423 lines
15 KiB
Python
423 lines
15 KiB
Python
|
from datetime import datetime, timedelta
|
||
|
|
from importlib import reload
|
||
|
|
import string
|
||
|
|
import sys
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas._libs.tslibs import iNaT
|
||
|
|
|
||
|
|
from pandas import (
|
||
|
|
NA,
|
||
|
|
Categorical,
|
||
|
|
CategoricalDtype,
|
||
|
|
Index,
|
||
|
|
Interval,
|
||
|
|
NaT,
|
||
|
|
Series,
|
||
|
|
Timedelta,
|
||
|
|
Timestamp,
|
||
|
|
cut,
|
||
|
|
date_range,
|
||
|
|
)
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
class TestAstypeAPI:
|
||
|
|
def test_arg_for_errors_in_astype(self):
|
||
|
|
# see GH#14878
|
||
|
|
ser = Series([1, 2, 3])
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
r"Expected value of kwarg 'errors' to be one of \['raise', "
|
||
|
|
r"'ignore'\]\. Supplied value is 'False'"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ser.astype(np.float64, errors=False)
|
||
|
|
|
||
|
|
ser.astype(np.int8, errors="raise")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype_class", [dict, Series])
|
||
|
|
def test_astype_dict_like(self, dtype_class):
|
||
|
|
# see GH#7271
|
||
|
|
ser = Series(range(0, 10, 2), name="abc")
|
||
|
|
|
||
|
|
dt1 = dtype_class({"abc": str})
|
||
|
|
result = ser.astype(dt1)
|
||
|
|
expected = Series(["0", "2", "4", "6", "8"], name="abc")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
dt2 = dtype_class({"abc": "float64"})
|
||
|
|
result = ser.astype(dt2)
|
||
|
|
expected = Series([0.0, 2.0, 4.0, 6.0, 8.0], dtype="float64", name="abc")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
dt3 = dtype_class({"abc": str, "def": str})
|
||
|
|
msg = (
|
||
|
|
"Only the Series name can be used for the key in Series dtype "
|
||
|
|
r"mappings\."
|
||
|
|
)
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
ser.astype(dt3)
|
||
|
|
|
||
|
|
dt4 = dtype_class({0: str})
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
ser.astype(dt4)
|
||
|
|
|
||
|
|
# GH#16717
|
||
|
|
# if dtypes provided is empty, it should error
|
||
|
|
if dtype_class is Series:
|
||
|
|
dt5 = dtype_class({}, dtype=object)
|
||
|
|
else:
|
||
|
|
dt5 = dtype_class({})
|
||
|
|
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
ser.astype(dt5)
|
||
|
|
|
||
|
|
|
||
|
|
class TestAstype:
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["All"])
|
||
|
|
def test_astype_empty_constructor_equality(self, dtype):
|
||
|
|
# see GH#15524
|
||
|
|
|
||
|
|
if dtype not in (
|
||
|
|
"S",
|
||
|
|
"V", # poor support (if any) currently
|
||
|
|
"M",
|
||
|
|
"m", # Generic timestamps raise a ValueError. Already tested.
|
||
|
|
):
|
||
|
|
init_empty = Series([], dtype=dtype)
|
||
|
|
with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
|
||
|
|
as_type_empty = Series([]).astype(dtype)
|
||
|
|
tm.assert_series_equal(init_empty, as_type_empty)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [str, np.str_])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"series",
|
||
|
|
[
|
||
|
|
Series([string.digits * 10, tm.rands(63), tm.rands(64), tm.rands(1000)]),
|
||
|
|
Series([string.digits * 10, tm.rands(63), tm.rands(64), np.nan, 1.0]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_astype_str_map(self, dtype, series):
|
||
|
|
# see GH#4405
|
||
|
|
result = series.astype(dtype)
|
||
|
|
expected = series.map(str)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_float_to_period(self):
|
||
|
|
result = Series([np.nan]).astype("period[D]")
|
||
|
|
expected = Series([NaT], dtype="period[D]")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_no_pandas_dtype(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/pull/24866
|
||
|
|
ser = Series([1, 2], dtype="int64")
|
||
|
|
# Don't have PandasDtype in the public API, so we use `.array.dtype`,
|
||
|
|
# which is a PandasDtype.
|
||
|
|
result = ser.astype(ser.array.dtype)
|
||
|
|
tm.assert_series_equal(result, ser)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
|
||
|
|
def test_astype_generic_timestamp_no_frequency(self, dtype, request):
|
||
|
|
# see GH#15524, GH#15987
|
||
|
|
data = [1]
|
||
|
|
s = Series(data)
|
||
|
|
|
||
|
|
if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
|
||
|
|
mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
|
||
|
|
request.node.add_marker(mark)
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
fr"The '{dtype.__name__}' dtype has no unit\. "
|
||
|
|
fr"Please pass in '{dtype.__name__}\[ns\]' instead."
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.astype(dtype)
|
||
|
|
|
||
|
|
def test_astype_dt64_to_str(self):
|
||
|
|
# GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
|
||
|
|
dti = date_range("2012-01-01", periods=3)
|
||
|
|
result = Series(dti).astype(str)
|
||
|
|
expected = Series(["2012-01-01", "2012-01-02", "2012-01-03"], dtype=object)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_dt64tz_to_str(self):
|
||
|
|
# GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
|
||
|
|
dti_tz = date_range("2012-01-01", periods=3, tz="US/Eastern")
|
||
|
|
result = Series(dti_tz).astype(str)
|
||
|
|
expected = Series(
|
||
|
|
[
|
||
|
|
"2012-01-01 00:00:00-05:00",
|
||
|
|
"2012-01-02 00:00:00-05:00",
|
||
|
|
"2012-01-03 00:00:00-05:00",
|
||
|
|
],
|
||
|
|
dtype=object,
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_datetime(self):
|
||
|
|
s = Series(iNaT, dtype="M8[ns]", index=range(5))
|
||
|
|
|
||
|
|
s = s.astype("O")
|
||
|
|
assert s.dtype == np.object_
|
||
|
|
|
||
|
|
s = Series([datetime(2001, 1, 2, 0, 0)])
|
||
|
|
|
||
|
|
s = s.astype("O")
|
||
|
|
assert s.dtype == np.object_
|
||
|
|
|
||
|
|
s = Series([datetime(2001, 1, 2, 0, 0) for i in range(3)])
|
||
|
|
|
||
|
|
s[1] = np.nan
|
||
|
|
assert s.dtype == "M8[ns]"
|
||
|
|
|
||
|
|
s = s.astype("O")
|
||
|
|
assert s.dtype == np.object_
|
||
|
|
|
||
|
|
def test_astype_datetime64tz(self):
|
||
|
|
s = Series(date_range("20130101", periods=3, tz="US/Eastern"))
|
||
|
|
|
||
|
|
# astype
|
||
|
|
result = s.astype(object)
|
||
|
|
expected = Series(s.astype(object), dtype=object)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = Series(s.values).dt.tz_localize("UTC").dt.tz_convert(s.dt.tz)
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
# astype - object, preserves on construction
|
||
|
|
result = Series(s.astype(object))
|
||
|
|
expected = s.astype(object)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# astype - datetime64[ns, tz]
|
||
|
|
result = Series(s.values).astype("datetime64[ns, US/Eastern]")
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
result = Series(s.values).astype(s.dtype)
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
result = s.astype("datetime64[ns, CET]")
|
||
|
|
expected = Series(date_range("20130101 06:00:00", periods=3, tz="CET"))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_str_cast_dt64(self):
|
||
|
|
# see GH#9757
|
||
|
|
ts = Series([Timestamp("2010-01-04 00:00:00")])
|
||
|
|
s = ts.astype(str)
|
||
|
|
|
||
|
|
expected = Series(["2010-01-04"])
|
||
|
|
tm.assert_series_equal(s, expected)
|
||
|
|
|
||
|
|
ts = Series([Timestamp("2010-01-04 00:00:00", tz="US/Eastern")])
|
||
|
|
s = ts.astype(str)
|
||
|
|
|
||
|
|
expected = Series(["2010-01-04 00:00:00-05:00"])
|
||
|
|
tm.assert_series_equal(s, expected)
|
||
|
|
|
||
|
|
def test_astype_str_cast_td64(self):
|
||
|
|
# see GH#9757
|
||
|
|
|
||
|
|
td = Series([Timedelta(1, unit="d")])
|
||
|
|
ser = td.astype(str)
|
||
|
|
|
||
|
|
expected = Series(["1 days"])
|
||
|
|
tm.assert_series_equal(ser, expected)
|
||
|
|
|
||
|
|
def test_dt64_series_astype_object(self):
|
||
|
|
dt64ser = Series(date_range("20130101", periods=3))
|
||
|
|
result = dt64ser.astype(object)
|
||
|
|
assert isinstance(result.iloc[0], datetime)
|
||
|
|
assert result.dtype == np.object_
|
||
|
|
|
||
|
|
def test_td64_series_astype_object(self):
|
||
|
|
tdser = Series(["59 Days", "59 Days", "NaT"], dtype="timedelta64[ns]")
|
||
|
|
result = tdser.astype(object)
|
||
|
|
assert isinstance(result.iloc[0], timedelta)
|
||
|
|
assert result.dtype == np.object_
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"values",
|
||
|
|
[
|
||
|
|
Series(["x", "y", "z"], dtype="string"),
|
||
|
|
Series(["x", "y", "z"], dtype="category"),
|
||
|
|
Series(3 * [Timestamp("2020-01-01", tz="UTC")]),
|
||
|
|
Series(3 * [Interval(0, 1)]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("errors", ["raise", "ignore"])
|
||
|
|
def test_astype_ignores_errors_for_extension_dtypes(self, values, errors):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/35471
|
||
|
|
if errors == "ignore":
|
||
|
|
expected = values
|
||
|
|
result = values.astype(float, errors="ignore")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
else:
|
||
|
|
msg = "(Cannot cast)|(could not convert)"
|
||
|
|
with pytest.raises((ValueError, TypeError), match=msg):
|
||
|
|
values.astype(float, errors=errors)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
|
||
|
|
def test_astype_from_float_to_str(self, dtype):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/36451
|
||
|
|
s = Series([0.1], dtype=dtype)
|
||
|
|
result = s.astype(str)
|
||
|
|
expected = Series(["0.1"])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"value, string_value",
|
||
|
|
[
|
||
|
|
(None, "None"),
|
||
|
|
(np.nan, "nan"),
|
||
|
|
(NA, "<NA>"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_astype_to_str_preserves_na(self, value, string_value):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/36904
|
||
|
|
s = Series(["a", "b", value], dtype=object)
|
||
|
|
result = s.astype(str)
|
||
|
|
expected = Series(["a", "b", string_value], dtype=object)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", ["float32", "float64", "int64", "int32"])
|
||
|
|
def test_astype(self, dtype):
|
||
|
|
s = Series(np.random.randn(5), name="foo")
|
||
|
|
as_typed = s.astype(dtype)
|
||
|
|
|
||
|
|
assert as_typed.dtype == dtype
|
||
|
|
assert as_typed.name == s.name
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("value", [np.nan, np.inf])
|
||
|
|
@pytest.mark.parametrize("dtype", [np.int32, np.int64])
|
||
|
|
def test_astype_cast_nan_inf_int(self, dtype, value):
|
||
|
|
# gh-14265: check NaN and inf raise error when converting to int
|
||
|
|
msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
|
||
|
|
s = Series([value])
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.astype(dtype)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [int, np.int8, np.int64])
|
||
|
|
def test_astype_cast_object_int_fail(self, dtype):
|
||
|
|
arr = Series(["car", "house", "tree", "1"])
|
||
|
|
msg = r"invalid literal for int\(\) with base 10: 'car'"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
arr.astype(dtype)
|
||
|
|
|
||
|
|
def test_astype_cast_object_int(self):
|
||
|
|
arr = Series(["1", "2", "3", "4"], dtype=object)
|
||
|
|
result = arr.astype(int)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, Series(np.arange(1, 5)))
|
||
|
|
|
||
|
|
def test_astype_unicode(self):
|
||
|
|
# see GH#7758: A bit of magic is required to set
|
||
|
|
# default encoding to utf-8
|
||
|
|
digits = string.digits
|
||
|
|
test_series = [
|
||
|
|
Series([digits * 10, tm.rands(63), tm.rands(64), tm.rands(1000)]),
|
||
|
|
Series(["データーサイエンス、お前はもう死んでいる"]),
|
||
|
|
]
|
||
|
|
|
||
|
|
former_encoding = None
|
||
|
|
|
||
|
|
if sys.getdefaultencoding() == "utf-8":
|
||
|
|
test_series.append(Series(["野菜食べないとやばい".encode()]))
|
||
|
|
|
||
|
|
for s in test_series:
|
||
|
|
res = s.astype("unicode")
|
||
|
|
expec = s.map(str)
|
||
|
|
tm.assert_series_equal(res, expec)
|
||
|
|
|
||
|
|
# Restore the former encoding
|
||
|
|
if former_encoding is not None and former_encoding != "utf-8":
|
||
|
|
reload(sys)
|
||
|
|
sys.setdefaultencoding(former_encoding)
|
||
|
|
|
||
|
|
def test_astype_bytes(self):
|
||
|
|
# GH#39474
|
||
|
|
result = Series(["foo", "bar", "baz"]).astype(bytes)
|
||
|
|
assert result.dtypes == np.dtype("S3")
|
||
|
|
|
||
|
|
|
||
|
|
class TestAstypeCategorical:
|
||
|
|
def test_astype_categorical_invalid_conversions(self):
|
||
|
|
# invalid conversion (these are NOT a dtype)
|
||
|
|
cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
|
||
|
|
ser = Series(np.random.randint(0, 10000, 100)).sort_values()
|
||
|
|
ser = cut(ser, range(0, 10500, 500), right=False, labels=cat)
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
"dtype '<class 'pandas.core.arrays.categorical.Categorical'>' "
|
||
|
|
"not understood"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
ser.astype(Categorical)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
ser.astype("object").astype(Categorical)
|
||
|
|
|
||
|
|
def test_astype_categoricaldtype(self):
|
||
|
|
s = Series(["a", "b", "a"])
|
||
|
|
result = s.astype(CategoricalDtype(["a", "b"], ordered=True))
|
||
|
|
expected = Series(Categorical(["a", "b", "a"], ordered=True))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = s.astype(CategoricalDtype(["a", "b"], ordered=False))
|
||
|
|
expected = Series(Categorical(["a", "b", "a"], ordered=False))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = s.astype(CategoricalDtype(["a", "b", "c"], ordered=False))
|
||
|
|
expected = Series(
|
||
|
|
Categorical(["a", "b", "a"], categories=["a", "b", "c"], ordered=False)
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("name", [None, "foo"])
|
||
|
|
@pytest.mark.parametrize("dtype_ordered", [True, False])
|
||
|
|
@pytest.mark.parametrize("series_ordered", [True, False])
|
||
|
|
def test_astype_categorical_to_categorical(
|
||
|
|
self, name, dtype_ordered, series_ordered
|
||
|
|
):
|
||
|
|
# GH#10696, GH#18593
|
||
|
|
s_data = list("abcaacbab")
|
||
|
|
s_dtype = CategoricalDtype(list("bac"), ordered=series_ordered)
|
||
|
|
s = Series(s_data, dtype=s_dtype, name=name)
|
||
|
|
|
||
|
|
# unspecified categories
|
||
|
|
dtype = CategoricalDtype(ordered=dtype_ordered)
|
||
|
|
result = s.astype(dtype)
|
||
|
|
exp_dtype = CategoricalDtype(s_dtype.categories, dtype_ordered)
|
||
|
|
expected = Series(s_data, name=name, dtype=exp_dtype)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# different categories
|
||
|
|
dtype = CategoricalDtype(list("adc"), dtype_ordered)
|
||
|
|
result = s.astype(dtype)
|
||
|
|
expected = Series(s_data, name=name, dtype=dtype)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
if dtype_ordered is False:
|
||
|
|
# not specifying ordered, so only test once
|
||
|
|
expected = s
|
||
|
|
result = s.astype("category")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_bool_missing_to_categorical(self):
|
||
|
|
# GH-19182
|
||
|
|
s = Series([True, False, np.nan])
|
||
|
|
assert s.dtypes == np.object_
|
||
|
|
|
||
|
|
result = s.astype(CategoricalDtype(categories=[True, False]))
|
||
|
|
expected = Series(Categorical([True, False, np.nan], categories=[True, False]))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_astype_categories_raises(self):
|
||
|
|
# deprecated GH#17636, removed in GH#27141
|
||
|
|
s = Series(["a", "b", "a"])
|
||
|
|
with pytest.raises(TypeError, match="got an unexpected"):
|
||
|
|
s.astype("category", categories=["a", "b"], ordered=True)
|