test_mask.py

"""
Tests for DataFrame.mask; tests DataFrame.where as a side-effect.
"""

import numpy as np

from pandas import (
    NA,
    DataFrame,
    Float64Dtype,
    Int64Dtype,
    Series,
    StringDtype,
    Timedelta,
    isna,
)
import pandas._testing as tm


class TestDataFrameMask:
    def test_mask(self):
        df = DataFrame(np.random.randn(5, 3))
        cond = df > 0

        rs = df.where(cond, np.nan)
        tm.assert_frame_equal(rs, df.mask(df <= 0))
        tm.assert_frame_equal(rs, df.mask(~cond))

        other = DataFrame(np.random.randn(5, 3))
        rs = df.where(cond, other)
        tm.assert_frame_equal(rs, df.mask(df <= 0, other))
        tm.assert_frame_equal(rs, df.mask(~cond, other))

    def test_mask2(self):
        # see GH#21891
        df = DataFrame([1, 2])
        res = df.mask([[True], [False]])

        exp = DataFrame([np.nan, 2])
        tm.assert_frame_equal(res, exp)

    def test_mask_inplace(self):
        # GH#8801
        df = DataFrame(np.random.randn(5, 3))
        cond = df > 0

        rdf = df.copy()

        return_value = rdf.where(cond, inplace=True)
        assert return_value is None
        tm.assert_frame_equal(rdf, df.where(cond))
        tm.assert_frame_equal(rdf, df.mask(~cond))

        rdf = df.copy()
        return_value = rdf.where(cond, -df, inplace=True)
        assert return_value is None
        tm.assert_frame_equal(rdf, df.where(cond, -df))
        tm.assert_frame_equal(rdf, df.mask(~cond, -df))

    def test_mask_edge_case_1xN_frame(self):
        # GH#4071
        df = DataFrame([[1, 2]])
        res = df.mask(DataFrame([[True, False]]))
        expec = DataFrame([[np.nan, 2]])
        tm.assert_frame_equal(res, expec)

    def test_mask_callable(self):
        # GH#12533
        df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        result = df.mask(lambda x: x > 4, lambda x: x + 1)
        exp = DataFrame([[1, 2, 3], [4, 6, 7], [8, 9, 10]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.mask(df > 4, df + 1))

        # return ndarray and scalar
        result = df.mask(lambda x: (x % 2 == 0).values, lambda x: 99)
        exp = DataFrame([[1, 99, 3], [99, 5, 99], [7, 99, 9]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, df.mask(df % 2 == 0, 99))

        # chain
        result = (df + 2).mask(lambda x: x > 8, lambda x: x + 10)
        exp = DataFrame([[3, 4, 5], [6, 7, 8], [19, 20, 21]])
        tm.assert_frame_equal(result, exp)
        tm.assert_frame_equal(result, (df + 2).mask((df + 2) > 8, (df + 2) + 10))

    def test_mask_dtype_bool_conversion(self):
        # GH#3733
        df = DataFrame(data=np.random.randn(100, 50))
        df = df.where(df > 0)  # create nans
        bools = df > 0
        mask = isna(df)
        expected = bools.astype(object).mask(mask)
        result = bools.mask(mask)
        tm.assert_frame_equal(result, expected)


def test_mask_stringdtype(frame_or_series):
    # GH 40824
    obj = DataFrame(
        {"A": ["foo", "bar", "baz", NA]},
        index=["id1", "id2", "id3", "id4"],
        dtype=StringDtype(),
    )
    filtered_obj = DataFrame(
        {"A": ["this", "that"]}, index=["id2", "id3"], dtype=StringDtype()
    )
    expected = DataFrame(
        {"A": [NA, "this", "that", NA]},
        index=["id1", "id2", "id3", "id4"],
        dtype=StringDtype(),
    )
    if frame_or_series is Series:
        obj = obj["A"]
        filtered_obj = filtered_obj["A"]
        expected = expected["A"]

    filter_ser = Series([False, True, True, False])
    result = obj.mask(filter_ser, filtered_obj)

    tm.assert_equal(result, expected)


def test_mask_where_dtype_timedelta():
    # https://github.com/pandas-dev/pandas/issues/39548
    df = DataFrame([Timedelta(i, unit="d") for i in range(5)])

    expected = DataFrame(np.full(5, np.nan, dtype="timedelta64[ns]"))
    tm.assert_frame_equal(df.mask(df.notna()), expected)

    expected = DataFrame(
        [np.nan, np.nan, np.nan, Timedelta("3 day"), Timedelta("4 day")]
    )
    tm.assert_frame_equal(df.where(df > Timedelta(2, unit="d")), expected)


def test_mask_return_dtype():
    # GH#50488
    ser = Series([0.0, 1.0, 2.0, 3.0], dtype=Float64Dtype())
    cond = ~ser.isna()
    other = Series([True, False, True, False])
    excepted = Series([1.0, 0.0, 1.0, 0.0], dtype=ser.dtype)
    result = ser.mask(cond, other)
    tm.assert_series_equal(result, excepted)


def test_mask_inplace_no_other():
    # GH#51685
    df = DataFrame({"a": [1.0, 2.0], "b": ["x", "y"]})
    cond = DataFrame({"a": [True, False], "b": [False, True]})
    df.mask(cond, inplace=True)
    expected = DataFrame({"a": [np.nan, 2], "b": ["x", np.nan]})
    tm.assert_frame_equal(df, expected)


def test_mask_with_na():
    # See GH #52955, if cond is NA, propagate in mask
    df = DataFrame([[1, NA], [NA, 2]], dtype=Int64Dtype())

    result1 = df.mask(df % 2 == 1, 0)
    expected1 = DataFrame([[0, NA], [NA, 2]], dtype=Int64Dtype())
    tm.assert_frame_equal(result1, expected1)

    result2 = df.mask(df[0] % 2 == 1, 0)
    expected2 = DataFrame([[0, 0], [NA, 2]], dtype=Int64Dtype())
    tm.assert_frame_equal(result2, expected2)