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import numpy as np
import pytest
from pandas.compat.numpy import np_version_gte1p25
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 = (
r"Can only compare identically-labeled \(both index and columns\) 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 = (
r"Can only compare identically-labeled \(both index and columns\) 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)
@pytest.mark.parametrize(
"val1,val2",
[(4, pd.NA), (pd.NA, pd.NA), (pd.NA, 4)],
)
def test_compare_ea_and_np_dtype(val1, val2):
# GH 48966
arr = [4.0, val1]
ser = pd.Series([1, val2], dtype="Int64")
df1 = pd.DataFrame({"a": arr, "b": [1.0, 2]})
df2 = pd.DataFrame({"a": ser, "b": [1.0, 2]})
expected = pd.DataFrame(
{
("a", "self"): arr,
("a", "other"): ser,
("b", "self"): np.nan,
("b", "other"): np.nan,
}
)
if val1 is pd.NA and val2 is pd.NA:
# GH#18463 TODO: is this really the desired behavior?
expected.loc[1, ("a", "self")] = np.nan
if val1 is pd.NA and np_version_gte1p25:
# can't compare with numpy array if it contains pd.NA
with pytest.raises(TypeError, match="boolean value of NA is ambiguous"):
result = df1.compare(df2, keep_shape=True)
else:
result = df1.compare(df2, keep_shape=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"df1_val,df2_val,diff_self,diff_other",
[
(4, 3, 4, 3),
(4, 4, pd.NA, pd.NA),
(4, pd.NA, 4, pd.NA),
(pd.NA, pd.NA, pd.NA, pd.NA),
],
)
def test_compare_nullable_int64_dtype(df1_val, df2_val, diff_self, diff_other):
# GH 48966
df1 = pd.DataFrame({"a": pd.Series([df1_val, pd.NA], dtype="Int64"), "b": [1.0, 2]})
df2 = df1.copy()
df2.loc[0, "a"] = df2_val
expected = pd.DataFrame(
{
("a", "self"): pd.Series([diff_self, pd.NA], dtype="Int64"),
("a", "other"): pd.Series([diff_other, pd.NA], dtype="Int64"),
("b", "self"): np.nan,
("b", "other"): np.nan,
}
)
result = df1.compare(df2, keep_shape=True)
tm.assert_frame_equal(result, expected)