From 24162bbbb9a31d611add3bb3ceb0f5df30d6ebdd Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Sun, 9 Mar 2025 20:06:25 +0000
Subject: [PATCH 1/7] Remove ydata-profiling  library dependency

---
 poetry.lock                                   | 547 ++++--------------
 pyproject.toml                                |   1 -
 .../data_validation/DatasetDescription.py     |  25 +-
 validmind/tests/data_validation/Skewness.py   |  13 +-
 validmind/utils.py                            |  21 +
 5 files changed, 153 insertions(+), 454 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index b7757c1ba..c226d7233 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.0 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -214,13 +214,13 @@ trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "anywidget"
-version = "0.9.13"
+version = "0.9.15"
 description = "custom jupyter widgets made easy"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "anywidget-0.9.13-py3-none-any.whl", hash = "sha256:43d1658f1043b8c95cd350b2f5deccb123fd37810a36f656d6163aefe8163705"},
-    {file = "anywidget-0.9.13.tar.gz", hash = "sha256:c655455bf51f82182eb23c5947d37cc41f0b1ffacaf7e2b763147a2332cb3f07"},
+    {file = "anywidget-0.9.15-py3-none-any.whl", hash = "sha256:fd7876332e47f380e0428f552f26b7227f5694d4e0a257bbc23354d9b9e9a73c"},
+    {file = "anywidget-0.9.15.tar.gz", hash = "sha256:1891c11897aaf7cff8809f996413f618f97d786b6097f7e46266423969a726a0"},
 ]
 
 [package.dependencies]
@@ -229,8 +229,7 @@ psygnal = ">=0.8.1"
 typing-extensions = ">=4.2.0"
 
 [package.extras]
-dev = ["comm (>=0.1.0)", "watchfiles (>=0.18.0)"]
-test = ["ipython (<8.13)", "msgspec", "mypy (==1.10.0)", "pydantic", "pytest", "pytest-cov", "ruff"]
+dev = ["watchfiles (>=0.18.0)"]
 
 [[package]]
 name = "appdirs"
@@ -1201,20 +1200,6 @@ files = [
     {file = "Cython-0.29.37.tar.gz", hash = "sha256:f813d4a6dd94adee5d4ff266191d1d95bf6d4164a4facc535422c021b2504cfb"},
 ]
 
-[[package]]
-name = "dacite"
-version = "1.9.2"
-description = "Simple creation of data classes from dictionaries."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "dacite-1.9.2-py3-none-any.whl", hash = "sha256:053f7c3f5128ca2e9aceb66892b1a3c8936d02c686e707bee96e19deef4bc4a0"},
-    {file = "dacite-1.9.2.tar.gz", hash = "sha256:6ccc3b299727c7aa17582f0021f6ae14d5de47c7227932c47fec4cdfefd26f09"},
-]
-
-[package.extras]
-dev = ["black", "coveralls", "mypy", "pre-commit", "pylint", "pytest (>=5)", "pytest-benchmark", "pytest-cov"]
-
 [[package]]
 name = "dataclasses-json"
 version = "0.6.7"
@@ -1276,48 +1261,48 @@ vision = ["Pillow (>=9.4.0)"]
 
 [[package]]
 name = "debugpy"
-version = "1.8.12"
+version = "1.8.13"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "debugpy-1.8.13-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32b6857f8263a969ce2ca098f228e5cc0604d277447ec05911a8c46cf3e7e307"},
+    {file = "debugpy-1.8.13-cp39-cp39-win32.whl", hash = "sha256:f14d2c4efa1809da125ca62df41050d9c7cd9cb9e380a2685d1e453c4d450ccb"},
+    {file = "debugpy-1.8.13-cp39-cp39-win_amd64.whl", hash = "sha256:ea869fe405880327497e6945c09365922c79d2a1eed4c3ae04d77ac7ae34b2b5"},
+    {file = "debugpy-1.8.13-py2.py3-none-any.whl", hash = "sha256:d4ba115cdd0e3a70942bd562adba9ec8c651fe69ddde2298a1be296fc331906f"},
+    {file = "debugpy-1.8.13.tar.gz", hash = "sha256:837e7bef95bdefba426ae38b9a94821ebdc5bea55627879cd48165c90b9e50ce"},
 ]
 
 [[package]]
 name = "decorator"
-version = "5.1.1"
+version = "5.2.1"
 description = "Decorators for Humans"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
-    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
-    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+    {file = "decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a"},
+    {file = "decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360"},
 ]
 
 [[package]]
@@ -1894,16 +1879,6 @@ files = [
     {file = "html2text-2024.2.26.tar.gz", hash = "sha256:05f8e367d15aaabc96415376776cdd11afd5127a77fce6e36afc60c563ca2c32"},
 ]
 
-[[package]]
-name = "htmlmin"
-version = "0.1.12"
-description = "An HTML Minifier"
-optional = false
-python-versions = "*"
-files = [
-    {file = "htmlmin-0.1.12.tar.gz", hash = "sha256:50c1ef4630374a5d723900096a961cff426dff46b48f34d194a81bbe14eca178"},
-]
-
 [[package]]
 name = "httpcore"
 version = "1.0.7"
@@ -1951,13 +1926,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.29.1"
+version = "0.29.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.29.1-py3-none-any.whl", hash = "sha256:352f69caf16566c7b6de84b54a822f6238e17ddd8ae3da4f8f2272aea5b198d5"},
-    {file = "huggingface_hub-0.29.1.tar.gz", hash = "sha256:9524eae42077b8ff4fc459ceb7a514eca1c1232b775276b009709fe2a084f250"},
+    {file = "huggingface_hub-0.29.2-py3-none-any.whl", hash = "sha256:c56f20fca09ef19da84dcde2b76379ecdaddf390b083f59f166715584953307d"},
+    {file = "huggingface_hub-0.29.2.tar.gz", hash = "sha256:590b29c0dcbd0ee4b7b023714dc1ad8563fe4a68a91463438b74e980d28afaf3"},
 ]
 
 [package.dependencies]
@@ -2011,23 +1986,6 @@ files = [
 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
-[[package]]
-name = "imagehash"
-version = "4.3.1"
-description = "Image Hashing library"
-optional = false
-python-versions = "*"
-files = [
-    {file = "ImageHash-4.3.1-py2.py3-none-any.whl", hash = "sha256:5ad9a5cde14fe255745a8245677293ac0d67f09c330986a351f34b614ba62fb5"},
-    {file = "ImageHash-4.3.1.tar.gz", hash = "sha256:7038d1b7f9e0585beb3dd8c0a956f02b95a346c0b5f24a9e8cc03ebadaf0aa70"},
-]
-
-[package.dependencies]
-numpy = "*"
-pillow = "*"
-PyWavelets = "*"
-scipy = "*"
-
 [[package]]
 name = "imagesize"
 version = "1.4.1"
@@ -2284,28 +2242,28 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jeepney"
-version = "0.8.0"
+version = "0.9.0"
 description = "Low-level, pure Python DBus protocol wrapper."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jeepney-0.8.0-py3-none-any.whl", hash = "sha256:c0a454ad016ca575060802ee4d590dd912e35c122fa04e70306de3d076cce755"},
-    {file = "jeepney-0.8.0.tar.gz", hash = "sha256:5efe48d255973902f6badc3ce55e2aa6c5c3b3bc642059ef3a91247bcfcc5806"},
+    {file = "jeepney-0.9.0-py3-none-any.whl", hash = "sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683"},
+    {file = "jeepney-0.9.0.tar.gz", hash = "sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732"},
 ]
 
 [package.extras]
 test = ["async-timeout", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"]
-trio = ["async_generator", "trio"]
+trio = ["trio"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.5"
+version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
-    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
+    {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
+    {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
 ]
 
 [package.dependencies]
@@ -3455,17 +3413,6 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
-[[package]]
-name = "multimethod"
-version = "1.10"
-description = "Multiple argument dispatching."
-optional = false
-python-versions = ">=3.8"
-files = [
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 polars = ["polars (>=0.20.3)"]
 pyarrow = ["pyarrow (>=11.0.0)"]
 pyspark = ["pyspark (>=3.5.0)"]
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 [[package]]
 name = "nbclient"
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 [[package]]
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 [[package]]
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+version = "1.65.5"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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 [package.dependencies]
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 [[package]]
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 version = "0.7.5"
@@ -5250,20 +5158,20 @@ cli = ["click (>=5.0)"]
 
 [[package]]
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 urllib3 = ">=1.26.0"
 
-[[package]]
-name = "typeguard"
-version = "4.4.0"
-description = "Run-time type checker for Python"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "typeguard-4.4.0.tar.gz", hash = "sha256:463bd8697a65a4aa576a63767c369b1ecfba8a5ba735edfe3223127b6ecfa28c"},
-]
-
-[package.dependencies]
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
-typing-extensions = ">=4.10.0"
-
-[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.3.0)"]
-test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
-
 [[package]]
 name = "types-python-dateutil"
 version = "2.9.0.20241206"
@@ -7401,13 +7251,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.29.2"
+version = "20.29.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -7419,34 +7269,6 @@ platformdirs = ">=3.9.1,<5"
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-[[package]]
-name = "visions"
-version = "0.7.6"
-description = "Visions"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-multimethod = ">=1.4"
-networkx = ">=2.4"
-numpy = ">=1.23.2"
-pandas = ">=2.0.0"
-Pillow = {version = "*", optional = true, markers = "extra == \"type_image_path\""}
-
-[package.extras]
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-dev = ["IPython", "Sphinx-copybutton", "black (>=20.8b1)", "isort (>=5.0.9)", "mypy (>=0.770)", "nbsphinx", "recommonmark (>=0.6.0)", "setuptools (>=46.1.3)", "sphinx-autodoc-typehints (>=1.10.3)", "sphinx-rtd-theme (>=0.4.3)", "wheel (>=0.34.2)"]
-plotting = ["matplotlib", "pydot", "pygraphviz"]
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-type-geometry = ["shapely"]
-type-image-path = ["Pillow", "imagehash"]
-
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -7525,92 +7347,6 @@ files = [
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-    {file = "wordcloud-1.9.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e137493365770f59655c7308ff76addc95ada2c6bd50ac119e4c33091e2e4e08"},
-    {file = "wordcloud-1.9.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:665f8e7de3dcc1e43aa5bdd9560d56ed51026ba638a33472eede2b9051108adb"},
-    {file = "wordcloud-1.9.4.tar.gz", hash = "sha256:b273d8a5ded97d3ead904046b49464dcb71119ee79df875072a4c105cadd347a"},
-]
-
-[package.dependencies]
-matplotlib = "*"
-numpy = ">=1.6.1"
-pillow = "*"
-
 [[package]]
 name = "xgboost"
 version = "2.1.4"
@@ -7900,43 +7636,6 @@ idna = ">=2.0"
 multidict = ">=4.0"
 propcache = ">=0.2.0"
 
-[[package]]
-name = "ydata-profiling"
-version = "4.12.2"
-description = "Generate profile report for pandas DataFrame"
-optional = false
-python-versions = "<3.13,>=3.7"
-files = [
-    {file = "ydata-profiling-4.12.2.tar.gz", hash = "sha256:af9a6dcadf1a8cc73aa9e59c1d1f5f02c798bf5510817bf33e735ebf7c838c02"},
-    {file = "ydata_profiling-4.12.2-py2.py3-none-any.whl", hash = "sha256:b3d2df646a9694dab221d52548ca4d5af0462d5f05996e215fdee35f1e45b391"},
-]
-
-[package.dependencies]
-dacite = ">=1.8"
-htmlmin = "0.1.12"
-imagehash = "4.3.1"
-jinja2 = ">=2.11.1,<3.2"
-matplotlib = ">=3.5"
-multimethod = ">=1.4,<2"
-numpy = ">=1.16.0,<2.2"
-pandas = ">1.1,<1.4.0 || >1.4.0,<3"
-phik = ">=0.11.1,<0.13"
-pydantic = ">=2"
-PyYAML = ">=5.0.0,<6.1"
-requests = ">=2.24.0,<3"
-scipy = ">=1.4.1,<1.16"
-seaborn = ">=0.10.1,<0.14"
-statsmodels = ">=0.13.2,<1"
-tqdm = ">=4.48.2,<5"
-typeguard = ">=3,<5"
-visions = {version = ">=0.7.5,<0.8.0", extras = ["type-image-path"]}
-wordcloud = ">=1.9.3"
-
-[package.extras]
-notebook = ["ipywidgets (>=7.5.1)", "jupyter (>=1.0.0)"]
-numba = ["numba (>=0.56.0,<1)"]
-unicode = ["tangled-up-in-unicode (==0.2.0)"]
-
 [[package]]
 name = "yfinance"
 version = "0.2.54"
@@ -7991,4 +7690,4 @@ pytorch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<3.12"
-content-hash = "f5ba5f0244769e7e6e75d1f82a33b6267b57ec6d456a13f1da1b3a3399eff3f5"
+content-hash = "9411d6ae4599ee60805e84a2dc96b933955bd69dc88c9415184a400b9032c364"
diff --git a/pyproject.toml b/pyproject.toml
index 97133b421..db442b0be 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,7 +56,6 @@ torch = {version = ">=1.10.0", optional = true}
 tqdm = "*"
 transformers = {version = "^4.32.0", optional = true}
 xgboost = ">=1.5.2,<3"
-ydata-profiling = "*"
 yfinance = "^0.2.48"
 
 [tool.poetry.group.dev.dependencies]
diff --git a/validmind/tests/data_validation/DatasetDescription.py b/validmind/tests/data_validation/DatasetDescription.py
index f8f3d0699..64fe81db7 100644
--- a/validmind/tests/data_validation/DatasetDescription.py
+++ b/validmind/tests/data_validation/DatasetDescription.py
@@ -6,12 +6,10 @@
 from collections import Counter
 
 import numpy as np
-from ydata_profiling.config import Settings
-from ydata_profiling.model.typeset import ProfilingTypeSet
 
 from validmind import RawData, tags, tasks
-from validmind.errors import UnsupportedColumnTypeError
 from validmind.logging import get_logger
+from validmind.utils import infer_datatypes
 from validmind.vm_models import VMDataset
 
 DEFAULT_HISTOGRAM_BINS = 10
@@ -20,25 +18,6 @@
 logger = get_logger(__name__)
 
 
-def infer_datatypes(df):
-    column_type_mappings = {}
-    typeset = ProfilingTypeSet(Settings())
-    variable_types = typeset.infer_type(df)
-
-    for column, type in variable_types.items():
-        if str(type) == "Unsupported":
-            if df[column].isnull().all():
-                column_type_mappings[column] = {"id": column, "type": "Null"}
-            else:
-                raise UnsupportedColumnTypeError(
-                    f"Unsupported type for column {column}. Please review all values in this dataset column."
-                )
-        else:
-            column_type_mappings[column] = {"id": column, "type": str(type)}
-
-    return list(column_type_mappings.values())
-
-
 def get_numerical_histograms(df, column):
     """
     Returns a collection of histograms for a numerical column, each one
@@ -50,7 +29,7 @@ def get_numerical_histograms(df, column):
     # bins='sturges'. Cannot use 'auto' until we review and fix its performance
     #  on datasets with too many unique values
     #
-    # 'sturges': R’s default method, only accounts for data size. Only optimal
+    # 'sturges': R's default method, only accounts for data size. Only optimal
     # for gaussian data and underestimates number of bins for large non-gaussian datasets.
     default_hist = np.histogram(values_cleaned, bins="sturges")
 
diff --git a/validmind/tests/data_validation/Skewness.py b/validmind/tests/data_validation/Skewness.py
index c472159fd..2c7550f75 100644
--- a/validmind/tests/data_validation/Skewness.py
+++ b/validmind/tests/data_validation/Skewness.py
@@ -2,10 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from ydata_profiling.config import Settings
-from ydata_profiling.model.typeset import ProfilingTypeSet
-
 from validmind import tags, tasks
+from validmind.utils import infer_datatypes
 
 
 @tags("data_quality", "tabular_data")
@@ -49,8 +47,11 @@ def Skewness(dataset, max_threshold=1):
     - Subjective threshold for risk grading, requiring expert input and recurrent iterations for refinement.
     """
 
-    typeset = ProfilingTypeSet(Settings())
-    dataset_types = typeset.infer_type(dataset.df)
+    # Use the imported infer_datatypes function
+    dataset_types = infer_datatypes(dataset.df)
+
+    # Convert the list of dictionaries to a dictionary for easy access
+    dataset_types_dict = {item["id"]: item["type"] for item in dataset_types}
 
     skewness = dataset.df.skew(numeric_only=True)
 
@@ -58,7 +59,7 @@ def Skewness(dataset, max_threshold=1):
     passed = True
 
     for col in skewness.index:
-        if str(dataset_types[col]) != "Numeric":
+        if dataset_types_dict.get(col) != "Numeric":
             continue
 
         col_skewness = skewness[col]
diff --git a/validmind/utils.py b/validmind/utils.py
index 4a9c07b46..7c4a80b6c 100644
--- a/validmind/utils.py
+++ b/validmind/utils.py
@@ -601,3 +601,24 @@ def serialize(obj):
     elif isinstance(obj, (pd.DataFrame, pd.Series)):
         return ""  # Simple empty string for non-serializable objects
     return obj
+
+
+def infer_datatypes(df):
+    column_type_mappings = {}
+    # Use pandas to infer data types
+    for column in df.columns:
+        dtype = df[column].dtype
+        if pd.api.types.is_numeric_dtype(dtype):
+            column_type_mappings[column] = {"id": column, "type": "Numeric"}
+        elif pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(
+            dtype
+        ):
+            column_type_mappings[column] = {"id": column, "type": "Categorical"}
+        elif pd.api.types.is_bool_dtype(dtype):
+            column_type_mappings[column] = {"id": column, "type": "Boolean"}
+        elif pd.api.types.is_datetime64_any_dtype(dtype):
+            column_type_mappings[column] = {"id": column, "type": "Datetime"}
+        else:
+            column_type_mappings[column] = {"id": column, "type": "Unsupported"}
+
+    return list(column_type_mappings.values())

From f688339b3ab52cc72325b6cec4ad1250cd5a2030 Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Sun, 9 Mar 2025 20:47:39 +0000
Subject: [PATCH 2/7] poetry lock update

---
 poetry.lock | 78 +++--------------------------------------------------
 1 file changed, 4 insertions(+), 74 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 3b53137cd..7a8719eba 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3459,35 +3459,6 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
-[[package]]
-name = "narwhals"
-version = "1.29.1"
-description = "Extremely lightweight compatibility layer between dataframe libraries"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "narwhals-1.29.1-py3-none-any.whl", hash = "sha256:2f68cfbb2562672c4dfa54f158ed8c2828e9920ef784981cd9114e419c444216"},
-    {file = "narwhals-1.29.1.tar.gz", hash = "sha256:c408acf09e90c116f247cf34f24a3a89d147e3e235b1d3c708cfd1960baf320a"},
-]
-
-[package.extras]
-core = ["duckdb", "pandas", "polars", "pyarrow", "pyarrow-stubs"]
-cudf = ["cudf (>=24.10.0)"]
-dask = ["dask[dataframe] (>=2024.8)"]
-dev = ["covdefaults", "hypothesis", "mypy (>=1.15.0,<1.16.0)", "pandas-stubs", "pre-commit", "pyright", "pytest", "pytest-cov", "pytest-env", "pytest-randomly", "typing-extensions"]
-docs = ["black", "duckdb", "jinja2", "markdown-exec[ansi]", "mkdocs", "mkdocs-autorefs", "mkdocs-material", "mkdocstrings-python (>=1.16)", "mkdocstrings[python]", "pandas", "polars (>=1.0.0)", "pyarrow"]
-duckdb = ["duckdb (>=1.0)"]
-extra = ["scikit-learn"]
-ibis = ["ibis-framework (>=6.0.0)", "packaging", "pyarrow-hotfix", "rich"]
-modin = ["modin"]
-pandas = ["pandas (>=0.25.3)"]
-polars = ["polars (>=0.20.3)"]
-pyarrow = ["pyarrow (>=11.0.0)"]
-pyspark = ["pyspark (>=3.5.0)"]
-sqlframe = ["sqlframe (>=3.22.0)"]
-tests = ["covdefaults", "hypothesis", "pytest", "pytest-cov", "pytest-env", "pytest-randomly", "typing-extensions"]
-typing = ["mypy (>=1.15.0,<1.16.0)", "pandas-stubs", "pyright", "typing-extensions"]
-
 [[package]]
 name = "nbclient"
 version = "0.10.1"
@@ -3934,13 +3905,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.65.4"
+version = "1.65.5"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "openai-1.65.4-py3-none-any.whl", hash = "sha256:15566d46574b94eae3d18efc2f9a4ebd1366d1d44bfc1bdafeea7a5cf8271bcb"},
-    {file = "openai-1.65.4.tar.gz", hash = "sha256:0b08c58625d556f5c6654701af1023689c173eb0989ce8f73c7fd0eb22203c76"},
+    {file = "openai-1.65.5-py3-none-any.whl", hash = "sha256:5948a504e7b4003d921cfab81273813793a31c25b1d7b605797c01757e0141f1"},
+    {file = "openai-1.65.5.tar.gz", hash = "sha256:17d39096bbcaf6c86580244b493a59e16613460147f0ba5ab6e608cdb6628149"},
 ]
 
 [package.dependencies]
@@ -7633,47 +7604,6 @@ idna = ">=2.0"
 multidict = ">=4.0"
 propcache = ">=0.2.0"
 
-[[package]]
-name = "ydata-profiling"
-version = "4.13.0"
-description = "Generate profile report for pandas DataFrame"
-optional = false
-python-versions = "<3.13,>=3.7"
-files = [
-    {file = "ydata-profiling-4.13.0.tar.gz", hash = "sha256:07541bde9d93169f72f9616beae09312826e6a3f5b7e4d9df05e6edbf39d876c"},
-    {file = "ydata_profiling-4.13.0-py2.py3-none-any.whl", hash = "sha256:b6f027766bdcdd61f70694b7fa8c4ae3131e0e332aad1dc1797c49bb68c3c42a"},
-]
-
-[package.dependencies]
-dacite = ">=1.8"
-htmlmin = "0.1.12"
-imagehash = "4.3.1"
-jinja2 = ">=2.11.1,<3.2"
-matplotlib = ">=3.5,<=3.10"
-multimethod = ">=1.4,<2"
-numba = ">=0.56.0,<1"
-numpy = ">=1.16.0,<2.2"
-pandas = ">1.1,<1.4.0 || >1.4.0,<3.0"
-phik = ">=0.11.1,<0.13"
-pydantic = ">=2"
-PyYAML = ">=5.0.0,<6.1"
-requests = ">=2.24.0,<3"
-scipy = ">=1.4.1,<1.14"
-seaborn = ">=0.10.1,<0.14"
-statsmodels = ">=0.13.2,<1"
-tqdm = ">=4.48.2,<5"
-typeguard = ">=3,<5"
-visions = {version = ">=0.7.5,<0.7.7", extras = ["type-image-path"]}
-wordcloud = ">=1.9.3"
-
-[package.extras]
-dev = ["autodoc-pydantic", "black (>=20.8b1)", "isort (>=5.0.7)", "myst-parser (>=0.18.1)", "pre-commit (>=2.8.2)", "sphinx-autodoc-typehints (>=1.10.3)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=0.4.3)", "twine", "virtualenv (>=20.0.33)", "wheel"]
-docs = ["mike (>=2.1.1,<2.2.0)", "mkdocs (>=1.6.0,<1.7.0)", "mkdocs-badges", "mkdocs-material (>=9.0.12,<10.0.0)", "mkdocs-material-extensions (>=1.1.1,<2.0.0)", "mkdocs-table-reader-plugin (<=2.2.0)", "mkdocstrings[python] (>=0.20.0,<1.0.0)"]
-notebook = ["ipywidgets (>=7.5.1)", "jupyter (>=1.0.0)"]
-spark = ["numpy (>=1.16.0,<1.24)", "pandas (>1.1,!=1.4.0,<2)", "pyarrow (>=2.0.0)", "pyspark (>=2.3.0)", "visions[type-image-path] (==0.7.5)"]
-test = ["codecov", "coverage (>=6.5,<8)", "kaggle", "nbval", "pyarrow", "pytest", "pytest-cov", "pytest-spark", "twine (>=3.1.1)"]
-unicode = ["tangled-up-in-unicode (==0.2.0)"]
-
 [[package]]
 name = "yfinance"
 version = "0.2.54"
@@ -7728,4 +7658,4 @@ pytorch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<3.12"
-content-hash = "0765c86a78b5cf83a7a88a16e23f0c5e3a410de541ec12321e82db6f4321c675"
+content-hash = "3cbb25b087a59f1d06dc2ffa07c16b055f08bf1f66aa655cc9de475132da79b9"

From 46920f81ad62b4a642b019c3780bc4a72c427c8c Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Mon, 10 Mar 2025 11:06:58 +0000
Subject: [PATCH 3/7] update infer_datatypes function to infer Text type

---
 validmind/utils.py | 181 +++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 176 insertions(+), 5 deletions(-)

diff --git a/validmind/utils.py b/validmind/utils.py
index 7c4a80b6c..abc90cd4c 100644
--- a/validmind/utils.py
+++ b/validmind/utils.py
@@ -603,21 +603,192 @@ def serialize(obj):
     return obj
 
 
-def infer_datatypes(df):
+def is_text_column(series, threshold=0.05):
+    """
+    Determines if a series is likely to contain text data using heuristics.
+
+    Args:
+        series (pd.Series): The pandas Series to analyze
+        threshold (float): The minimum threshold to classify a pattern match as significant
+
+    Returns:
+        bool: True if the series likely contains text data, False otherwise
+    """
+    # Filter to non-null string values and sample if needed
+    string_series = series.dropna().astype(str)
+    if len(string_series) == 0:
+        return False
+    if len(string_series) > 1000:
+        string_series = string_series.sample(1000, random_state=42)
+
+    # Calculate basic metrics
+    total_values = len(string_series)
+    unique_ratio = len(string_series.unique()) / total_values if total_values > 0 else 0
+    avg_length = string_series.str.len().mean()
+    avg_words = string_series.str.split(r"\s+").str.len().mean()
+
+    # Check for special text patterns
+    patterns = {
+        "url": r"https?://\S+|www\.\S+",
+        "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
+        "filepath": r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+',
+    }
+
+    # Check if any special patterns exceed threshold
+    for pattern in patterns.values():
+        if string_series.str.contains(pattern, regex=True, na=False).mean() > threshold:
+            return True
+
+    # Calculate proportion of alphabetic characters
+    total_chars = string_series.str.len().sum()
+    if total_chars > 0:
+        alpha_ratio = string_series.str.count(r"[a-zA-Z]").sum() / total_chars
+    else:
+        alpha_ratio = 0
+
+    # Check for free-form text indicators
+    text_indicators = [
+        unique_ratio > 0.8 and avg_length > 20,  # High uniqueness and long strings
+        unique_ratio > 0.4
+        and avg_length > 15
+        and string_series.str.contains(r"[.,;:!?]", regex=True, na=False).mean()
+        > 0.3,  # Moderate uniqueness with punctuation
+        string_series.str.contains(
+            r"\b\w+\b\s+\b\w+\b\s+\b\w+\b\s+\b\w+\b", regex=True, na=False
+        ).mean()
+        > 0.3,  # Contains long phrases
+        avg_words > 5 and alpha_ratio > 0.6,  # Many words with mostly letters
+        unique_ratio > 0.95 and avg_length > 10,  # Very high uniqueness
+    ]
+
+    return any(text_indicators)
+
+
+def get_column_type_detail(df, column):
+    """
+    Get detailed column type information beyond basic type detection.
+    Similar to ydata-profiling's type system.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing the column
+        column (str): Column name to analyze
+
+    Returns:
+        dict: Detailed type information including primary type and subtype
+    """
+    series = df[column]
+    dtype = series.dtype
+
+    # Initialize result with basic type
+    result = {"id": column, "type": "Unknown"}
+
+    # Numeric types
+    if pd.api.types.is_numeric_dtype(dtype):
+        if pd.api.types.is_integer_dtype(dtype):
+            result["type"] = "Numeric"
+            result["subtype"] = "Integer"
+        elif pd.api.types.is_float_dtype(dtype):
+            result["type"] = "Numeric"
+            result["subtype"] = "Float"
+        else:
+            result["type"] = "Numeric"
+            result["subtype"] = "Other"
+
+    # Boolean type
+    elif pd.api.types.is_bool_dtype(dtype):
+        result["type"] = "Boolean"
+
+    # Datetime types
+    elif pd.api.types.is_datetime64_any_dtype(dtype):
+        result["type"] = "Datetime"
+
+    # String/object/categorical types need more analysis
+    elif pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(
+        dtype
+    ):
+        # Convert to strings for analysis
+        string_series = series.dropna().astype(str)
+
+        if len(string_series) == 0:
+            result["type"] = "Categorical"
+            return result
+
+        # Check for common patterns
+        url_pattern = r"https?://\S+|www\.\S+"
+        email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"
+        filepath_pattern = r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+'
+
+        url_ratio = string_series.str.contains(url_pattern, regex=True, na=False).mean()
+        email_ratio = string_series.str.contains(
+            email_pattern, regex=True, na=False
+        ).mean()
+        filepath_ratio = string_series.str.contains(
+            filepath_pattern, regex=True, na=False
+        ).mean()
+
+        # Check if general text using enhanced function
+        if url_ratio > 0.7:
+            result["type"] = "Text"
+            result["subtype"] = "URL"
+        elif email_ratio > 0.7:
+            result["type"] = "Text"
+            result["subtype"] = "Email"
+        elif filepath_ratio > 0.7:
+            result["type"] = "Text"
+            result["subtype"] = "Path"
+        elif is_text_column(series):
+            result["type"] = "Text"
+            result["subtype"] = "FreeText"
+        else:
+            result["type"] = "Categorical"
+
+            # Further analyze categorical data
+            n_unique = series.nunique()
+            if n_unique == 2:
+                result["subtype"] = "Binary"
+            else:
+                result["subtype"] = "Nominal"
+
+    return result
+
+
+def infer_datatypes(df, detailed=False):
+    """
+    Infer data types for columns in a DataFrame.
+
+    Args:
+        df (pd.DataFrame): DataFrame to analyze
+        detailed (bool): Whether to return detailed type information including subtypes
+
+    Returns:
+        list: Column type mappings
+    """
+    if detailed:
+        return [get_column_type_detail(df, column) for column in df.columns]
+
     column_type_mappings = {}
     # Use pandas to infer data types
     for column in df.columns:
+        # Check if all values are None
+        if df[column].isna().all():
+            column_type_mappings[column] = {"id": column, "type": "Null"}
+            continue
+
         dtype = df[column].dtype
         if pd.api.types.is_numeric_dtype(dtype):
             column_type_mappings[column] = {"id": column, "type": "Numeric"}
-        elif pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(
-            dtype
-        ):
-            column_type_mappings[column] = {"id": column, "type": "Categorical"}
         elif pd.api.types.is_bool_dtype(dtype):
             column_type_mappings[column] = {"id": column, "type": "Boolean"}
         elif pd.api.types.is_datetime64_any_dtype(dtype):
             column_type_mappings[column] = {"id": column, "type": "Datetime"}
+        elif pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(
+            dtype
+        ):
+            # Check if this is more likely to be text than categorical
+            if is_text_column(df[column]):
+                column_type_mappings[column] = {"id": column, "type": "Text"}
+            else:
+                column_type_mappings[column] = {"id": column, "type": "Categorical"}
         else:
             column_type_mappings[column] = {"id": column, "type": "Unsupported"}
 

From 7ba18f9ce80e4ab63bf6770f55b840ea850a309a Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Mon, 10 Mar 2025 11:07:17 +0000
Subject: [PATCH 4/7] update test

---
 .../data_validation/test_DatasetDescription.py  | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/tests/unit_tests/data_validation/test_DatasetDescription.py b/tests/unit_tests/data_validation/test_DatasetDescription.py
index 16b9f2b9a..dff7646ca 100644
--- a/tests/unit_tests/data_validation/test_DatasetDescription.py
+++ b/tests/unit_tests/data_validation/test_DatasetDescription.py
@@ -22,13 +22,13 @@ def setUp(self):
                     [True, False, True, True, False, True, False], dtype=bool
                 ),  # Explicitly boolean
                 "text": [
-                    "hello",
-                    "world",
+                    "hello@gmail.com",
+                    "this is a longer text",
                     "hello world",
-                    "test",
-                    "hello",
-                    "test",
-                    "world",
+                    "this is a longer text",
+                    "this is a longer text",
+                    "another example of text",
+                    "this is a longer text",
                 ],  # Text
                 "all_null": [
                     None,
@@ -104,6 +104,8 @@ def test_returns_expected_structure(self):
 
     def test_column_types_and_stats(self):
         result = DatasetDescription(self.vm_dataset)
+        print(self.vm_dataset.df.head())
+        print(self.vm_dataset.df.dtypes)
         description, _ = result
         column_info = {
             item["Name"]: item for item in description["Dataset Description"]
@@ -128,8 +130,9 @@ def test_column_types_and_stats(self):
         self.assertEqual(column_info["boolean"]["Count"], 7)  # All present
 
         # Check text column
+        print(column_info)
         self.assertEqual(column_info["text"]["Type"], "Text")
-        self.assertEqual(column_info["text"]["Distinct"], 4)  # 4 unique strings
+        self.assertEqual(column_info["text"]["Distinct"],4)  # 4 unique strings
         self.assertEqual(column_info["text"]["Missing"], 0)  # No missing values
         self.assertEqual(column_info["text"]["Count"], 7)  # All present
 

From bcda30fd86aac5b6db28af7e1d1f3db4ae91a210 Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Mon, 10 Mar 2025 11:19:04 +0000
Subject: [PATCH 5/7] remove lint error

---
 validmind/utils.py | 117 ++++++++++++++++++++++-----------------------
 1 file changed, 57 insertions(+), 60 deletions(-)

diff --git a/validmind/utils.py b/validmind/utils.py
index abc90cd4c..20429d418 100644
--- a/validmind/utils.py
+++ b/validmind/utils.py
@@ -664,6 +664,52 @@ def is_text_column(series, threshold=0.05):
     return any(text_indicators)
 
 
+def _get_numeric_type_detail(column, dtype, series):
+    """Helper function to determine numeric type details."""
+    if pd.api.types.is_integer_dtype(dtype):
+        return {"type": "Numeric", "subtype": "Integer"}
+    elif pd.api.types.is_float_dtype(dtype):
+        return {"type": "Numeric", "subtype": "Float"}
+    else:
+        return {"type": "Numeric", "subtype": "Other"}
+
+
+def _get_text_type_detail(series):
+    """Helper function to determine text/categorical type details."""
+    string_series = series.dropna().astype(str)
+
+    if len(string_series) == 0:
+        return {"type": "Categorical"}
+
+    # Check for common patterns
+    url_pattern = r"https?://\S+|www\.\S+"
+    email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"
+    filepath_pattern = r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+'
+
+    url_ratio = string_series.str.contains(url_pattern, regex=True, na=False).mean()
+    email_ratio = string_series.str.contains(email_pattern, regex=True, na=False).mean()
+    filepath_ratio = string_series.str.contains(
+        filepath_pattern, regex=True, na=False
+    ).mean()
+
+    # Check if general text using enhanced function
+    if url_ratio > 0.7:
+        return {"type": "Text", "subtype": "URL"}
+    elif email_ratio > 0.7:
+        return {"type": "Text", "subtype": "Email"}
+    elif filepath_ratio > 0.7:
+        return {"type": "Text", "subtype": "Path"}
+    elif is_text_column(series):
+        return {"type": "Text", "subtype": "FreeText"}
+
+    # Must be categorical
+    n_unique = series.nunique()
+    if n_unique == 2:
+        return {"type": "Categorical", "subtype": "Binary"}
+    else:
+        return {"type": "Categorical", "subtype": "Nominal"}
+
+
 def get_column_type_detail(df, column):
     """
     Get detailed column type information beyond basic type detection.
@@ -679,75 +725,26 @@ def get_column_type_detail(df, column):
     series = df[column]
     dtype = series.dtype
 
-    # Initialize result with basic type
+    # Initialize result with id and basic type
     result = {"id": column, "type": "Unknown"}
 
-    # Numeric types
-    if pd.api.types.is_numeric_dtype(dtype):
-        if pd.api.types.is_integer_dtype(dtype):
-            result["type"] = "Numeric"
-            result["subtype"] = "Integer"
-        elif pd.api.types.is_float_dtype(dtype):
-            result["type"] = "Numeric"
-            result["subtype"] = "Float"
-        else:
-            result["type"] = "Numeric"
-            result["subtype"] = "Other"
+    # Determine type details based on dtype
+    type_detail = None
 
-    # Boolean type
+    if pd.api.types.is_numeric_dtype(dtype):
+        type_detail = _get_numeric_type_detail(column, dtype, series)
     elif pd.api.types.is_bool_dtype(dtype):
-        result["type"] = "Boolean"
-
-    # Datetime types
+        type_detail = {"type": "Boolean"}
     elif pd.api.types.is_datetime64_any_dtype(dtype):
-        result["type"] = "Datetime"
-
-    # String/object/categorical types need more analysis
+        type_detail = {"type": "Datetime"}
     elif pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(
         dtype
     ):
-        # Convert to strings for analysis
-        string_series = series.dropna().astype(str)
+        type_detail = _get_text_type_detail(series)
 
-        if len(string_series) == 0:
-            result["type"] = "Categorical"
-            return result
-
-        # Check for common patterns
-        url_pattern = r"https?://\S+|www\.\S+"
-        email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"
-        filepath_pattern = r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+'
-
-        url_ratio = string_series.str.contains(url_pattern, regex=True, na=False).mean()
-        email_ratio = string_series.str.contains(
-            email_pattern, regex=True, na=False
-        ).mean()
-        filepath_ratio = string_series.str.contains(
-            filepath_pattern, regex=True, na=False
-        ).mean()
-
-        # Check if general text using enhanced function
-        if url_ratio > 0.7:
-            result["type"] = "Text"
-            result["subtype"] = "URL"
-        elif email_ratio > 0.7:
-            result["type"] = "Text"
-            result["subtype"] = "Email"
-        elif filepath_ratio > 0.7:
-            result["type"] = "Text"
-            result["subtype"] = "Path"
-        elif is_text_column(series):
-            result["type"] = "Text"
-            result["subtype"] = "FreeText"
-        else:
-            result["type"] = "Categorical"
-
-            # Further analyze categorical data
-            n_unique = series.nunique()
-            if n_unique == 2:
-                result["subtype"] = "Binary"
-            else:
-                result["subtype"] = "Nominal"
+    # Update result with type details
+    if type_detail:
+        result.update(type_detail)
 
     return result
 

From 02f0253f0ac1db6162079d1f746886f2230bd303 Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Mon, 10 Mar 2025 11:24:56 +0000
Subject: [PATCH 6/7] remove print statements

---
 tests/unit_tests/data_validation/test_DatasetDescription.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit_tests/data_validation/test_DatasetDescription.py b/tests/unit_tests/data_validation/test_DatasetDescription.py
index dff7646ca..6f7b1e7ef 100644
--- a/tests/unit_tests/data_validation/test_DatasetDescription.py
+++ b/tests/unit_tests/data_validation/test_DatasetDescription.py
@@ -104,8 +104,6 @@ def test_returns_expected_structure(self):
 
     def test_column_types_and_stats(self):
         result = DatasetDescription(self.vm_dataset)
-        print(self.vm_dataset.df.head())
-        print(self.vm_dataset.df.dtypes)
         description, _ = result
         column_info = {
             item["Name"]: item for item in description["Dataset Description"]
@@ -130,7 +128,6 @@ def test_column_types_and_stats(self):
         self.assertEqual(column_info["boolean"]["Count"], 7)  # All present
 
         # Check text column
-        print(column_info)
         self.assertEqual(column_info["text"]["Type"], "Text")
         self.assertEqual(column_info["text"]["Distinct"],4)  # 4 unique strings
         self.assertEqual(column_info["text"]["Missing"], 0)  # No missing values

From 08659c059b757d762e108e166c017d9aa0d03c97 Mon Sep 17 00:00:00 2001
From: Anil Sorathiya <anil@validmind.ai>
Date: Tue, 11 Mar 2025 14:35:01 +0000
Subject: [PATCH 7/7] resolve warning message

---
 validmind/utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/validmind/utils.py b/validmind/utils.py
index 20429d418..4ba0a1a96 100644
--- a/validmind/utils.py
+++ b/validmind/utils.py
@@ -631,7 +631,7 @@ def is_text_column(series, threshold=0.05):
     patterns = {
         "url": r"https?://\S+|www\.\S+",
         "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
-        "filepath": r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+',
+        "filepath": r'(?:[a-zA-Z]:|[\\/])(?:[\\/][^\\/:*?"<>|]+)+',
     }
 
     # Check if any special patterns exceed threshold
@@ -684,7 +684,7 @@ def _get_text_type_detail(series):
     # Check for common patterns
     url_pattern = r"https?://\S+|www\.\S+"
     email_pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"
-    filepath_pattern = r'([a-zA-Z]:|[\\/])([\\/][^\\/:*?"<>|]+)+'
+    filepath_pattern = r'(?:[a-zA-Z]:|[\\/])(?:[\\/][^\\/:*?"<>|]+)+'
 
     url_ratio = string_series.str.contains(url_pattern, regex=True, na=False).mean()
     email_ratio = string_series.str.contains(email_pattern, regex=True, na=False).mean()