cleanup

Signed-off-by: Anatoly Myachev <[email protected]>

modin/core/dataframe/base/dataframe/utils.py

@@ -24,7 +24,7 @@
 import pandas
 from pandas._typing import IndexLabel
 from pandas.api.types import is_scalar
-from pandas.core.dtypes.common import is_float_dtype, is_numeric_dtype
+from pandas.core.dtypes.common import is_integer_dtype
 
 
 class Axis(Enum):  # noqa: PR01
@@ -170,10 +170,7 @@ def is_trivial_index(index: pandas.Index) -> bool:
         return True
     if isinstance(index, pandas.RangeIndex):
         return index.start == 0 and index.step == 1
-    if not (
-        isinstance(index, pandas.Index)
-        and (is_numeric_dtype(index) and not is_float_dtype(index))
-    ):
+    if not (isinstance(index, pandas.Index) and is_integer_dtype(index)):
         return False
     return (
         index.is_monotonic_increasing

modin/core/dataframe/pandas/metadata/dtypes.py

@@ -496,7 +496,7 @@ def _merge_dtypes(
                 # in the 'dtypes_matrix'
                 series = pandas.Series(dtypes, name=i)
                 dtypes_matrix = pandas.concat([dtypes_matrix, series], axis=1)
-                if val._know_all_names and val._remaining_dtype is None:
+                if not (val._know_all_names and val._remaining_dtype is None):
                     dtypes_matrix.fillna(
                         value={
                             # If we encountered a 'NaN' while 'val' describes all the columns, then

modin/core/storage_formats/pandas/aggregations.py

@@ -56,7 +56,7 @@ def corr_method(
             min_periods: int = 1,
             numeric_only: bool = True,
         ) -> PandasQueryCompiler:
-            if method != "pearson" or qc._modin_frame._pandas_backend == "pyarrow":
+            if method != "pearson" or qc.get_backend() == "pyarrow":
                 return super(type(qc), qc).corr(
                     method=method, min_periods=min_periods, numeric_only=numeric_only
                 )