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312 lines
10 KiB
312 lines
10 KiB
1 year ago
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from functools import partial
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import re
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.api.types import is_extension_array_dtype
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dtypes = [
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"int64",
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"Int64",
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{"A": "int64", "B": "Int64"},
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]
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@pytest.mark.parametrize("dtype", dtypes)
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def test_unary_unary(dtype):
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# unary input, unary output
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values = np.array([[-1, -1], [1, 1]], dtype="int64")
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df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
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result = np.positive(df)
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expected = pd.DataFrame(
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np.positive(values), index=df.index, columns=df.columns
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).astype(dtype)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("dtype", dtypes)
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def test_unary_binary(request, dtype):
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# unary input, binary output
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if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
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request.node.add_marker(
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pytest.mark.xfail(
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reason="Extension / mixed with multiple outputs not implemented."
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)
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)
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values = np.array([[-1, -1], [1, 1]], dtype="int64")
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df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
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result_pandas = np.modf(df)
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assert isinstance(result_pandas, tuple)
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assert len(result_pandas) == 2
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expected_numpy = np.modf(values)
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for result, b in zip(result_pandas, expected_numpy):
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expected = pd.DataFrame(b, index=df.index, columns=df.columns)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("dtype", dtypes)
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def test_binary_input_dispatch_binop(dtype):
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# binop ufuncs are dispatched to our dunder methods.
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values = np.array([[-1, -1], [1, 1]], dtype="int64")
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df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype)
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result = np.add(df, df)
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expected = pd.DataFrame(
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np.add(values, values), index=df.index, columns=df.columns
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).astype(dtype)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"func,arg,expected",
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[
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(np.add, 1, [2, 3, 4, 5]),
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(
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partial(np.add, where=[[False, True], [True, False]]),
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np.array([[1, 1], [1, 1]]),
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[0, 3, 4, 0],
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),
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(np.power, np.array([[1, 1], [2, 2]]), [1, 2, 9, 16]),
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(np.subtract, 2, [-1, 0, 1, 2]),
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(
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partial(np.negative, where=np.array([[False, True], [True, False]])),
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None,
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[0, -2, -3, 0],
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),
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],
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)
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def test_ufunc_passes_args(func, arg, expected):
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# GH#40662
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arr = np.array([[1, 2], [3, 4]])
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df = pd.DataFrame(arr)
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result_inplace = np.zeros_like(arr)
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# 1-argument ufunc
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if arg is None:
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result = func(df, out=result_inplace)
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else:
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result = func(df, arg, out=result_inplace)
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expected = np.array(expected).reshape(2, 2)
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tm.assert_numpy_array_equal(result_inplace, expected)
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expected = pd.DataFrame(expected)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("dtype_a", dtypes)
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@pytest.mark.parametrize("dtype_b", dtypes)
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def test_binary_input_aligns_columns(request, dtype_a, dtype_b):
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if (
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is_extension_array_dtype(dtype_a)
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or isinstance(dtype_a, dict)
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or is_extension_array_dtype(dtype_b)
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or isinstance(dtype_b, dict)
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):
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request.node.add_marker(
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pytest.mark.xfail(
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reason="Extension / mixed with multiple inputs not implemented."
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)
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)
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df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}).astype(dtype_a)
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if isinstance(dtype_a, dict) and isinstance(dtype_b, dict):
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dtype_b = dtype_b.copy()
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dtype_b["C"] = dtype_b.pop("B")
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df2 = pd.DataFrame({"A": [1, 2], "C": [3, 4]}).astype(dtype_b)
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# As of 2.0, align first before applying the ufunc
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result = np.heaviside(df1, df2)
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expected = np.heaviside(
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np.array([[1, 3, np.nan], [2, 4, np.nan]]),
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np.array([[1, np.nan, 3], [2, np.nan, 4]]),
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)
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expected = pd.DataFrame(expected, index=[0, 1], columns=["A", "B", "C"])
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tm.assert_frame_equal(result, expected)
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result = np.heaviside(df1, df2.values)
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expected = pd.DataFrame([[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"])
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("dtype", dtypes)
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def test_binary_input_aligns_index(request, dtype):
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if is_extension_array_dtype(dtype) or isinstance(dtype, dict):
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request.node.add_marker(
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pytest.mark.xfail(
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reason="Extension / mixed with multiple inputs not implemented."
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)
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)
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df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "b"]).astype(dtype)
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df2 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "c"]).astype(dtype)
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result = np.heaviside(df1, df2)
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expected = np.heaviside(
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np.array([[1, 3], [3, 4], [np.nan, np.nan]]),
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np.array([[1, 3], [np.nan, np.nan], [3, 4]]),
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)
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# TODO(FloatArray): this will be Float64Dtype.
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expected = pd.DataFrame(expected, index=["a", "b", "c"], columns=["A", "B"])
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tm.assert_frame_equal(result, expected)
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result = np.heaviside(df1, df2.values)
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expected = pd.DataFrame(
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[[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"], index=["a", "b"]
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)
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tm.assert_frame_equal(result, expected)
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def test_binary_frame_series_raises():
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# We don't currently implement
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df = pd.DataFrame({"A": [1, 2]})
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with pytest.raises(NotImplementedError, match="logaddexp"):
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np.logaddexp(df, df["A"])
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with pytest.raises(NotImplementedError, match="logaddexp"):
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np.logaddexp(df["A"], df)
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def test_unary_accumulate_axis():
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# https://github.com/pandas-dev/pandas/issues/39259
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df = pd.DataFrame({"a": [1, 3, 2, 4]})
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result = np.maximum.accumulate(df)
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expected = pd.DataFrame({"a": [1, 3, 3, 4]})
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tm.assert_frame_equal(result, expected)
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df = pd.DataFrame({"a": [1, 3, 2, 4], "b": [0.1, 4.0, 3.0, 2.0]})
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result = np.maximum.accumulate(df)
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# in theory could preserve int dtype for default axis=0
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expected = pd.DataFrame({"a": [1.0, 3.0, 3.0, 4.0], "b": [0.1, 4.0, 4.0, 4.0]})
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tm.assert_frame_equal(result, expected)
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result = np.maximum.accumulate(df, axis=0)
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tm.assert_frame_equal(result, expected)
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result = np.maximum.accumulate(df, axis=1)
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expected = pd.DataFrame({"a": [1.0, 3.0, 2.0, 4.0], "b": [1.0, 4.0, 3.0, 4.0]})
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tm.assert_frame_equal(result, expected)
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def test_frame_outer_disallowed():
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df = pd.DataFrame({"A": [1, 2]})
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with pytest.raises(NotImplementedError, match=""):
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# deprecation enforced in 2.0
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np.subtract.outer(df, df)
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def test_alignment_deprecation_enforced():
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# Enforced in 2.0
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# https://github.com/pandas-dev/pandas/issues/39184
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df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]})
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s1 = pd.Series([1, 2], index=["a", "b"])
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s2 = pd.Series([1, 2], index=["b", "c"])
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# binary dataframe / dataframe
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expected = pd.DataFrame({"a": [2, 4, 6], "b": [8, 10, 12]})
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with tm.assert_produces_warning(None):
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# aligned -> no warning!
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result = np.add(df1, df1)
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tm.assert_frame_equal(result, expected)
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result = np.add(df1, df2.values)
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tm.assert_frame_equal(result, expected)
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result = np.add(df1, df2)
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expected = pd.DataFrame({"a": [np.nan] * 3, "b": [5, 7, 9], "c": [np.nan] * 3})
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tm.assert_frame_equal(result, expected)
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result = np.add(df1.values, df2)
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expected = pd.DataFrame({"b": [2, 4, 6], "c": [8, 10, 12]})
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tm.assert_frame_equal(result, expected)
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# binary dataframe / series
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expected = pd.DataFrame({"a": [2, 3, 4], "b": [6, 7, 8]})
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with tm.assert_produces_warning(None):
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# aligned -> no warning!
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result = np.add(df1, s1)
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tm.assert_frame_equal(result, expected)
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result = np.add(df1, s2.values)
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tm.assert_frame_equal(result, expected)
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expected = pd.DataFrame(
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{"a": [np.nan] * 3, "b": [5.0, 6.0, 7.0], "c": [np.nan] * 3}
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)
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result = np.add(df1, s2)
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tm.assert_frame_equal(result, expected)
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msg = "Cannot apply ufunc <ufunc 'add'> to mixed DataFrame and Series inputs."
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with pytest.raises(NotImplementedError, match=msg):
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np.add(s2, df1)
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def test_alignment_deprecation_many_inputs_enforced():
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# Enforced in 2.0
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# https://github.com/pandas-dev/pandas/issues/39184
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# test that the deprecation also works with > 2 inputs -> using a numba
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# written ufunc for this because numpy itself doesn't have such ufuncs
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numba = pytest.importorskip("numba")
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@numba.vectorize([numba.float64(numba.float64, numba.float64, numba.float64)])
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def my_ufunc(x, y, z):
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return x + y + z
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df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]})
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df3 = pd.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]})
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result = my_ufunc(df1, df2, df3)
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expected = pd.DataFrame(np.full((3, 3), np.nan), columns=["a", "b", "c"])
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tm.assert_frame_equal(result, expected)
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# all aligned -> no warning
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with tm.assert_produces_warning(None):
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result = my_ufunc(df1, df1, df1)
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expected = pd.DataFrame([[3.0, 12.0], [6.0, 15.0], [9.0, 18.0]], columns=["a", "b"])
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tm.assert_frame_equal(result, expected)
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# mixed frame / arrays
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msg = (
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r"operands could not be broadcast together with shapes \(3,3\) \(3,3\) \(3,2\)"
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)
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with pytest.raises(ValueError, match=msg):
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my_ufunc(df1, df2, df3.values)
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# single frame -> no warning
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with tm.assert_produces_warning(None):
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result = my_ufunc(df1, df2.values, df3.values)
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tm.assert_frame_equal(result, expected)
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# takes indices of first frame
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msg = (
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r"operands could not be broadcast together with shapes \(3,2\) \(3,3\) \(3,3\)"
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)
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with pytest.raises(ValueError, match=msg):
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my_ufunc(df1.values, df2, df3)
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def test_array_ufuncs_for_many_arguments():
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# GH39853
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def add3(x, y, z):
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return x + y + z
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ufunc = np.frompyfunc(add3, 3, 1)
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df = pd.DataFrame([[1, 2], [3, 4]])
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result = ufunc(df, df, 1)
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expected = pd.DataFrame([[3, 5], [7, 9]], dtype=object)
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tm.assert_frame_equal(result, expected)
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ser = pd.Series([1, 2])
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msg = (
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"Cannot apply ufunc <ufunc 'add3 (vectorized)'> "
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"to mixed DataFrame and Series inputs."
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)
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with pytest.raises(NotImplementedError, match=re.escape(msg)):
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ufunc(df, df, ser)
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