entity_percentages_diff = {}
         for key in ["word_level", "true_char_level", "postprocess_char_level"]:
-            entity_percentages_diff[key] = utils.find_diff_of_dicts(
+            entity_percentages_diff[key] = profiler_utils.find_diff_of_dicts(
                 self.entity_percentages[key], other_profile.entity_percentages[key]
             )
-            entity_counts_diff[key] = utils.find_diff_of_dicts(
+            entity_counts_diff[key] = profiler_utils.find_diff_of_dicts(
                 self.entity_counts[key], other_profile.entity_counts[key]
             )
 
diff --git a/dataprofiler/profilers/unstructured_text_profile.py b/dataprofiler/profilers/unstructured_text_profile.py
index ffc9eb503..96b7d0625 100644
--- a/dataprofiler/profilers/unstructured_text_profile.py
+++ b/dataprofiler/profilers/unstructured_text_profile.py
@@ -9,7 +9,7 @@
 from numpy import ndarray
 from pandas import DataFrame, Series
 
-from . import utils
+from . import profiler_utils
 from .base_column_profilers import BaseColumnProfiler
 from .profiler_options import TextProfilerOptions
 
@@ -610,18 +610,20 @@ def diff(self, other_profile: TextProfiler, options: dict = None) -> dict:
             other_word_count = {k.lower(): v for k, v in other_word_count.items()}
 
         diff: dict = {}
-        diff["vocab"] = utils.find_diff_of_lists_and_sets(
+        diff["vocab"] = profiler_utils.find_diff_of_lists_and_sets(
             list(self.vocab_count.keys()), list(other_profile.vocab_count.keys())
         )
 
-        diff["vocab_count"] = utils.find_diff_of_dicts_with_diff_keys(
+        diff["vocab_count"] = profiler_utils.find_diff_of_dicts_with_diff_keys(
             dict(self.vocab_count.most_common(self._top_k_chars)),
             dict(other_profile.vocab_count.most_common(self._top_k_chars)),
         )
 
-        diff["words"] = utils.find_diff_of_lists_and_sets(self_words, other_words)
+        diff["words"] = profiler_utils.find_diff_of_lists_and_sets(
+            self_words, other_words
+        )
 
-        diff["word_count"] = utils.find_diff_of_dicts_with_diff_keys(
+        diff["word_count"] = profiler_utils.find_diff_of_dicts_with_diff_keys(
             self_word_count, other_word_count
         )
 
diff --git a/dataprofiler/rng_utils.py b/dataprofiler/rng_utils.py
new file mode 100644
index 000000000..329066658
--- /dev/null
+++ b/dataprofiler/rng_utils.py
@@ -0,0 +1,19 @@
+"""Create a random number generator using a manual seed DATAPROFILER_SEED."""
+import os
+import warnings
+
+import numpy as np
+
+from . import settings
+
+
+def get_random_number_generator() -> np.random._generator.Generator:
+    """Create a random number generator using a manual seed DATAPROFILER_SEED."""
+    rng = np.random.default_rng(settings._seed)
+    if "DATAPROFILER_SEED" in os.environ and settings._seed is None:
+        seed: str = os.environ.get("DATAPROFILER_SEED", "")
+        try:
+            rng = np.random.default_rng(int(seed))
+        except ValueError:
+            warnings.warn("Seed should be an integer", RuntimeWarning)
+    return rng
diff --git a/dataprofiler/tests/profilers/profiler_options/test_datalabeler_options.py b/dataprofiler/tests/profilers/profiler_options/test_datalabeler_options.py
index ef906e084..e2794c78a 100644
--- a/dataprofiler/tests/profilers/profiler_options/test_datalabeler_options.py
+++ b/dataprofiler/tests/profilers/profiler_options/test_datalabeler_options.py
@@ -196,7 +196,7 @@ def test_json_encode(self):
         self.assertDictEqual(expected, json.loads(serialized))
 
     @mock.patch(
-        "dataprofiler.profilers.utils.DataLabeler",
+        "dataprofiler.profilers.profiler_utils.DataLabeler",
         spec=BaseDataLabeler,
     )
     def test_json_decode(self, mock_BaseDataLabeler):
diff --git a/dataprofiler/tests/profilers/profiler_options/test_float_options.py b/dataprofiler/tests/profilers/profiler_options/test_float_options.py
index 044faa04e..9b67e3534 100644
--- a/dataprofiler/tests/profilers/profiler_options/test_float_options.py
+++ b/dataprofiler/tests/profilers/profiler_options/test_float_options.py
@@ -94,7 +94,7 @@ def test_json_encode(self):
                     "data": {"is_enabled": True},
                 },
                 "histogram_and_quantiles": {
-                    "class": "HistogramOption",
+                    "class": "HistogramAndQuantilesOption",
                     "data": mock.ANY,
                 },
                 "bias_correction": {
diff --git a/dataprofiler/tests/profilers/profiler_options/test_histogram_and_quantiles_option.py b/dataprofiler/tests/profilers/profiler_options/test_histogram_and_quantiles_option.py
new file mode 100644
index 000000000..17abad647
--- /dev/null
+++ b/dataprofiler/tests/profilers/profiler_options/test_histogram_and_quantiles_option.py
@@ -0,0 +1,311 @@
+import json
+
+from dataprofiler.profilers.json_decoder import load_option
+from dataprofiler.profilers.json_encoder import ProfileEncoder
+from dataprofiler.profilers.profiler_options import HistogramAndQuantilesOption
+
+from .. import utils as test_utils
+from .test_boolean_option import TestBooleanOption
+
+
+class TestHistogramAndQuantilesOption(TestBooleanOption):
+
+    option_class = HistogramAndQuantilesOption
+    keys = []
+
+    def test_init(self):
+        option = self.get_options()
+        self.assertTrue(option.is_enabled)
+        self.assertEqual(option.bin_count_or_method, "auto")
+        self.assertEqual(option.num_quantiles, 1000)
+
+    def test_set_helper(self):
+        option = self.get_options()
+
+        # validate, variable path being passed
+        expected_error = (
+            "type object 'test.bin_count_or_method' has no attribute 'is_enabled'"
+        )
+        with self.assertRaisesRegex(AttributeError, expected_error):
+            option._set_helper({"bin_count_or_method.is_enabled": True}, "test")
+
+        # validate, variable path being passed
+        expected_error = (
+            "type object 'test.num_quantiles' has no attribute 'is_enabled'"
+        )
+        with self.assertRaisesRegex(AttributeError, expected_error):
+            option._set_helper({"num_quantiles.is_enabled": True}, "test")
+
+    def test_set(self):
+        option = self.get_options()
+
+        params_to_check = [
+            dict(prop="is_enabled", value_list=[False, True]),
+            dict(
+                prop="bin_count_or_method",
+                value_list=[
+                    None,
+                    "auto",
+                    "fd",
+                    "doane",
+                    "scott",
+                    "rice",
+                    "sturges",
+                    "sqrt",
+                    ["sturges", "doane"],
+                    1,
+                    10,
+                    100,
+                    1000,
+                    99,
+                    10000000,
+                ],
+            ),
+        ]
+
+        # this code can be abstracted to limit code everywhere else
+        # AKA, params_to_check would be the only needed code plus raise errors
+        def _assert_set_helper(prop, value):
+            option.set({prop: value})
+            self.assertEqual(value, getattr(option, prop), msg=prop)
+
+        for params in params_to_check:
+            prop, value_list = params["prop"], params["value_list"]
+            for value in value_list:
+                _assert_set_helper(prop, value)
+
+        # Treat bin_count_or_method as a BooleanOption
+        expected_error = (
+            "type object 'bin_count_or_method' has no attribute 'is_enabled'"
+        )
+        with self.assertRaisesRegex(AttributeError, expected_error):
+            option.set({"bin_count_or_method.is_enabled": True})
+
+        # Treat num_quantiles as a BooleanOption
+        expected_error = "type object 'num_quantiles' has no attribute 'is_enabled'"
+        with self.assertRaisesRegex(AttributeError, expected_error):
+            option.set({"num_quantiles.is_enabled": True})
+
+        # Test set option for num_quantiles
+        option.set({"num_quantiles": 50})
+        self.assertEqual(option.num_quantiles, 50)
+
+    def test_validate_helper(self):
+        super().test_validate_helper()
+
+        optpth = self.get_options_path()
+
+        # Default configuration
+        option = self.get_options(num_quantiles=1000)
+        self.assertEqual([], option._validate_helper())
+
+        # Valid configurations
+        option = self.get_options(num_quantiles=50)
+        self.assertEqual([], option._validate_helper())
+        option = self.get_options(num_quantiles=2000)
+        self.assertEqual([], option._validate_helper())
+        option = self.get_options(num_quantiles=1)
+        self.assertEqual([], option._validate_helper())
+
+        # Option num_quantiles
+        option = self.get_options(num_quantiles="Hello World")
+        expected_error = [f"{optpth}.num_quantiles must be a positive integer."]
+        self.assertSetEqual(set(expected_error), set(option._validate_helper()))
+
+        # Option num_quantiles cannot be a float, must be an int
+        option = self.get_options(num_quantiles=1.1)
+        expected_error = [f"{optpth}.num_quantiles must be a positive integer."]
+        self.assertSetEqual(set(expected_error), set(option._validate_helper()))
+
+        # Option num_quantiles may not be zero, must be greater than one(1)
+        option = self.get_options(num_quantiles=0)
+        expected_error = [f"{optpth}.num_quantiles must be a positive integer."]
+        self.assertSetEqual(set(expected_error), set(option._validate_helper()))
+
+        # Option num_quantiles cannot be a negative integer
+        option = self.get_options(num_quantiles=-5)
+        expected_error = [f"{optpth}.num_quantiles must be a positive integer."]
+        self.assertSetEqual(set(expected_error), set(option._validate_helper()))
+
+    def test_validate(self):
+
+        super().test_validate()
+
+        optpth = self.get_options_path()
+
+        params_to_check = [
+            # non errors
+            dict(prop="is_enabled", value_list=[False, True], errors=[]),
+            dict(
+                prop="bin_count_or_method",
+                value_list=[
+                    "auto",
+                    "fd",
+                    "doane",
+                    "scott",
+                    "rice",
+                    "sturges",
+                    "sqrt",
+                    ["sturges", "doane"],
+                    1,
+                    10,
+                    100,
+                    1000,
+                    99,
+                    10000000,
+                ],
+                errors=[],
+            ),
+            # errors
+            dict(
+                prop="bin_count_or_method",
+                value_list=[
+                    -1,
+                    1.2,
+                    1.0,
+                    [],
+                    False,
+                    "whoops",
+                    ["doane", "incorrect"],
+                    "1",
+                ],
+                errors=[
+                    "HistogramAndQuantilesOption.bin_count_or_method must be an integer "
+                    "more than 1, a string, or list of strings from the "
+                    "following: ['auto', 'fd', 'doane', 'scott', 'rice', "
+                    "'sturges', 'sqrt']."
+                ],
+            ),
+        ]
+
+        # this code can be abstracted to limit code everywhere else
+        # AKA, for loop below could be abstracted to a utils func
+
+        # Default configuration is valid
+        option = self.get_options()
+        self.assertIsNone(option.validate(raise_error=False))
+
+        for params in params_to_check:
+            prop, value_list, expected_errors = (
+                params["prop"],
+                params["value_list"],
+                params["errors"],
+            )
+            option = self.get_options()
+            for value in value_list:
+                setattr(option, prop, value)
+                validate_errors = option.validate(raise_error=False)
+                if expected_errors:
+                    self.assertListEqual(
+                        expected_errors,
+                        validate_errors,
+                        msg=f"Errored for prop: {prop}, value: {value}.",
+                    )
+                else:
+                    self.assertIsNone(
+                        validate_errors,
+                        msg=f"Errored for prop: {prop}, value: {value}.",
+                    )
+
+        # this time testing raising an error
+        option.bin_count_or_method = "fake method"
+        expected_error = (
+            r"HistogramAndQuantilesOption.bin_count_or_method must be an integer more than "
+            r"1, a string, or list of strings from the following: "
+            r"\['auto', 'fd', 'doane', 'scott', 'rice', 'sturges', 'sqrt']."
+        )
+        with self.assertRaisesRegex(ValueError, expected_error):
+            option.validate()
+
+        # Valid configurations
+        option = self.get_options(num_quantiles=50)
+        self.assertEqual([], option._validate_helper())
+        option = self.get_options(num_quantiles=2000)
+        self.assertEqual([], option._validate_helper())
+        option = self.get_options(num_quantiles=1)
+        self.assertEqual([], option._validate_helper())
+
+        # Option num_quantiles cannot be a string, must be an int
+        option = self.get_options(num_quantiles="Hello World")
+        expected_error = f"{optpth}.num_quantiles must be a positive integer"
+        with self.assertRaisesRegex(ValueError, expected_error):
+            option.validate()
+
+        # Option num_quantiles cannot be a float, must be an int
+        option = self.get_options(num_quantiles=1.1)
+        expected_error = f"{optpth}.num_quantiles must be a positive integer"
+        with self.assertRaisesRegex(ValueError, expected_error):
+            option.validate()
+
+        # Option num_quantiles must be a positive integer
+        option = self.get_options(num_quantiles=0)
+        expected_error = f"{optpth}.num_quantiles must be a positive integer"
+        with self.assertRaisesRegex(ValueError, expected_error):
+            option.validate()
+
+        # Option num_quantiles cannot be a negative integer
+        option = self.get_options(num_quantiles=-5)
+        expected_error = f"{optpth}.num_quantiles must be a positive integer"
+        with self.assertRaisesRegex(ValueError, expected_error):
+            option.validate()
+
+    def test_eq(self):
+        super().test_eq()
+
+        options = self.get_options()
+        options2 = self.get_options()
+        options.bin_count_or_method = "sturges"
+        self.assertNotEqual(options, options2)
+        options2.bin_count_or_method = "doane"
+        self.assertNotEqual(options, options2)
+        options2.bin_count_or_method = "sturges"
+        self.assertEqual(options, options2)
+        options.num_quantiles = 30
+        self.assertNotEqual(options, options2)
+        options2.num_quantiles = 50
+        self.assertNotEqual(options, options2)
+        options2.num_quantiles = 30
+        self.assertEqual(options, options2)
+
+    def test_json_encode(self):
+        option = HistogramAndQuantilesOption(
+            is_enabled=False, bin_count_or_method="doane"
+        )
+
+        serialized = json.dumps(option, cls=ProfileEncoder)
+
+        expected = {
+            "class": "HistogramAndQuantilesOption",
+            "data": {
+                "bin_count_or_method": "doane",
+                "num_quantiles": 1000,
+                "is_enabled": False,
+            },
+        }
+
+        self.assertDictEqual(expected, json.loads(serialized))
+
+    def test_json_decode_warn(self):
+        old_histogram = {
+            "class": "HistogramOption",
+            "data": {
+                "bin_count_or_method": "doane",
+                "is_enabled": False,
+            },
+        }
+
+        expected = HistogramAndQuantilesOption(
+            is_enabled=False, bin_count_or_method="doane"
+        )
+
+        expected_string = json.dumps(old_histogram, cls=ProfileEncoder)
+
+        expected_warning = (
+            "HistogramOption will be deprecated in the future. During the JSON encode "
+            "process, HistogramOption is mapped to HistogramAndQuantilesOption. "
+            "Please begin utilizing the new HistogramAndQuantilesOption class."
+        )
+
+        with self.assertWarnsRegex(DeprecationWarning, expected_warning):
+            deserialized = load_option(json.loads(expected_string))
+            test_utils.assert_profiles_equal(deserialized, expected)
diff --git a/dataprofiler/tests/profilers/profiler_options/test_histogram_option.py b/dataprofiler/tests/profilers/profiler_options/test_histogram_option.py
deleted file mode 100644
index 4bf3a3b16..000000000
--- a/dataprofiler/tests/profilers/profiler_options/test_histogram_option.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import json
-
-from dataprofiler.profilers.json_encoder import ProfileEncoder
-from dataprofiler.profilers.profiler_options import HistogramOption
-
-from .test_boolean_option import TestBooleanOption
-
-
-class TestHistogramOption(TestBooleanOption):
-
-    option_class = HistogramOption
-    keys = []
-
-    def test_init(self):
-        option = self.get_options()
-        self.assertTrue(option.is_enabled)
-        self.assertEqual(option.bin_count_or_method, "auto")
-
-    def test_set_helper(self):
-        option = self.get_options()
-
-        # validate, variable path being passed
-        expected_error = (
-            "type object 'test.bin_count_or_method' has no " "attribute 'is_enabled'"
-        )
-        with self.assertRaisesRegex(AttributeError, expected_error):
-            option._set_helper({"bin_count_or_method.is_enabled": True}, "test")
-
-    def test_set(self):
-        option = self.get_options()
-
-        params_to_check = [
-            dict(prop="is_enabled", value_list=[False, True]),
-            dict(
-                prop="bin_count_or_method",
-                value_list=[
-                    None,
-                    "auto",
-                    "fd",
-                    "doane",
-                    "scott",
-                    "rice",
-                    "sturges",
-                    "sqrt",
-                    ["sturges", "doane"],
-                    1,
-                    10,
-                    100,
-                    1000,
-                    99,
-                    10000000,
-                ],
-            ),
-        ]
-
-        # this code can be abstracted to limit code everywhere else
-        # AKA, params_to_check would be the only needed code plus raise errors
-        def _assert_set_helper(prop, value):
-            option.set({prop: value})
-            self.assertEqual(value, getattr(option, prop), msg=prop)
-
-        for params in params_to_check:
-            prop, value_list = params["prop"], params["value_list"]
-            for value in value_list:
-                _assert_set_helper(prop, value)
-
-        # Treat bin_count_or_method as a BooleanOption
-        expected_error = (
-            "type object 'bin_count_or_method' has no attribute " "'is_enabled'"
-        )
-        with self.assertRaisesRegex(AttributeError, expected_error):
-            option.set({"bin_count_or_method.is_enabled": True})
-
-    def test_validate_helper(self):
-        super().test_validate_helper()
-
-    def test_validate(self):
-
-        super().test_validate()
-
-        params_to_check = [
-            # non errors
-            dict(prop="is_enabled", value_list=[False, True], errors=[]),
-            dict(
-                prop="bin_count_or_method",
-                value_list=[
-                    "auto",
-                    "fd",
-                    "doane",
-                    "scott",
-                    "rice",
-                    "sturges",
-                    "sqrt",
-                    ["sturges", "doane"],
-                    1,
-                    10,
-                    100,
-                    1000,
-                    99,
-                    10000000,
-                ],
-                errors=[],
-            ),
-            # errors
-            dict(
-                prop="bin_count_or_method",
-                value_list=[
-                    -1,
-                    1.2,
-                    1.0,
-                    [],
-                    False,
-                    "whoops",
-                    ["doane", "incorrect"],
-                    "1",
-                ],
-                errors=[
-                    "HistogramOption.bin_count_or_method must be an integer "
-                    "more than 1, a string, or list of strings from the "
-                    "following: ['auto', 'fd', 'doane', 'scott', 'rice', "
-                    "'sturges', 'sqrt']."
-                ],
-            ),
-        ]
-
-        # # this code can be abstracted to limit code everywhere else
-        # # AKA, for loop below could be abstracted to a utils func
-
-        # Default configuration is valid
-        option = self.get_options()
-        self.assertIsNone(option.validate(raise_error=False))
-
-        for params in params_to_check:
-            prop, value_list, expected_errors = (
-                params["prop"],
-                params["value_list"],
-                params["errors"],
-            )
-            option = self.get_options()
-            for value in value_list:
-                setattr(option, prop, value)
-                validate_errors = option.validate(raise_error=False)
-                if expected_errors:
-                    self.assertListEqual(
-                        expected_errors,
-                        validate_errors,
-                        msg=f"Errored for prop: {prop}, value: {value}.",
-                    )
-                else:
-                    self.assertIsNone(
-                        validate_errors,
-                        msg=f"Errored for prop: {prop}, value: {value}.",
-                    )
-
-        # this time testing raising an error
-        option.bin_count_or_method = "fake method"
-        expected_error = (
-            r"HistogramOption.bin_count_or_method must be an integer more than "
-            r"1, a string, or list of strings from the following: "
-            r"\['auto', 'fd', 'doane', 'scott', 'rice', 'sturges', 'sqrt']."
-        )
-        with self.assertRaisesRegex(ValueError, expected_error):
-            option.validate()
-
-    def test_eq(self):
-        super().test_eq()
-
-        options = self.get_options()
-        options2 = self.get_options()
-        options.bin_count_or_method = "sturges"
-        self.assertNotEqual(options, options2)
-        options2.bin_count_or_method = "doane"
-        self.assertNotEqual(options, options2)
-        options2.bin_count_or_method = "sturges"
-        self.assertEqual(options, options2)
-
-    def test_json_encode(self):
-        option = HistogramOption(is_enabled=False, bin_count_or_method="doane")
-
-        serialized = json.dumps(option, cls=ProfileEncoder)
-
-        expected = {
-            "class": "HistogramOption",
-            "data": {
-                "bin_count_or_method": "doane",
-                "is_enabled": False,
-            },
-        }
-
-        self.assertDictEqual(expected, json.loads(serialized))
diff --git a/dataprofiler/tests/profilers/profiler_options/test_int_options.py b/dataprofiler/tests/profilers/profiler_options/test_int_options.py
index 317d1ff64..b767f3f3e 100644
--- a/dataprofiler/tests/profilers/profiler_options/test_int_options.py
+++ b/dataprofiler/tests/profilers/profiler_options/test_int_options.py
@@ -86,7 +86,7 @@ def test_json_encode(self):
                     "data": {"is_enabled": True},
                 },
                 "histogram_and_quantiles": {
-                    "class": "HistogramOption",
+                    "class": "HistogramAndQuantilesOption",
                     "data": mock.ANY,
                 },
                 "bias_correction": {
diff --git a/dataprofiler/tests/profilers/profiler_options/test_numerical_options.py b/dataprofiler/tests/profilers/profiler_options/test_numerical_options.py
index 03d6c01db..ad0833d80 100644
--- a/dataprofiler/tests/profilers/profiler_options/test_numerical_options.py
+++ b/dataprofiler/tests/profilers/profiler_options/test_numerical_options.py
@@ -422,7 +422,7 @@ def test_json_encode(self):
                     "data": {"is_enabled": True},
                 },
                 "histogram_and_quantiles": {
-                    "class": "HistogramOption",
+                    "class": "HistogramAndQuantilesOption",
                     "data": mock.ANY,
                 },
                 "bias_correction": {
diff --git a/dataprofiler/tests/profilers/profiler_options/test_text_options.py b/dataprofiler/tests/profilers/profiler_options/test_text_options.py
index 57814126d..b26509e91 100644
--- a/dataprofiler/tests/profilers/profiler_options/test_text_options.py
+++ b/dataprofiler/tests/profilers/profiler_options/test_text_options.py
@@ -128,7 +128,7 @@ def test_json_encode(self):
                     "data": {"is_enabled": False},
                 },
                 "histogram_and_quantiles": {
-                    "class": "HistogramOption",
+                    "class": "HistogramAndQuantilesOption",
                     "data": mock.ANY,
                 },
                 "bias_correction": {
diff --git a/dataprofiler/tests/profilers/test_base_column_profilers.py b/dataprofiler/tests/profilers/test_base_column_profilers.py
index eb2ed764d..4ab7182cf 100644
--- a/dataprofiler/tests/profilers/test_base_column_profilers.py
+++ b/dataprofiler/tests/profilers/test_base_column_profilers.py
@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from dataprofiler.profilers import utils
+from dataprofiler.profilers import profiler_utils
 from dataprofiler.profilers.base_column_profilers import (
     BaseColumnPrimitiveTypeProfiler,
     BaseColumnProfiler,
@@ -185,7 +185,7 @@ def test_cannot_instantiate(self):
     def test_combine_unqiue_sets(self):
         a = [1, 2, 3]
         b = [3, 1, 4, -1]
-        c = utils._combine_unique_sets(a, b)
+        c = profiler_utils._combine_unique_sets(a, b)
         self.assertCountEqual([1, 2, 3, 4, -1], c)
 
     def test__init__(self):
diff --git a/dataprofiler/tests/profilers/test_column_profile_compilers.py b/dataprofiler/tests/profilers/test_column_profile_compilers.py
index 957bb694f..35617d1e8 100644
--- a/dataprofiler/tests/profilers/test_column_profile_compilers.py
+++ b/dataprofiler/tests/profilers/test_column_profile_compilers.py
@@ -611,7 +611,7 @@ def test_json_decode_after_update(self):
 
 
 @mock.patch(
-    "dataprofiler.profilers.utils.DataLabeler",
+    "dataprofiler.profilers.profiler_utils.DataLabeler",
     spec=BaseDataLabeler,
 )
 @mock.patch(
diff --git a/dataprofiler/tests/profilers/test_data_labeler_column_profile.py b/dataprofiler/tests/profilers/test_data_labeler_column_profile.py
index 5b25f939c..35f448aea 100644
--- a/dataprofiler/tests/profilers/test_data_labeler_column_profile.py
+++ b/dataprofiler/tests/profilers/test_data_labeler_column_profile.py
@@ -7,7 +7,7 @@
 import pandas as pd
 
 from dataprofiler.labelers import BaseDataLabeler
-from dataprofiler.profilers import utils
+from dataprofiler.profilers import profiler_utils
 from dataprofiler.profilers.data_labeler_column_profile import DataLabelerColumn
 from dataprofiler.profilers.json_decoder import load_column_profile
 from dataprofiler.profilers.json_encoder import ProfileEncoder
@@ -376,7 +376,7 @@ def test_diff(self, mock_instance):
 
             diff = profiler1.diff(profiler2)
             expected_diff = {
-                "data_label": utils.find_diff_of_lists_and_sets(
+                "data_label": profiler_utils.find_diff_of_lists_and_sets(
                     ["a", "b", "c"], ["b", "c", "d"]
                 ),
                 "avg_predictions": {"a": "unchanged", "b": -0.70, "c": 0.70},
@@ -485,7 +485,9 @@ def test_json_encode_after_update(self, mock_instance):
 
         self.assertEqual(expected, serialized)
 
-    @mock.patch("dataprofiler.profilers.utils.DataLabeler", spec=BaseDataLabeler)
+    @mock.patch(
+        "dataprofiler.profilers.profiler_utils.DataLabeler", spec=BaseDataLabeler
+    )
     def test_json_decode(self, mock_utils_DataLabeler, mock_BaseDataLabeler):
         self._setup_data_labeler_mock(mock_BaseDataLabeler)
         mock_utils_DataLabeler.load_from_library.side_effect = mock_BaseDataLabeler
@@ -526,7 +528,9 @@ def test_json_decode(self, mock_utils_DataLabeler, mock_BaseDataLabeler):
             class_as_dict["data"]["data_labeler"] = {"from_disk": "test"}
             deserialized = load_column_profile(class_as_dict, config)
 
-    @mock.patch("dataprofiler.profilers.utils.DataLabeler", spec=BaseDataLabeler)
+    @mock.patch(
+        "dataprofiler.profilers.profiler_utils.DataLabeler", spec=BaseDataLabeler
+    )
     def test_json_decode_after_update(
         self, mock_utils_DataLabeler, mock_BaseDataLabeler
     ):
diff --git a/dataprofiler/tests/profilers/test_float_column_profile.py b/dataprofiler/tests/profilers/test_float_column_profile.py
index 86e721a33..b7a2bfab7 100644
--- a/dataprofiler/tests/profilers/test_float_column_profile.py
+++ b/dataprofiler/tests/profilers/test_float_column_profile.py
@@ -37,6 +37,7 @@ def test_base_case(self):
         self.assertTrue(profiler.stddev is np.nan)
         self.assertIsNone(profiler.histogram_selection)
         self.assertIsNone(profiler.quantiles)
+        self.assertEqual(profiler._num_quantiles, 1000)
         self.assertIsNone(profiler.data_type_ratio)
 
     def test_single_data_variance_case(self):
@@ -1837,9 +1838,10 @@ def test_json_encode_after_update(self, time):
         data = np.array([0.0, 5.0, 10.0])
         df = pd.Series(data).apply(str)
 
-        int_options = FloatOptions()
-        int_options.histogram_and_quantiles.bin_count_or_method = 5
-        profiler = FloatColumn("0.0", int_options)
+        float_options = FloatOptions()
+        float_options.histogram_and_quantiles.bin_count_or_method = 5
+        float_options.histogram_and_quantiles.num_quantiles = 4
+        profiler = FloatColumn("0.0", float_options)
 
         mocked_quantiles = [0.25, 0.50, 0.75]
         with mock.patch.object(
@@ -1884,7 +1886,7 @@ def test_json_encode_after_update(self, time):
                     "_mode_is_enabled": True,
                     "num_zeros": 1,
                     "num_negatives": 0,
-                    "_num_quantiles": 1000,
+                    "_num_quantiles": 4,
                     "histogram_methods": expected_historam_methods,
                     "_stored_histogram": {
                         "total_loss": 2.0,
diff --git a/dataprofiler/tests/profilers/test_int_column_profile.py b/dataprofiler/tests/profilers/test_int_column_profile.py
index 01e624d20..d224a57a0 100644
--- a/dataprofiler/tests/profilers/test_int_column_profile.py
+++ b/dataprofiler/tests/profilers/test_int_column_profile.py
@@ -37,6 +37,7 @@ def test_base_case(self):
         self.assertTrue(profiler.stddev is np.nan)
         self.assertIsNone(profiler.histogram_selection)
         self.assertIsNone(profiler.quantiles)
+        self.assertEqual(profiler._num_quantiles, 1000)
         self.assertIsNone(profiler.data_type_ratio)
 
     def test_single_data_variance_case(self):
diff --git a/dataprofiler/tests/profilers/test_numeric_stats_mixin_profile.py b/dataprofiler/tests/profilers/test_numeric_stats_mixin_profile.py
index d01a7c382..4294dfd40 100644
--- a/dataprofiler/tests/profilers/test_numeric_stats_mixin_profile.py
+++ b/dataprofiler/tests/profilers/test_numeric_stats_mixin_profile.py
@@ -420,7 +420,6 @@ def test_from_dict_helper(self):
         )
         expected_profile._stored_histogram = mock_saved_profile["_stored_histogram"]
         expected_profile.quantiles = None
-
         expected_profile._stored_histogram["histogram"] = {
             "bin_counts": None,
             "bin_edges": None,
diff --git a/dataprofiler/tests/profilers/test_profile_builder.py b/dataprofiler/tests/profilers/test_profile_builder.py
index 7a309e946..9507fe20f 100644
--- a/dataprofiler/tests/profilers/test_profile_builder.py
+++ b/dataprofiler/tests/profilers/test_profile_builder.py
@@ -327,7 +327,12 @@ def test_correlation(self, *mock):
         # sum((x - np.mean(x))*(y-np.mean(y))) /
         # np.sqrt(sum((x - np.mean(x)**2)))/np.sqrt(sum((y - np.mean(y)**2)))
         profile_options = dp.ProfilerOptions()
-        profile_options.set({"correlation.is_enabled": True})
+        profile_options.set(
+            {
+                "correlation.is_enabled": True,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
 
         # data with a sole numeric column
         data = pd.DataFrame([1.0, 8.0, 1.0, -2.0, 5.0])
@@ -580,7 +585,12 @@ def test_merge_correlation(self, *mocks):
 
     def test_correlation_update(self):
         profile_options = dp.ProfilerOptions()
-        profile_options.set({"correlation.is_enabled": True})
+        profile_options.set(
+            {
+                "correlation.is_enabled": True,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
 
         # Test with all numeric columns
         data = pd.DataFrame(
@@ -776,12 +786,14 @@ def test_correlation_selected_columns(self, *mocks):
     def test_chi2(self, *mocks):
         # Empty
         data = pd.DataFrame([])
-        profiler = dp.StructuredProfiler(data)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         self.assertIsNone(profiler.chi2_matrix)
 
         # Single column
         data = pd.DataFrame({"a": ["y", "y", "n", "n", "y"]})
-        profiler = dp.StructuredProfiler(data)
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         expected_mat = np.array([1])
         self.assertEqual(expected_mat, profiler.chi2_matrix)
 
@@ -793,7 +805,7 @@ def test_chi2(self, *mocks):
             }
         )
 
-        profiler = dp.StructuredProfiler(data)
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         expected_mat = np.array(
             [[1, 0.309924, 0.404638], [0.309924, 1, 0.548812], [0.404638, 0.548812, 1]]
         )
@@ -808,7 +820,7 @@ def test_chi2(self, *mocks):
             }
         )
 
-        profiler = dp.StructuredProfiler(data)
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         expected_mat = np.array(
             [[1, 0.007295, 0.007295], [0.007295, 1, 0.015609], [0.007295, 0.015609, 1]]
         )
@@ -823,7 +835,7 @@ def test_chi2(self, *mocks):
             }
         )
 
-        profiler = dp.StructuredProfiler(data)
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         expected_mat = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
         np.testing.assert_array_almost_equal(expected_mat, profiler.chi2_matrix)
 
@@ -840,8 +852,10 @@ def test_merge_chi2(self, *mocks):
                 "c": ["n", "maybe", "n", "n", "n", "y", "y"],
             }
         )
-        profiler1 = dp.StructuredProfiler(None)
-        profiler2 = dp.StructuredProfiler(data)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+        profiler1 = dp.StructuredProfiler(None, options=profile_options)
+        profiler2 = dp.StructuredProfiler(data, options=profile_options)
         with mock.patch(
             "dataprofiler.profilers.profile_builder."
             "StructuredProfiler._add_error_checks"
@@ -862,8 +876,8 @@ def test_merge_chi2(self, *mocks):
 
         data1 = data[:4]
         data2 = data[4:]
-        profiler1 = dp.StructuredProfiler(data1)
-        profiler2 = dp.StructuredProfiler(data2)
+        profiler1 = dp.StructuredProfiler(data1, options=profile_options)
+        profiler2 = dp.StructuredProfiler(data2, options=profile_options)
         profiler3 = profiler1 + profiler2
         expected_mat = np.array(
             [[1, 0.309924, 0.404638], [0.309924, 1, 0.548812], [0.404638, 0.548812, 1]]
@@ -880,8 +894,8 @@ def test_merge_chi2(self, *mocks):
         )
         data1 = data[:4]
         data2 = data[4:]
-        profiler1 = dp.StructuredProfiler(data1)
-        profiler2 = dp.StructuredProfiler(data2)
+        profiler1 = dp.StructuredProfiler(data1, options=profile_options)
+        profiler2 = dp.StructuredProfiler(data2, options=profile_options)
         profiler3 = profiler1 + profiler2
         expected_mat = np.array(
             [[1, 0.007295, 0.007295], [0.007295, 1, 0.015609], [0.007295, 0.015609, 1]]
@@ -918,7 +932,9 @@ def test_update_chi2(self, *mocks):
             }
         )
         data2 = pd.DataFrame({"a": [], "b": [], "c": []})
-        profiler = dp.StructuredProfiler(data1)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+        profiler = dp.StructuredProfiler(data1, options=profile_options)
         profiler.update_profile(data2)
         expected_mat = np.array(
             [[1, 0.309924, 0.404638], [0.309924, 1, 0.548812], [0.404638, 0.548812, 1]]
@@ -934,7 +950,7 @@ def test_update_chi2(self, *mocks):
         )
         data1 = data[:4]
         data2 = data[4:]
-        profiler = dp.StructuredProfiler(data1)
+        profiler = dp.StructuredProfiler(data1, options=profile_options)
         profiler.update_profile(data2)
         expected_mat = np.array(
             [[1, 0.309924, 0.404638], [0.309924, 1, 0.548812], [0.404638, 0.548812, 1]]
@@ -952,7 +968,7 @@ def test_update_chi2(self, *mocks):
 
         data1 = data[:4]
         data2 = data[4:]
-        profiler = dp.StructuredProfiler(data1)
+        profiler = dp.StructuredProfiler(data1, options=profile_options)
         profiler.update_profile(data2)
         expected_mat = np.array(
             [[1, 0.007295, 0.007295], [0.007295, 1, 0.015609], [0.007295, 0.015609, 1]]
@@ -969,7 +985,7 @@ def test_update_chi2(self, *mocks):
         )
         data1 = data[:4]
         data2 = data[4:]
-        profiler = dp.StructuredProfiler(data1)
+        profiler = dp.StructuredProfiler(data1, options=profile_options)
         profiler.update_profile(data2)
         expected_mat = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
         np.testing.assert_array_almost_equal(expected_mat, profiler.chi2_matrix)
@@ -1203,7 +1219,12 @@ def test_report_remove_disabled_flag(self):
         # with options to disable FloatColumn `precision`
         # and with remove_disabled_flag == True
         profiler_options = ProfilerOptions()
-        profiler_options.set({"precision.is_enabled": False})
+        profiler_options.set(
+            {
+                "precision.is_enabled": False,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
         profiler = dp.StructuredProfiler(data=data, options=profiler_options)
         report = profiler.report(report_options={"remove_disabled_flag": True})
 
@@ -1215,7 +1236,12 @@ def test_report_remove_disabled_flag(self):
         # with options to disable NumericalMixIn cal `min`
         # and with remove_disabled_flag == True
         profiler_options = ProfilerOptions()
-        profiler_options.set({"min.is_enabled": False})
+        profiler_options.set(
+            {
+                "min.is_enabled": False,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
         profiler = dp.StructuredProfiler(data=data, options=profiler_options)
         report = profiler.report(report_options={"remove_disabled_flag": True})
 
@@ -1225,7 +1251,12 @@ def test_report_remove_disabled_flag(self):
         # with options to disable TextColumn cal `vocab`
         # and with remove_disabled_flag == True
         profiler_options = ProfilerOptions()
-        profiler_options.set({"vocab.is_enabled": False})
+        profiler_options.set(
+            {
+                "vocab.is_enabled": False,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
         profiler = dp.StructuredProfiler(data=data, options=profiler_options)
         report = profiler.report(report_options={"remove_disabled_flag": True})
 
@@ -1234,7 +1265,12 @@ def test_report_remove_disabled_flag(self):
 
         # with profiler options and default remove_disabled_flag
         profiler_options = ProfilerOptions()
-        profiler_options.set({"min.is_enabled": False})
+        profiler_options.set(
+            {
+                "min.is_enabled": False,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
         profiler = dp.StructuredProfiler(data=data, options=profiler_options)
         report = profiler.report()
 
@@ -1242,7 +1278,9 @@ def test_report_remove_disabled_flag(self):
             self.assertIn("min", report["data_stats"][iter_value]["statistics"])
 
         # w/o profiler options and default remove_disabled_flag
-        profiler = dp.StructuredProfiler(data=data)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+        profiler = dp.StructuredProfiler(data=data, options=profiler_options)
         report = profiler.report()
 
         for iter_value in range(0, len(data.columns) - 1):
@@ -1370,7 +1408,11 @@ def recursive_test_helper(report, prev_key=None):
 
     def test_data_label_assigned(self):
         # only use 5 samples
-        trained_schema = dp.StructuredProfiler(self.aws_dataset, samples_per_update=5)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+        trained_schema = dp.StructuredProfiler(
+            self.aws_dataset, samples_per_update=5, options=profile_options
+        )
         report = trained_schema.report()
         has_non_null_column = False
         for i in range(len(report["data_stats"])):
@@ -1545,7 +1587,7 @@ def test_save_and_load_json_file(self):
                 save_profile.save(save_method="json")
                 mock_file.seek(0)
                 with mock.patch(
-                    "dataprofiler.profilers.utils.DataLabeler.load_from_library",
+                    "dataprofiler.profilers.profiler_utils.DataLabeler.load_from_library",
                     return_value=data_labeler,
                 ):
                     load_profile = dp.StructuredProfiler.load("mock.json", "JSON")
@@ -1754,7 +1796,10 @@ def test_duplicate_columns(self):
             [[1, 2, 3, 4, 5, 6], [10, 20, 30, 40, 50, 60]],
             columns=["a", "b", "a", "b", "c", "d"],
         )
-        profiler = dp.StructuredProfiler(data)
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
+
+        profiler = dp.StructuredProfiler(data, options=profile_options)
 
         # Ensure columns are correctly allocated to profiles in list
         expected_mapping = {"a": [0, 2], "b": [1, 3], "c": [4], "d": [5]}
@@ -1812,9 +1857,11 @@ def test_unique_col_permutation(self, *mocks):
         perm_data = pd.DataFrame(
             [[4, 3, 2, 1], [8, 7, 6, 5]], columns=["d", "c", "b", "a"]
         )
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
 
         # Test via add
-        first_profiler = dp.StructuredProfiler(data)
+        first_profiler = dp.StructuredProfiler(data, options=profile_options)
         perm_profiler = dp.StructuredProfiler(perm_data)
         profiler = first_profiler + perm_profiler
 
@@ -1834,7 +1881,7 @@ def test_unique_col_permutation(self, *mocks):
             )
 
         # Test via update
-        profiler = dp.StructuredProfiler(data)
+        profiler = dp.StructuredProfiler(data, options=profile_options)
         profiler.update_profile(perm_data)
 
         for col_idx in range(len(profiler._profile)):
@@ -2462,7 +2509,7 @@ def test_json_encode_after_update(self, mock_DataLabeler, *mocks):
         spec=BaseDataLabeler,
     )
     @mock.patch(
-        "dataprofiler.profilers.utils.DataLabeler",
+        "dataprofiler.profilers.profiler_utils.DataLabeler",
         spec=BaseDataLabeler,
     )
     def test_json_decode(self, mock_utils_DataLabeler, mock_DataLabeler, *mocks):
@@ -2496,7 +2543,7 @@ def test_json_decode(self, mock_utils_DataLabeler, mock_DataLabeler, *mocks):
         spec=BaseDataLabeler,
     )
     @mock.patch(
-        "dataprofiler.profilers.utils.DataLabeler",
+        "dataprofiler.profilers.profiler_utils.DataLabeler",
         spec=BaseDataLabeler,
     )
     def test_json_decode_after_update(
@@ -3166,7 +3213,7 @@ def test_json_encode_after_update(self, mock_DataLabeler, *mocks):
         spec=BaseDataLabeler,
     )
     @mock.patch(
-        "dataprofiler.profilers.utils.DataLabeler",
+        "dataprofiler.profilers.profiler_utils.DataLabeler",
         spec=BaseDataLabeler,
     )
     def test_json_decode(self, mock_utils_DataLabeler, mock_DataLabeler, *mocks):
@@ -3187,7 +3234,7 @@ def test_json_decode(self, mock_utils_DataLabeler, mock_DataLabeler, *mocks):
         spec=BaseDataLabeler,
     )
     @mock.patch(
-        "dataprofiler.profilers.utils.DataLabeler",
+        "dataprofiler.profilers.profiler_utils.DataLabeler",
         spec=BaseDataLabeler,
     )
     def test_json_decode_after_update(
@@ -4047,11 +4094,13 @@ def test_report_remove_disabled_flag(self):
     def test_save_and_load_pkl_file(self):
         data_folder = "dataprofiler/tests/data/"
         test_files = ["txt/code.txt", "txt/sentence-10x.txt"]
+        profile_options = dp.ProfilerOptions()
+        profile_options.set({"structured_options.multiprocess.is_enabled": False})
 
         for test_file in test_files:
             # Create Data and StructuredProfiler objects
             data = dp.Data(os.path.join(data_folder, test_file))
-            save_profile = UnstructuredProfiler(data)
+            save_profile = UnstructuredProfiler(data, options=profile_options)
 
             # If profile _empty_line_count = 0, it won't test if the variable is
             # saved correctly since that is also the default value. Ensure
@@ -4112,7 +4161,12 @@ def test_save_and_load_no_labeler(self):
         data = "this is my test data: 123-456-7890"
 
         profile_options = dp.ProfilerOptions()
-        profile_options.set({"data_labeler.is_enabled": False})
+        profile_options.set(
+            {
+                "data_labeler.is_enabled": False,
+                "structured_options.multiprocess.is_enabled": False,
+            }
+        )
 
         save_profile = dp.UnstructuredProfiler(data, options=profile_options)
 
diff --git a/dataprofiler/tests/profilers/test_utils.py b/dataprofiler/tests/profilers/test_profiler_utils.py
similarity index 67%
rename from dataprofiler/tests/profilers/test_utils.py
rename to dataprofiler/tests/profilers/test_profiler_utils.py
index 10350b2b2..4eee1963a 100644
--- a/dataprofiler/tests/profilers/test_utils.py
+++ b/dataprofiler/tests/profilers/test_profiler_utils.py
@@ -7,26 +7,26 @@
 
 import dataprofiler as dp
 from dataprofiler.labelers.base_data_labeler import BaseDataLabeler
-from dataprofiler.profilers import utils
+from dataprofiler.profilers import profiler_utils
 
 
 class TestShuffleInChunks(unittest.TestCase):
     """
-    Validates utils.shuffle_in_chunks is properly working.
+    Validates profiler_utils.shuffle_in_chunks is properly working.
     """
 
     def test_full_sample(self):
         """
         Check if can shuffle full sample.
         """
-        sample = next(utils.shuffle_in_chunks(data_length=10, chunk_size=10))
+        sample = next(profiler_utils.shuffle_in_chunks(data_length=10, chunk_size=10))
         self.assertCountEqual(sample, list(range(10)))
 
     def test_even_chunk_sample(self):
         """
         Check if can shuffle sample where chunk size is evenly divisible.
         """
-        sample_gen = utils.shuffle_in_chunks(data_length=12, chunk_size=3)
+        sample_gen = profiler_utils.shuffle_in_chunks(data_length=12, chunk_size=3)
 
         all_values = set()
         num_chunks = 0
@@ -41,7 +41,7 @@ def test_uneven_chunk_sample(self):
         """
         Check if can shuffle sample where chunk size is not evenly divisible.
         """
-        sample_gen = utils.shuffle_in_chunks(data_length=100, chunk_size=7)
+        sample_gen = profiler_utils.shuffle_in_chunks(data_length=100, chunk_size=7)
 
         all_values = set()
         num_chunks = 0
@@ -60,50 +60,65 @@ def test_find_diff(self):
 
         # Ensure lists and sets are handled appropriately
         self.assertEqual(
-            [[], [3, 2], [2]], utils.find_diff_of_lists_and_sets([3, 2], [2, 3, 2])
+            [[], [3, 2], [2]],
+            profiler_utils.find_diff_of_lists_and_sets([3, 2], [2, 3, 2]),
         )
         self.assertEqual(
-            [[1], [2, 3], [4]], utils.find_diff_of_lists_and_sets([1, 2, 3], [2, 3, 4])
+            [[1], [2, 3], [4]],
+            profiler_utils.find_diff_of_lists_and_sets([1, 2, 3], [2, 3, 4]),
         )
-        self.assertEqual("unchanged", utils.find_diff_of_lists_and_sets({3, 2}, {2, 3}))
         self.assertEqual(
-            [[1], [2, 3], [4]], utils.find_diff_of_lists_and_sets({1, 2, 3}, {2, 3, 4})
+            "unchanged", profiler_utils.find_diff_of_lists_and_sets({3, 2}, {2, 3})
         )
-        self.assertEqual("unchanged", utils.find_diff_of_lists_and_sets({2, 3}, [2, 3]))
         self.assertEqual(
-            [[1], [2, 3], [4]], utils.find_diff_of_lists_and_sets([1, 2, 3], {2, 3, 4})
+            [[1], [2, 3], [4]],
+            profiler_utils.find_diff_of_lists_and_sets({1, 2, 3}, {2, 3, 4}),
         )
         self.assertEqual(
-            [None, {1, 2}], utils.find_diff_of_lists_and_sets(None, {1, 2})
+            "unchanged", profiler_utils.find_diff_of_lists_and_sets({2, 3}, [2, 3])
+        )
+        self.assertEqual(
+            [[1], [2, 3], [4]],
+            profiler_utils.find_diff_of_lists_and_sets([1, 2, 3], {2, 3, 4}),
+        )
+        self.assertEqual(
+            [None, {1, 2}], profiler_utils.find_diff_of_lists_and_sets(None, {1, 2})
+        )
+        self.assertEqual(
+            "unchanged", profiler_utils.find_diff_of_lists_and_sets(None, None)
         )
-        self.assertEqual("unchanged", utils.find_diff_of_lists_and_sets(None, None))
 
         # Ensure ints and floats are handled appropriately
-        self.assertEqual(1, utils.find_diff_of_numbers(5, 4))
-        self.assertEqual(1.0, utils.find_diff_of_numbers(5.0, 4.0))
-        self.assertEqual(1.0, utils.find_diff_of_numbers(5.0, 4))
-        self.assertEqual("unchanged", utils.find_diff_of_numbers(5.0, 5.0))
-        self.assertEqual("unchanged", utils.find_diff_of_numbers(5, 5.0))
-        self.assertEqual([4, None], utils.find_diff_of_numbers(4, None))
-        self.assertEqual("unchanged", utils.find_diff_of_numbers(None, None))
+        self.assertEqual(1, profiler_utils.find_diff_of_numbers(5, 4))
+        self.assertEqual(1.0, profiler_utils.find_diff_of_numbers(5.0, 4.0))
+        self.assertEqual(1.0, profiler_utils.find_diff_of_numbers(5.0, 4))
+        self.assertEqual("unchanged", profiler_utils.find_diff_of_numbers(5.0, 5.0))
+        self.assertEqual("unchanged", profiler_utils.find_diff_of_numbers(5, 5.0))
+        self.assertEqual([4, None], profiler_utils.find_diff_of_numbers(4, None))
+        self.assertEqual("unchanged", profiler_utils.find_diff_of_numbers(None, None))
 
         # Ensure strings are handled appropriately
         self.assertEqual(
-            "unchanged", utils.find_diff_of_strings_and_bools("Hello", "Hello")
+            "unchanged", profiler_utils.find_diff_of_strings_and_bools("Hello", "Hello")
+        )
+        self.assertEqual(
+            ["Hello", "team"],
+            profiler_utils.find_diff_of_strings_and_bools("Hello", "team"),
         )
         self.assertEqual(
-            ["Hello", "team"], utils.find_diff_of_strings_and_bools("Hello", "team")
+            "unchanged", profiler_utils.find_diff_of_strings_and_bools(None, None)
         )
-        self.assertEqual("unchanged", utils.find_diff_of_strings_and_bools(None, None))
 
         # Ensure dates are handled appropriately
         a = datetime(2021, 6, 28)
         b = datetime(2021, 6, 27, 1)
-        self.assertEqual("unchanged", utils.find_diff_of_dates(a, a))
-        self.assertEqual("+23:00:00", utils.find_diff_of_dates(a, b))
-        self.assertEqual("-23:00:00", utils.find_diff_of_dates(b, a))
-        self.assertEqual(["06/28/21 00:00:00", None], utils.find_diff_of_dates(a, None))
-        self.assertEqual("unchanged", utils.find_diff_of_dates(None, None))
+        self.assertEqual("unchanged", profiler_utils.find_diff_of_dates(a, a))
+        self.assertEqual("+23:00:00", profiler_utils.find_diff_of_dates(a, b))
+        self.assertEqual("-23:00:00", profiler_utils.find_diff_of_dates(b, a))
+        self.assertEqual(
+            ["06/28/21 00:00:00", None], profiler_utils.find_diff_of_dates(a, None)
+        )
+        self.assertEqual("unchanged", profiler_utils.find_diff_of_dates(None, None))
 
         # Ensure that differencing dictionaries is handled appropriately
         dict1 = {
@@ -131,7 +146,9 @@ def test_find_diff(self):
             "f": ["hi2", None],
             "g": [None, 15],
         }
-        self.assertDictEqual(expected_diff, utils.find_diff_of_dicts(dict1, dict2))
+        self.assertDictEqual(
+            expected_diff, profiler_utils.find_diff_of_dicts(dict1, dict2)
+        )
 
         dict1 = {
             "nested_key_one": {"fruit": ["apple", "banana", "orange"], "yes_no": False},
@@ -167,7 +184,9 @@ def test_find_diff(self):
             "additional_key": [None, "random_string"],
         }
 
-        self.assertDictEqual(expected_diff, utils.find_diff_of_dicts(dict1, dict2))
+        self.assertDictEqual(
+            expected_diff, profiler_utils.find_diff_of_dicts(dict1, dict2)
+        )
 
     def test_diff_of_dicts_with_diff_keys(self):
         dict1 = {"unique1": 1, "shared1": 2, "shared2": 3}
@@ -181,11 +200,13 @@ def test_diff_of_dicts_with_diff_keys(self):
 
         # Assert difference is appropriate
         self.assertListEqual(
-            expected, utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
+            expected, profiler_utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
         )
 
         # Assert empty dicts are unchanged
-        self.assertEqual("unchanged", utils.find_diff_of_dicts_with_diff_keys({}, {}))
+        self.assertEqual(
+            "unchanged", profiler_utils.find_diff_of_dicts_with_diff_keys({}, {})
+        )
 
         # Assert all edge cases work
         a = datetime(2021, 6, 28)
@@ -215,7 +236,7 @@ def test_diff_of_dicts_with_diff_keys(self):
             {"unique2": 5},
         ]
         self.assertListEqual(
-            expected, utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
+            expected, profiler_utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
         )
 
         dict1 = {
@@ -259,14 +280,14 @@ def test_diff_of_dicts_with_diff_keys(self):
         ]
 
         self.assertListEqual(
-            expected, utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
+            expected, profiler_utils.find_diff_of_dicts_with_diff_keys(dict1, dict2)
         )
 
     def test_list_diff_with_nan(self):
         # when lists are same length
         list_1 = [np.nan, 1.5, 6.7]
         list_2 = [np.nan, 1.5, np.nan]
-        diff_1 = utils.find_diff_of_lists_and_sets(list_1, list_2)
+        diff_1 = profiler_utils.find_diff_of_lists_and_sets(list_1, list_2)
         expected_diff_1 = [[6.7], [np.nan, 1.5], [np.nan]]
 
         for x, y in zip(diff_1, expected_diff_1):
@@ -276,7 +297,7 @@ def test_list_diff_with_nan(self):
         # when lists aren't the same length
         list_3 = [np.nan, 1.5, 6.7, np.nan, np.nan, np.nan]
         list_4 = [4.2, 1.5, np.nan]
-        diff_2 = utils.find_diff_of_lists_and_sets(list_3, list_4)
+        diff_2 = profiler_utils.find_diff_of_lists_and_sets(list_3, list_4)
         expected_diff_2 = [[6.7, np.nan, np.nan, np.nan], [np.nan, 1.5], [4.2]]
 
         for x, y in zip(diff_2, expected_diff_2):
@@ -285,7 +306,7 @@ def test_list_diff_with_nan(self):
 
         list_5 = [np.nan, np.nan]
         list_6 = [np.nan]
-        diff_3 = utils.find_diff_of_lists_and_sets(list_5, list_6)
+        diff_3 = profiler_utils.find_diff_of_lists_and_sets(list_5, list_6)
         expected_diff_3 = [[np.nan], [np.nan], []]
 
         for x, y in zip(diff_3, expected_diff_3):
@@ -294,7 +315,7 @@ def test_list_diff_with_nan(self):
 
         list_7 = [np.nan, 3]
         list_8 = [np.nan, 3]
-        diff_4 = utils.find_diff_of_lists_and_sets(list_7, list_8)
+        diff_4 = profiler_utils.find_diff_of_lists_and_sets(list_7, list_8)
         expected_diff_4 = "unchanged"
 
         self.assertEqual(diff_4, expected_diff_4)
@@ -307,7 +328,7 @@ def test_find_diff_of_matrices(self):
 
         # Check matrix subtraction of same size matrices
         expected_matrix = [[-10.0, np.nan, 3.0], [3.0, 0.0, 4.0], [np.nan, -12.0, 8.0]]
-        diff_matrix = utils.find_diff_of_matrices(matrix1, matrix2)
+        diff_matrix = profiler_utils.find_diff_of_matrices(matrix1, matrix2)
         comparison = (
             (expected_matrix == diff_matrix)
             | (np.isnan(expected_matrix) & np.isnan(diff_matrix))
@@ -315,13 +336,15 @@ def test_find_diff_of_matrices(self):
         self.assertEqual(True, comparison)
 
         # Check matrix subtraction of same exact matrices
-        self.assertEqual("unchanged", utils.find_diff_of_matrices(matrix1, matrix1))
+        self.assertEqual(
+            "unchanged", profiler_utils.find_diff_of_matrices(matrix1, matrix1)
+        )
         # Check matrix subtraction with different sized matrices
         matrix1 = [[1, 2], [1, 2]]
-        self.assertIsNone(utils.find_diff_of_matrices(matrix1, matrix2))
+        self.assertIsNone(profiler_utils.find_diff_of_matrices(matrix1, matrix2))
 
         # Check matrix with none
-        self.assertIsNone(utils.find_diff_of_matrices(matrix1, None))
+        self.assertIsNone(profiler_utils.find_diff_of_matrices(matrix1, None))
 
     def test_get_memory_size(self):
         """
@@ -333,27 +356,30 @@ def test_get_memory_size(self):
             "Currently only supports the memory size unit "
             r"in \['B', 'K', 'M', 'G'\]",
         ):
-            utils.get_memory_size([], unit="wrong_unit")
+            profiler_utils.get_memory_size([], unit="wrong_unit")
 
         # test with different data sizes
-        self.assertEqual(0, utils.get_memory_size([]))
+        self.assertEqual(0, profiler_utils.get_memory_size([]))
         self.assertEqual(
-            33 / 1024**2, utils.get_memory_size(["This is test, a Test sentence.!!!"])
+            33 / 1024**2,
+            profiler_utils.get_memory_size(["This is test, a Test sentence.!!!"]),
         )
         self.assertEqual(
             33 / 1024**2,
-            utils.get_memory_size(["This is test,", " a Test sentence.!!!"]),
+            profiler_utils.get_memory_size(["This is test,", " a Test sentence.!!!"]),
         )
         self.assertEqual(
             33 / 1024**3,
-            utils.get_memory_size(["This is test, a Test sentence.!!!"], unit="G"),
+            profiler_utils.get_memory_size(
+                ["This is test, a Test sentence.!!!"], unit="G"
+            ),
         )
 
 
 @mock.patch("dataprofiler.profilers.profile_builder.DataLabeler", spec=BaseDataLabeler)
 class TestProfileDistributedMerge(unittest.TestCase):
     """
-    Validates utils.merge_profile_list is properly working.
+    Validates profiler_utils.merge_profile_list is properly working.
     """
 
     @staticmethod
@@ -393,7 +419,9 @@ def test_merge_profile_list(self, mock_data_labeler, *mocks):
         profile_two = dp.Profiler(data[2:])
 
         list_of_profiles = [profile_one, profile_two]
-        single_profile = utils.merge_profile_list(list_of_profiles=list_of_profiles)
+        single_profile = profiler_utils.merge_profile_list(
+            list_of_profiles=list_of_profiles
+        )
         single_report = single_profile.report()
 
         self.assertEqual(1, len(single_report["data_stats"]))
@@ -428,7 +456,9 @@ def test_odd_merge_profile_list(self, mock_data_labeler, *mocks):
         profile_three = dp.Profiler(data[2:])
 
         list_of_profiles = [profile_one, profile_two, profile_three]
-        single_profile = utils.merge_profile_list(list_of_profiles=list_of_profiles)
+        single_profile = profiler_utils.merge_profile_list(
+            list_of_profiles=list_of_profiles
+        )
         single_report = single_profile.report()
 
         self.assertEqual(1, len(single_report["data_stats"]))
@@ -439,3 +469,52 @@ def test_odd_merge_profile_list(self, mock_data_labeler, *mocks):
 
         self.assertEqual(1, single_report["data_stats"][0]["statistics"]["min"])
         self.assertEqual(60.0, single_report["data_stats"][0]["statistics"]["max"])
+
+
+class TestAutoMultiProcessToggle(unittest.TestCase):
+
+    """
+    Validate profile_utils.auto_multiprocess_toggle is properly working.
+    """
+
+    def test_auto_multiprocess_toggle(self):
+        rows_threshold = 5
+        cols_threshold = 10
+
+        # Test for no multiprocessing for sufficiently small datasets
+        data = pd.DataFrame(np.random.random((2, 5)))
+        self.assertFalse(
+            profiler_utils.auto_multiprocess_toggle(
+                data, rows_threshold, cols_threshold
+            )
+        )
+        data = pd.DataFrame(np.random.random((5, 10)))
+        self.assertFalse(
+            profiler_utils.auto_multiprocess_toggle(
+                data, rows_threshold, cols_threshold
+            )
+        )
+
+        # Test for multiprocessing with only rows passing threshold
+        data = pd.DataFrame(np.random.random((6, 10)))
+        self.assertTrue(
+            profiler_utils.auto_multiprocess_toggle(
+                data, rows_threshold, cols_threshold
+            )
+        )
+
+        # Test for multiprocessing with only columns passing threshold
+        data = pd.DataFrame(np.random.random((5, 11)))
+        self.assertTrue(
+            profiler_utils.auto_multiprocess_toggle(
+                data, rows_threshold, cols_threshold
+            )
+        )
+
+        # Test for multiprocessing with both rows and columns passing threshold
+        data = pd.DataFrame(np.random.random((6, 11)))
+        self.assertTrue(
+            profiler_utils.auto_multiprocess_toggle(
+                data, rows_threshold, cols_threshold
+            )
+        )
diff --git a/dataprofiler/tests/profilers/test_text_column_profile.py b/dataprofiler/tests/profilers/test_text_column_profile.py
index 0d578c6e9..98b87acbe 100644
--- a/dataprofiler/tests/profilers/test_text_column_profile.py
+++ b/dataprofiler/tests/profilers/test_text_column_profile.py
@@ -8,7 +8,7 @@
 import numpy as np
 import pandas as pd
 
-from dataprofiler.profilers import TextColumn, utils
+from dataprofiler.profilers import TextColumn, profiler_utils
 from dataprofiler.profilers.json_decoder import load_column_profile
 from dataprofiler.profilers.json_encoder import ProfileEncoder
 from dataprofiler.profilers.profiler_options import TextOptions
@@ -584,7 +584,7 @@ def test_diff(self):
             "median_absolute_deviation": -0.5,
             "variance": profile1["variance"] - profile2["variance"],
             "stddev": profile1["stddev"] - profiler2["stddev"],
-            "vocab": utils.find_diff_of_lists_and_sets(
+            "vocab": profiler_utils.find_diff_of_lists_and_sets(
                 profile1["vocab"], profile2["vocab"]
             ),
             "t-test": {
diff --git a/dataprofiler/tests/profilers/test_unstructured_labeler_profile.py b/dataprofiler/tests/profilers/test_unstructured_labeler_profile.py
index d3a47108b..c7aa8b0c5 100644
--- a/dataprofiler/tests/profilers/test_unstructured_labeler_profile.py
+++ b/dataprofiler/tests/profilers/test_unstructured_labeler_profile.py
@@ -4,7 +4,7 @@
 
 import pandas as pd
 
-from dataprofiler.profilers import utils
+from dataprofiler.profilers import profiler_utils
 from dataprofiler.profilers.unstructured_labeler_profile import (
     UnstructuredLabelerProfile,
 )
diff --git a/dataprofiler/tests/profilers/utils.py b/dataprofiler/tests/profilers/utils.py
index bef54763d..d85dbf380 100644
--- a/dataprofiler/tests/profilers/utils.py
+++ b/dataprofiler/tests/profilers/utils.py
@@ -11,7 +11,7 @@
 from dataprofiler.profilers.column_profile_compilers import BaseCompiler
 from dataprofiler.profilers.profile_builder import BaseProfiler, StructuredColProfiler
 from dataprofiler.profilers.profiler_options import BaseOption
-from dataprofiler.profilers.utils import find_diff_of_dicts
+from dataprofiler.profilers.profiler_utils import find_diff_of_dicts
 from dataprofiler.tests.test_utils import patched_assert_warns
 
 
diff --git a/dataprofiler/tests/test_rng_utils.py b/dataprofiler/tests/test_rng_utils.py
new file mode 100644
index 000000000..6ee2ed35c
--- /dev/null
+++ b/dataprofiler/tests/test_rng_utils.py
@@ -0,0 +1,53 @@
+"""Validates that generator intakes DATAPROFILER_SEED properly."""
+import os
+import unittest
+import unittest.mock
+
+from .. import rng_utils
+
+
+class TestGetRandomNumberGenerator(unittest.TestCase):
+    """Validates get_random_number_generator() is properly working."""
+
+    @unittest.mock.patch.dict(os.environ, {"DATAPROFILER_SEED": "0"})
+    @unittest.mock.patch("dataprofiler.rng_utils.settings._seed", new=123)
+    def test_dataprofiler_seed_true_settings_seed_false(self):
+        """Test for DATAPROFILER_SEED in os.environ and settings._seed!=None."""
+        with unittest.mock.patch("numpy.random.default_rng") as mock_np_generator:
+            rng_utils.get_random_number_generator()
+            self.assertEqual(mock_np_generator.call_count, 1)
+            mock_np_generator.assert_called_with(123)
+
+    @unittest.mock.patch("dataprofiler.rng_utils.settings._seed", new=None)
+    @unittest.mock.patch.dict("os.environ", clear=True)
+    def test_dataprofiler_seed_false_settings_seed_true(self):
+        """Test for DATAPROFILER_SEED not in os.environ and settings._seed==None."""
+        with unittest.mock.patch("numpy.random.default_rng") as mock_np_generator:
+            rng_utils.get_random_number_generator()
+            self.assertEqual(mock_np_generator.call_count, 1)
+            mock_np_generator.assert_called_with(None)
+
+    @unittest.mock.patch.dict(os.environ, {"DATAPROFILER_SEED": "123"})
+    @unittest.mock.patch("dataprofiler.rng_utils.settings._seed", new=None)
+    def test_dataprofiler_seed_true_settings_seed_true(self):
+        """Test for DATAPROFILER_SEED in os.environ and settings._seed==None."""
+        with unittest.mock.patch("numpy.random.default_rng") as mock_np_generator:
+            rng_utils.get_random_number_generator()
+            self.assertEqual(mock_np_generator.call_count, 2)
+            mock_np_generator.assert_called_with(123)
+
+    @unittest.mock.patch("dataprofiler.rng_utils.settings._seed", new=123)
+    @unittest.mock.patch.dict("os.environ", clear=True)
+    def test_dataprofiler_seed_false_settings_seed_false(self):
+        """Test for DATAPROFILER_SEED not in os.environ and settings._seed!=None."""
+        with unittest.mock.patch("numpy.random.default_rng") as mock_np_generator:
+            rng_utils.get_random_number_generator()
+            self.assertEqual(mock_np_generator.call_count, 1)
+            mock_np_generator.assert_called_with(123)
+
+    @unittest.mock.patch.dict(os.environ, {"DATAPROFILER_SEED": "George Washington"})
+    @unittest.mock.patch("dataprofiler.rng_utils.settings._seed", new=None)
+    def test_warning_raised(self):
+        """Test that warning raises if seed is not an integer."""
+        with self.assertWarnsRegex(RuntimeWarning, "Seed should be an integer"):
+            rng_utils.get_random_number_generator()
diff --git a/dataprofiler/version.py b/dataprofiler/version.py
index 070b4c9e3..0808d3c3e 100644
--- a/dataprofiler/version.py
+++ b/dataprofiler/version.py
@@ -2,7 +2,7 @@
 
 MAJOR = 0
 MINOR = 10
-MICRO = 2
+MICRO = 3
 POST = None  # otherwise None
 
 VERSION = "%d.%d.%d" % (MAJOR, MINOR, MICRO)
diff --git a/examples/add_new_model_to_data_labeler.ipynb b/examples/add_new_model_to_data_labeler.ipynb
index 3f59297bc..1495e6a85 100644
--- a/examples/add_new_model_to_data_labeler.ipynb
+++ b/examples/add_new_model_to_data_labeler.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "228bb2a6",
    "metadata": {},
@@ -9,6 +10,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cab7a569",
    "metadata": {},
@@ -39,6 +41,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e90728ab",
    "metadata": {},
@@ -47,6 +50,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3d61981c",
    "metadata": {},
@@ -75,6 +79,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "745ed0d4",
    "metadata": {},
@@ -83,6 +88,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7375b0c0",
    "metadata": {},
@@ -105,15 +111,19 @@
    "source": [
     "import tensorflow as tf\n",
     "import numpy as np\n",
-    "from dataprofiler.labelers.character_level_cnn_model import CharacterLevelCnnModel, F1Score, \\\n",
-    "                                                            create_glove_char, build_embd_dictionary\n",
+    "from dataprofiler.labelers.character_level_cnn_model import (\n",
+    "    CharacterLevelCnnModel,\n",
+    "    create_glove_char,\n",
+    "    build_embd_dictionary,\n",
+    ")\n",
     "from dataprofiler.labelers.base_model import BaseModel\n",
+    "from dataprofiler.labelers.labeler_utils import F1Score\n",
+    "\n",
     "\n",
     "# CharacterLevelLstmModel derives from CharacterLevelCnnModel\n",
     "#########################################################\n",
     "#########################################################\n",
     "class CharacterLevelLstmModel(CharacterLevelCnnModel):\n",
-    "\n",
     "    # boolean if the label mapping requires the mapping for index 0 reserved\n",
     "    requires_zero_mapping = True\n",
     "\n",
@@ -121,26 +131,26 @@
     "        \"\"\"\n",
     "        LSTM Model Initializer\n",
     "        \"\"\"\n",
-    "                \n",
+    "\n",
     "        # parameter initialization\n",
     "        if not parameters:\n",
     "            parameters = {}\n",
-    "        parameters.setdefault('max_length', 3400)\n",
-    "        parameters.setdefault('max_char_encoding_id', 127)\n",
-    "        parameters.setdefault('dim_embed', 64)\n",
-    "        parameters.setdefault('size_fc', [32, 32])\n",
-    "        parameters.setdefault('dropout', 0.1)\n",
+    "        parameters.setdefault(\"max_length\", 3400)\n",
+    "        parameters.setdefault(\"max_char_encoding_id\", 127)\n",
+    "        parameters.setdefault(\"dim_embed\", 64)\n",
+    "        parameters.setdefault(\"size_fc\", [32, 32])\n",
+    "        parameters.setdefault(\"dropout\", 0.1)\n",
     "        # new parameters for LSTM model\n",
     "        #########################################################\n",
     "        #########################################################\n",
-    "        parameters.setdefault('size_lstm', [64])\n",
-    "        parameters.setdefault('rec_dropout', 0.1)\n",
-    "        parameters.setdefault('activation', \"tanh\")\n",
-    "        parameters.setdefault('recurrent_activation', \"sigmoid\")\n",
+    "        parameters.setdefault(\"size_lstm\", [64])\n",
+    "        parameters.setdefault(\"rec_dropout\", 0.1)\n",
+    "        parameters.setdefault(\"activation\", \"tanh\")\n",
+    "        parameters.setdefault(\"recurrent_activation\", \"sigmoid\")\n",
     "        #########################################################\n",
     "        #########################################################\n",
-    "        parameters.setdefault('default_label', \"UNKNOWN\")\n",
-    "        parameters['pad_label'] = 'PAD'\n",
+    "        parameters.setdefault(\"default_label\", \"UNKNOWN\")\n",
+    "        parameters[\"pad_label\"] = \"PAD\"\n",
     "        self._epoch_id = 0\n",
     "\n",
     "        # reconstruct flags for model\n",
@@ -155,36 +165,66 @@
     "        present.\n",
     "        \"\"\"\n",
     "        errors = []\n",
-    "        list_of_necessary_params = ['max_length', 'max_char_encoding_id',\n",
-    "                                    'dim_embed', 'size_fc', 'dropout',\n",
-    "                                    'size_lstm', 'rec_dropout', 'activation', \n",
-    "                                    'recurrent_activation', 'default_label', \n",
-    "                                    'pad_label']\n",
+    "        list_of_necessary_params = [\n",
+    "            \"max_length\",\n",
+    "            \"max_char_encoding_id\",\n",
+    "            \"dim_embed\",\n",
+    "            \"size_fc\",\n",
+    "            \"dropout\",\n",
+    "            \"size_lstm\",\n",
+    "            \"rec_dropout\",\n",
+    "            \"activation\",\n",
+    "            \"recurrent_activation\",\n",
+    "            \"default_label\",\n",
+    "            \"pad_label\",\n",
+    "        ]\n",
     "        # Make sure the necessary parameters are present and valid.\n",
     "        for param in parameters:\n",
-    "            if param in ['max_length', 'max_char_encoding_id', 'dim_embed',\n",
-    "                         'size_conv']:\n",
-    "                if not isinstance(parameters[param], (int, float)) \\\n",
-    "                        or parameters[param] < 0:\n",
-    "                    errors.append(param + \" must be a valid integer or float \"\n",
-    "                                          \"greater than 0.\")\n",
-    "            elif param in ['dropout', 'rec_dropout']: # additional check for rec_dropout\n",
-    "                if not isinstance(parameters[param], (int, float)) \\\n",
-    "                        or parameters[param] < 0 or parameters[param] > 1:\n",
-    "                    errors.append(param + \" must be a valid integer or float \"\n",
-    "                                          \"from 0 to 1.\")\n",
-    "            elif param == 'size_fc' or param == 'size_lstm': # additional check for size_lstm\n",
-    "                if not isinstance(parameters[param], list) \\\n",
-    "                        or len(parameters[param]) == 0:\n",
-    "                    errors.append(param + \" must be a non-empty list of \"\n",
-    "                                          \"integers.\")\n",
+    "            if param in [\n",
+    "                \"max_length\",\n",
+    "                \"max_char_encoding_id\",\n",
+    "                \"dim_embed\",\n",
+    "                \"size_conv\",\n",
+    "            ]:\n",
+    "                if (\n",
+    "                    not isinstance(parameters[param], (int, float))\n",
+    "                    or parameters[param] < 0\n",
+    "                ):\n",
+    "                    errors.append(\n",
+    "                        param + \" must be a valid integer or float \" \"greater than 0.\"\n",
+    "                    )\n",
+    "            elif param in [\n",
+    "                \"dropout\",\n",
+    "                \"rec_dropout\",\n",
+    "            ]:  # additional check for rec_dropout\n",
+    "                if (\n",
+    "                    not isinstance(parameters[param], (int, float))\n",
+    "                    or parameters[param] < 0\n",
+    "                    or parameters[param] > 1\n",
+    "                ):\n",
+    "                    errors.append(\n",
+    "                        param + \" must be a valid integer or float \" \"from 0 to 1.\"\n",
+    "                    )\n",
+    "            elif (\n",
+    "                param == \"size_fc\" or param == \"size_lstm\"\n",
+    "            ):  # additional check for size_lstm\n",
+    "                if (\n",
+    "                    not isinstance(parameters[param], list)\n",
+    "                    or len(parameters[param]) == 0\n",
+    "                ):\n",
+    "                    errors.append(param + \" must be a non-empty list of \" \"integers.\")\n",
     "                else:\n",
     "                    for item in parameters[param]:\n",
     "                        if not isinstance(item, int):\n",
-    "                            errors.append(param + \" must be a non-empty \"\n",
-    "                                                  \"list of integers.\")\n",
+    "                            errors.append(\n",
+    "                                param + \" must be a non-empty \" \"list of integers.\"\n",
+    "                            )\n",
     "                            break\n",
-    "            elif param in ['default_label', 'activation', 'recurrent_activation']: # additional check for activation and recurrent_activation\n",
+    "            elif param in [\n",
+    "                \"default_label\",\n",
+    "                \"activation\",\n",
+    "                \"recurrent_activation\",\n",
+    "            ]:  # additional check for activation and recurrent_activation\n",
     "                if not isinstance(parameters[param], str):\n",
     "                    error = str(param) + \" must be a string.\"\n",
     "                    errors.append(error)\n",
@@ -194,7 +234,7 @@
     "            if param not in list_of_necessary_params:\n",
     "                errors.append(param + \" is not an accepted parameter.\")\n",
     "        if errors:\n",
-    "            raise ValueError('\\n'.join(errors))\n",
+    "            raise ValueError(\"\\n\".join(errors))\n",
     "\n",
     "    def _construct_model(self):\n",
     "        \"\"\"\n",
@@ -204,41 +244,44 @@
     "        :return: None\n",
     "        \"\"\"\n",
     "        num_labels = self.num_labels\n",
-    "        default_ind = self.label_mapping[self._parameters['default_label']]\n",
+    "        default_ind = self.label_mapping[self._parameters[\"default_label\"]]\n",
     "\n",
     "        # Reset model\n",
     "        tf.keras.backend.clear_session()\n",
     "\n",
     "        # generate glove embedding\n",
-    "        create_glove_char(self._parameters['dim_embed'])\n",
+    "        create_glove_char(self._parameters[\"dim_embed\"])\n",
     "\n",
     "        # generate model\n",
     "        self._model = tf.keras.models.Sequential()\n",
     "\n",
     "        # default parameters\n",
-    "        max_length = self._parameters['max_length']\n",
-    "        max_char_encoding_id = self._parameters['max_char_encoding_id']\n",
+    "        max_length = self._parameters[\"max_length\"]\n",
+    "        max_char_encoding_id = self._parameters[\"max_char_encoding_id\"]\n",
     "\n",
     "        # Encoding layer\n",
     "        def encoding_function(input_str):\n",
     "            char_in_vector = CharacterLevelLstmModel._char_encoding_layer(\n",
-    "                input_str, max_char_encoding_id, max_length)\n",
+    "                input_str, max_char_encoding_id, max_length\n",
+    "            )\n",
     "            return char_in_vector\n",
     "\n",
     "        self._model.add(tf.keras.layers.Input(shape=(None,), dtype=tf.string))\n",
     "\n",
     "        self._model.add(\n",
-    "            tf.keras.layers.Lambda(encoding_function,\n",
-    "                                   output_shape=tuple([max_length])))\n",
+    "            tf.keras.layers.Lambda(encoding_function, output_shape=tuple([max_length]))\n",
+    "        )\n",
     "\n",
     "        # Create a pre-trained weight matrix\n",
     "        # character encoding indices range from 0 to max_char_encoding_id,\n",
     "        # we add one extra index for out-of-vocabulary character\n",
     "        embed_file = os.path.join(\n",
-    "            \"../dataprofiler/labelers\", \"embeddings/glove-reduced-{}D.txt\".format(\n",
-    "                self._parameters['dim_embed']))\n",
-    "        embedding_matrix = np.zeros((max_char_encoding_id + 2,\n",
-    "                                     self._parameters['dim_embed']))\n",
+    "            \"../dataprofiler/labelers\",\n",
+    "            \"embeddings/glove-reduced-{}D.txt\".format(self._parameters[\"dim_embed\"]),\n",
+    "        )\n",
+    "        embedding_matrix = np.zeros(\n",
+    "            (max_char_encoding_id + 2, self._parameters[\"dim_embed\"])\n",
+    "        )\n",
     "        embedding_dict = build_embd_dictionary(embed_file)\n",
     "\n",
     "        input_shape = tuple([max_length])\n",
@@ -247,70 +290,74 @@
     "            if chr(ascii_num) in embedding_dict:\n",
     "                embedding_matrix[ascii_num + 1] = embedding_dict[chr(ascii_num)]\n",
     "\n",
-    "        self._model.add(tf.keras.layers.Embedding(\n",
-    "            max_char_encoding_id + 2,\n",
-    "            self._parameters['dim_embed'],\n",
-    "            weights=[embedding_matrix],\n",
-    "            input_length=input_shape[0],\n",
-    "            trainable=True))\n",
-    "            \n",
+    "        self._model.add(\n",
+    "            tf.keras.layers.Embedding(\n",
+    "                max_char_encoding_id + 2,\n",
+    "                self._parameters[\"dim_embed\"],\n",
+    "                weights=[embedding_matrix],\n",
+    "                input_length=input_shape[0],\n",
+    "                trainable=True,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
     "        # Add the lstm layers\n",
     "        #########################################################\n",
     "        #########################################################\n",
-    "        for size in self._parameters['size_lstm']:\n",
+    "        for size in self._parameters[\"size_lstm\"]:\n",
     "            self._model.add(\n",
-    "                tf.keras.layers.LSTM(units=size, \n",
-    "                                     recurrent_dropout=self._parameters['rec_dropout'], \n",
-    "                                     activation=self._parameters['activation'],\n",
-    "                                     recurrent_activation=self._parameters['recurrent_activation'],\n",
-    "                                     return_sequences=True))\n",
-    "            if self._parameters['dropout']:\n",
-    "                self._model.add(tf.keras.layers.Dropout(self._parameters['dropout']))\n",
+    "                tf.keras.layers.LSTM(\n",
+    "                    units=size,\n",
+    "                    recurrent_dropout=self._parameters[\"rec_dropout\"],\n",
+    "                    activation=self._parameters[\"activation\"],\n",
+    "                    recurrent_activation=self._parameters[\"recurrent_activation\"],\n",
+    "                    return_sequences=True,\n",
+    "                )\n",
+    "            )\n",
+    "            if self._parameters[\"dropout\"]:\n",
+    "                self._model.add(tf.keras.layers.Dropout(self._parameters[\"dropout\"]))\n",
     "        #########################################################\n",
     "        #########################################################\n",
     "\n",
     "        # Add the fully connected layers\n",
-    "        for size in self._parameters['size_fc']:\n",
-    "            self._model.add(\n",
-    "                tf.keras.layers.Dense(units=size, activation='relu'))\n",
-    "            if self._parameters['dropout']:\n",
-    "                self._model.add(\n",
-    "                    tf.keras.layers.Dropout(self._parameters['dropout']))\n",
+    "        for size in self._parameters[\"size_fc\"]:\n",
+    "            self._model.add(tf.keras.layers.Dense(units=size, activation=\"relu\"))\n",
+    "            if self._parameters[\"dropout\"]:\n",
+    "                self._model.add(tf.keras.layers.Dropout(self._parameters[\"dropout\"]))\n",
     "\n",
     "        # Add the final Softmax layer\n",
-    "        self._model.add(\n",
-    "            tf.keras.layers.Dense(num_labels, activation='softmax'))\n",
+    "        self._model.add(tf.keras.layers.Dense(num_labels, activation=\"softmax\"))\n",
     "\n",
     "        # Output the model into a .pb file for TensorFlow\n",
     "        argmax_layer = tf.keras.backend.argmax(self._model.output)\n",
     "\n",
     "        # Create confidence layers\n",
     "        final_predicted_layer = CharacterLevelLstmModel._argmax_threshold_layer(\n",
-    "            num_labels, threshold=0.0, default_ind=default_ind)\n",
+    "            num_labels, threshold=0.0, default_ind=default_ind\n",
+    "        )\n",
     "\n",
-    "        argmax_outputs = self._model.outputs + \\\n",
-    "                         [argmax_layer,\n",
-    "                          final_predicted_layer(argmax_layer, self._model.output)]\n",
+    "        argmax_outputs = self._model.outputs + [\n",
+    "            argmax_layer,\n",
+    "            final_predicted_layer(argmax_layer, self._model.output),\n",
+    "        ]\n",
     "        self._model = tf.keras.Model(self._model.inputs, argmax_outputs)\n",
     "\n",
     "        # Compile the model\n",
-    "        softmax_output_layer_name = self._model.outputs[0].name.split('/')[0]\n",
+    "        softmax_output_layer_name = self._model.outputs[0].name.split(\"/\")[0]\n",
     "        losses = {softmax_output_layer_name: \"categorical_crossentropy\"}\n",
     "\n",
     "        # use f1 score metric\n",
-    "        f1_score_training = F1Score(num_classes=num_labels, average='micro')\n",
-    "        metrics = {softmax_output_layer_name: ['acc', f1_score_training]}\n",
+    "        f1_score_training = F1Score(num_classes=num_labels, average=\"micro\")\n",
+    "        metrics = {softmax_output_layer_name: [\"acc\", f1_score_training]}\n",
     "\n",
-    "        self._model.compile(loss=losses,\n",
-    "                            optimizer=\"adam\",\n",
-    "                            metrics=metrics)\n",
+    "        self._model.compile(loss=losses, optimizer=\"adam\", metrics=metrics)\n",
     "\n",
     "        self._epoch_id = 0\n",
     "        self._model_num_labels = num_labels\n",
-    "        self._model_default_ind = default_ind\n"
+    "        self._model_default_ind = default_ind"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d66bd25c",
    "metadata": {},
@@ -319,6 +366,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "479f407a",
    "metadata": {},
@@ -365,6 +413,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "14b78c69",
    "metadata": {},
@@ -406,6 +455,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cc60ff8a",
    "metadata": {},
diff --git a/examples/structured_profilers.ipynb b/examples/structured_profilers.ipynb
index 10f9eddd2..b6a4409c9 100644
--- a/examples/structured_profilers.ipynb
+++ b/examples/structured_profilers.ipynb
@@ -245,7 +245,7 @@
     "\n",
     "Below, let's remove the histogram and increase the number of samples to the labeler component (1,000 samples). \n",
     "\n",
-    "Full list of options in the Profiler section of the [DataProfiler documentation](https://capitalone.github.io/DataProfiler)."
+    "Full list of options in the Profiler section of the [DataProfiler documentation](https://capitalone.github.io/DataProfiler/profile_options.html)."
    ]
   },
   {
diff --git a/examples/unstructured_profilers.ipynb b/examples/unstructured_profilers.ipynb
index 82169af5a..9ab754cc7 100644
--- a/examples/unstructured_profilers.ipynb
+++ b/examples/unstructured_profilers.ipynb
@@ -178,7 +178,7 @@
     "\n",
     "Below, let's remove the vocab count and set the stop words. \n",
     "\n",
-    "Full list of options in the Profiler section of the [DataProfiler documentation](https://capitalone.github.io/DataProfiler)."
+    "Full list of options in the Profiler section of the [DataProfiler documentation](https://capitalone.github.io/DataProfiler/profile_options.html)."
    ]
   },
   {
diff --git a/requirements.txt b/requirements.txt
index 994ec78de..7c8aa0b99 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,9 +10,10 @@ fastavro>=1.0.0.post1
 python-snappy>=0.5.4
 charset-normalizer>=1.3.6
 psutil>=4.0.0
-scipy>=1.4.1,<1.11.0
+scipy>=1.10.0
 requests>=2.28.1
 networkx>=2.5.1
 typing-extensions>=3.10.0.2
 HLL>=2.0.3
 datasketches>=4.1.0
+packaging>=23.0