diff --git a/.gitignore b/.gitignore
index 0a12bc7be..2f5d5259b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -134,6 +134,3 @@ venv.bak/
 env3/
 
 *.bak
-
-#Pipfiles
-Pipfile*
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fd1768f0b..a2047e1bf 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@ repos:
   # Black: format Python code
   # https://github.com/psf/black/blob/master/.pre-commit-hooks.yaml
   - repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 24.10.0
     hooks:
       - id: black
         types: [file, python]
@@ -38,7 +38,7 @@ repos:
   # Mypy: Optional static type checking
   # https://github.com/pre-commit/mirrors-mypy
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.982
+    rev: v1.11.2
     hooks:
       - id: mypy
         exclude: (^dataprofiler/tests/|^resources/|^examples|venv*/)
@@ -108,16 +108,19 @@ repos:
     rev: "0.48"
     hooks:
       - id: check-manifest
-        additional_dependencies: ['h5py', 'wheel', 'future', 'numpy<2.0.0', 'pandas',
-        'python-dateutil', 'pytz', 'pyarrow', 'chardet', 'fastavro',
-        'python-snappy', 'charset-normalizer', 'psutil', 'scipy', 'requests',
-        'networkx','typing-extensions', 'HLL', 'datasketches', 'boto3']
+        additional_dependencies:
+          [
+            'matplotlib', 'h5py', 'wheel', 'future', 'numpy<2.0.0', 'pandas',
+            'python-dateutil', 'pytz', 'pyarrow', 'chardet', 'fastavro',
+            'python-snappy', 'charset-normalizer', 'psutil', 'scipy', 'requests',
+            'networkx','typing-extensions', 'HLL', 'datasketches', 'boto3',
+          ]
   # Pyupgrade - standardize and modernize Python syntax for newer versions of the language
   - repo: https://github.com/asottile/pyupgrade
     rev: v3.3.0
     hooks:
       - id: pyupgrade
-        args: ["--py38-plus"]
+        args: ["--py39-plus"]
   # Autoflake - cleanup unused variables and imports
   - repo: https://github.com/PyCQA/autoflake
     rev: v2.0.0
diff --git a/MANIFEST.in b/MANIFEST.in
index 0ace6ebe9..9a62e405e 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -17,4 +17,4 @@ recursive-include resources *.json
 recursive-include resources *.pb
 recursive-include resources *.py
 
-recursive-include dataprofiler/labelers/embeddings/ *.txt
+recursive-include dataprofiler/labelers/embeddings/*.txt
diff --git a/Makefile b/Makefile
index f659ae309..5867b94be 100644
--- a/Makefile
+++ b/Makefile
@@ -1,12 +1,19 @@
-setup: requirements.txt requirements-dev.txt requirements-test.txt
-	python3 -m venv venv
+PYTHON_VERSION ?= python3.9
+VENV_DIR ?= venv
+REQ_FILES := requirements.txt requirements-dev.txt requirements-test.txt requirements-ml.txt requirements-reports.txt
 
-	. venv/bin/activate && \
-	pip3 install -r requirements.txt && \
-	pip3 install -r requirements-dev.txt && \
-	pip3 install -r requirements-ml.txt && \
-	pip3 install -r requirements-reports.txt && \
-	pip3 install -r requirements-test.txt && \
+check-python:
+	@$(PYTHON_VERSION) --version | grep -E "Python (3\.9|3\.10|3\.11)" || \
+	(echo "Python 3.9, 3.10, or 3.11 is required. Ensure $(PYTHON_VERSION) is installed and try again." && exit 1)
+
+setup: check-python $(REQ_FILES)
+	@$(PYTHON_VERSION) -m venv $(VENV_DIR)
+	. $(VENV_DIR)/bin/activate && \
+	pip3 install --no-cache-dir -r requirements-ml.txt && \
+	pip3 install --no-cache-dir -r requirements.txt && \
+	pip3 install --no-cache-dir -r requirements-dev.txt && \
+	pip3 install --no-cache-dir -r requirements-reports.txt && \
+	pip3 install --no-cache-dir -r requirements-test.txt && \
 	pip3 install -e . && \
 	pre-commit install && \
 	pre-commit run
@@ -15,4 +22,15 @@ format:
 	pre-commit run
 
 test:
-	DATAPROFILER_SEED=0 python3 -m unittest discover -p "test*.py"
+	DATAPROFILER_SEED=0 $(VENV_DIR)/bin/python -m unittest discover -p "test*.py"
+
+clean:
+	rm -rf .pytest_cache __pycache__
+
+help:
+	@echo "Makefile Commands:"
+	@echo "  setup       - Set up the virtual environment with Python $(PYTHON_VERSION)"
+	@echo "  format      - Format the code using pre-commit hooks"
+	@echo "  test        - Run unit tests with unittest"
+	@echo "  clean       - Remove temporary files (caches), but keep the virtual environment"
+	@echo "  help        - Display this help message"
diff --git a/dataprofiler/__init__.py b/dataprofiler/__init__.py
index 5f218bd85..f23cf6494 100644
--- a/dataprofiler/__init__.py
+++ b/dataprofiler/__init__.py
@@ -1,4 +1,5 @@
 """Package for dataprofiler."""
+
 from . import settings
 from .data_readers.data import Data
 from .dp_logging import get_logger, set_verbosity
diff --git a/dataprofiler/_typing.py b/dataprofiler/_typing.py
index fa362d1b1..399d3ad43 100644
--- a/dataprofiler/_typing.py
+++ b/dataprofiler/_typing.py
@@ -1,9 +1,10 @@
 """Contains typing aliases."""
-from typing import Dict, List, NewType, Union
+
+from typing import NewType, Union
 
 import numpy as np
 import pandas as pd
 
 DataArray = Union[pd.DataFrame, pd.Series, np.ndarray]
-JSONType = Union[str, int, float, bool, None, List, Dict]
+JSONType = Union[str, int, float, bool, None, list, dict]
 Url = NewType("Url", str)
diff --git a/dataprofiler/data_readers/avro_data.py b/dataprofiler/data_readers/avro_data.py
index 720b9d1f7..79769f800 100644
--- a/dataprofiler/data_readers/avro_data.py
+++ b/dataprofiler/data_readers/avro_data.py
@@ -1,6 +1,7 @@
 """Contains class for saving and loading spreadsheet data."""
+
 from io import BytesIO, StringIO
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 import fastavro
 
@@ -20,7 +21,7 @@ def __init__(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[Any] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Initialize Data class for loading datasets of type AVRO.
@@ -60,7 +61,7 @@ def file_encoding(self, value: Any) -> None:
         """
         pass
 
-    def _load_data_from_file(self, input_file_path: str) -> List:
+    def _load_data_from_file(self, input_file_path: str) -> list:
         """Load data from file."""
         with FileOrBufferHandler(input_file_path, "rb") as input_file:
             # Currently, string reading with 'r' option has the unicode issue,
@@ -68,14 +69,14 @@ def _load_data_from_file(self, input_file_path: str) -> List:
             # some special compression codec, e.g., snappy. Then, binary mode
             # reading is currently used to get the dict-formatted lines.
             df_reader = fastavro.reader(input_file)
-            lines: List = list()
+            lines: list = list()
             for line in df_reader:
                 lines.append(line)
             return lines
 
     @classmethod
     def is_match(
-        cls, file_path: Union[str, StringIO, BytesIO], options: Optional[Dict] = None
+        cls, file_path: Union[str, StringIO, BytesIO], options: Optional[dict] = None
     ) -> bool:
         """
         Test the given file to check if the file has valid AVRO format or not.
@@ -103,7 +104,7 @@ def is_match(
         return is_valid_avro
 
     @classmethod
-    def _get_nested_key(cls, dict_line: Dict, nested_key: Dict) -> Dict:
+    def _get_nested_key(cls, dict_line: dict, nested_key: dict) -> dict:
         """
         Update nested keys from a dictionary and the current nested key.
 
@@ -131,7 +132,7 @@ def _get_nested_key(cls, dict_line: Dict, nested_key: Dict) -> Dict:
         return nested_key
 
     @classmethod
-    def _get_nested_keys_from_dicts(cls, dicts: List[Dict]) -> Dict:
+    def _get_nested_keys_from_dicts(cls, dicts: list[dict]) -> dict:
         """
         Extract nested keys from a list of dictionaries.
 
@@ -143,13 +144,13 @@ def _get_nested_keys_from_dicts(cls, dicts: List[Dict]) -> Dict:
         :type dicts: list(dict)
         :return: a dictionary containing nested keys
         """
-        nested_keys: Dict = {}
+        nested_keys: dict = {}
         for dict_line in dicts:
             nested_keys = cls._get_nested_key(dict_line, nested_keys)
         return nested_keys
 
     @classmethod
-    def _get_schema_avro(cls, nested_keys: Dict, schema_avro: Dict) -> Dict:
+    def _get_schema_avro(cls, nested_keys: dict, schema_avro: dict) -> dict:
         """
         Update avro schema from the nested keys and the current avro schema.
 
@@ -190,7 +191,7 @@ def _get_schema_avro(cls, nested_keys: Dict, schema_avro: Dict) -> Dict:
             if type(value) is dict:
                 # here, the null option to specify keys not required
                 # for every lines
-                schema_avro_temp: Dict[str, Any] = {
+                schema_avro_temp: dict[str, Any] = {
                     "name": key,
                     "type": [{"name": key, "type": "record", "fields": []}, "null"],
                 }
diff --git a/dataprofiler/data_readers/base_data.py b/dataprofiler/data_readers/base_data.py
index 27d8d5deb..74c695755 100644
--- a/dataprofiler/data_readers/base_data.py
+++ b/dataprofiler/data_readers/base_data.py
@@ -1,9 +1,11 @@
 """Contains abstract class for data loading and saving."""
+
 import locale
 import sys
 from collections import OrderedDict
+from collections.abc import Generator
 from io import StringIO
-from typing import Any, Dict, Generator, List, Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -21,7 +23,7 @@ class BaseData:
     info: Optional[str] = None
 
     def __init__(
-        self, input_file_path: Optional[str], data: Any, options: Dict
+        self, input_file_path: Optional[str], data: Any, options: dict
     ) -> None:
         """
         Initialize Base class for loading a dataset.
@@ -42,7 +44,7 @@ def __init__(
 
         # Public properties
         self.input_file_path = input_file_path
-        self.options: Optional[Dict] = options
+        self.options: Optional[dict] = options
 
         # 'Private' properties
         #  _data_formats: dict containing data_formats (key) and function
@@ -56,10 +58,10 @@ def __init__(
         #               constant across function calls.
         #  _tmp_file_name: randomly set variables for file name usable by system
         #  _file_encoding: contains the suggested file encoding for reading data
-        self._data_formats: Dict[str, Any] = OrderedDict()
+        self._data_formats: dict[str, Any] = OrderedDict()
         self._selected_data_format: Optional[str] = None
         self._data: Optional[Any] = data
-        self._batch_info: Dict = dict(perm=list(), iter=0)
+        self._batch_info: dict = dict(perm=list(), iter=0)
         self._tmp_file_name: Optional[str] = None
         self._file_encoding: Optional[str] = options.get("encoding", None)
 
@@ -137,7 +139,7 @@ def file_encoding(self, value: str) -> None:
         self._file_encoding = value
 
     @staticmethod
-    def _check_and_return_options(options: Optional[Dict]) -> Dict:
+    def _check_and_return_options(options: Optional[dict]) -> dict:
         """Return options or raise error."""
         if not options:
             options = dict()
@@ -151,7 +153,7 @@ def _load_data(self, data: Optional[Any] = None) -> None:
 
     def get_batch_generator(
         self, batch_size: int
-    ) -> Generator[Union[pd.DataFrame, List], None, None]:
+    ) -> Generator[Union[pd.DataFrame, list], None, None]:
         """Get batch generator."""
         data_length = len(self.data)
         indices = np.random.permutation(data_length)
@@ -162,12 +164,12 @@ def get_batch_generator(
                 yield list(self.data[k] for k in indices[i : i + batch_size])
 
     @classmethod
-    def is_match(cls, input_file_path: str, options: Optional[Dict]) -> bool:
+    def is_match(cls, input_file_path: str, options: Optional[dict]) -> bool:
         """Return true if match, false otherwise."""
         raise NotImplementedError()
 
     def reload(
-        self, input_file_path: Optional[str], data: Any, options: Optional[Dict]
+        self, input_file_path: Optional[str], data: Any, options: Optional[dict]
     ) -> None:
         """
         Reload the data class with a new dataset.
diff --git a/dataprofiler/data_readers/csv_data.py b/dataprofiler/data_readers/csv_data.py
index 7e13d4075..5cc1fe299 100644
--- a/dataprofiler/data_readers/csv_data.py
+++ b/dataprofiler/data_readers/csv_data.py
@@ -1,10 +1,11 @@
 """Contains class that saves and loads spreadsheet data."""
+
 import csv
 import random
 import re
 from collections import Counter
 from io import StringIO
-from typing import Dict, List, Optional, Tuple, Union, cast
+from typing import Optional, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -26,7 +27,7 @@ def __init__(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[pd.DataFrame] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ):
         """
         Initialize Data class for loading datasets of type CSV.
@@ -82,7 +83,7 @@ def __init__(
         self._selected_data_format: str = options.get("data_format", "dataframe")
         self._delimiter: Optional[str] = options.get("delimiter", None)
         self._quotechar: Optional[str] = options.get("quotechar", None)
-        self._selected_columns: List[str] = options.get("selected_columns", list())
+        self._selected_columns: list[str] = options.get("selected_columns", list())
         self._header: Optional[Union[str, int]] = options.get("header", "auto")
         self._checked_header: bool = "header" in options and self._header != "auto"
         self._default_delimiter: str = ","
@@ -97,7 +98,7 @@ def __init__(
                 self._quotechar = self._default_quotechar
 
     @property
-    def selected_columns(self) -> List[str]:
+    def selected_columns(self) -> list[str]:
         """Return selected columns."""
         return self._selected_columns
 
@@ -127,7 +128,7 @@ def is_structured(self) -> bool:
         return self.data_format == "dataframe"
 
     @staticmethod
-    def _check_and_return_options(options: Optional[Dict]) -> Dict:
+    def _check_and_return_options(options: Optional[dict]) -> dict:
         """
         Ensure options are valid inputs to the data reader.
 
@@ -184,9 +185,9 @@ def _check_and_return_options(options: Optional[Dict]) -> Dict:
     def _guess_delimiter_and_quotechar(
         data_as_str: str,
         quotechar: Optional[str] = None,
-        preferred: List[str] = [",", "\t"],
-        omitted: List[str] = ['"', "'"],
-    ) -> Tuple[Optional[str], Optional[str]]:
+        preferred: list[str] = [",", "\t"],
+        omitted: list[str] = ['"', "'"],
+    ) -> tuple[Optional[str], Optional[str]]:
         r"""
         Automatically check for what delimiter exists in a text document.
 
@@ -207,7 +208,7 @@ def _guess_delimiter_and_quotechar(
         vocab = Counter(data_as_str)
         if "\n" in vocab:
             vocab.pop("\n")
-        omitted_list: List[str] = omitted
+        omitted_list: list[str] = omitted
         if quotechar is not None:
             omitted_list = omitted + [quotechar]
         for char in omitted_list:
@@ -384,7 +385,7 @@ def _guess_header_row(
             quotechar = '"'
 
         # Determine type for every cell
-        header_check_list: List[List[str]] = []
+        header_check_list: list[list[str]] = []
         only_string_flag = True  # Requires additional checks
         for row in data_as_str.split("\n"):
 
@@ -403,7 +404,7 @@ def _guess_header_row(
 
         # Flags differences in types between each row (true/false)
         potential_header = header_check_list[0]
-        differences: List[List[bool]] = []
+        differences: list[list[bool]] = []
         for i in range(0, len(header_check_list)):
             differences.append([])
 
@@ -612,7 +613,7 @@ def _load_data_from_file(self, input_file_path: str) -> pd.DataFrame:
             encoding=self.file_encoding,
         )
 
-    def _get_data_as_records(self, data: pd.DataFrame) -> List[str]:
+    def _get_data_as_records(self, data: pd.DataFrame) -> list[str]:
         """Return data as records."""
         sep = self.delimiter if self.delimiter else self._default_delimiter
         quote = self.quotechar if self.quotechar else self._default_quotechar
@@ -621,7 +622,7 @@ def _get_data_as_records(self, data: pd.DataFrame) -> List[str]:
         return super()._get_data_as_records(data)
 
     @classmethod
-    def is_match(cls, file_path: str, options: Optional[Dict] = None) -> bool:
+    def is_match(cls, file_path: str, options: Optional[dict] = None) -> bool:
         """
         Check if first 1000 lines of given file has valid delimited format.
 
@@ -745,7 +746,7 @@ def reload(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[pd.DataFrame] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ):
         """
         Reload the data class with a new dataset.
diff --git a/dataprofiler/data_readers/data.py b/dataprofiler/data_readers/data.py
index 8a3e6d94a..e2dcfe8d4 100644
--- a/dataprofiler/data_readers/data.py
+++ b/dataprofiler/data_readers/data.py
@@ -1,7 +1,7 @@
 """Contains factory class reading various kinds of data."""
 
 from io import BytesIO
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 from .. import dp_logging
 from .avro_data import AVROData
@@ -18,7 +18,7 @@
 class Data:
     """Factory class for reading various kinds of data."""
 
-    data_classes: List[Dict] = [
+    data_classes: list[dict] = [
         dict(data_class=JSONData, kwargs=dict()),
         dict(data_class=GraphData, kwargs=dict()),
         dict(data_class=CSVData, kwargs=dict()),
@@ -32,7 +32,7 @@ def __new__(
         input_file_path: Optional[Union[str, BytesIO]] = None,
         data: Optional[Any] = None,
         data_type: Optional[str] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ):
         """
         Create Factory Data object.
diff --git a/dataprofiler/data_readers/data_utils.py b/dataprofiler/data_readers/data_utils.py
index 611d25dc3..4065a6b42 100644
--- a/dataprofiler/data_readers/data_utils.py
+++ b/dataprofiler/data_readers/data_utils.py
@@ -1,4 +1,5 @@
 """Contains functions for data readers."""
+
 import json
 import logging
 import os
@@ -6,21 +7,12 @@
 import re
 import urllib
 from collections import OrderedDict
+from collections.abc import Generator, Iterator
 from io import BytesIO, StringIO, TextIOWrapper
 from itertools import islice
 from math import floor, log, log1p
-from typing import (
-    Any,
-    Dict,
-    Generator,
-    Iterator,
-    List,
-    Optional,
-    Pattern,
-    Tuple,
-    Union,
-    cast,
-)
+from re import Pattern
+from typing import Any, Optional, Union, cast
 
 import boto3
 import botocore
@@ -39,7 +31,7 @@
 logger = dp_logging.get_child_logger(__name__)
 
 
-def data_generator(data_list: List[str]) -> Generator[str, None, None]:
+def data_generator(data_list: list[str]) -> Generator[str, None, None]:
     """
     Take a list and return a generator on the list.
 
@@ -122,10 +114,10 @@ def unicode_to_str(data: JSONType, ignore_dicts: bool = False) -> JSONType:
 
 
 def json_to_dataframe(
-    json_lines: List[JSONType],
-    selected_columns: Optional[List[str]] = None,
+    json_lines: list[JSONType],
+    selected_columns: Optional[list[str]] = None,
     read_in_string: bool = False,
-) -> Tuple[pd.DataFrame, pd.Series]:
+) -> tuple[pd.DataFrame, pd.Series]:
     """
     Take list of json objects and return dataframe representing json list.
 
@@ -165,9 +157,9 @@ def json_to_dataframe(
 
 def read_json_df(
     data_generator: Generator,
-    selected_columns: Optional[List[str]] = None,
+    selected_columns: Optional[list[str]] = None,
     read_in_string: bool = False,
-) -> Tuple[pd.DataFrame, pd.Series]:
+) -> tuple[pd.DataFrame, pd.Series]:
     """
     Return an iterator that returns a chunk of data as dataframe in each call.
 
@@ -193,7 +185,7 @@ def read_json_df(
         each call as well as original dtypes of the dataframe columns.
     :rtype: tuple(pd.DataFrame, pd.Series(dtypes))
     """
-    lines: List[JSONType] = list()
+    lines: list[JSONType] = list()
     k = 0
     while True:
         try:
@@ -222,9 +214,9 @@ def read_json_df(
 
 def read_json(
     data_generator: Iterator,
-    selected_columns: Optional[List[str]] = None,
+    selected_columns: Optional[list[str]] = None,
     read_in_string: bool = False,
-) -> List[JSONType]:
+) -> list[JSONType]:
     """
     Return the lines of a json.
 
@@ -249,7 +241,7 @@ def read_json(
     :return: returns the lines of a json file
     :rtype: list(dict)
     """
-    lines: List[JSONType] = list()
+    lines: list[JSONType] = list()
     k = 0
     while True:
         try:
@@ -372,7 +364,7 @@ def read_csv_df(
     delimiter: Optional[str],
     header: Optional[int],
     sample_nrows: Optional[int] = None,
-    selected_columns: List[str] = [],
+    selected_columns: list[str] = [],
     read_in_string: bool = False,
     encoding: Optional[str] = "utf-8",
 ) -> pd.DataFrame:
@@ -393,7 +385,7 @@ def read_csv_df(
     :return: Iterator
     :rtype: pd.DataFrame
     """
-    args: Dict[str, Any] = {
+    args: dict[str, Any] = {
         "delimiter": delimiter,
         "header": header,
         "iterator": True,
@@ -476,9 +468,9 @@ def convert_unicode_col_to_utf8(input_df: pd.DataFrame) -> pd.DataFrame:
 def sample_parquet(
     file_path: str,
     sample_nrows: int,
-    selected_columns: Optional[List[str]] = None,
+    selected_columns: Optional[list[str]] = None,
     read_in_string: bool = False,
-) -> Tuple[pd.DataFrame, pd.Series]:
+) -> tuple[pd.DataFrame, pd.Series]:
     """
     Read parquet file, sample specified number of rows from it and return a data frame.
 
@@ -521,9 +513,9 @@ def sample_parquet(
 def read_parquet_df(
     file_path: str,
     sample_nrows: Optional[int] = None,
-    selected_columns: Optional[List[str]] = None,
+    selected_columns: Optional[list[str]] = None,
     read_in_string: bool = False,
-) -> Tuple[pd.DataFrame, pd.Series]:
+) -> tuple[pd.DataFrame, pd.Series]:
     """
     Return an iterator that returns one row group each time.
 
@@ -569,7 +561,7 @@ def read_parquet_df(
 
 def read_text_as_list_of_strs(
     file_path: str, encoding: Optional[str] = None
-) -> List[str]:
+) -> list[str]:
     """
     Return list of strings relative to the chunk size.
 
@@ -744,7 +736,7 @@ def find_nth_loc(
     search_query: Optional[str] = None,
     n: int = 0,
     ignore_consecutive: bool = True,
-) -> Tuple[int, int]:
+) -> tuple[int, int]:
     """
     Search string via search_query and return nth index in which query occurs.
 
@@ -877,7 +869,7 @@ def is_valid_url(url_as_string: Any) -> TypeGuard[Url]:
     return all([result.scheme, result.netloc])
 
 
-def url_to_bytes(url_as_string: Url, options: Dict) -> BytesIO:
+def url_to_bytes(url_as_string: Url, options: dict) -> BytesIO:
     """
     Read in URL and converts it to a byte stream.
 
diff --git a/dataprofiler/data_readers/filepath_or_buffer.py b/dataprofiler/data_readers/filepath_or_buffer.py
index 56c21e28e..f5ee552c6 100644
--- a/dataprofiler/data_readers/filepath_or_buffer.py
+++ b/dataprofiler/data_readers/filepath_or_buffer.py
@@ -1,6 +1,7 @@
 """Contains functions and classes for handling filepaths and buffers."""
+
 from io import BytesIO, StringIO, TextIOWrapper
-from typing import IO, Any, Optional, Type, Union, cast
+from typing import IO, Any, Optional, Union, cast
 
 from typing_extensions import TypeGuard
 
@@ -54,7 +55,7 @@ def __init__(
         self.seek_whence: int = seek_whence
         self._encoding: Optional[str] = encoding
         self.original_type: Union[
-            Type[str], Type[StringIO], Type[BytesIO], Type[IO]
+            type[str], type[StringIO], type[BytesIO], type[IO]
         ] = type(filepath_or_buffer)
         self._is_wrapped: bool = False
 
diff --git a/dataprofiler/data_readers/graph_data.py b/dataprofiler/data_readers/graph_data.py
index 337408a68..be49e3c00 100644
--- a/dataprofiler/data_readers/graph_data.py
+++ b/dataprofiler/data_readers/graph_data.py
@@ -1,6 +1,7 @@
 """Contains class for identifying, reading, and loading graph data."""
+
 import csv
-from typing import Dict, List, Optional, Union, cast
+from typing import Optional, Union, cast
 
 import networkx as nx
 
@@ -19,7 +20,7 @@ def __init__(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[nx.Graph] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Initialize Data class for identifying, reading, and loading graph data.
@@ -64,14 +65,14 @@ def __init__(
 
         self._source_node: Optional[int] = options.get("source_node", None)
         self._destination_node: Optional[int] = options.get("destination_node", None)
-        self._target_keywords: List[str] = options.get(
+        self._target_keywords: list[str] = options.get(
             "target_keywords", ["target", "destination", "dst"]
         )
-        self._source_keywords: List[str] = options.get(
+        self._source_keywords: list[str] = options.get(
             "source_keywords", ["source", "src", "origin"]
         )
-        self._graph_keywords: List[str] = options.get("graph_keywords", ["node"])
-        self._column_names: Optional[List[str]] = options.get("column_names", None)
+        self._graph_keywords: list[str] = options.get("graph_keywords", ["node"])
+        self._column_names: Optional[list[str]] = options.get("column_names", None)
         self._delimiter: Optional[str] = options.get("delimiter", None)
         self._quotechar: Optional[str] = options.get("quotechar", None)
         self._header: Optional[Union[str, int]] = options.get("header", "auto")
@@ -82,7 +83,7 @@ def __init__(
 
     @classmethod
     def _find_target_string_in_column(
-        self, column_names: List[str], keyword_list: List[str]
+        self, column_names: list[str], keyword_list: list[str]
     ) -> int:
         """Find out if col name contains keyword that could refer to target node col."""
         column_name_symbols = ["_", ".", "-"]
@@ -116,9 +117,9 @@ def csv_column_names(
         header: Optional[int],
         delimiter: Optional[str],
         encoding: str = "utf-8",
-    ) -> List[str]:
+    ) -> list[str]:
         """Fetch a list of column names from the csv file."""
-        column_names: List[str] = []
+        column_names: list[str] = []
         if delimiter is None:
             delimiter = ","
         if header is None:
@@ -141,7 +142,7 @@ def csv_column_names(
         return column_names
 
     @classmethod
-    def is_match(cls, file_path: str, options: Optional[Dict] = None) -> bool:
+    def is_match(cls, file_path: str, options: Optional[dict] = None) -> bool:
         """
         Determine whether the file is a graph.
 
@@ -157,16 +158,16 @@ def is_match(cls, file_path: str, options: Optional[Dict] = None) -> bool:
         header: int = options.get("header", 0)
         delimiter: str = options.get("delimiter", ",")
         encoding: str = options.get("encoding", "utf-8")
-        column_names: List[str] = cls.csv_column_names(
+        column_names: list[str] = cls.csv_column_names(
             file_path, header, delimiter, encoding
         )
-        source_keywords: List[str] = options.get(
+        source_keywords: list[str] = options.get(
             "source_keywords", ["source", "src", "origin"]
         )
-        target_keywords: List[str] = options.get(
+        target_keywords: list[str] = options.get(
             "target_keywords", ["target", "destination", "dst"]
         )
-        graph_keywords: List[str] = options.get("graph_keywords", ["node"])
+        graph_keywords: list[str] = options.get("graph_keywords", ["node"])
         source_index: int = cls._find_target_string_in_column(
             column_names, source_keywords
         )
diff --git a/dataprofiler/data_readers/json_data.py b/dataprofiler/data_readers/json_data.py
index 93e5d7e6f..f1e378fcd 100644
--- a/dataprofiler/data_readers/json_data.py
+++ b/dataprofiler/data_readers/json_data.py
@@ -1,10 +1,11 @@
 """Contains class to save and load json data."""
+
 import json
 import re
 import warnings
 from collections import OrderedDict
 from io import StringIO
-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -25,7 +26,7 @@ def __init__(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[Union[str, pd.DataFrame]] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ):
         """
         Initialize Data class for loading datasets of type JSON.
@@ -71,23 +72,23 @@ def __init__(
 
         self._data_formats["records"] = self._get_data_as_records
         self._data_formats["json"] = self._get_data_as_json
-        self._data_formats[
-            "flattened_dataframe"
-        ] = self._get_data_as_flattened_dataframe
+        self._data_formats["flattened_dataframe"] = (
+            self._get_data_as_flattened_dataframe
+        )
         self._selected_data_format: str = options.get(
             "data_format", "flattened_dataframe"
         )
-        self._payload_keys: List[str] = options.get("payload_keys", ["data", "payload"])
+        self._payload_keys: list[str] = options.get("payload_keys", ["data", "payload"])
         if not isinstance(self._payload_keys, list):
             self._payload_keys = [self._payload_keys]
         self._key_separator: str = options.get("key_separator", ".")
-        self._selected_keys: Optional[List[str]] = options.get("selected_keys", list())
+        self._selected_keys: Optional[list[str]] = options.get("selected_keys", list())
         self._metadata: Optional[pd.DataFrame] = None
         if data is not None:
             self._load_data(data)
 
     @property
-    def selected_keys(self) -> Optional[List[str]]:
+    def selected_keys(self) -> Optional[list[str]]:
         """Return selected keys."""
         return self._selected_keys
 
@@ -280,7 +281,7 @@ def _load_data_from_file(self, input_file_path: str) -> JSONType:
                 )
             return data
 
-    def _get_data_as_records(self, data: Union[pd.DataFrame, Dict, List]) -> List[str]:
+    def _get_data_as_records(self, data: Union[pd.DataFrame, dict, list]) -> list[str]:
         """
         Extract the data as a record format.
 
@@ -297,7 +298,7 @@ def _get_data_as_records(self, data: Union[pd.DataFrame, Dict, List]) -> List[st
             )
         return super()._get_data_as_records(data)
 
-    def _get_data_as_json(self, data: Union[pd.DataFrame, Dict, List]) -> List[str]:
+    def _get_data_as_json(self, data: Union[pd.DataFrame, dict, list]) -> list[str]:
         """
         Extract the data as a json format.
 
@@ -310,7 +311,7 @@ def _get_data_as_json(self, data: Union[pd.DataFrame, Dict, List]) -> List[str]:
         char_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)
         return list(map("".join, zip(*[iter(data)] * char_per_line)))
 
-    def _get_data_as_df(self, data: Union[pd.DataFrame, Dict, List]) -> pd.DataFrame:
+    def _get_data_as_df(self, data: Union[pd.DataFrame, dict, list]) -> pd.DataFrame:
         """
         Extract the data as pandas formats it.
 
@@ -329,7 +330,7 @@ def _get_data_as_df(self, data: Union[pd.DataFrame, Dict, List]) -> pd.DataFrame
         return data
 
     @classmethod
-    def _convert_flat_to_nested_cols(cls, dic: Dict, separator: str = ".") -> Dict:
+    def _convert_flat_to_nested_cols(cls, dic: dict, separator: str = ".") -> dict:
         """
         Convert a flat dict to nested dict.
 
@@ -366,7 +367,7 @@ def _convert_flat_to_nested_cols(cls, dic: Dict, separator: str = ".") -> Dict:
 
     @classmethod
     def is_match(
-        cls, file_path: Union[str, StringIO], options: Optional[Dict] = None
+        cls, file_path: Union[str, StringIO], options: Optional[dict] = None
     ) -> bool:
         """
         Test whether first 1000 lines of file has valid JSON format or not.
@@ -425,7 +426,7 @@ def reload(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[Union[str, pd.DataFrame]] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Reload the data class with a new dataset.
diff --git a/dataprofiler/data_readers/parquet_data.py b/dataprofiler/data_readers/parquet_data.py
index 4fa567b8d..ee6253162 100644
--- a/dataprofiler/data_readers/parquet_data.py
+++ b/dataprofiler/data_readers/parquet_data.py
@@ -1,6 +1,7 @@
 """Contains class to save and load parquet data."""
+
 from io import BytesIO, StringIO
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 import pandas as pd
 import pyarrow.parquet as pq
@@ -19,7 +20,7 @@ def __init__(
         self,
         input_file_path: Optional[str] = None,
         data: Optional[Union[pd.DataFrame, str]] = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ):
         """
         Initialize Data class for loading datasets of type PARQUET.
@@ -60,7 +61,7 @@ def __init__(
         self._data_formats["records"] = self._get_data_as_records
         self._data_formats["json"] = self._get_data_as_json
         self._selected_data_format: str = options.get("data_format", "dataframe")
-        self._selected_columns: List[str] = options.get("selected_columns", list())
+        self._selected_columns: list[str] = options.get("selected_columns", list())
         self._sample_nrows: Optional[int] = options.get("sample_nrows", None)
 
         if data is not None:
@@ -80,7 +81,7 @@ def file_encoding(self, value: Any) -> None:
         pass
 
     @property
-    def selected_columns(self) -> List[str]:
+    def selected_columns(self) -> list[str]:
         """Return selected columns."""
         return self._selected_columns
 
@@ -114,14 +115,14 @@ def _load_data_from_file(self, input_file_path: str) -> pd.DataFrame:
         self._original_df_dtypes = original_df_dtypes
         return data
 
-    def _get_data_as_records(self, data: pd.DataFrame) -> List[str]:
+    def _get_data_as_records(self, data: pd.DataFrame) -> list[str]:
         """Return data records."""
         # split into row samples separate by `\n`
         data = data.to_json(orient="records", lines=True)
         data = data.splitlines()
         return super()._get_data_as_records(data)
 
-    def _get_data_as_json(self, data: pd.DataFrame) -> List[str]:
+    def _get_data_as_json(self, data: pd.DataFrame) -> list[str]:
         """Return json data."""
         data = data.to_json(orient="records")
         chars_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)
@@ -129,7 +130,7 @@ def _get_data_as_json(self, data: pd.DataFrame) -> List[str]:
 
     @classmethod
     def is_match(
-        cls, file_path: Union[str, StringIO, BytesIO], options: Optional[Dict] = None
+        cls, file_path: Union[str, StringIO, BytesIO], options: Optional[dict] = None
     ) -> bool:
         """
         Test the given file to check if the file has valid Parquet format.
@@ -164,7 +165,7 @@ def reload(
         self,
         input_file_path: Optional[str] = None,
         data: Any = None,
-        options: Optional[Dict] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Reload the data class with a new dataset.
diff --git a/dataprofiler/data_readers/structured_mixins.py b/dataprofiler/data_readers/structured_mixins.py
index 3587291f1..c1c3e9cca 100644
--- a/dataprofiler/data_readers/structured_mixins.py
+++ b/dataprofiler/data_readers/structured_mixins.py
@@ -1,6 +1,7 @@
 """Contains mixin data class for loading datasets of tye SpreadSheet."""
+
 from logging import Logger
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Optional, Union, cast
 
 import pandas as pd
 
@@ -26,10 +27,10 @@ class SpreadSheetDataMixin:
     """
 
     def __init__(
-        self, input_file_path: Optional[str], data: Any, options: Dict
+        self, input_file_path: Optional[str], data: Any, options: dict
     ) -> None:
         """Initialize spreadsheet mixin object."""
-        self._data_formats: Dict = dict()
+        self._data_formats: dict = dict()
         self._data_formats["dataframe"] = self._get_data_as_df
         self._original_df_dtypes: Optional[pd.Series]
         self.input_file_path: Optional[str] = input_file_path
@@ -69,7 +70,7 @@ def _get_data_as_df(self, data: pd.DataFrame) -> pd.DataFrame:
             )
         return data
 
-    def _get_data_as_records(self, data: Any) -> List[str]:
+    def _get_data_as_records(self, data: Any) -> list[str]:
         """Return data records."""
         records_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)
         data = [
@@ -80,4 +81,4 @@ def _get_data_as_records(self, data: Any) -> List[str]:
             )
             for i in range((len(data) + records_per_line - 1) // records_per_line)
         ]
-        return cast(List[str], data)
+        return cast(list[str], data)
diff --git a/dataprofiler/data_readers/text_data.py b/dataprofiler/data_readers/text_data.py
index fd9dc2413..117ca2e37 100644
--- a/dataprofiler/data_readers/text_data.py
+++ b/dataprofiler/data_readers/text_data.py
@@ -1,7 +1,7 @@
 """Contains class for saving and loading text files."""
 
 from io import StringIO
-from typing import Dict, List, Optional, Union, cast
+from typing import Optional, Union, cast
 
 from . import data_utils
 from .base_data import BaseData
@@ -15,8 +15,8 @@ class TextData(BaseData):
     def __init__(
         self,
         input_file_path: Optional[str] = None,
-        data: Optional[List[str]] = None,
-        options: Optional[Dict] = None,
+        data: Optional[list[str]] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Initialize Data class for loading datasets of type TEXT.
@@ -74,7 +74,7 @@ def is_structured(self) -> bool:
         """Determine compatibility with StructuredProfiler."""
         return False
 
-    def _load_data(self, data: Optional[List[str]] = None) -> None:
+    def _load_data(self, data: Optional[list[str]] = None) -> None:
         """Load data."""
         if data is not None:
             self._data = data
@@ -83,7 +83,7 @@ def _load_data(self, data: Optional[List[str]] = None) -> None:
                 cast(str, self.input_file_path), self.file_encoding
             )
 
-    def _get_data_as_text(self, data: Union[str, List[str]]) -> List[str]:
+    def _get_data_as_text(self, data: Union[str, list[str]]) -> list[str]:
         """Return data as text."""
         if isinstance(data, list) and len(data) and isinstance(data[0], str):
             data = "".join(data)
@@ -105,7 +105,7 @@ def tokenize(self) -> None:
         raise NotImplementedError("Tokenizing does not currently exist for text data.")
 
     @classmethod
-    def is_match(cls, file_path: str, options: Optional[Dict] = None) -> bool:
+    def is_match(cls, file_path: str, options: Optional[dict] = None) -> bool:
         """
         Return True if all are text files.
 
@@ -127,8 +127,8 @@ def is_match(cls, file_path: str, options: Optional[Dict] = None) -> bool:
     def reload(
         self,
         input_file_path: Optional[str] = None,
-        data: Optional[List[str]] = None,
-        options: Optional[Dict] = None,
+        data: Optional[list[str]] = None,
+        options: Optional[dict] = None,
     ) -> None:
         """
         Reload the data class with a new dataset.
diff --git a/dataprofiler/labelers/__init__.py b/dataprofiler/labelers/__init__.py
index 1b2302fcf..a355ead23 100644
--- a/dataprofiler/labelers/__init__.py
+++ b/dataprofiler/labelers/__init__.py
@@ -26,6 +26,7 @@
         2. structured_model
         3. regex_model
 """
+
 # import data labelers
 # import models
 from .base_data_labeler import BaseDataLabeler, TrainableDataLabeler
diff --git a/dataprofiler/labelers/base_data_labeler.py b/dataprofiler/labelers/base_data_labeler.py
index 201f78998..efb387377 100644
--- a/dataprofiler/labelers/base_data_labeler.py
+++ b/dataprofiler/labelers/base_data_labeler.py
@@ -1,4 +1,5 @@
 """Contains abstract classes from which labeler classes will inherit."""
+
 from __future__ import annotations
 
 import json
diff --git a/dataprofiler/labelers/base_model.py b/dataprofiler/labelers/base_model.py
index 032c2ea38..fcfcddbec 100644
--- a/dataprofiler/labelers/base_model.py
+++ b/dataprofiler/labelers/base_model.py
@@ -1,11 +1,12 @@
 """Contains abstract classes for labeling data."""
+
 from __future__ import annotations
 
 import abc
 import copy
 import inspect
 import warnings
-from typing import Any, Callable, Type, TypeVar, cast
+from typing import Any, Callable, TypeVar, cast
 
 from dataprofiler._typing import DataArray
 
@@ -22,7 +23,7 @@ def __new__(
     ) -> type[T]:
         """Create auto registration object and return new class."""
         new_class = cast(
-            Type[T],
+            type[T],
             super().__new__(cls, clsname, bases, attrs),
         )
         new_class._register_subclass()
diff --git a/dataprofiler/labelers/char_load_tf_model.py b/dataprofiler/labelers/char_load_tf_model.py
index a4a44e03a..57087c0d0 100644
--- a/dataprofiler/labelers/char_load_tf_model.py
+++ b/dataprofiler/labelers/char_load_tf_model.py
@@ -1,4 +1,5 @@
 """Contains class for training data labeler model."""
+
 from __future__ import annotations
 
 import copy
@@ -537,9 +538,9 @@ def predict(
                 confidences[
                     allocation_index : allocation_index + num_samples_in_batch
                 ] = model_output[0].numpy()
-            predictions[
-                allocation_index : allocation_index + num_samples_in_batch
-            ] = model_output[1].numpy()
+            predictions[allocation_index : allocation_index + num_samples_in_batch] = (
+                model_output[1].numpy()
+            )
 
             allocation_index += num_samples_in_batch
 
diff --git a/dataprofiler/labelers/character_level_cnn_model.py b/dataprofiler/labelers/character_level_cnn_model.py
index 2cbb7051a..78fd2152e 100644
--- a/dataprofiler/labelers/character_level_cnn_model.py
+++ b/dataprofiler/labelers/character_level_cnn_model.py
@@ -1,4 +1,5 @@
 """Contains classes for char data labeling."""
+
 from __future__ import annotations
 
 import copy
@@ -877,9 +878,9 @@ def predict(
                 confidences[
                     allocation_index : allocation_index + num_samples_in_batch
                 ] = model_output[0].numpy()
-            predictions[
-                allocation_index : allocation_index + num_samples_in_batch
-            ] = model_output[1].numpy()
+            predictions[allocation_index : allocation_index + num_samples_in_batch] = (
+                model_output[1].numpy()
+            )
             sentence_lengths[
                 allocation_index : allocation_index + num_samples_in_batch
             ] = list(map(lambda x: len(x[0]), batch_data))
diff --git a/dataprofiler/labelers/classification_report_utils.py b/dataprofiler/labelers/classification_report_utils.py
index 28e742e32..3d248bcfa 100644
--- a/dataprofiler/labelers/classification_report_utils.py
+++ b/dataprofiler/labelers/classification_report_utils.py
@@ -1,4 +1,5 @@
 """Contains functions for classification."""
+
 from __future__ import annotations
 
 import warnings
diff --git a/dataprofiler/labelers/column_name_model.py b/dataprofiler/labelers/column_name_model.py
index d698cfd6a..e5ff59184 100644
--- a/dataprofiler/labelers/column_name_model.py
+++ b/dataprofiler/labelers/column_name_model.py
@@ -1,4 +1,5 @@
 """Contains class for column name data labeling model."""
+
 from __future__ import annotations
 
 import json
@@ -175,7 +176,7 @@ def _reconstruct_model(self) -> None:
         pass
 
     def _need_to_reconstruct_model(self) -> bool:
-        pass
+        raise NotImplementedError()
 
     def reset_weights(self) -> None:
         """Reset weights function."""
diff --git a/dataprofiler/labelers/data_labelers.py b/dataprofiler/labelers/data_labelers.py
index a6d9932b7..48dbc7834 100644
--- a/dataprofiler/labelers/data_labelers.py
+++ b/dataprofiler/labelers/data_labelers.py
@@ -1,4 +1,5 @@
 """Module to train and choose between structured and unstructured data labelers."""
+
 from __future__ import annotations
 
 import os
diff --git a/dataprofiler/labelers/data_processing.py b/dataprofiler/labelers/data_processing.py
index d53980a35..a849863a6 100644
--- a/dataprofiler/labelers/data_processing.py
+++ b/dataprofiler/labelers/data_processing.py
@@ -1,4 +1,5 @@
 """Contains pre-built processors for data labeling/processing."""
+
 from __future__ import annotations
 
 import abc
@@ -11,7 +12,8 @@
 import types
 import warnings
 from collections import Counter
-from typing import Any, Generator, Iterable, TypeVar, cast
+from collections.abc import Generator, Iterable
+from typing import Any, TypeVar, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -173,9 +175,11 @@ def process(
         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] | tuple[
-        np.ndarray, np.ndarray
-    ] | np.ndarray:
+    ) -> (
+        Generator[tuple[np.ndarray, np.ndarray] | np.ndarray, None, None]
+        | tuple[np.ndarray, np.ndarray]
+        | np.ndarray
+    ):
         """Preprocess data."""
         raise NotImplementedError()
 
@@ -1947,9 +1951,11 @@ def _validate_parameters(self, parameters: dict) -> None:
                 # being changed and is already set
                 aggregation_func = parameters.get(
                     "aggregation_func",
-                    self._parameters.get("aggregation_func")
-                    if hasattr(self, "_parameters")
-                    else None,
+                    (
+                        self._parameters.get("aggregation_func")
+                        if hasattr(self, "_parameters")
+                        else None
+                    ),
                 )
                 if value is None and aggregation_func == "priority":
                     errors.append(
diff --git a/dataprofiler/labelers/labeler_utils.py b/dataprofiler/labelers/labeler_utils.py
index 3a24886f3..2883042b3 100644
--- a/dataprofiler/labelers/labeler_utils.py
+++ b/dataprofiler/labelers/labeler_utils.py
@@ -1,10 +1,11 @@
 """Contains functions for the data labeler."""
+
 from __future__ import annotations
 
 import logging
 import os
 import warnings
-from typing import Any, Callable, Dict, cast
+from typing import Any, Callable, cast
 
 import numpy as np
 import scipy
@@ -169,7 +170,7 @@ def evaluate_accuracy(
         conf_mat_pd.to_csv(confusion_matrix_file)
 
     f1_report: dict = cast(
-        Dict,
+        dict,
         classification_report(
             conf_mat, labels=label_indexes, target_names=label_names, output_dict=True
         ),
diff --git a/dataprofiler/labelers/regex_model.py b/dataprofiler/labelers/regex_model.py
index c6a690c17..3c8a27ab2 100644
--- a/dataprofiler/labelers/regex_model.py
+++ b/dataprofiler/labelers/regex_model.py
@@ -1,4 +1,5 @@
 """Contains class for regex data labeling model."""
+
 from __future__ import annotations
 
 import copy
@@ -166,7 +167,7 @@ def _reconstruct_model(self) -> None:
         pass
 
     def _need_to_reconstruct_model(self) -> bool:
-        pass
+        raise NotImplementedError()
 
     def reset_weights(self) -> None:
         """Reset weights."""
diff --git a/dataprofiler/labelers/utils.py b/dataprofiler/labelers/utils.py
index 2d587f7b4..54676a176 100644
--- a/dataprofiler/labelers/utils.py
+++ b/dataprofiler/labelers/utils.py
@@ -1,7 +1,8 @@
 """Contains functions for checking for installations/dependencies."""
+
 import sys
 import warnings
-from typing import Any, Callable, List
+from typing import Any, Callable
 
 
 def warn_missing_module(labeler_function: str, module_name: str) -> None:
@@ -22,7 +23,7 @@ def warn_missing_module(labeler_function: str, module_name: str) -> None:
     warnings.warn(warning_msg, RuntimeWarning, stacklevel=3)
 
 
-def require_module(names: List[str]) -> Callable:
+def require_module(names: list[str]) -> Callable:
     """
     Check if a set of modules exists in sys.modules prior to running function.
 
diff --git a/dataprofiler/plugins/__init__.py b/dataprofiler/plugins/__init__.py
index fbe52182e..1673a15ec 100644
--- a/dataprofiler/plugins/__init__.py
+++ b/dataprofiler/plugins/__init__.py
@@ -1,7 +1,7 @@
 import importlib
 import os
 
-from .decorators import plugin_decorator, plugins_dict
+from dataprofiler.plugins.decorators import plugins_dict
 
 
 def load_plugins():
diff --git a/dataprofiler/plugins/decorators.py b/dataprofiler/plugins/decorators.py
index c781f4300..d839707ca 100644
--- a/dataprofiler/plugins/decorators.py
+++ b/dataprofiler/plugins/decorators.py
@@ -1,8 +1,9 @@
 """Contains function for generating plugins data."""
+
 from collections import defaultdict
-from typing import Any, DefaultDict, Dict
+from typing import Any, DefaultDict
 
-plugins_dict: DefaultDict[str, Dict[str, Any]] = defaultdict(dict)
+plugins_dict: DefaultDict[str, dict[str, Any]] = defaultdict(dict)
 
 
 def plugin_decorator(typ, name):
diff --git a/dataprofiler/profilers/__init__.py b/dataprofiler/profilers/__init__.py
index 4b068fcb0..14834794b 100644
--- a/dataprofiler/profilers/__init__.py
+++ b/dataprofiler/profilers/__init__.py
@@ -1,4 +1,5 @@
 """Package for providing statistics and predictions for a given dataset."""
+
 from . import json_decoder
 from .base_column_profilers import BaseColumnProfiler
 from .categorical_column_profile import CategoricalColumn
diff --git a/dataprofiler/profilers/categorical_column_profile.py b/dataprofiler/profilers/categorical_column_profile.py
index 1ca630900..d64f5aa5b 100644
--- a/dataprofiler/profilers/categorical_column_profile.py
+++ b/dataprofiler/profilers/categorical_column_profile.py
@@ -1,4 +1,5 @@
 """Contains class for categorical column profiler."""
+
 from __future__ import annotations
 
 import math
@@ -277,28 +278,28 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
 
         # These stats are only diffed if both profiles are categorical
         if self.is_match and other_profile.is_match:
-            differences["statistics"][
-                "chi2-test"
-            ] = profiler_utils.perform_chi_squared_test_for_homogeneity(
-                self._categories,
-                self.sample_size,
-                other_profile._categories,
-                other_profile.sample_size,
+            differences["statistics"]["chi2-test"] = (
+                profiler_utils.perform_chi_squared_test_for_homogeneity(
+                    self._categories,
+                    self.sample_size,
+                    other_profile._categories,
+                    other_profile.sample_size,
+                )
             )
-            differences["statistics"][
-                "categories"
-            ] = profiler_utils.find_diff_of_lists_and_sets(
-                self.categories, other_profile.categories
+            differences["statistics"]["categories"] = (
+                profiler_utils.find_diff_of_lists_and_sets(
+                    self.categories, other_profile.categories
+                )
             )
-            differences["statistics"][
-                "gini_impurity"
-            ] = profiler_utils.find_diff_of_numbers(
-                self.gini_impurity, other_profile.gini_impurity
+            differences["statistics"]["gini_impurity"] = (
+                profiler_utils.find_diff_of_numbers(
+                    self.gini_impurity, other_profile.gini_impurity
+                )
             )
-            differences["statistics"][
-                "unalikeability"
-            ] = profiler_utils.find_diff_of_numbers(
-                self.unalikeability, other_profile.unalikeability
+            differences["statistics"]["unalikeability"] = (
+                profiler_utils.find_diff_of_numbers(
+                    self.unalikeability, other_profile.unalikeability
+                )
             )
             cat_count1 = dict(
                 sorted(self._categories.items(), key=itemgetter(1), reverse=True)
@@ -326,9 +327,9 @@ def diff(self, other_profile: CategoricalColumn, options: dict = None) -> dict:
                     )
                 differences["statistics"]["psi"] = total_psi
 
-            differences["statistics"][
-                "categorical_count"
-            ] = profiler_utils.find_diff_of_dicts(self_cat_count, other_cat_count)
+            differences["statistics"]["categorical_count"] = (
+                profiler_utils.find_diff_of_dicts(self_cat_count, other_cat_count)
+            )
 
         return differences
 
diff --git a/dataprofiler/profilers/column_profile_compilers.py b/dataprofiler/profilers/column_profile_compilers.py
index 07edf13dc..cfeb8c699 100644
--- a/dataprofiler/profilers/column_profile_compilers.py
+++ b/dataprofiler/profilers/column_profile_compilers.py
@@ -1,4 +1,5 @@
 """For generating a report."""
+
 from __future__ import annotations
 
 import abc
diff --git a/dataprofiler/profilers/data_labeler_column_profile.py b/dataprofiler/profilers/data_labeler_column_profile.py
index d9bfe1ee9..4a3478efa 100644
--- a/dataprofiler/profilers/data_labeler_column_profile.py
+++ b/dataprofiler/profilers/data_labeler_column_profile.py
@@ -1,8 +1,9 @@
 """Contains class for for profiling data labeler col."""
+
 from __future__ import annotations
 
 import operator
-from typing import Dict, cast
+from typing import cast
 
 import numpy as np
 from pandas import DataFrame, Series
@@ -264,7 +265,7 @@ def data_label(self) -> str | None:
             map(operator.itemgetter(0), ordered_top_k_rank[is_value_close])
         )
         top_label = ordered_top_k_rank[0][0]
-        if cast(Dict, self.label_representation)[top_label] < self._min_top_label_prob:
+        if cast(dict, self.label_representation)[top_label] < self._min_top_label_prob:
             return "could not determine"
         return data_label
 
diff --git a/dataprofiler/profilers/datetime_column_profile.py b/dataprofiler/profilers/datetime_column_profile.py
index af99283a9..1042ea0c0 100644
--- a/dataprofiler/profilers/datetime_column_profile.py
+++ b/dataprofiler/profilers/datetime_column_profile.py
@@ -1,4 +1,5 @@
 """Contains class for profiling datetime column."""
+
 from __future__ import annotations
 
 import datetime
@@ -216,7 +217,7 @@ def _validate_datetime(date: str, date_format: str) -> datetime.datetime | float
         :return: either the str converted into a date format, or Nan
         """
         try:
-            converted_date: (datetime.datetime | float) = datetime.datetime.strptime(
+            converted_date: datetime.datetime | float = datetime.datetime.strptime(
                 date, date_format
             )
         except (ValueError, TypeError):
@@ -237,7 +238,7 @@ def _replace_day_suffix(date: str, pattern: re.Pattern) -> str | float:
         """
         try:
             new_date: str | float = pattern.sub(r"\1", date)
-        except (TypeError):
+        except TypeError:
             new_date = np.nan
         return new_date
 
diff --git a/dataprofiler/profilers/float_column_profile.py b/dataprofiler/profilers/float_column_profile.py
index bc426a447..3d6ede326 100644
--- a/dataprofiler/profilers/float_column_profile.py
+++ b/dataprofiler/profilers/float_column_profile.py
@@ -1,4 +1,5 @@
 """Float profile analysis for individual col within structured profiling."""
+
 from __future__ import annotations
 
 import copy
diff --git a/dataprofiler/profilers/graph_profiler.py b/dataprofiler/profilers/graph_profiler.py
index 0680a29a7..345a0f2e5 100644
--- a/dataprofiler/profilers/graph_profiler.py
+++ b/dataprofiler/profilers/graph_profiler.py
@@ -1,4 +1,5 @@
 """Class and functions to calculate and profile properties of graph data."""
+
 from __future__ import annotations
 
 import importlib
diff --git a/dataprofiler/profilers/helpers/__init__.py b/dataprofiler/profilers/helpers/__init__.py
index 43393433d..2c72b2f35 100644
--- a/dataprofiler/profilers/helpers/__init__.py
+++ b/dataprofiler/profilers/helpers/__init__.py
@@ -1,4 +1,5 @@
 """This package provides helper functions for generating reports."""
+
 from .report_helpers import _prepare_report, calculate_quantiles
 
 __all__ = [
diff --git a/dataprofiler/profilers/helpers/report_helpers.py b/dataprofiler/profilers/helpers/report_helpers.py
index 0588252c9..44ac8fb1a 100644
--- a/dataprofiler/profilers/helpers/report_helpers.py
+++ b/dataprofiler/profilers/helpers/report_helpers.py
@@ -1,4 +1,5 @@
 """Contains helper functions for generating report."""
+
 from __future__ import annotations
 
 import math
diff --git a/dataprofiler/profilers/histogram_utils.py b/dataprofiler/profilers/histogram_utils.py
index df230c4c7..033e384bc 100644
--- a/dataprofiler/profilers/histogram_utils.py
+++ b/dataprofiler/profilers/histogram_utils.py
@@ -7,8 +7,9 @@
 A copy of the license for numpy is available here:
 https://github.com/numpy/numpy/blob/main/LICENSE.txt
 """
+
 import operator
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import numpy as np
 from numpy.lib.histograms import (  # type: ignore[attr-defined]
@@ -238,10 +239,10 @@ def _calc_scott_bin_width_from_profile(profile):
 
 def _get_bin_edges(
     a: np.ndarray,
-    bins: Union[str, int, List],
-    range: Optional[Tuple[int, int]],
+    bins: Union[str, int, list],
+    range: Optional[tuple[int, int]],
     weights: Optional[np.ndarray],
-) -> Tuple[None, int]:
+) -> tuple[None, int]:
     """
     Compute the bins used internally by `histogram`.
 
diff --git a/dataprofiler/profilers/int_column_profile.py b/dataprofiler/profilers/int_column_profile.py
index 014465c71..fe38eec60 100644
--- a/dataprofiler/profilers/int_column_profile.py
+++ b/dataprofiler/profilers/int_column_profile.py
@@ -1,4 +1,5 @@
 """Int profile analysis for individual col within structured profiling."""
+
 from __future__ import annotations
 
 import numpy as np
diff --git a/dataprofiler/profilers/json_decoder.py b/dataprofiler/profilers/json_decoder.py
index fb4ff8cb9..eb09db0d7 100644
--- a/dataprofiler/profilers/json_decoder.py
+++ b/dataprofiler/profilers/json_decoder.py
@@ -1,4 +1,5 @@
 """Contains methods to decode components of a Profiler."""
+
 from __future__ import annotations
 
 import warnings
@@ -116,9 +117,9 @@ def get_structured_col_profiler_class(class_name: str) -> type[StructuredColProf
     :type class_name: str representing name of class
     :return: subclass of StructuredColProfiler object
     """
-    struct_col_profiler_class: None | (
-        type[StructuredColProfiler]
-    ) = _structured_col_profiler.get(class_name)
+    struct_col_profiler_class: None | (type[StructuredColProfiler]) = (
+        _structured_col_profiler.get(class_name)
+    )
     if struct_col_profiler_class is None:
         raise ValueError(
             f"Invalid structured col profiler class {class_name} " f"failed to load."
@@ -153,9 +154,9 @@ def load_column_profile(
         JSON
 
     """
-    column_profiler_cls: type[
-        BaseColumnProfiler[BaseColumnProfiler]
-    ] = get_column_profiler_class(serialized_json["class"])
+    column_profiler_cls: type[BaseColumnProfiler[BaseColumnProfiler]] = (
+        get_column_profiler_class(serialized_json["class"])
+    )
     return column_profiler_cls.load_from_dict(serialized_json["data"], config)
 
 
diff --git a/dataprofiler/profilers/numerical_column_stats.py b/dataprofiler/profilers/numerical_column_stats.py
index fa0666a66..202a704f8 100644
--- a/dataprofiler/profilers/numerical_column_stats.py
+++ b/dataprofiler/profilers/numerical_column_stats.py
@@ -6,7 +6,7 @@
 import copy
 import itertools
 import warnings
-from typing import Any, Callable, Dict, List, TypeVar, cast
+from typing import Any, Callable, TypeVar, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -257,9 +257,9 @@ def _add_helper_merge_profile_histograms(
 
         if self.user_set_histogram_bin is None:
             for method in self.histogram_bin_method_names:
-                self.histogram_methods[method][
-                    "suggested_bin_count"
-                ] = histogram_utils._calculate_bins_from_profile(self, method)
+                self.histogram_methods[method]["suggested_bin_count"] = (
+                    histogram_utils._calculate_bins_from_profile(self, method)
+                )
 
         self._get_quantiles()
 
@@ -1040,10 +1040,7 @@ def _merge_biased_kurtosis(
             / N**3
         )
         third_term = (
-            6
-            * delta**2
-            * (match_count1**2 * M2_2 + match_count2**2 * M2_1)
-            / N**2
+            6 * delta**2 * (match_count1**2 * M2_2 + match_count2**2 * M2_1) / N**2
         )
         fourth_term = 4 * delta * (match_count1 * M3_2 - match_count2 * M3_1) / N
         M4 = first_term + second_term + third_term + fourth_term
@@ -1111,7 +1108,7 @@ def _estimate_mode_from_histogram(self) -> list[float]:
         mode: npt.NDArray[np.float64] = (
             bin_edges[highest_idxs] + bin_edges[highest_idxs + 1]  # type: ignore
         ) / 2
-        return cast(List[float], mode.tolist())
+        return cast(list[float], mode.tolist())
 
     def _estimate_stats_from_histogram(self) -> np.float64:
         # test estimated mean and var
@@ -1548,7 +1545,7 @@ def _get_best_histogram_for_profile(self) -> dict:
                     self.histogram_selection = method
                     best_hist_loss = hist_loss
 
-        return cast(Dict, self.histogram_methods[self.histogram_selection]["histogram"])
+        return cast(dict, self.histogram_methods[self.histogram_selection]["histogram"])
 
     def _get_percentile(self, percentiles: np.ndarray | list[float]) -> list[float]:
         """
@@ -1586,7 +1583,7 @@ def _get_percentile(self, percentiles: np.ndarray | list[float]) -> list[float]:
         )
         if median_value:
             quantiles[percentiles == 50] = median_value
-        return cast(List[float], quantiles.tolist())
+        return cast(list[float], quantiles.tolist())
 
     @staticmethod
     def _fold_histogram(
diff --git a/dataprofiler/profilers/order_column_profile.py b/dataprofiler/profilers/order_column_profile.py
index 308262324..6ee96ed56 100644
--- a/dataprofiler/profilers/order_column_profile.py
+++ b/dataprofiler/profilers/order_column_profile.py
@@ -1,8 +1,9 @@
 """Index profile analysis for individual col within structured profiling."""
+
 from __future__ import annotations
 
 from abc import abstractmethod
-from typing import Protocol, Type, TypeVar, cast
+from typing import Protocol, TypeVar, cast
 
 import numpy as np
 from pandas import DataFrame, Series
@@ -24,8 +25,8 @@ def __lt__(self: CT, other: CT) -> bool:
 CT = TypeVar("CT", bound=Comparable)
 
 # bc type in class attr causing issues, need to alias
-AliasFloatType = Type[np.float64]
-AliasStrType = Type[str]
+AliasFloatType = type[np.float64]
+AliasStrType = type[str]
 
 
 class OrderColumn(BaseColumnProfiler["OrderColumn"]):
diff --git a/dataprofiler/profilers/profile_builder.py b/dataprofiler/profilers/profile_builder.py
index 6e512658f..542c2536f 100644
--- a/dataprofiler/profilers/profile_builder.py
+++ b/dataprofiler/profilers/profile_builder.py
@@ -10,9 +10,10 @@
 import re
 import warnings
 from collections import OrderedDict, defaultdict
+from collections.abc import Generator
 from datetime import datetime
 from multiprocessing.pool import Pool
-from typing import Any, Generator, List, Optional, TypeVar, cast
+from typing import Any, Optional, TypeVar, cast
 
 import networkx as nx
 import numpy as np
@@ -1919,10 +1920,10 @@ def diff(  # type: ignore[override]
             col_name = other_profile._profile[i].name
             other_profile_schema[col_name].append(i)
 
-        report["global_stats"][
-            "profile_schema"
-        ] = profiler_utils.find_diff_of_dicts_with_diff_keys(
-            self_profile_schema, other_profile_schema
+        report["global_stats"]["profile_schema"] = (
+            profiler_utils.find_diff_of_dicts_with_diff_keys(
+                self_profile_schema, other_profile_schema
+            )
         )
 
         # Only find the diff of columns if the schemas are exactly the same
@@ -2049,7 +2050,7 @@ def profile(self) -> list[StructuredColProfiler]:
 
         :return: list[StructuredColProfiler]
         """
-        return cast(List[StructuredColProfiler], super().profile)
+        return cast(list[StructuredColProfiler], super().profile)
 
     def report(self, report_options: dict = None) -> dict:
         """Return a report."""
@@ -2101,9 +2102,9 @@ def report(self, report_options: dict = None) -> dict:
                 self.options.null_replication_metrics.is_enabled
                 and i in self._null_replication_metrics
             ):
-                report["data_stats"][i][
-                    "null_replication_metrics"
-                ] = self._null_replication_metrics[i]
+                report["data_stats"][i]["null_replication_metrics"] = (
+                    self._null_replication_metrics[i]
+                )
 
         return _prepare_report(report, output_format, omit_keys)
 
@@ -2610,9 +2611,11 @@ def _update_null_replication_metrics(self, clean_samples: dict) -> None:
 
         total_row_sum = np.asarray(
             [
-                get_data_type_profiler(profile).sum
-                if get_data_type(profile) not in [None, "datetime"]
-                else np.nan
+                (
+                    get_data_type_profiler(profile).sum
+                    if get_data_type(profile) not in [None, "datetime"]
+                    else np.nan
+                )
                 for profile in self._profile
             ]
         )
@@ -2704,17 +2707,21 @@ def _merge_null_replication_metrics(self, other: StructuredProfiler) -> dict:
 
         self_row_sum = np.asarray(
             [
-                get_data_type_profiler(profile).sum
-                if get_data_type(profile)
-                else np.nan
+                (
+                    get_data_type_profiler(profile).sum
+                    if get_data_type(profile)
+                    else np.nan
+                )
                 for profile in self._profile
             ]
         )
         other_row_sum = np.asarray(
             [
-                get_data_type_profiler(profile).sum
-                if get_data_type(profile)
-                else np.nan
+                (
+                    get_data_type_profiler(profile).sum
+                    if get_data_type(profile)
+                    else np.nan
+                )
                 for profile in other._profile
             ]
         )
diff --git a/dataprofiler/profilers/profiler_options.py b/dataprofiler/profilers/profiler_options.py
index 038acf806..077aa3b62 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 ..plugins.__init__ import get_plugins
+from ..plugins import get_plugins
 from . import profiler_utils
 from .json_decoder import load_option
 
@@ -32,7 +32,7 @@ def properties(self) -> dict[str, BooleanOption]:
         """
         return copy.deepcopy(self.__dict__)
 
-    def _set_helper(self, options: dict[str, bool], variable_path: str) -> None:
+    def _set_helper(self, options: dict, variable_path: str) -> None:
         """
         Set all the options.
 
@@ -100,7 +100,7 @@ def _set_helper(self, options: dict[str, bool], variable_path: str) -> None:
                     f"type object '{error_path}' has no attribute '{option}'"
                 )
 
-    def set(self, options: dict[str, bool]) -> None:
+    def set(self, options: dict) -> None:
         """
         Set all the options.
 
diff --git a/dataprofiler/profilers/profiler_utils.py b/dataprofiler/profilers/profiler_utils.py
index e38e1b041..e0d883a07 100644
--- a/dataprofiler/profilers/profiler_utils.py
+++ b/dataprofiler/profilers/profiler_utils.py
@@ -1,4 +1,5 @@
 """Contains functions for profilers."""
+
 from __future__ import annotations
 
 import collections
@@ -10,20 +11,10 @@
 import time
 import warnings
 from abc import abstractmethod
+from collections.abc import Generator, Iterator
 from itertools import islice
 from multiprocessing.pool import Pool
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    Generator,
-    Iterator,
-    Protocol,
-    TypeVar,
-    cast,
-    overload,
-)
+from typing import TYPE_CHECKING, Any, Callable, Protocol, TypeVar, cast, overload
 
 import numpy as np
 import psutil
@@ -51,7 +42,7 @@ def recursive_dict_update(d: dict, update_d: dict) -> dict:
         if isinstance(v, collections.abc.Mapping) and isinstance(
             d.get(k, None), collections.abc.Mapping
         ):
-            d[k] = recursive_dict_update(d.get(k, {}), cast(Dict, v))
+            d[k] = recursive_dict_update(d.get(k, {}), cast(dict, v))
         else:
             d[k] = v
     return d
@@ -417,13 +408,11 @@ def __sub__(self: T, other: T) -> Any:
 def find_diff_of_numbers(
     stat1: int | float | np.float64 | np.int64 | None,
     stat2: int | float | np.float64 | np.int64 | None,
-) -> Any:
-    ...
+) -> Any: ...
 
 
 @overload
-def find_diff_of_numbers(stat1: T | None, stat2: T | None) -> Any:
-    ...
+def find_diff_of_numbers(stat1: T | None, stat2: T | None) -> Any: ...
 
 
 def find_diff_of_numbers(stat1, stat2):
diff --git a/dataprofiler/profilers/text_column_profile.py b/dataprofiler/profilers/text_column_profile.py
index bea8dbd68..eb79643fd 100644
--- a/dataprofiler/profilers/text_column_profile.py
+++ b/dataprofiler/profilers/text_column_profile.py
@@ -1,4 +1,5 @@
 """Text profile analysis for individual col within structured profiling.."""
+
 from __future__ import annotations
 
 import itertools
diff --git a/dataprofiler/profilers/unstructured_labeler_profile.py b/dataprofiler/profilers/unstructured_labeler_profile.py
index 1c7b16c0f..22789c4e3 100644
--- a/dataprofiler/profilers/unstructured_labeler_profile.py
+++ b/dataprofiler/profilers/unstructured_labeler_profile.py
@@ -1,4 +1,5 @@
 """Profile analysis for applying labels within unstructured profiling."""
+
 from __future__ import annotations
 
 from collections import defaultdict
diff --git a/dataprofiler/profilers/unstructured_text_profile.py b/dataprofiler/profilers/unstructured_text_profile.py
index 96b7d0625..3f1b6dd7f 100644
--- a/dataprofiler/profilers/unstructured_text_profile.py
+++ b/dataprofiler/profilers/unstructured_text_profile.py
@@ -1,4 +1,5 @@
 """For profiling unstructured text data."""
+
 from __future__ import annotations
 
 import itertools
diff --git a/dataprofiler/reports/graphs.py b/dataprofiler/reports/graphs.py
index 1f0b43018..8c0e16133 100644
--- a/dataprofiler/reports/graphs.py
+++ b/dataprofiler/reports/graphs.py
@@ -1,10 +1,11 @@
 """Contains functions for generating graph data report."""
+
 # !/usr/bin/env python3
 from __future__ import annotations
 
 import math
 import warnings
-from typing import TYPE_CHECKING, List, Union, cast
+from typing import TYPE_CHECKING, Union, cast
 
 if TYPE_CHECKING:
     from ..profilers.float_column_profile import FloatColumn
@@ -17,6 +18,7 @@
     import matplotlib.patches
     import matplotlib.pyplot as plt
     import seaborn as sns
+    from matplotlib.pyplot.figure import Figure, SubFigure
 except ImportError:
     # don't require if using graphs will below recommend to install if not
     # installed
@@ -32,7 +34,7 @@ def plot_histograms(
     profiler: StructuredProfiler,
     column_names: list[int | str] | None = None,
     column_inds: list[int] | None = None,
-) -> matplotlib.pyplot.figure:
+) -> Figure | SubFigure | None:
     """
     Plot the histograms of column names that are int or float columns.
 
@@ -72,7 +74,7 @@ def plot_histograms(
     if not column_names and not column_inds:
         inds_to_graph = list(range(len(profile_list)))
     elif not column_inds:
-        for column in cast(List[Union[str, int]], column_names):
+        for column in cast(list[Union[str, int]], column_names):
             col = column
             if isinstance(col, str):
                 col = col.lower()
@@ -108,7 +110,7 @@ def is_index_graphable_column(ind_to_graph: int) -> bool:
             "No plots were constructed"
             " because no int or float columns were found in columns"
         )
-        return
+        return None
 
     # get proper tile format for graph
     n = len(inds_to_graph)
@@ -153,7 +155,7 @@ def plot_col_histogram(
     data_type_profiler: IntColumn | FloatColumn,
     ax: matplotlib.axes.Axes | None = None,
     title: str | None = None,
-) -> matplotlib.axes.Axes:
+) -> matplotlib.axes.Axes | None:
     """
     Take input of a Int or Float Column and plot the histogram.
 
@@ -179,10 +181,11 @@ def plot_col_histogram(
         ax=ax,
     )
 
-    ax.set(xlabel="bins")
-    if title is None:
-        title = str(data_type_profiler.name)
-    ax.set_title(title)
+    if ax:
+        ax.set(xlabel="bins")
+        if title is None:
+            title = str(data_type_profiler.name)
+        ax.set_title(title)
     return ax
 
 
@@ -191,7 +194,7 @@ def plot_missing_values_matrix(
     profiler: StructuredProfiler,
     ax: matplotlib.axes.Axes | None = None,
     title: str | None = None,
-) -> matplotlib.pyplot.figure:
+) -> Figure | SubFigure | None:
     """
     Generate matrix of bar graphs for missing value locations in cols of struct dataset.
 
@@ -215,7 +218,7 @@ def plot_col_missing_values(
     col_profiler_list: list[StructuredColProfiler],
     ax: matplotlib.axes.Axes | None = None,
     title: str | None = None,
-) -> matplotlib.pyplot.figure:
+) -> Figure | SubFigure | None:
     """
     Generate bar graph of missing value locations within a col.
 
@@ -243,7 +246,7 @@ def plot_col_missing_values(
         warnings.warn(
             "There was no data in the profiles to plot missing " "column values."
         )
-        return
+        return None
 
     # bar width settings and height settings for each null value
     # width = 1, height = 1 would be no gaps
@@ -262,7 +265,8 @@ def plot_col_missing_values(
         ax = fig.add_subplot(111)
         is_own_fig = True
     # in case user passed their own axes
-    fig = ax.figure
+    else:
+        fig = cast(Figure, ax.figure)
 
     # loop through eac column plotting their null values
     for col_id, col_profiler in enumerate(col_profiler_list):
diff --git a/dataprofiler/reports/utils.py b/dataprofiler/reports/utils.py
index a10b8fe59..a578f9c29 100644
--- a/dataprofiler/reports/utils.py
+++ b/dataprofiler/reports/utils.py
@@ -1,7 +1,8 @@
 """Contains functions for checking for installations/dependencies."""
+
 import sys
 import warnings
-from typing import Any, Callable, List, TypeVar, cast
+from typing import Any, Callable, TypeVar, cast
 
 # Generic type for the return of the function "require_module()"
 F = TypeVar("F", bound=Callable[..., Any])
@@ -25,7 +26,7 @@ def warn_missing_module(graph_func: str, module_name: str) -> None:
     warnings.warn(warning_msg, RuntimeWarning, stacklevel=3)
 
 
-def require_module(names: List[str]) -> Callable[[F], F]:
+def require_module(names: list[str]) -> Callable[[F], F]:
     """
     Check if a set of modules exists in sys.modules prior to running function.
 
diff --git a/dataprofiler/rng_utils.py b/dataprofiler/rng_utils.py
index 329066658..2fd14f0f7 100644
--- a/dataprofiler/rng_utils.py
+++ b/dataprofiler/rng_utils.py
@@ -1,4 +1,5 @@
 """Create a random number generator using a manual seed DATAPROFILER_SEED."""
+
 import os
 import warnings
 
diff --git a/dataprofiler/settings.py b/dataprofiler/settings.py
index 1ba017f4e..a81c3477c 100644
--- a/dataprofiler/settings.py
+++ b/dataprofiler/settings.py
@@ -1,2 +1,3 @@
 """Configure settings for dataprofiler."""
+
 _seed = None
diff --git a/dataprofiler/tests/plugins/test_plugins.py b/dataprofiler/tests/plugins/test_plugins.py
index ec148a526..91677e5c1 100644
--- a/dataprofiler/tests/plugins/test_plugins.py
+++ b/dataprofiler/tests/plugins/test_plugins.py
@@ -2,7 +2,7 @@
 from collections import defaultdict
 from unittest import mock
 
-from dataprofiler.plugins.__init__ import get_plugins, load_plugins
+from dataprofiler.plugins import get_plugins, load_plugins
 from dataprofiler.plugins.decorators import plugin_decorator, plugins_dict
 
 
@@ -24,12 +24,12 @@ def test_plugin():
             test_get_dict = get_plugins("test")
             self.assertDictEqual({"mock_test": test_plugin}, test_get_dict)
 
-    @mock.patch("dataprofiler.plugins.__init__.importlib.util")
-    @mock.patch("dataprofiler.plugins.__init__.os.path.isdir")
-    @mock.patch("dataprofiler.plugins.__init__.os.listdir")
+    @mock.patch("dataprofiler.plugins.importlib.util")
+    @mock.patch("dataprofiler.plugins.os.path.isdir")
+    @mock.patch("dataprofiler.plugins.os.listdir")
     def test_load_plugin(self, mock_listdir, mock_isdir, mock_importlib_util):
-        mock_listdir.side_effect = (
-            lambda folder_dir: ["__pycache__", "py"]
+        mock_listdir.side_effect = lambda folder_dir: (
+            ["__pycache__", "py"]
             if folder_dir.endswith("plugins")
             else ["stillnotrealpy", "a.json", None]
         )
@@ -38,10 +38,8 @@ def test_load_plugin(self, mock_listdir, mock_isdir, mock_importlib_util):
         load_plugins()
         mock_importlib_util.spec_from_file_location.assert_not_called()
 
-        mock_listdir.side_effect = (
-            lambda folder_dir: ["folder"]
-            if folder_dir.endswith("plugins")
-            else ["file.py"]
+        mock_listdir.side_effect = lambda folder_dir: (
+            ["folder"] if folder_dir.endswith("plugins") else ["file.py"]
         )
         mock_spec = mock.Mock()
         mock_importlib_util.spec_from_file_location.return_value = mock_spec
diff --git a/dataprofiler/tests/profilers/test_profile_builder.py b/dataprofiler/tests/profilers/test_profile_builder.py
index c4e604737..f9bbf14ae 100644
--- a/dataprofiler/tests/profilers/test_profile_builder.py
+++ b/dataprofiler/tests/profilers/test_profile_builder.py
@@ -88,6 +88,12 @@ def setUpClass(cls):
                 cls.aws_dataset, len(cls.aws_dataset), options=profiler_options
             )
 
+    @classmethod
+    def tearDownClass(cls):
+        from dataprofiler import dp_logging
+
+        dp_logging.set_verbosity(logging.INFO)
+
     @mock.patch(
         "dataprofiler.profilers.profile_builder.ColumnPrimitiveTypeProfileCompiler"
     )
diff --git a/dataprofiler/tests/profilers/test_profiler_utils.py b/dataprofiler/tests/profilers/test_profiler_utils.py
index 4eee1963a..0ea0c0fc0 100644
--- a/dataprofiler/tests/profilers/test_profiler_utils.py
+++ b/dataprofiler/tests/profilers/test_profiler_utils.py
@@ -472,7 +472,6 @@ def test_odd_merge_profile_list(self, mock_data_labeler, *mocks):
 
 
 class TestAutoMultiProcessToggle(unittest.TestCase):
-
     """
     Validate profile_utils.auto_multiprocess_toggle is properly working.
     """
diff --git a/dataprofiler/tests/space_time_analysis/dataset_generation.py b/dataprofiler/tests/space_time_analysis/dataset_generation.py
index a983921da..6b23de32d 100644
--- a/dataprofiler/tests/space_time_analysis/dataset_generation.py
+++ b/dataprofiler/tests/space_time_analysis/dataset_generation.py
@@ -1,7 +1,7 @@
 import copy
 import json
 import string
-from typing import List, Optional
+from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -55,7 +55,7 @@ def convert_data_to_df(
     np_data: np.array,
     path: Optional[str] = None,
     index: bool = False,
-    column_names: Optional[List[str]] = None,
+    column_names: Optional[list[str]] = None,
 ) -> pd.DataFrame:
     """
     Converts np array to a pandas dataframe
@@ -129,7 +129,7 @@ def random_floats(
 
 def random_string(
     rng: Generator,
-    chars: Optional[List[str]] = None,
+    chars: Optional[list[str]] = None,
     num_rows: int = 1,
     str_len_min: int = 1,
     str_len_max: int = 256,
@@ -244,7 +244,7 @@ def random_datetimes(
 
 
 def random_categorical(
-    rng: Generator, categories: Optional[List[str]] = None, num_rows: int = 1
+    rng: Generator, categories: Optional[list[str]] = None, num_rows: int = 1
 ) -> np.array:
     """
     Randomly generates an array of categorical chosen out of categories
@@ -311,7 +311,7 @@ def random_text(
 
 def generate_dataset_by_class(
     rng: Generator,
-    columns_to_generate: Optional[List[dict]] = None,
+    columns_to_generate: Optional[list[dict]] = None,
     dataset_length: int = 100000,
     path: Optional[str] = None,
 ) -> pd.DataFrame:
diff --git a/dataprofiler/tests/space_time_analysis/structured_space_time_analysis.py b/dataprofiler/tests/space_time_analysis/structured_space_time_analysis.py
index df57854f6..51207413c 100644
--- a/dataprofiler/tests/space_time_analysis/structured_space_time_analysis.py
+++ b/dataprofiler/tests/space_time_analysis/structured_space_time_analysis.py
@@ -1,10 +1,11 @@
 """Contains space and time analysis tests for the Dataprofiler"""
+
 import json
 import os
 import random
 import time
 from collections import defaultdict
-from typing import Dict, List, Optional
+from typing import Optional
 
 import memray
 import numpy as np
@@ -32,7 +33,7 @@
 def dp_profile_space_analysis(
     data: pd.DataFrame,
     path: str,
-    options: Optional[Dict] = None,
+    options: Optional[dict] = None,
 ) -> StructuredProfiler:
     """
     Generate memray bin file of the space analysis of dp.Profiler function
@@ -68,12 +69,12 @@ def dp_merge_space_analysis(profile: StructuredProfiler, path: str):
 
 def dp_space_time_analysis(
     rng: Generator,
-    sample_sizes: List,
+    sample_sizes: list,
     data: pd.DataFrame,
     path: str = "./time_analysis/structured_profiler_times.json",
     percent_to_nan: float = 0.0,
     allow_subsampling: bool = True,
-    options: Optional[Dict] = None,
+    options: Optional[dict] = None,
     space_analysis=True,
     time_analysis=True,
 ):
diff --git a/dataprofiler/tests/test_data_profiler.py b/dataprofiler/tests/test_data_profiler.py
index 9ebdfa039..54a5f2d82 100644
--- a/dataprofiler/tests/test_data_profiler.py
+++ b/dataprofiler/tests/test_data_profiler.py
@@ -18,6 +18,7 @@
 class TestDataProfiler(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
+        import dataprofiler as dp
 
         test_dir = os.path.join(MODULE_PATH, "data")
         cls.input_file_names = [
@@ -25,6 +26,7 @@ def setUpClass(cls):
                 path=os.path.join(test_dir, "csv/aws_honeypot_marx_geo.csv"), type="csv"
             ),
         ]
+        dp.settings._seed = None
 
     def test_set_seed(self):
         import dataprofiler as dp
diff --git a/dataprofiler/tests/test_rng_utils.py b/dataprofiler/tests/test_rng_utils.py
index 6ee2ed35c..8a4c4d229 100644
--- a/dataprofiler/tests/test_rng_utils.py
+++ b/dataprofiler/tests/test_rng_utils.py
@@ -1,4 +1,5 @@
 """Validates that generator intakes DATAPROFILER_SEED properly."""
+
 import os
 import unittest
 import unittest.mock
diff --git a/dataprofiler/version.py b/dataprofiler/version.py
index 1136efae1..b4e4c2b9c 100644
--- a/dataprofiler/version.py
+++ b/dataprofiler/version.py
@@ -1,8 +1,8 @@
 """File contains the version number for the package."""
 
 MAJOR = 0
-MINOR = 11
-MICRO = 0
+MINOR = 13
+MICRO = 1
 POST = None  # otherwise None
 
 VERSION = "%d.%d.%d" % (MAJOR, MINOR, MICRO)
diff --git a/resources/__init__.py b/resources/__init__.py
index dd86bffe7..b1d2822d9 100644
--- a/resources/__init__.py
+++ b/resources/__init__.py
@@ -1,2 +1,3 @@
 """Contains resources for labelers."""
+
 # empty init
diff --git a/setup.cfg b/setup.cfg
index dd0e2235f..6ec3e8a61 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -15,6 +15,9 @@ line_length=88
 warn_return_any = True
 warn_unused_configs = True
 ignore_missing_imports = True
+no_implicit_optional = False
+exclude = ^dataprofiler/tests/|^resources/|^examples|venv*/
+disable_error_code = override
 
 [check-manifest]
 ignore-default-rules=True
diff --git a/setup.py b/setup.py
index f8b5eaf8e..eeca6629b 100644
--- a/setup.py
+++ b/setup.py
@@ -54,7 +54,7 @@
 setup(
     name="DataProfiler",
     version=__version__,
-    python_requires=">=3.8",
+    python_requires=">=3.9",
     description=DESCRIPTION,
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",
diff --git a/tox.ini b/tox.ini
index 90d06af06..4ee6081bd 100644
--- a/tox.ini
+++ b/tox.ini
@@ -16,7 +16,7 @@ deps =
     -rrequirements-reports.txt
     -rrequirements-test.txt
 commands =
-    python3 -m pytest dataprofiler/tests/ --cov=dataprofiler --cov-fail-under=80 --cov-report=xml:dist/coverage.xml --forked
+    python3 -m pytest dataprofiler/tests/ --cov=dataprofiler --cov-fail-under=80 --cov-report=xml:coverage.xml --forked
 
 # add "docs" to `envlist` to run the docs build
 #[testenv:docs]