Add more tests for the dataframe interchange protocol

protocol/tests/conftest.py

@@ -0,0 +1,7 @@
+import pytest
+import pandas as pd
+
+
+@pytest.fixture
+def constructor_frame(data):
+    return pd.DataFrame(data)

protocol/tests/test_protocol.py

@@ -0,0 +1,142 @@
+import pytest
+import numpy as np
+from conftest import *
+
+
+@pytest.mark.parametrize("test_data",
+                         [
+                             ({'a': [np.array([1, 2, 3]), np.array([4, 5, 6])],
+                               'b': [np.array([1.5, 2.0, 3.2]), np.array([4.1, 5.7, 6.9])]},
+                              np.object_, None),
+                             ({'a': [1.5, 2.5, 3.5], 'b': [9.2, 10.5, 11.8]}, np.float64, None),
+                             ({'A': [1, 2, 3, 4], 'B': [1, 2, 3, 4]}, np.int64, np.float64)
+                         ],
+                         ids=["array_data", "float_data", "int_data"])
+def test_only_one_data(test_data):
+    data, dtype, new_dtype = test_data
+    columns = list(data.keys())
+    df = constructor_frame(data)
+    df2 = df.__dataframe__()
+    new_dtype = dtype if new_dtype is None else new_dtype
+    assert df.columns.values.tolist() == columns
+    val = len(df[columns[0]])-1
+    column_size = df.size
+    for column in columns:
+        assert df[column].tolist() == df[column].tolist()
+        assert df[column].dtype.type is dtype
+        assert df2.get_column_by_name(column).null_count == 0
+        assert df2.get_column_by_name(column).size == column_size
+        assert df2.get_column_by_name(column).offset == 0
+        assert not df2["x"].is_masked
+        n = np.random.randint(0, val)
+        (df[column])[n] = None
+        assert df[column].dtype.type is new_dtype
+        assert df2.get_column_by_name(column).null_count == 1
+
+
+def test_float_int():
+    df = constructor_frame({'a': [1, 2, 3], 'b': [3, 4, 5],
+                       'c': [1.5, 2.5, 3.5], 'd': [9, 10, 11]})
+    df2 = df.__dataframe__()
+    columns = ['a', 'b', 'c', 'd']
+    assert df.columns.values.tolist() == columns
+    for column in columns:
+        assert df[column].tolist() == df[column].tolist()
+        if column is 'c':
+            assert df[column].dtype.type is np.float64
+        else:
+            assert df[column].dtype.type is np.int64
+
+        assert df2.get_column_by_name(column).null_count == 0
+        assert df2.get_column_by_name(column).size == 3
+        assert df2.get_column_by_name(column).offset == 0
+
+        n = np.random.randint(0, 2)
+        (df[column])[n] = None
+        assert df[column].dtype.type is np.float64
+        assert df2.get_column_by_name(column).null_count == 1
+
+
+def test_mixed_intfloatbool():
+    df = constructor_frame({"x": np.array([True, True, False]),
+                       "y": np.array([1, 2, 0]),
+                       "z": np.array([9.2, 10.5, 11.8])})
+    df2 = df.__dataframe__()
+    columns = ['x', 'y', 'z']
+    assert df.columns.values.tolist() == columns
+    for column in columns:
+        assert df[column].tolist() == df[column].tolist()
+        assert df2.get_column_by_name(column).null_count == 0
+        assert df2.get_column_by_name(column).size == 3
+        assert df2.get_column_by_name(column).offset == 0
+
+    assert df["x"].dtype.type is np.bool_
+    assert df["y"].dtype.type is np.int32
+    assert df["z"].dtype.type is np.float64
+
+    assert df2.get_column_by_name("x")._allow_copy == True
+
+    for column in columns:
+        n = np.random.randint(0, 2)
+        (df[column])[n] = None
+        if column is "x":
+            assert df[column].dtype.type is np.object_
+        else:
+            assert df[column].dtype.type is np.float64
+        assert df2.get_column_by_name(column).null_count == 1
+
+
+def test_string_dtype():
+    df = constructor_frame({"A": ["a", "b", "cdef", "", "g"]})
+    df2 = df.__dataframe__()
+    columns = ['A']
+    assert df.columns.values.tolist() == columns
+    for column in columns:
+        assert df[column].tolist() == df[column].tolist()
+        assert df[column].dtype.type is np.object_
+        assert df2.get_column_by_name(column).null_count == 0
+
+
+def test_categorical():
+    df = constructor_frame({"year": [2012, 2013, 2015, 2019], "weekday": [0, 1, 4, 6]})
+    df = df.categorize("year", min_value=2012, max_value=2019)
+    df = df.categorize("weekday", labels=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"])
+    # Some detailed testing for correctness of dtype and null handling:
+    col = df.__dataframe__().get_column_by_name("year")
+    assert col.describe_categorical == (False, True, {0: 2012, 1: 2013, 2: 2014, 3: 2015, 4: 2016, 5: 2017, 6: 2018, 7: 2019})
+    assert col.describe_null == (0, None)
+    col2 = df.__dataframe__().get_column_by_name("weekday")
+    assert col2.describe_categorical == (False, True, {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"})
+    assert col2.describe_null == (0, None)
+
+
+def test_dataframe():
+    df = constructor_frame({"x": [True, True, False], "y": [1, 2, 0], "z": [9.2, 10.5, 11.8]})
+    df2 = df.__dataframe__()
+    assert df2._allow_copy == True
+    assert df2.num_columns() == 3
+    assert df2.num_rows() == 3
+    assert df2.num_chunks() == 1
+    assert df2.column_names() == ["x", "y", "z"]
+    assert df2.select_columns((0, 2))._df[:, 0].tolist() == df2.select_columns_by_name(("x", "z"))._df[:, 0].tolist()
+    assert df2.select_columns((0, 2))._df[:, 1].tolist() == df2.select_columns_by_name(("x", "z"))._df[:, 1].tolist()
+
+
+def test_chunks():
+    df = constructor_frame({"x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]})
+    df2 = df.__dataframe__()
+    chunk_iter = iter(df2.get_chunks(3))
+    chunk = next(chunk_iter)
+    assert chunk.num_rows() == 4
+    chunk = next(chunk_iter)
+    assert chunk.num_rows() == 4
+    chunk = next(chunk_iter)
+    assert chunk.num_rows() == 2
+    with pytest.raises(StopIteration):
+        chunk = next(chunk_iter)
+
+
+def test_get_chunks():
+    df = constructor_frame({"x": [1]})
+    df2 = df.__dataframe__()
+    assert df2.get_chunks() == 1