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import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
class BaseMissingTests:
def test_isna(self, data_missing):
expected = np.array([True, False])
result = pd.isna(data_missing)
tm.assert_numpy_array_equal(result, expected)
result = pd.Series(data_missing).isna()
expected = pd.Series(expected)
tm.assert_series_equal(result, expected)
# GH 21189
result = pd.Series(data_missing).drop([0, 1]).isna()
expected = pd.Series([], dtype=bool)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("na_func", ["isna", "notna"])
def test_isna_returns_copy(self, data_missing, na_func):
result = pd.Series(data_missing)
expected = result.copy()
mask = getattr(result, na_func)()
if isinstance(mask.dtype, pd.SparseDtype):
mask = np.array(mask)
mask[:] = True
tm.assert_series_equal(result, expected)
def test_dropna_array(self, data_missing):
result = data_missing.dropna()
expected = data_missing[[1]]
tm.assert_extension_array_equal(result, expected)
def test_dropna_series(self, data_missing):
ser = pd.Series(data_missing)
result = ser.dropna()
expected = ser.iloc[[1]]
tm.assert_series_equal(result, expected)
def test_dropna_frame(self, data_missing):
df = pd.DataFrame({"A": data_missing}, columns=pd.Index(["A"], dtype=object))
# defaults
result = df.dropna()
expected = df.iloc[[1]]
tm.assert_frame_equal(result, expected)
# axis = 1
result = df.dropna(axis="columns")
expected = pd.DataFrame(index=pd.RangeIndex(2), columns=pd.Index([]))
tm.assert_frame_equal(result, expected)
# multiple
df = pd.DataFrame({"A": data_missing, "B": [1, np.nan]})
result = df.dropna()
expected = df.iloc[:0]
tm.assert_frame_equal(result, expected)
def test_fillna_scalar(self, data_missing):
valid = data_missing[1]
result = data_missing.fillna(valid)
expected = data_missing.fillna(valid)
tm.assert_extension_array_equal(result, expected)
@pytest.mark.filterwarnings(
"ignore:Series.fillna with 'method' is deprecated:FutureWarning"
)
def test_fillna_limit_pad(self, data_missing):
arr = data_missing.take([1, 0, 0, 0, 1])
result = pd.Series(arr).ffill(limit=2)
expected = pd.Series(data_missing.take([1, 1, 1, 0, 1]))
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"limit_area, input_ilocs, expected_ilocs",
[
("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
],
)
def test_ffill_limit_area(
self, data_missing, limit_area, input_ilocs, expected_ilocs
):
# GH#56616
arr = data_missing.take(input_ilocs)
result = pd.Series(arr).ffill(limit_area=limit_area)
expected = pd.Series(data_missing.take(expected_ilocs))
tm.assert_series_equal(result, expected)
@pytest.mark.filterwarnings(
"ignore:Series.fillna with 'method' is deprecated:FutureWarning"
)
def test_fillna_limit_backfill(self, data_missing):
arr = data_missing.take([1, 0, 0, 0, 1])
result = pd.Series(arr).fillna(method="backfill", limit=2)
expected = pd.Series(data_missing.take([1, 0, 1, 1, 1]))
tm.assert_series_equal(result, expected)
def test_fillna_no_op_returns_copy(self, data):
data = data[~data.isna()]
valid = data[0]
result = data.fillna(valid)
assert result is not data
tm.assert_extension_array_equal(result, data)
result = data._pad_or_backfill(method="backfill")
assert result is not data
tm.assert_extension_array_equal(result, data)
def test_fillna_series(self, data_missing):
fill_value = data_missing[1]
ser = pd.Series(data_missing)
result = ser.fillna(fill_value)
expected = pd.Series(
data_missing._from_sequence(
[fill_value, fill_value], dtype=data_missing.dtype
)
)
tm.assert_series_equal(result, expected)
# Fill with a series
result = ser.fillna(expected)
tm.assert_series_equal(result, expected)
# Fill with a series not affecting the missing values
result = ser.fillna(ser)
tm.assert_series_equal(result, ser)
def test_fillna_series_method(self, data_missing, fillna_method):
fill_value = data_missing[1]
if fillna_method == "ffill":
data_missing = data_missing[::-1]
result = getattr(pd.Series(data_missing), fillna_method)()
expected = pd.Series(
data_missing._from_sequence(
[fill_value, fill_value], dtype=data_missing.dtype
)
)
tm.assert_series_equal(result, expected)
def test_fillna_frame(self, data_missing):
fill_value = data_missing[1]
result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
expected = pd.DataFrame(
{
"A": data_missing._from_sequence(
[fill_value, fill_value], dtype=data_missing.dtype
),
"B": [1, 2],
}
)
tm.assert_frame_equal(result, expected)
def test_fillna_fill_other(self, data):
result = pd.DataFrame({"A": data, "B": [np.nan] * len(data)}).fillna({"B": 0.0})
expected = pd.DataFrame({"A": data, "B": [0.0] * len(result)})
tm.assert_frame_equal(result, expected)
def test_use_inf_as_na_no_effect(self, data_missing):
ser = pd.Series(data_missing)
expected = ser.isna()
msg = "use_inf_as_na option is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
with pd.option_context("mode.use_inf_as_na", True):
result = ser.isna()
tm.assert_series_equal(result, expected)
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