[python] Faster categorical column names selection (#4787)

* Faster categorical column names selection (#1)

* Faster categorical column names selection

Change slow and redundant dataframe query by select_dtypes into a dataframe.dtypes list comprehension

* Update compat with CategoricalDtype

* sort imports

* import CategoricalDtype from pandas.api.types

* add categorical import try/except

python-package/lightgbm/basic.py

@@ -17,7 +17,8 @@
 import numpy as np
 import scipy.sparse
 
-from .compat import PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse, pd_DataFrame, pd_Series
+from .compat import (PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse, pd_CategoricalDtype, pd_DataFrame,
+                     pd_Series)
 from .libpath import find_lib_path
 
 ZERO_THRESHOLD = 1e-35
@@ -567,7 +568,7 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
             raise ValueError('Input data must be 2 dimensional and non empty.')
         if feature_name == 'auto' or feature_name is None:
             data = data.rename(columns=str)
-        cat_cols = list(data.select_dtypes(include=['category']).columns)
+        cat_cols = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)]
         cat_cols_not_ordered = [col for col in cat_cols if not data[col].cat.ordered]
         if pandas_categorical is None:  # train dataset
             pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]

python-package/lightgbm/compat.py

@@ -7,6 +7,10 @@
     from pandas import Series as pd_Series
     from pandas import concat
     from pandas.api.types import is_sparse as is_dtype_sparse
+    try:
+        from pandas import CategoricalDtype as pd_CategoricalDtype
+    except ImportError:
+        from pandas.api.types import CategoricalDtype as pd_CategoricalDtype
     PANDAS_INSTALLED = True
 except ImportError:
     PANDAS_INSTALLED = False
@@ -23,6 +27,12 @@ class pd_DataFrame:  # type: ignore
         def __init__(self, *args, **kwargs):
             pass
 
+    class pd_CategoricalDtype:
+        """Dummy class for pandas.CategoricalDtype."""
+
+        def __init__(self, *args, **kwargs):
+            pass
+
     concat = None
     is_dtype_sparse = None