183 lines
6.0 KiB
Python
183 lines
6.0 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)
|