Add support for pandas 2 (#4216)

* Squashed changes

* Ignored index

* Disabled column checking

* Reverted deleted code

* Updated pyproject.toml

* Replaced version check code

Author: christopherbunn

.github/meta.yaml

@@ -25,7 +25,7 @@ outputs:
         - setuptools ==58.0.4
       run:
         - numpy >=1.21.0
-        - pandas >=1.5.0, <2.0.0
+        - pandas >=1.5.0
         - dask >=2022.2.0, !=2022.10.1
         - scipy >=1.5.0
         - scikit-learn >=1.3.0

core-requirements.txt

@@ -1,5 +1,5 @@
 numpy>=1.21.0
-pandas>=1.5.0, <2.0.0
+pandas>=1.5.0
 scipy>=1.5.0
 scikit-learn>=1.3.0
 scikit-optimize>=0.9.0

docs/source/release_notes.rst

@@ -5,6 +5,7 @@ Release Notes
         * Updated regression metrics to handle multioutput dataframes as well as single output series :pr:`4233`
         * Added stacking and unstacking utility functions to work with multiseries data :pr:`4250`
     * Fixes
+        * Added support for pandas 2 :pr:`4216`
     * Changes
         * Unpinned sktime version :pr:`4214`
         * Bumped minimum lightgbm version to 4.0.0 for nullable type handling :pr:`4237`

docs/source/user_guide/timeseries.ipynb

@@ -996,8 +996,8 @@
     "        ),\n",
     "        # Plot prediction intervals\n",
     "        go.Scatter(\n",
-    "            x=X_forecast_dates[\"Date\"].append(X_forecast_dates[\"Date\"][::-1]),\n",
-    "            y=y_upper.append(y_lower[::-1]),\n",
+    "            x=pd.concat([X_forecast_dates[\"Date\"], X_forecast_dates[\"Date\"][::-1]]),\n",
+    "            y=pd.concat([y_upper, y_lower[::-1]]),\n",
     "            fill=\"toself\",\n",
     "            fillcolor=\"rgba(255,0,0,0.2)\",\n",
     "            line=dict(color=\"rgba(255,0,0,0.2)\"),\n",

evalml/model_understanding/visualizations.py

@@ -472,8 +472,8 @@ def get_linear_coefficients(estimator, features=None):
     coef_.name = "Coefficients"
     coef_.index = features
     coef_ = coef_.sort_values()
-    coef_ = pd.Series(estimator._component_obj.intercept_, index=["Intercept"]).append(
-        coef_,
+    coef_ = pd.concat(
+        [pd.Series(estimator._component_obj.intercept_, index=["Intercept"]), coef_],
     )
 
     return coef_

evalml/pipelines/components/transformers/encoders/onehot_encoder.py

@@ -152,7 +152,7 @@ def fit(self, X, y=None):
                         random_state=self._initial_state,
                     )
                     value_counts = value_counts.sort_values(
-                        [col],
+                        value_counts.iloc[:, 0].name,
                         ascending=False,
                         kind="mergesort",
                     )

evalml/pipelines/components/transformers/preprocessing/decomposer.py

@@ -151,7 +151,9 @@ def determine_periodicity(
                 period is detected, returns None.
 
         """
-        X, y = cls._handle_nullable_types(cls, X, y)
+        # Only need to handle nullable types on pandas < 2. Kept for backwards compatibility with pandas 1.x.
+        if int(pd.__version__.split(".")[0]) < 2:
+            X, y = cls._handle_nullable_types(cls, X, y)
 
         def _get_rel_max_from_acf(y):
             """Determines the relative maxima of the target's autocorrelation."""

evalml/pipelines/components/transformers/preprocessing/polynomial_decomposer.py

@@ -267,7 +267,7 @@ def inverse_transform(self, y_t: pd.Series) -> tuple[pd.DataFrame, pd.Series]:
                     index=truncated_y_t.index,
                 ),
             )
-        y = y_in_sample.append(y_out_of_sample)
+        y = pd.concat([y_in_sample, y_out_of_sample])
         y.index = original_index
         return y
 

evalml/pipelines/components/transformers/preprocessing/stl_decomposer.py

@@ -245,7 +245,7 @@ def transform(
                     index=truncated_y.index,
                 ),
             )
-        y_t = y_in_sample.append(y_out_of_sample)
+        y_t = pd.concat([y_in_sample, y_out_of_sample])
         y_t.index = original_index
         return X, y_t
 
@@ -317,7 +317,7 @@ def inverse_transform(self, y_t: pd.Series) -> tuple[pd.DataFrame, pd.Series]:
                     index=truncated_y_t.index,
                 ),
             )
-        y = y_in_sample.append(y_out_of_sample)
+        y = pd.concat([y_in_sample, y_out_of_sample])
         y.index = original_index
         return y
 

evalml/preprocessing/utils.py

@@ -184,19 +184,17 @@ def target_distribution(targets):
 
     Examples:
         >>> y = pd.Series([1, 2, 4, 1, 3, 3, 1, 2])
-        >>> target_distribution(y)
+        >>> print(target_distribution(y).to_string())
         Targets
         1    37.50%
         2    25.00%
         3    25.00%
         4    12.50%
-        dtype: object
         >>> y = pd.Series([True, False, False, False, True])
-        >>> target_distribution(y)
+        >>> print(target_distribution(y).to_string())
         Targets
         False    60.00%
         True     40.00%
-        dtype: object
     """
     distribution = targets.value_counts() / len(targets)
     return distribution.mul(100).apply("{:.2f}%".format).rename_axis("Targets")