255 lines
8.0 KiB
Python
255 lines
8.0 KiB
Python
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
|
||
|
|
def test_compare_axis(align_axis):
|
||
|
|
# GH#30429
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
columns=["col1", "col2", "col3"],
|
||
|
|
)
|
||
|
|
df2 = df.copy()
|
||
|
|
df2.loc[0, "col1"] = "c"
|
||
|
|
df2.loc[2, "col3"] = 4.0
|
||
|
|
|
||
|
|
result = df.compare(df2, align_axis=align_axis)
|
||
|
|
|
||
|
|
if align_axis in (1, "columns"):
|
||
|
|
indices = pd.Index([0, 2])
|
||
|
|
columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]])
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]],
|
||
|
|
index=indices,
|
||
|
|
columns=columns,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]])
|
||
|
|
columns = pd.Index(["col1", "col3"])
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]],
|
||
|
|
index=indices,
|
||
|
|
columns=columns,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"keep_shape, keep_equal",
|
||
|
|
[
|
||
|
|
(True, False),
|
||
|
|
(False, True),
|
||
|
|
(True, True),
|
||
|
|
# False, False case is already covered in test_compare_axis
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_compare_various_formats(keep_shape, keep_equal):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
columns=["col1", "col2", "col3"],
|
||
|
|
)
|
||
|
|
df2 = df.copy()
|
||
|
|
df2.loc[0, "col1"] = "c"
|
||
|
|
df2.loc[2, "col3"] = 4.0
|
||
|
|
|
||
|
|
result = df.compare(df2, keep_shape=keep_shape, keep_equal=keep_equal)
|
||
|
|
|
||
|
|
if keep_shape:
|
||
|
|
indices = pd.Index([0, 1, 2])
|
||
|
|
columns = pd.MultiIndex.from_product(
|
||
|
|
[["col1", "col2", "col3"], ["self", "other"]]
|
||
|
|
)
|
||
|
|
if keep_equal:
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[
|
||
|
|
["a", "c", 1.0, 1.0, 1.0, 1.0],
|
||
|
|
["b", "b", 2.0, 2.0, 2.0, 2.0],
|
||
|
|
["c", "c", np.nan, np.nan, 3.0, 4.0],
|
||
|
|
],
|
||
|
|
index=indices,
|
||
|
|
columns=columns,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[
|
||
|
|
["a", "c", np.nan, np.nan, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, np.nan, np.nan, 3.0, 4.0],
|
||
|
|
],
|
||
|
|
index=indices,
|
||
|
|
columns=columns,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
indices = pd.Index([0, 2])
|
||
|
|
columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]])
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[["a", "c", 1.0, 1.0], ["c", "c", 3.0, 4.0]], index=indices, columns=columns
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compare_with_equal_nulls():
|
||
|
|
# We want to make sure two NaNs are considered the same
|
||
|
|
# and dropped where applicable
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
columns=["col1", "col2", "col3"],
|
||
|
|
)
|
||
|
|
df2 = df.copy()
|
||
|
|
df2.loc[0, "col1"] = "c"
|
||
|
|
|
||
|
|
result = df.compare(df2)
|
||
|
|
indices = pd.Index([0])
|
||
|
|
columns = pd.MultiIndex.from_product([["col1"], ["self", "other"]])
|
||
|
|
expected = pd.DataFrame([["a", "c"]], index=indices, columns=columns)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compare_with_non_equal_nulls():
|
||
|
|
# We want to make sure the relevant NaNs do not get dropped
|
||
|
|
# even if the entire row or column are NaNs
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
columns=["col1", "col2", "col3"],
|
||
|
|
)
|
||
|
|
df2 = df.copy()
|
||
|
|
df2.loc[0, "col1"] = "c"
|
||
|
|
df2.loc[2, "col3"] = np.nan
|
||
|
|
|
||
|
|
result = df.compare(df2)
|
||
|
|
|
||
|
|
indices = pd.Index([0, 2])
|
||
|
|
columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]])
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
[["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, np.nan]],
|
||
|
|
index=indices,
|
||
|
|
columns=columns,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("align_axis", [0, 1])
|
||
|
|
def test_compare_multi_index(align_axis):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}
|
||
|
|
)
|
||
|
|
df.columns = pd.MultiIndex.from_arrays([["a", "a", "b"], ["col1", "col2", "col3"]])
|
||
|
|
df.index = pd.MultiIndex.from_arrays([["x", "x", "y"], [0, 1, 2]])
|
||
|
|
|
||
|
|
df2 = df.copy()
|
||
|
|
df2.iloc[0, 0] = "c"
|
||
|
|
df2.iloc[2, 2] = 4.0
|
||
|
|
|
||
|
|
result = df.compare(df2, align_axis=align_axis)
|
||
|
|
|
||
|
|
if align_axis == 0:
|
||
|
|
indices = pd.MultiIndex.from_arrays(
|
||
|
|
[["x", "x", "y", "y"], [0, 0, 2, 2], ["self", "other", "self", "other"]]
|
||
|
|
)
|
||
|
|
columns = pd.MultiIndex.from_arrays([["a", "b"], ["col1", "col3"]])
|
||
|
|
data = [["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]]
|
||
|
|
else:
|
||
|
|
indices = pd.MultiIndex.from_arrays([["x", "y"], [0, 2]])
|
||
|
|
columns = pd.MultiIndex.from_arrays(
|
||
|
|
[
|
||
|
|
["a", "a", "b", "b"],
|
||
|
|
["col1", "col1", "col3", "col3"],
|
||
|
|
["self", "other", "self", "other"],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
data = [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]]
|
||
|
|
|
||
|
|
expected = pd.DataFrame(data=data, index=indices, columns=columns)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compare_unaligned_objects():
|
||
|
|
# test DataFrames with different indices
|
||
|
|
msg = "Can only compare identically-labeled DataFrame objects"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"])
|
||
|
|
df2 = pd.DataFrame([1, 2, 3], index=["a", "b", "d"])
|
||
|
|
df1.compare(df2)
|
||
|
|
|
||
|
|
# test DataFrames with different shapes
|
||
|
|
msg = "Can only compare identically-labeled DataFrame objects"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df1 = pd.DataFrame(np.ones((3, 3)))
|
||
|
|
df2 = pd.DataFrame(np.zeros((2, 1)))
|
||
|
|
df1.compare(df2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compare_result_names():
|
||
|
|
# GH 44354
|
||
|
|
df1 = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
)
|
||
|
|
df2 = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"col1": ["c", "b", "c"],
|
||
|
|
"col2": [1.0, 2.0, np.nan],
|
||
|
|
"col3": [1.0, 2.0, np.nan],
|
||
|
|
},
|
||
|
|
)
|
||
|
|
result = df1.compare(df2, result_names=("left", "right"))
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
("col1", "left"): {0: "a", 2: np.nan},
|
||
|
|
("col1", "right"): {0: "c", 2: np.nan},
|
||
|
|
("col3", "left"): {0: np.nan, 2: 3.0},
|
||
|
|
("col3", "right"): {0: np.nan, 2: np.nan},
|
||
|
|
}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"result_names",
|
||
|
|
[
|
||
|
|
[1, 2],
|
||
|
|
"HK",
|
||
|
|
{"2": 2, "3": 3},
|
||
|
|
3,
|
||
|
|
3.0,
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_invalid_input_result_names(result_names):
|
||
|
|
# GH 44354
|
||
|
|
df1 = pd.DataFrame(
|
||
|
|
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
|
||
|
|
)
|
||
|
|
df2 = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"col1": ["c", "b", "c"],
|
||
|
|
"col2": [1.0, 2.0, np.nan],
|
||
|
|
"col3": [1.0, 2.0, np.nan],
|
||
|
|
},
|
||
|
|
)
|
||
|
|
with pytest.raises(
|
||
|
|
TypeError,
|
||
|
|
match=(
|
||
|
|
f"Passing 'result_names' as a {type(result_names)} is not "
|
||
|
|
"supported. Provide 'result_names' as a tuple instead."
|
||
|
|
),
|
||
|
|
):
|
||
|
|
df1.compare(df2, result_names=result_names)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compare_ea_and_np_dtype():
|
||
|
|
# GH#44014
|
||
|
|
df1 = pd.DataFrame({"a": [4.0, 4], "b": [1.0, 2]})
|
||
|
|
df2 = pd.DataFrame({"a": pd.Series([1, pd.NA], dtype="Int64"), "b": [1.0, 2]})
|
||
|
|
result = df1.compare(df2, keep_shape=True)
|
||
|
|
expected = pd.DataFrame(
|
||
|
|
{
|
||
|
|
("a", "self"): [4.0, np.nan],
|
||
|
|
("a", "other"): pd.Series([1, pd.NA], dtype="Int64"),
|
||
|
|
("b", "self"): np.nan,
|
||
|
|
("b", "other"): np.nan,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|