[WIP] support koalas and modin

add tests for koalas

fix type issues with koalas patch to pd.Series, DataFrame

add datatype koalas tests

finish writing initial test suite for koalas

fix regressions

configure koalas

fix regressions

update pylint dep

update deps

update black

fix lint

use context manager for koalas ops_on_diff_frames

updates

update pre-commit mypy

typing ignore

fix docs

install hypothesis for koalas ci

don't cover modin import check

better handling of timestamp

fix koalas

wip

wip

wip

coverage

hypothesis health check

Author: cosmicBboy

.github/workflows/ci-tests.yml

@@ -145,6 +145,13 @@ jobs:
           --non-interactive
           --session "tests-${{ matrix.python-version }}(extra='strategies', pandas='${{ matrix.pandas-version }}')"
 
+      - name: Unit Tests - Koalas
+        run: >
+          nox
+          -db virtualenv -r -v
+          --non-interactive
+          --session "tests-${{ matrix.python-version }}(extra='koalas', pandas='${{ matrix.pandas-version }}')"
+
       - name: Upload coverage to Codecov
         uses: "codecov/codecov-action@v1"
 

docs/source/conf.py

@@ -64,10 +64,18 @@
 else:
     SKIP_STRATEGY = False
 
+try:
+    import koalas
+except ImportError:
+    KOALAS_INSTALLED = True
+else:
+    KOALAS_INSTALLED = False
+
 SKIP = sys.version_info < (3, 6)
 PY36 = sys.version_info < (3, 7)
 SKIP_PANDAS_LT_V1 = version.parse(pd.__version__).release < (1, 0) or PY36
 SKIP_SCALING = True
+SKIP_SCHEMA_MODEL = SKIP_PANDAS_LT_V1 or KOALAS_INSTALLED
 """
 
 doctest_default_flags = (

docs/source/schema_models.rst

@@ -194,13 +194,13 @@ You must give a **type**, not an **instance**.
 :red:`✘` Bad:
 
 .. testcode:: dataframe_schema_model
-    :skipif: SKIP_PANDAS_LT_V1
+    :skipif: SKIP_SCHEMA_MODEL
 
     class Schema(pa.SchemaModel):
         a: Series[pd.StringDtype()]
 
 .. testoutput:: dataframe_schema_model
-    :skipif: SKIP_PANDAS_LT_V1
+    :skipif: SKIP_SCHEMA_MODEL
 
     Traceback (most recent call last):
     ...

environment.yml

@@ -19,14 +19,18 @@ dependencies:
   - frictionless
   - pyarrow
 
+  # koalas extra
+  - koalas
+  - pyspark
+
   # testing and dependencies
   - black >= 20.8b1
 
   # testing
   - isort >= 5.7.0
   - codecov
   - mypy >= 0.902 # mypy no longer bundle stubs for third-party libraries
-  - pylint = v2.11.1
+  - pylint = 2.11.1
   - pytest
   - pytest-cov
   - pytest-xdist

noxfile.py

@@ -184,7 +184,7 @@ def install_extras(
             specs.append(
                 spec if spec != "pandas" else f"pandas{pandas_version}"
             )
-    if extra == "core":
+    if extra in {"core", "koalas"}:
         specs.append(REQUIRES["all"]["hypothesis"])
 
     # this is a temporary measure to install setuptools due to this issue:

pandera/__init__.py

@@ -1,6 +1,7 @@
 """A flexible and expressive pandas validation library."""
 import platform
 
+from pandera import external_config
 from pandera.dtypes import (
     Bool,
     Category,
@@ -56,4 +57,5 @@
 from .version import __version__
 
 if platform.system() != "Windows":
+    # pylint: disable=ungrouped-imports
     from pandera.dtypes import Complex256, Float128

pandera/check_utils.py

@@ -1,9 +1,88 @@
 """Utility functions for validation."""
 
-from typing import Optional, Tuple, Union
+from functools import lru_cache
+from typing import NamedTuple, Optional, Tuple, Union
 
 import pandas as pd
 
+SupportedTypes = NamedTuple(
+    "SupportedTypes",
+    (
+        ("table_types", Tuple[type]),
+        ("field_types", Tuple[type]),
+        ("index_types", Tuple[type]),
+        ("multiindex_types", Tuple[type]),
+    ),
+)
+
+
+@lru_cache(maxsize=None)
+def _supported_types():
+    # pylint: disable=import-outside-toplevel
+    table_types = [pd.DataFrame]
+    field_types = [pd.Series]
+    index_types = [pd.Index]
+    multiindex_types = [pd.MultiIndex]
+
+    try:
+        import databricks.koalas as ks
+
+        table_types.append(ks.DataFrame)
+        field_types.append(ks.Series)
+        index_types.append(ks.Index)
+        multiindex_types.append(ks.MultiIndex)
+    except ImportError:
+        pass
+    try:  # pragma: no cover
+        import modin.pandas as mpd
+
+        table_types.append(mpd.DataFrame)
+        field_types.append(mpd.Series)
+        index_types.append(mpd.Index)
+        multiindex_types.append(mpd.MultiIndex)
+    except ImportError:
+        pass
+
+    return SupportedTypes(
+        tuple(table_types),
+        tuple(field_types),
+        tuple(index_types),
+        tuple(multiindex_types),
+    )
+
+
+def is_table(obj):
+    """Verifies whether an object is table-like.
+
+    Where a table is a 2-dimensional data matrix of rows and columns, which
+    can be indexed in multiple different ways.
+    """
+    return isinstance(obj, _supported_types().table_types)
+
+
+def is_field(obj):
+    """Verifies whether an object is field-like.
+
+    Where a field is a columnar representation of data in a table-like
+    data structure.
+    """
+    return isinstance(obj, _supported_types().field_types)
+
+
+def is_index(obj):
+    """Verifies whether an object is a table index."""
+    return isinstance(obj, _supported_types().index_types)
+
+
+def is_multiindex(obj):
+    """Verifies whether an object is a multi-level table index."""
+    return isinstance(obj, _supported_types().multiindex_types)
+
+
+def is_supported_check_obj(obj):
+    """Verifies whether an object is table- or field-like."""
+    return is_table(obj) or is_field(obj)
+
 
 def prepare_series_check_output(
     check_obj: Union[pd.Series, pd.DataFrame],
@@ -25,9 +104,20 @@ def prepare_series_check_output(
         check_output = check_output | isna
     failure_cases = check_obj[~check_output]
     if not failure_cases.empty and n_failure_cases is not None:
-        failure_cases = failure_cases.groupby(check_output).head(
-            n_failure_cases
-        )
+        # NOTE: this is a hack to support koalas, since you can't use groupby
+        # on a dataframe with another dataframe
+        if type(failure_cases).__module__.startswith("databricks.koalas"):
+            failure_cases = (
+                failure_cases.rename("failure_cases")
+                .to_frame()
+                .assign(check_output=check_output)
+                .groupby("check_output")
+                .head(n_failure_cases)["failure_cases"]
+            )
+        else:
+            failure_cases = failure_cases.groupby(check_output).head(
+                n_failure_cases
+            )
     return check_output, failure_cases
 
 

pandera/checks.py

@@ -369,27 +369,27 @@ def __call__(
             ``failure_cases``: subset of the check_object that failed.
         """
         # prepare check object
-        if isinstance(df_or_series, pd.Series) or (
-            column is not None and isinstance(df_or_series, pd.DataFrame)
+        if check_utils.is_field(df_or_series) or (
+            column is not None and check_utils.is_table(df_or_series)
         ):
             check_obj = self._prepare_series_input(df_or_series, column)
-        elif isinstance(df_or_series, pd.DataFrame):
+        elif check_utils.is_table(df_or_series):
             check_obj = self._prepare_dataframe_input(df_or_series)
         else:
             raise ValueError(
-                f"object of type {df_or_series} not supported. Must be a "
-                "Series, a dictionary of Series, or DataFrame"
+                f"object of type {type(df_or_series)} not supported. Must be "
+                "a Series, a dictionary of Series, or DataFrame"
             )
 
         # apply check function to check object
         check_fn = partial(self._check_fn, **self._check_kwargs)
 
         if self.element_wise:
             check_output = (
-                check_obj.apply(check_fn, axis=1)
-                if isinstance(check_obj, pd.DataFrame)
-                else check_obj.map(check_fn)
-                if isinstance(check_obj, pd.Series)
+                check_obj.apply(check_fn, axis=1)  # type: ignore
+                if check_utils.is_table(check_obj)
+                else check_obj.map(check_fn)  # type: ignore
+                if check_utils.is_field(check_obj)
                 else check_fn(check_obj)
             )
         else:
@@ -401,12 +401,12 @@ def __call__(
         if (
             isinstance(check_obj, dict)
             or isinstance(check_output, bool)
-            or not isinstance(check_output, (pd.Series, pd.DataFrame))
+            or not check_utils.is_supported_check_obj(check_output)
             or check_obj.shape[0] != check_output.shape[0]
             or (check_obj.index != check_output.index).all()
         ):
             failure_cases = None
-        elif isinstance(check_output, pd.Series):
+        elif check_utils.is_field(check_output):
             (
                 check_output,
                 failure_cases,
@@ -416,7 +416,7 @@ def __call__(
                 ignore_na=self.ignore_na,
                 n_failure_cases=self.n_failure_cases,
             )
-        elif isinstance(check_output, pd.DataFrame):
+        elif check_utils.is_table(check_output):
             (
                 check_output,
                 failure_cases,
@@ -434,12 +434,11 @@ def __call__(
 
         check_passed = (
             check_output.all()
-            if isinstance(check_output, pd.Series)
+            if check_utils.is_field(check_output)
             else check_output.all(axis=None)
-            if isinstance(check_output, pd.DataFrame)
+            if check_utils.is_table(check_output)
             else check_output
         )
-
         return CheckResult(
             check_output, check_passed, check_obj, failure_cases
         )

pandera/engines/pandas_engine.py

@@ -444,12 +444,28 @@ class NpString(numpy_engine.String):
     """Specializes numpy_engine.String.coerce to handle pd.NA values."""
 
     def coerce(self, data_container: PandasObject) -> np.ndarray:
-        # Convert to object first to avoid
-        # TypeError: object cannot be converted to an IntegerDtype
-        data_container = data_container.astype(object)
-        return data_container.where(
-            data_container.isna(), data_container.astype(str)
-        )
+        def _to_str(obj):
+            # NOTE: this is a hack to handle the following case:
+            # koalas.Index doesn't support .where method yet, use numpy
+            reverter = None
+            if type(obj).__module__.startswith("databricks.koalas"):
+                # pylint: disable=import-outside-toplevel
+                import databricks.koalas as ks
+
+                if isinstance(obj, ks.Index):
+                    obj = obj.to_series()
+                    reverter = ks.Index
+            else:
+                obj = obj.astype(object)
+
+            obj = (
+                obj.astype(str)
+                if obj.notna().all(axis=None)
+                else obj.where(obj.isna(), obj.astype(str))
+            )
+            return obj if reverter is None else reverter(obj)
+
+        return _to_str(data_container)
 
     def check(self, pandera_dtype: dtypes.DataType) -> bool:
         return isinstance(pandera_dtype, (numpy_engine.Object, type(self)))
@@ -471,6 +487,7 @@ def check(self, pandera_dtype: dtypes.DataType) -> bool:
         object,
         np.object_,
         np.bytes_,
+        np.string_,
     ],
 )
 
@@ -517,7 +534,18 @@ def __post_init__(self):
 
     def coerce(self, data_container: PandasObject) -> PandasObject:
         def _to_datetime(col: pd.Series) -> pd.Series:
-            col = pd.to_datetime(col, **self.to_datetime_kwargs)
+            # NOTE: this is a hack to support koalas. This needs to be
+            # thoroughly tested, right now koalas returns NA when a dtype value
+            # can't be coerced into the target dtype.
+            to_datetime_fn = pd.to_datetime
+            if type(col).__module__.startswith(
+                "databricks.koalas"
+            ):  # pragma: no cover
+                # pylint: disable=import-outside-toplevel
+                import databricks.koalas as ks
+
+                to_datetime_fn = ks.to_datetime
+            col = to_datetime_fn(col, **self.to_datetime_kwargs)
             return col.astype(self.type)
 
         if isinstance(data_container, pd.DataFrame):