Alle Dateien aus dem Pythonkurs
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import numpy as np
import pytest
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Series,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
def test_where_unsafe_int(any_signed_int_numpy_dtype):
s = Series(np.arange(10), dtype=any_signed_int_numpy_dtype)
mask = s < 5
s[mask] = range(2, 7)
expected = Series(
list(range(2, 7)) + list(range(5, 10)),
dtype=any_signed_int_numpy_dtype,
)
tm.assert_series_equal(s, expected)
def test_where_unsafe_float(float_numpy_dtype):
s = Series(np.arange(10), dtype=float_numpy_dtype)
mask = s < 5
s[mask] = range(2, 7)
data = list(range(2, 7)) + list(range(5, 10))
expected = Series(data, dtype=float_numpy_dtype)
tm.assert_series_equal(s, expected)
@pytest.mark.parametrize(
"dtype,expected_dtype",
[
(np.int8, np.float64),
(np.int16, np.float64),
(np.int32, np.float64),
(np.int64, np.float64),
(np.float32, np.float32),
(np.float64, np.float64),
],
)
def test_where_unsafe_upcast(dtype, expected_dtype):
# see gh-9743
s = Series(np.arange(10), dtype=dtype)
values = [2.5, 3.5, 4.5, 5.5, 6.5]
mask = s < 5
expected = Series(values + list(range(5, 10)), dtype=expected_dtype)
warn = (
None
if np.dtype(dtype).kind == np.dtype(expected_dtype).kind == "f"
else FutureWarning
)
with tm.assert_produces_warning(warn, match="incompatible dtype"):
s[mask] = values
tm.assert_series_equal(s, expected)
def test_where_unsafe():
# see gh-9731
s = Series(np.arange(10), dtype="int64")
values = [2.5, 3.5, 4.5, 5.5]
mask = s > 5
expected = Series(list(range(6)) + values, dtype="float64")
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
s[mask] = values
tm.assert_series_equal(s, expected)
# see gh-3235
s = Series(np.arange(10), dtype="int64")
mask = s < 5
s[mask] = range(2, 7)
expected = Series(list(range(2, 7)) + list(range(5, 10)), dtype="int64")
tm.assert_series_equal(s, expected)
assert s.dtype == expected.dtype
s = Series(np.arange(10), dtype="int64")
mask = s > 5
s[mask] = [0] * 4
expected = Series([0, 1, 2, 3, 4, 5] + [0] * 4, dtype="int64")
tm.assert_series_equal(s, expected)
s = Series(np.arange(10))
mask = s > 5
msg = "cannot set using a list-like indexer with a different length than the value"
with pytest.raises(ValueError, match=msg):
s[mask] = [5, 4, 3, 2, 1]
with pytest.raises(ValueError, match=msg):
s[mask] = [0] * 5
# dtype changes
s = Series([1, 2, 3, 4])
result = s.where(s > 2, np.nan)
expected = Series([np.nan, np.nan, 3, 4])
tm.assert_series_equal(result, expected)
# GH 4667
# setting with None changes dtype
s = Series(range(10)).astype(float)
s[8] = None
result = s[8]
assert isna(result)
s = Series(range(10)).astype(float)
s[s > 8] = None
result = s[isna(s)]
expected = Series(np.nan, index=[9])
tm.assert_series_equal(result, expected)
def test_where():
s = Series(np.random.default_rng(2).standard_normal(5))
cond = s > 0
rs = s.where(cond).dropna()
rs2 = s[cond]
tm.assert_series_equal(rs, rs2)
rs = s.where(cond, -s)
tm.assert_series_equal(rs, s.abs())
rs = s.where(cond)
assert s.shape == rs.shape
assert rs is not s
# test alignment
cond = Series([True, False, False, True, False], index=s.index)
s2 = -(s.abs())
expected = s2[cond].reindex(s2.index[:3]).reindex(s2.index)
rs = s2.where(cond[:3])
tm.assert_series_equal(rs, expected)
expected = s2.abs()
expected.iloc[0] = s2[0]
rs = s2.where(cond[:3], -s2)
tm.assert_series_equal(rs, expected)
def test_where_error():
s = Series(np.random.default_rng(2).standard_normal(5))
cond = s > 0
msg = "Array conditional must be same shape as self"
with pytest.raises(ValueError, match=msg):
s.where(1)
with pytest.raises(ValueError, match=msg):
s.where(cond[:3].values, -s)
# GH 2745
s = Series([1, 2])
s[[True, False]] = [0, 1]
expected = Series([0, 2])
tm.assert_series_equal(s, expected)
# failures
msg = "cannot set using a list-like indexer with a different length than the value"
with pytest.raises(ValueError, match=msg):
s[[True, False]] = [0, 2, 3]
with pytest.raises(ValueError, match=msg):
s[[True, False]] = []
@pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
def test_where_array_like(klass):
# see gh-15414
s = Series([1, 2, 3])
cond = [False, True, True]
expected = Series([np.nan, 2, 3])
result = s.where(klass(cond))
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"cond",
[
[1, 0, 1],
Series([2, 5, 7]),
["True", "False", "True"],
[Timestamp("2017-01-01"), pd.NaT, Timestamp("2017-01-02")],
],
)
def test_where_invalid_input(cond):
# see gh-15414: only boolean arrays accepted
s = Series([1, 2, 3])
msg = "Boolean array expected for the condition"
with pytest.raises(ValueError, match=msg):
s.where(cond)
msg = "Array conditional must be same shape as self"
with pytest.raises(ValueError, match=msg):
s.where([True])
def test_where_ndframe_align():
msg = "Array conditional must be same shape as self"
s = Series([1, 2, 3])
cond = [True]
with pytest.raises(ValueError, match=msg):
s.where(cond)
expected = Series([1, np.nan, np.nan])
out = s.where(Series(cond))
tm.assert_series_equal(out, expected)
cond = np.array([False, True, False, True])
with pytest.raises(ValueError, match=msg):
s.where(cond)
expected = Series([np.nan, 2, np.nan])
out = s.where(Series(cond))
tm.assert_series_equal(out, expected)
def test_where_setitem_invalid():
# GH 2702
# make sure correct exceptions are raised on invalid list assignment
msg = (
lambda x: f"cannot set using a {x} indexer with a "
"different length than the value"
)
# slice
s = Series(list("abc"))
with pytest.raises(ValueError, match=msg("slice")):
s[0:3] = list(range(27))
s[0:3] = list(range(3))
expected = Series([0, 1, 2])
tm.assert_series_equal(s.astype(np.int64), expected)
# slice with step
s = Series(list("abcdef"))
with pytest.raises(ValueError, match=msg("slice")):
s[0:4:2] = list(range(27))
s = Series(list("abcdef"))
s[0:4:2] = list(range(2))
expected = Series([0, "b", 1, "d", "e", "f"])
tm.assert_series_equal(s, expected)
# neg slices
s = Series(list("abcdef"))
with pytest.raises(ValueError, match=msg("slice")):
s[:-1] = list(range(27))
s[-3:-1] = list(range(2))
expected = Series(["a", "b", "c", 0, 1, "f"])
tm.assert_series_equal(s, expected)
# list
s = Series(list("abc"))
with pytest.raises(ValueError, match=msg("list-like")):
s[[0, 1, 2]] = list(range(27))
s = Series(list("abc"))
with pytest.raises(ValueError, match=msg("list-like")):
s[[0, 1, 2]] = list(range(2))
# scalar
s = Series(list("abc"))
s[0] = list(range(10))
expected = Series([list(range(10)), "b", "c"])
tm.assert_series_equal(s, expected)
@pytest.mark.parametrize("size", range(2, 6))
@pytest.mark.parametrize(
"mask", [[True, False, False, False, False], [True, False], [False]]
)
@pytest.mark.parametrize(
"item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min]
)
# Test numpy arrays, lists and tuples as the input to be
# broadcast
@pytest.mark.parametrize(
"box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)]
)
def test_broadcast(size, mask, item, box):
# GH#8801, GH#4195
selection = np.resize(mask, size)
data = np.arange(size, dtype=float)
# Construct the expected series by taking the source
# data or item based on the selection
expected = Series(
[item if use_item else data[i] for i, use_item in enumerate(selection)]
)
s = Series(data)
s[selection] = item
tm.assert_series_equal(s, expected)
s = Series(data)
result = s.where(~selection, box(item))
tm.assert_series_equal(result, expected)
s = Series(data)
result = s.mask(selection, box(item))
tm.assert_series_equal(result, expected)
def test_where_inplace():
s = Series(np.random.default_rng(2).standard_normal(5))
cond = s > 0
rs = s.copy()
rs.where(cond, inplace=True)
tm.assert_series_equal(rs.dropna(), s[cond])
tm.assert_series_equal(rs, s.where(cond))
rs = s.copy()
rs.where(cond, -s, inplace=True)
tm.assert_series_equal(rs, s.where(cond, -s))
def test_where_dups():
# GH 4550
# where crashes with dups in index
s1 = Series(list(range(3)))
s2 = Series(list(range(3)))
comb = pd.concat([s1, s2])
result = comb.where(comb < 2)
expected = Series([0, 1, np.nan, 0, 1, np.nan], index=[0, 1, 2, 0, 1, 2])
tm.assert_series_equal(result, expected)
# GH 4548
# inplace updating not working with dups
comb[comb < 1] = 5
expected = Series([5, 1, 2, 5, 1, 2], index=[0, 1, 2, 0, 1, 2])
tm.assert_series_equal(comb, expected)
comb[comb < 2] += 10
expected = Series([5, 11, 2, 5, 11, 2], index=[0, 1, 2, 0, 1, 2])
tm.assert_series_equal(comb, expected)
def test_where_numeric_with_string():
# GH 9280
s = Series([1, 2, 3])
w = s.where(s > 1, "X")
assert not is_integer(w[0])
assert is_integer(w[1])
assert is_integer(w[2])
assert isinstance(w[0], str)
assert w.dtype == "object"
w = s.where(s > 1, ["X", "Y", "Z"])
assert not is_integer(w[0])
assert is_integer(w[1])
assert is_integer(w[2])
assert isinstance(w[0], str)
assert w.dtype == "object"
w = s.where(s > 1, np.array(["X", "Y", "Z"]))
assert not is_integer(w[0])
assert is_integer(w[1])
assert is_integer(w[2])
assert isinstance(w[0], str)
assert w.dtype == "object"
@pytest.mark.parametrize("dtype", ["timedelta64[ns]", "datetime64[ns]"])
def test_where_datetimelike_coerce(dtype):
ser = Series([1, 2], dtype=dtype)
expected = Series([10, 10])
mask = np.array([False, False])
rs = ser.where(mask, [10, 10])
tm.assert_series_equal(rs, expected)
rs = ser.where(mask, 10)
tm.assert_series_equal(rs, expected)
rs = ser.where(mask, 10.0)
tm.assert_series_equal(rs, expected)
rs = ser.where(mask, [10.0, 10.0])
tm.assert_series_equal(rs, expected)
rs = ser.where(mask, [10.0, np.nan])
expected = Series([10, np.nan], dtype="object")
tm.assert_series_equal(rs, expected)
def test_where_datetimetz():
# GH 15701
timestamps = ["2016-12-31 12:00:04+00:00", "2016-12-31 12:00:04.010000+00:00"]
ser = Series([Timestamp(t) for t in timestamps], dtype="datetime64[ns, UTC]")
rs = ser.where(Series([False, True]))
expected = Series([pd.NaT, ser[1]], dtype="datetime64[ns, UTC]")
tm.assert_series_equal(rs, expected)
def test_where_sparse():
# GH#17198 make sure we dont get an AttributeError for sp_index
ser = Series(pd.arrays.SparseArray([1, 2]))
result = ser.where(ser >= 2, 0)
expected = Series(pd.arrays.SparseArray([0, 2]))
tm.assert_series_equal(result, expected)
def test_where_empty_series_and_empty_cond_having_non_bool_dtypes():
# https://github.com/pandas-dev/pandas/issues/34592
ser = Series([], dtype=float)
result = ser.where([])
tm.assert_series_equal(result, ser)
def test_where_categorical(frame_or_series):
# https://github.com/pandas-dev/pandas/issues/18888
exp = frame_or_series(
pd.Categorical(["A", "A", "B", "B", np.nan], categories=["A", "B", "C"]),
dtype="category",
)
df = frame_or_series(["A", "A", "B", "B", "C"], dtype="category")
res = df.where(df != "C")
tm.assert_equal(exp, res)
def test_where_datetimelike_categorical(tz_naive_fixture):
# GH#37682
tz = tz_naive_fixture
dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None)
lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT])
rvals = pd.Categorical([dr[0], pd.NaT, dr[2]])
mask = np.array([True, True, False])
# DatetimeIndex.where
res = lvals.where(mask, rvals)
tm.assert_index_equal(res, dr)
# DatetimeArray.where
res = lvals._data._where(mask, rvals)
tm.assert_datetime_array_equal(res, dr._data)
# Series.where
res = Series(lvals).where(mask, rvals)
tm.assert_series_equal(res, Series(dr))
# DataFrame.where
res = pd.DataFrame(lvals).where(mask[:, None], pd.DataFrame(rvals))
tm.assert_frame_equal(res, pd.DataFrame(dr))