refactor code to work with pandas 2.0 (#660)

* Transform positional argument into keyword argument

From pandas 2.0 any only accepts keyworkd arguments
ref pandas-dev/pandas#44896

* Change how reciprocal is computed

I have not fully understood why this solve the problem, but splitting
the operation in 2 lines does not seem to work

* Catch warnings from pandas.to_datetime

Now pandas.to_datetime raises a warning when the column cannot be converted

* check_dtype=False in tests datetime features

Pandas dataframes created from python integers are created with int
column types `int64` but the operation tested returns `int32` which
caused issues

* Use droplevel before merging

Merging dfs with different column lelvels has been disallowed
ref pandas-dev/pandas#34862

* Change expected values for months

I am not sure why this caused an issue, maybe due to type casting?

* run black

* run black on tests

* isort _variable_type_checks.py

* Fix datetime_subtraction

---------

Co-authored-by: Claudio Salvatore Arcidiacono <claudio.arcidiacono@mollie.com>

feature_engine/datetime/datetime_subtraction.py

@@ -318,7 +318,7 @@ def _sub(self, dt_df: pd.DataFrame):
             new_df[new_varnames] = (
                 dt_df[self.variables_]
                 .sub(dt_df[reference], axis=0)
-                .apply(lambda s: s / np.timedelta64(1, self.output_unit))
+                .div(np.timedelta64(1, self.output_unit).astype("timedelta64[ns]"))
             )
 
         if self.new_variables_names is not None:

feature_engine/imputation/drop_missing_data.py

@@ -205,7 +205,7 @@ def return_na_data(self, X: pd.DataFrame) -> pd.DataFrame:
             idx = pd.isnull(X[self.variables_]).mean(axis=1) >= self.threshold
             idx = idx[idx]
         else:
-            idx = pd.isnull(X[self.variables_]).any(1)
+            idx = pd.isnull(X[self.variables_]).any(axis=1)
             idx = idx[idx]
 
         return X.loc[idx.index, :]

feature_engine/transformation/reciprocal.py

@@ -96,7 +96,6 @@ class ReciprocalTransformer(BaseNumericalTransformer):
     def __init__(
         self, variables: Union[None, int, str, List[Union[str, int]]] = None
     ) -> None:
-
         self.variables = _check_init_parameter_variables(variables)
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
@@ -152,8 +151,9 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame:
 
         # transform
         # for some reason reciprocal does not work with integers
-        X.loc[:, self.variables_] = X.loc[:, self.variables_].astype("float")
-        X.loc[:, self.variables_] = np.reciprocal(X.loc[:, self.variables_])
+        X.loc[:, self.variables_] = np.reciprocal(
+            X.loc[:, self.variables_].astype("float")
+        )
 
         return X
 

feature_engine/variable_handling/_variable_type_checks.py

@@ -1,3 +1,5 @@
+import warnings
+
 import pandas as pd
 from pandas.core.dtypes.common import is_categorical_dtype as is_categorical
 from pandas.core.dtypes.common import is_datetime64_any_dtype as is_datetime
@@ -6,7 +8,6 @@
 
 
 def _is_categorical_and_is_not_datetime(column: pd.Series) -> bool:
-
     # check for datetime only if object cannot be cast as numeric because
     # if it could pd.to_datetime would convert it to datetime regardless
     if is_object(column):
@@ -25,15 +26,16 @@ def _is_categories_num(column: pd.Series) -> bool:
 
 
 def _is_convertible_to_dt(column: pd.Series) -> bool:
-    return is_datetime(pd.to_datetime(column, errors="ignore", utc=True))
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        return is_datetime(pd.to_datetime(column, errors="ignore", utc=True))
 
 
 def _is_convertible_to_num(column: pd.Series) -> bool:
     return is_numeric(pd.to_numeric(column, errors="ignore"))
 
 
 def _is_categorical_and_is_datetime(column: pd.Series) -> bool:
-
     # check for datetime only if object cannot be cast as numeric because
     # if it could pd.to_datetime would convert it to datetime regardless
     if is_object(column):

tests/test_datetime/test_datetime_features.py

@@ -183,6 +183,7 @@ def test_extract_datetime_features_with_default_options(
         df_datetime_transformed[
             vars_non_dt + [var + feat for var in vars_dt for feat in feat_names_default]
         ],
+        check_dtype=False,
     )
 
 
@@ -198,6 +199,7 @@ def test_extract_datetime_features_from_specified_variables(
             + ["datetime_range", "date_obj2", "time_obj"]
             + ["date_obj1" + feat for feat in feat_names_default]
         ],
+        check_dtype=False,
     )
 
     # multiple datetime variables
@@ -215,6 +217,7 @@ def test_extract_datetime_features_from_specified_variables(
                 for feat in feat_names_default
             ]
         ],
+        check_dtype=False,
     )
 
     # multiple datetime variables in different order than they appear in the df
@@ -232,6 +235,7 @@ def test_extract_datetime_features_from_specified_variables(
                 for feat in feat_names_default
             ]
         ],
+        check_dtype=False,
     )
 
     # datetime variable is index
@@ -251,12 +255,15 @@ def test_extract_datetime_features_from_specified_variables(
             ],
             axis=1,
         ),
+        check_dtype=False,
     )
 
 
 def test_extract_all_datetime_features(df_datetime, df_datetime_transformed):
     X = DatetimeFeatures(features_to_extract="all").fit_transform(df_datetime)
-    pd.testing.assert_frame_equal(X, df_datetime_transformed.drop(vars_dt, axis=1))
+    pd.testing.assert_frame_equal(
+        X, df_datetime_transformed.drop(vars_dt, axis=1), check_dtype=False
+    )
 
 
 def test_extract_specified_datetime_features(df_datetime, df_datetime_transformed):
@@ -269,6 +276,7 @@ def test_extract_specified_datetime_features(df_datetime, df_datetime_transforme
             vars_non_dt
             + [var + "_" + feat for var in vars_dt for feat in ["semester", "week"]]
         ],
+        check_dtype=False,
     )
 
     # different order than they appear in the glossary
@@ -281,6 +289,7 @@ def test_extract_specified_datetime_features(df_datetime, df_datetime_transforme
             vars_non_dt
             + [var + "_" + feat for var in vars_dt for feat in ["hour", "day_of_week"]]
         ],
+        check_dtype=False,
     )
 
 
@@ -290,7 +299,9 @@ def test_extract_features_from_categorical_variable(
     cat_date = pd.DataFrame({"date_obj1": df_datetime["date_obj1"].astype("category")})
     X = DatetimeFeatures(variables="date_obj1").fit_transform(cat_date)
     pd.testing.assert_frame_equal(
-        X, df_datetime_transformed[["date_obj1" + feat for feat in feat_names_default]]
+        X,
+        df_datetime_transformed[["date_obj1" + feat for feat in feat_names_default]],
+        check_dtype=False,
     )
 
 
@@ -311,6 +322,7 @@ def test_extract_features_from_different_timezones(
         df_datetime_transformed[["time_obj_hour"]].apply(
             lambda x: x.subtract(time_zones)
         ),
+        check_dtype=False,
     )
     exp_err_msg = (
         "ValueError: variable(s) time_obj "
@@ -356,7 +368,7 @@ def test_extract_features_from_localized_tz_variables():
     # transform
     X = transformer.transform(tz_df)
     df_expected = pd.DataFrame({"date_var_hour": [1, 2, 2, 2, 2, 3, 3]})
-    pd.testing.assert_frame_equal(X, df_expected)
+    pd.testing.assert_frame_equal(X, df_expected, check_dtype=False)
 
     # when utc is True
     transformer = DatetimeFeatures(features_to_extract=["hour"], utc=True).fit(tz_df)
@@ -372,7 +384,7 @@ def test_extract_features_from_localized_tz_variables():
     # transform
     X = transformer.transform(tz_df)
     df_expected = pd.DataFrame({"date_var_hour": [5, 6, 6, 6, 6, 7, 7]})
-    pd.testing.assert_frame_equal(X, df_expected)
+    pd.testing.assert_frame_equal(X, df_expected, check_dtype=False)
 
 
 def test_extract_features_without_dropping_original_variables(
@@ -399,6 +411,7 @@ def test_extract_features_without_dropping_original_variables(
             ],
             axis=1,
         ),
+        check_dtype=False,
     )
 
 
@@ -435,6 +448,7 @@ def test_extract_features_with_different_datetime_parsing_options(df_datetime):
     pd.testing.assert_frame_equal(
         X,
         pd.DataFrame({"date_obj2_day_of_month": [10, 31, 30, 17]}),
+        check_dtype=False,
     )
 
     X = DatetimeFeatures(features_to_extract=["year"], yearfirst=True).fit_transform(
@@ -443,6 +457,7 @@ def test_extract_features_with_different_datetime_parsing_options(df_datetime):
     pd.testing.assert_frame_equal(
         X,
         pd.DataFrame({"date_obj2_year": [2010, 2009, 1995, 2004]}),
+        check_dtype=False,
     )
 
 
@@ -457,8 +472,7 @@ def test_get_feature_names_out(df_datetime, df_datetime_transformed):
         transformer.get_feature_names_out(input_features=vars_dt)
 
     with pytest.raises(ValueError):
-        transformer.get_feature_names_out(input_features=["date_obj1"])\
-
+        transformer.get_feature_names_out(input_features=["date_obj1"])
     # default features from 1 variable
     transformer = DatetimeFeatures(variables="date_obj1")
     X = transformer.fit_transform(df_datetime)

tests/test_time_series/test_forecasting/test_window_features.py

@@ -380,8 +380,10 @@ def test_multiple_windows(df_time):
     X = df_time.copy()
     num_vars = ["ambient_temp", "module_temp", "irradiation"]
     tmp = X[num_vars].rolling(2).agg(["sum", "mean"]).shift(periods=15, freq="min")
+    tmp.columns = tmp.columns.droplevel()
     X_tr = X.merge(tmp, left_index=True, right_index=True, how="left")
     tmp = X[num_vars].rolling(3).agg(["sum", "mean"]).shift(periods=15, freq="min")
+    tmp.columns = tmp.columns.droplevel()
     X_tr = X_tr.merge(tmp, left_index=True, right_index=True, how="left")
     X_tr.columns = transformer.get_feature_names_out()
 
@@ -404,13 +406,15 @@ def test_multiple_windows(df_time):
         .agg(["sum", "mean"])
         .shift(freq="30min")
     )
+    tmp.columns = tmp.columns.droplevel()
     X_tr = X.merge(tmp, left_index=True, right_index=True, how="left")
     tmp = (
         X[["ambient_temp", "irradiation"]]
         .rolling(3)
         .agg(["sum", "mean"])
         .shift(freq="30min")
     )
+    tmp.columns = tmp.columns.droplevel()
     X_tr = X_tr.merge(tmp, left_index=True, right_index=True, how="left")
     X_tr.columns = transformer.get_feature_names_out()