diff --git a/.gitignore b/.gitignore
index a30b5eb4..63479341 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,6 @@ _build/
 # PyCharm
 #########
 .idea/
+.vscode/settings.json
+.env
+.vscode/launch.json
diff --git a/poetry.lock b/poetry.lock
index 5f8f9338..e177aab3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -70,9 +70,6 @@ files = [
     {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
@@ -585,24 +582,6 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
-[[package]]
-name = "importlib-resources"
-version = "6.1.1"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
-]
-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -689,6 +668,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.21.1"
@@ -702,9 +692,7 @@ files = [
 
 [package.dependencies]
 attrs = ">=22.2.0"
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rpds-py = ">=0.7.1"
 
@@ -724,7 +712,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -1013,43 +1000,6 @@ nbformat = "*"
 sphinx = ">=1.8"
 traitlets = ">=5"
 
-[[package]]
-name = "numpy"
-version = "1.24.4"
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 [[package]]
 name = "numpy"
 version = "1.26.4"
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 [[package]]
 name = "pandas"
-version = "2.0.3"
+version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
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 name = "scipy"
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 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
-numpy = ">=1.21.6,<1.28.0"
+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
-dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
-doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "six"
@@ -2055,6 +2025,17 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
+]
+
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
@@ -2132,13 +2113,13 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2024.1"
+version = "2024.2"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
-    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+    {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+    {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
 ]
 
 [[package]]
@@ -2197,5 +2178,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8.1, <3.13"
-content-hash = "f908f8c1e11f3e2d1ce347673013bc3dd77eef943341e17c3106dd8fc31d6633"
+python-versions = ">=3.9, <=3.13"
+content-hash = "6374b0bba22da8a0e240ae5f655897a449c79134d4e4ef416ec5a1f270385d2f"
diff --git a/pygam/sklearn_api.py b/pygam/sklearn_api.py
new file mode 100644
index 00000000..f218ff11
--- /dev/null
+++ b/pygam/sklearn_api.py
@@ -0,0 +1,262 @@
+"""
+sklearn_api.py
+
+This module provides scikit-learn compatible classes for Generalized Additive Models (GAM) regressors and classifiers.
+It integrates pygam's GAM capabilities with scikit-learn's estimator interface, enabling seamless use in machine learning pipelines.
+"""
+
+# Standard library imports
+import numpy as np
+
+# Third-party imports
+from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Local application imports
+from pygam import GAM
+from pygam.terms import te, TermList, Term  # Import te for interactions
+from pygam.terms import s, f, l, intercept  # Import s, f, l for splines
+
+
+def create_default_terms(X, categorical_features=None):
+    """Generate default terms for each feature in X, handling categoricals."""
+    n_features = X.shape[1]
+    terms = []
+    if categorical_features is None:
+        categorical_features = []
+    for i in range(n_features):
+        if i in categorical_features:
+            terms.append(f(i))
+        elif not np.issubdtype(X[:, i].dtype, np.number):
+            terms.append(f(i))
+        else:
+            terms.append(s(i))
+    return terms
+
+
+class GAMRegressor(BaseEstimator, RegressorMixin):
+    """
+    GAMRegressor
+
+    A scikit-learn compatible regressor using Generalized Additive Models (GAM).
+
+    Parameters
+    ----------
+    distribution : str, default='normal'
+        The distribution of the response variable.
+    link : str, default='identity'
+        The link function.
+    terms : 'auto', None, or list of Term objects, default='auto'
+        The terms to include in the model. If 'auto', terms are automatically inferred based on X.
+        If None, no terms are used. If a list of Term objects, they are used as specified.
+    interactions : None or list of Term objects, optional
+        Interaction terms to include in the model.
+    callbacks : list, default=['deviance', 'diffs']
+        List of callbacks to monitor during training.
+    fit_intercept : bool, default=True
+        Whether to fit an intercept.
+    max_iter : int, default=100
+        Maximum number of iterations.
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+    verbose : bool, default=False
+        Verbosity mode.
+    categorical_features : list, optional
+        List of indices of categorical features.
+    **gam_params :
+        Additional parameters for the GAM model.
+
+    Attributes
+    ----------
+    model_ : GAM
+        The underlying pygam GAM model fitted to the data.
+    """
+
+    def __init__(
+        self,
+        distribution='normal',
+        link='identity',
+        terms='auto',
+        interactions=None,
+        callbacks=['deviance', 'diffs'],
+        fit_intercept=True,
+        max_iter=100,
+        tol=1e-4,
+        verbose=False,
+        categorical_features=None,
+        **gam_params,
+    ):
+        self.distribution = distribution
+        self.link = link
+        self.terms = terms
+        self.interactions = interactions
+        self.callbacks = callbacks
+        self.fit_intercept = fit_intercept
+        self.max_iter = max_iter
+        self.tol = tol
+        self.verbose = verbose
+        self.categorical_features = categorical_features
+        self.gam_params = gam_params
+
+    def fit(self, X, y):
+        if self.terms == 'auto':
+            self.terms_ = create_default_terms(X, self.categorical_features)
+        elif self.terms is None:
+            self.terms_ = []
+        else:
+            self.terms_ = self.terms
+
+        if self.interactions is not None:
+            self.interactions_ = [
+                te(*interaction) if isinstance(interaction, tuple) else interaction
+                for interaction in self.interactions
+            ]
+        else:
+            self.interactions_ = []
+
+        # Combine terms and interactions
+        terms = self.terms_ + self.interactions_
+        terms = TermList(*terms)
+
+        # Create the GAM model with the specified terms
+        self.model_ = GAM(
+            distribution=self.distribution,
+            link=self.link,
+            terms=terms,
+            callbacks=self.callbacks,
+            fit_intercept=self.fit_intercept,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            verbose=self.verbose,
+            **self.gam_params,
+        )
+        self.model_.fit(X, y)
+        return self
+
+    def predict(self, X):
+        return self.model_.predict(X)
+
+    def score(self, X, y):
+        return float(self.model_.statistics_.get('pseudo R-squared', 0))
+
+
+class GAMClassifier(BaseEstimator, ClassifierMixin):
+    """
+    GAMClassifier
+
+    A scikit-learn compatible classifier using Generalized Additive Models (GAM).
+
+    Parameters
+    ----------
+    distribution : str, default='binomial'
+        The distribution of the response variable.
+    link : str, default='logit'
+        The link function.
+    terms : 'auto', None, or list of Term objects, default='auto'
+        The terms to include in the model. If 'auto', terms are automatically inferred based on X.
+        If None, no terms are used. If a list of Term objects, they are used as specified.
+    interactions : None or list of Term objects, optional
+        Interaction terms to include in the model.
+    callbacks : list, default=['deviance', 'diffs', 'accuracy']
+        List of callbacks to monitor during training.
+    fit_intercept : bool, default=True
+        Whether to fit an intercept.
+    max_iter : int, default=100
+        Maximum number of iterations.
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+    verbose : bool, default=False
+        Verbosity mode.
+    categorical_features : list, optional
+        List of indices of categorical features.
+    **gam_params :
+        Additional parameters for the GAM model.
+
+    Attributes
+    ----------
+    model_ : GAM
+        The underlying pygam GAM model fitted to the data.
+    classes_ : array-like
+        Unique class labels.
+    """
+
+    def __init__(
+        self,
+        distribution='binomial',
+        link='logit',
+        terms='auto',
+        interactions=None,
+        callbacks=['deviance', 'diffs', 'accuracy'],
+        fit_intercept=True,
+        max_iter=100,
+        tol=1e-4,
+        verbose=False,
+        categorical_features=None,
+        **gam_params,
+    ):
+        self.distribution = distribution
+        self.link = link
+        self.terms = terms
+        self.interactions = interactions
+        self.callbacks = callbacks
+        self.fit_intercept = fit_intercept
+        self.max_iter = max_iter
+        self.tol = tol
+        self.verbose = verbose
+        self.categorical_features = categorical_features
+        self.gam_params = gam_params
+
+    def fit(self, X, y):
+        if self.terms == 'auto':
+            self.terms_ = create_default_terms(X, self.categorical_features)
+        elif self.terms is None:
+            self.terms_ = []
+        else:
+            self.terms_ = self.terms
+
+        if self.interactions is not None:
+            self.interactions_ = [
+                te(*interaction) if isinstance(interaction, tuple) else interaction
+                for interaction in self.interactions
+            ]
+        else:
+            self.interactions_ = []
+
+        # Combine terms and interactions
+        terms = self.terms_ + self.interactions_
+        terms = TermList(*terms)
+
+        # Create the GAM model with the specified terms
+        self.model_ = GAM(
+            distribution=self.distribution,
+            link=self.link,
+            terms=terms,
+            callbacks=self.callbacks,
+            fit_intercept=self.fit_intercept,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            verbose=self.verbose,
+            **self.gam_params,
+        )
+        self.model_.fit(X, y)
+        self.classes_ = np.unique(y)
+        return self
+
+    def predict(self, X):
+        proba = self.model_.predict(X)
+        if len(self.classes_) == 2:
+            return (proba >= 0.5).astype(int)
+        else:
+            return self.classes_[np.argmax(proba, axis=1)]
+
+    def predict_proba(self, X):
+        proba = self.model_.predict(X)
+        if len(self.classes_) == 2:
+            return np.vstack([1 - proba, proba]).T
+        else:
+            return proba  # Assume GAM model returns probabilities for each class
+
+    def score(self, X, y):
+        return accuracy_score(y, self.predict(X))
diff --git a/pygam/tests/test_penalties.py b/pygam/tests/test_penalties.py
index bf6dd68a..43b3ee24 100644
--- a/pygam/tests/test_penalties.py
+++ b/pygam/tests/test_penalties.py
@@ -23,13 +23,13 @@ def test_single_spline_penalty():
     monotonic_ and convexity_ should be 0.
     """
     coef = np.array(1.0)
-    assert np.alltrue(derivative(1, coef).A == 0.0)
-    assert np.alltrue(l2(1, coef).A == 1.0)
-    assert np.alltrue(monotonic_inc(1, coef).A == 0.0)
-    assert np.alltrue(monotonic_dec(1, coef).A == 0.0)
-    assert np.alltrue(convex(1, coef).A == 0.0)
-    assert np.alltrue(concave(1, coef).A == 0.0)
-    assert np.alltrue(none(1, coef).A == 0.0)
+    assert np.all(derivative(1, coef).A == 0.0)
+    assert np.all(l2(1, coef).A == 1.0)
+    assert np.all(monotonic_inc(1, coef).A == 0.0)
+    assert np.all(monotonic_dec(1, coef).A == 0.0)
+    assert np.all(convex(1, coef).A == 0.0)
+    assert np.all(concave(1, coef).A == 0.0)
+    assert np.all(none(1, coef).A == 0.0)
 
 
 def test_wrap_penalty():
diff --git a/pygam/tests/test_sklearn_api.py b/pygam/tests/test_sklearn_api.py
new file mode 100644
index 00000000..7c03fc16
--- /dev/null
+++ b/pygam/tests/test_sklearn_api.py
@@ -0,0 +1,125 @@
+import pytest
+import numpy as np
+from sklearn.datasets import make_regression, make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score, accuracy_score
+from pygam.sklearn_api import GAMRegressor, GAMClassifier
+
+@pytest.fixture
+def regression_data():
+    X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
+    return train_test_split(X, y, test_size=0.2, random_state=42)
+
+@pytest.fixture
+def classification_data():
+    X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=42)
+    return train_test_split(X, y, test_size=0.2, random_state=42)
+
+def test_gam_regressor_fit_predict(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor()
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert r2_score(y_test, predictions) >= 0  # Basic sanity check
+
+def test_gam_regressor_score(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor()
+    reg.fit(X_train, y_train)
+    score = reg.score(X_test, y_test)
+    assert isinstance(score, float)
+    assert score >= 0  # R-squared should be non-negative
+
+def test_gam_classifier_fit_predict(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert set(predictions).issubset({0, 1})  # Binary classification
+
+def test_gam_classifier_predict_proba(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    proba = clf.predict_proba(X_test)
+    assert proba.shape == (X_test.shape[0], 2)
+    assert np.allclose(proba.sum(axis=1), 1)
+
+def test_gam_classifier_score(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier()
+    clf.fit(X_train, y_train)
+    score = clf.score(X_test, y_test)
+    assert isinstance(score, float)
+    assert 0 <= score <= 1  # Accuracy between 0 and 1
+
+def test_gam_regressor_with_custom_params(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor(distribution='normal', link='identity', max_iter=200, tol=1e-5)
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert r2_score(y_test, predictions) >= 0
+
+def test_gam_classifier_with_custom_params(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier(distribution='binomial', link='logit', max_iter=200, tol=1e-5)
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    proba = clf.predict_proba(X_test)
+    assert accuracy_score(y_test, predictions) >= 0
+    assert proba.shape == (X_test.shape[0], 2)
+
+def test_gam_regressor_with_callbacks(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    reg = GAMRegressor(callbacks=['deviance', 'diffs'])
+    reg.fit(X_train, y_train)
+    assert 'deviance' in reg.model_.logs_
+    assert 'diffs' in reg.model_.logs_
+
+def test_gam_classifier_with_callbacks(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    clf = GAMClassifier(callbacks=['deviance', 'diffs', 'accuracy'])
+    clf.fit(X_train, y_train)
+    assert 'deviance' in clf.model_.logs_
+    assert 'diffs' in clf.model_.logs_
+    assert 'accuracy' in clf.model_.logs_
+
+def test_gam_regressor_gamma():
+    X = np.random.rand(100, 2)
+    y = np.random.gamma(shape=2.0, scale=1.0, size=100)
+    model = GAMRegressor(distribution='gamma')
+    model.fit(X, y)
+    predictions = model.predict(X)
+    assert predictions.shape == y.shape
+
+def test_gam_regressor_poisson():
+    X = np.random.rand(100, 2)
+    y = np.random.poisson(lam=3.0, size=100)
+    model = GAMRegressor(distribution='poisson')
+    model.fit(X, y)
+    predictions = model.predict(X)
+    assert predictions.shape == y.shape
+
+def test_gam_regressor_with_interactions(regression_data):
+    X_train, X_test, y_train, y_test = regression_data
+    interactions = [(0, 1), (2, 3)]  # Specify feature indices for interactions
+    reg = GAMRegressor(interactions=interactions)
+    reg.fit(X_train, y_train)
+    predictions = reg.predict(X_test)
+    assert predictions.shape == y_test.shape
+    assert r2_score(y_test, predictions) >= 0  # Basic sanity check
+
+def test_gam_classifier_with_interactions(classification_data):
+    X_train, X_test, y_train, y_test = classification_data
+    interactions = [(0, 1), (2, 3)]  # Specify feature indices for interactions
+    clf = GAMClassifier(interactions=interactions)
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    proba = clf.predict_proba(X_test)
+    assert predictions.shape == y_test.shape
+    assert set(predictions).issubset({0, 1})  # Binary classification
+    assert proba.shape == (X_test.shape[0], 2)
+    assert np.allclose(proba.sum(axis=1), 1)
+    assert accuracy_score(y_test, predictions) >= 0
diff --git a/pyproject.toml b/pyproject.toml
index 2fe8e8a0..2cf5ff35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,19 +7,17 @@ license = "Apache-2.0"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.8.1, <3.13"
+python = ">=3.9, <=3.13"
 numpy = [
   { version = ">=1.24.2,<1.25", python = "<3.9,>=3.8" },
   { version = ">=1.25", python = "<3.13,>=3.9" },
 ]
 progressbar2 = "^4.2.0"
-scipy = [
-  { version = ">=1.10.1,<1.11", python = "<3.9,>=3.8" },
-  { version = ">=1.11.1,<1.12", python = "<3.13,>=3.9" }
-]
+scipy = ">=1.11.4"
+scikit-learn = "^1.5.2"
+pandas = ">=1.4.0"  # Updated line
 
 [tool.poetry.group.dev.dependencies]
-pandas = ">=1.6"
 pytest = "^7.2.2"
 flake8 = "^6.0.0"
 codecov = "^2.1.12"
@@ -58,4 +56,4 @@ build-backend = "poetry_dynamic_versioning.backend"
 [tool.poetry-dynamic-versioning]
 enable = true
 vcs = "git"
-style = "semver"
+style = "semver"
\ No newline at end of file