Make Index constructor take Series or Index objects.

databricks/koalas/base.py

@@ -45,7 +45,6 @@
 from databricks.koalas.config import get_option, option_context
 from databricks.koalas.internal import (
     InternalFrame,
-    DEFAULT_SERIES_NAME,
     NATURAL_ORDER_COLUMN_NAME,
     SPARK_DEFAULT_INDEX_NAME,
 )
@@ -125,23 +124,17 @@ def align_diff_index_ops(func, this_index_ops: "IndexOpsMixin", *args) -> "Index
 
         with option_context("compute.default_index_type", "distributed-sequence"):
             if isinstance(this_index_ops, Index) and all(isinstance(col, Index) for col in cols):
-                return (
-                    cast(
-                        Series,
-                        column_op(func)(
-                            this_index_ops.to_series().reset_index(drop=True),
-                            *[
-                                arg.to_series().reset_index(drop=True)
-                                if isinstance(arg, Index)
-                                else arg
-                                for arg in args
-                            ]
-                        ),
-                    )
-                    .sort_index()
-                    .to_frame(DEFAULT_SERIES_NAME)
-                    .set_index(DEFAULT_SERIES_NAME)
-                    .index.rename(this_index_ops.name)
+                return Index(
+                    column_op(func)(
+                        this_index_ops.to_series().reset_index(drop=True),
+                        *[
+                            arg.to_series().reset_index(drop=True)
+                            if isinstance(arg, Index)
+                            else arg
+                            for arg in args
+                        ]
+                    ).sort_index(),
+                    name=this_index_ops.name,
                 )
             elif isinstance(this_index_ops, Series):
                 this = this_index_ops.reset_index()

databricks/koalas/indexes/base.py

@@ -104,12 +104,49 @@ class Index(IndexOpsMixin):
 
     >>> ks.Index(list('abc'))
     Index(['a', 'b', 'c'], dtype='object')
+
+    From a Series:
+
+    >>> s = ks.Series([1, 2, 3], index=[10, 20, 30])
+    >>> ks.Index(s)
+    Int64Index([1, 2, 3], dtype='int64')
+
+    From an Index:
+
+    >>> idx = ks.Index([1, 2, 3])
+    >>> ks.Index(idx)
+    Int64Index([1, 2, 3], dtype='int64')
     """
 
     def __new__(cls, data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs):
         if not is_hashable(name):
             raise TypeError("Index.name must be a hashable type")
 
+        if isinstance(data, Series):
+            if dtype is not None:
+                data = data.astype(dtype)
+            if name is not None:
+                data = data.rename(name)
+
+            internal = InternalFrame(
+                spark_frame=data._internal.spark_frame,
+                index_spark_columns=data._internal.data_spark_columns,
+                index_names=data._internal.column_labels,
+                index_dtypes=data._internal.data_dtypes,
+                column_labels=[],
+                data_spark_columns=[],
+                data_dtypes=[],
+            )
+            return DataFrame(internal).index
+        elif isinstance(data, Index):
+            if copy:
+                data = data.copy()
+            if dtype is not None:
+                data = data.astype(dtype)
+            if name is not None:
+                data = data.rename(name)
+            return data
+
         return ks.from_pandas(
             pd.Index(
                 data=data, dtype=dtype, copy=copy, name=name, tupleize_cols=tupleize_cols, **kwargs

databricks/koalas/indexes/datetimes.py

@@ -23,15 +23,13 @@
 from databricks import koalas as ks
 from databricks.koalas.indexes.base import Index
 from databricks.koalas.missing.indexes import MissingPandasLikeDatetimeIndex
+from databricks.koalas.series import Series
 
 
 class DatetimeIndex(Index):
     """
     Immutable ndarray-like of datetime64 data.
 
-    Represented internally as int64, and which can be boxed to Timestamp objects
-    that are subclasses of datetime and carry metadata.
-
     Parameters
     ----------
     data : array-like (1-dimensional), optional
@@ -63,7 +61,7 @@ class DatetimeIndex(Index):
         If True, parse dates in `data` with the day first order.
     yearfirst : bool, default False
         If True parse dates in `data` with the year first order.
-    dtype : numpy.dtype or DatetimeTZDtype or str, default None
+    dtype : numpy.dtype or str, default None
         Note that the only NumPy dtype allowed is ‘datetime64[ns]’.
     copy : bool, default False
         Make a copy of input ndarray.
@@ -79,6 +77,19 @@ class DatetimeIndex(Index):
     --------
     >>> ks.DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'])
     DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None)
+
+    From a Series:
+
+    >>> from datetime import datetime
+    >>> s = ks.Series([datetime(2021, 3, 1), datetime(2021, 3, 2)], index=[10, 20])
+    >>> ks.DatetimeIndex(s)
+    DatetimeIndex(['2021-03-01', '2021-03-02'], dtype='datetime64[ns]', freq=None)
+
+    From an Index:
+
+    >>> idx = ks.DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'])
+    >>> ks.DatetimeIndex(idx)
+    DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None)
     """
 
     def __new__(
@@ -97,6 +108,11 @@ def __new__(
         if not is_hashable(name):
             raise TypeError("Index.name must be a hashable type")
 
+        if isinstance(data, (Series, Index)):
+            if dtype is None:
+                dtype = "datetime64[ns]"
+            return Index(data, dtype=dtype, copy=copy, name=name)
+
         kwargs = dict(
             data=data,
             normalize=normalize,

databricks/koalas/indexes/numeric.py

@@ -18,6 +18,7 @@
 
 from databricks import koalas as ks
 from databricks.koalas.indexes.base import Index
+from databricks.koalas.series import Series
 
 
 class NumericIndex(Index):
@@ -65,12 +66,29 @@ class Int64Index(IntegerIndex):
     --------
     >>> ks.Int64Index([1, 2, 3])
     Int64Index([1, 2, 3], dtype='int64')
+
+    From a Series:
+
+    >>> s = ks.Series([1, 2, 3], index=[10, 20, 30])
+    >>> ks.Int64Index(s)
+    Int64Index([1, 2, 3], dtype='int64')
+
+    From an Index:
+
+    >>> idx = ks.Index([1, 2, 3])
+    >>> ks.Int64Index(idx)
+    Int64Index([1, 2, 3], dtype='int64')
     """
 
     def __new__(cls, data=None, dtype=None, copy=False, name=None):
         if not is_hashable(name):
             raise TypeError("Index.name must be a hashable type")
 
+        if isinstance(data, (Series, Index)):
+            if dtype is None:
+                dtype = "int64"
+            return Index(data, dtype=dtype, copy=copy, name=name)
+
         return ks.from_pandas(pd.Int64Index(data=data, dtype=dtype, copy=copy, name=name))
 
 
@@ -102,10 +120,27 @@ class Float64Index(NumericIndex):
     --------
     >>> ks.Float64Index([1.0, 2.0, 3.0])
     Float64Index([1.0, 2.0, 3.0], dtype='float64')
+
+    From a Series:
+
+    >>> s = ks.Series([1, 2, 3], index=[10, 20, 30])
+    >>> ks.Float64Index(s)
+    Float64Index([1.0, 2.0, 3.0], dtype='float64')
+
+    From an Index:
+
+    >>> idx = ks.Index([1, 2, 3])
+    >>> ks.Float64Index(idx)
+    Float64Index([1.0, 2.0, 3.0], dtype='float64')
     """
 
     def __new__(cls, data=None, dtype=None, copy=False, name=None):
         if not is_hashable(name):
             raise TypeError("Index.name must be a hashable type")
 
+        if isinstance(data, (Series, Index)):
+            if dtype is None:
+                dtype = "float64"
+            return Index(data, dtype=dtype, copy=copy, name=name)
+
         return ks.from_pandas(pd.Float64Index(data=data, dtype=dtype, copy=copy, name=name))

databricks/koalas/tests/test_indexes.py

@@ -45,7 +45,7 @@ def pdf(self):
     def kdf(self):
         return ks.from_pandas(self.pdf)
 
-    def test_index(self):
+    def test_index_basic(self):
         for pdf in [
             pd.DataFrame(np.random.randn(10, 5), index=np.random.randint(100, size=10)),
             pd.DataFrame(
@@ -63,6 +63,44 @@ def test_index(self):
             self.assert_eq(kdf.index, pdf.index)
             self.assert_eq(type(kdf.index).__name__, type(pdf.index).__name__)
 
+    def test_index_from_series(self):
+        pser = pd.Series([1, 2, 3], name="a", index=[10, 20, 30])
+        kser = ks.from_pandas(pser)
+
+        self.assert_eq(ks.Index(kser), pd.Index(pser))
+        self.assert_eq(ks.Index(kser, dtype="float"), pd.Index(pser, dtype="float"))
+        self.assert_eq(ks.Index(kser, name="x"), pd.Index(pser, name="x"))
+
+        if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
+            self.assert_eq(ks.Int64Index(kser), pd.Int64Index(pser))
+            self.assert_eq(ks.Float64Index(kser), pd.Float64Index(pser))
+        else:
+            self.assert_eq(ks.Int64Index(kser), pd.Int64Index(pser).rename("a"))
+            self.assert_eq(ks.Float64Index(kser), pd.Float64Index(pser).rename("a"))
+
+        pser = pd.Series([datetime(2021, 3, 1), datetime(2021, 3, 2)], name="x", index=[10, 20])
+        kser = ks.from_pandas(pser)
+
+        self.assert_eq(ks.Index(kser), pd.Index(pser))
+        self.assert_eq(ks.DatetimeIndex(kser), pd.DatetimeIndex(pser))
+
+    def test_index_from_index(self):
+        pidx = pd.Index([1, 2, 3], name="a")
+        kidx = ks.from_pandas(pidx)
+
+        self.assert_eq(ks.Index(kidx), pd.Index(pidx))
+        self.assert_eq(ks.Index(kidx, dtype="float"), pd.Index(pidx, dtype="float"))
+        self.assert_eq(ks.Index(kidx, name="x"), pd.Index(pidx, name="x"))
+
+        self.assert_eq(ks.Int64Index(kidx), pd.Int64Index(pidx))
+        self.assert_eq(ks.Float64Index(kidx), pd.Float64Index(pidx))
+
+        pidx = pd.DatetimeIndex(["2021-03-01", "2021-03-02"])
+        kidx = ks.from_pandas(pidx)
+
+        self.assert_eq(ks.Index(kidx), pd.Index(pidx))
+        self.assert_eq(ks.DatetimeIndex(kidx), pd.DatetimeIndex(pidx))
+
     def test_index_getattr(self):
         kidx = self.kdf.index
         item = "databricks"