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.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
Timedelta,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestSeriesDtypes:
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("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
def test_dtype(self, datetime_series):
assert datetime_series.dtype == np.dtype("float64")
assert datetime_series.dtypes == np.dtype("float64")
@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_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)
@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_str_cast_dt64(self):
# see gh-9757
ts = Series([Timestamp("2010-01-04 00:00:00")])
s = ts.astype(str)
expected = Series([str("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([str("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([str("1 days")])
tm.assert_series_equal(ser, expected)
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("utf-8")]))
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)
@pytest.mark.parametrize("dtype_class", [dict, Series])
def test_astype_dict_like(self, dtype_class):
# see gh-7271
s = Series(range(0, 10, 2), name="abc")
dt1 = dtype_class({"abc": str})
result = s.astype(dt1)
expected = Series(["0", "2", "4", "6", "8"], name="abc")
tm.assert_series_equal(result, expected)
dt2 = dtype_class({"abc": "float64"})
result = s.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):
s.astype(dt3)
dt4 = dtype_class({0: str})
with pytest.raises(KeyError, match=msg):
s.astype(dt4)
# GH16717
# 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):
s.astype(dt5)
def test_astype_categories_raises(self):
# deprecated 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)
def test_astype_from_categorical(self):
items = ["a", "b", "c", "a"]
s = Series(items)
exp = Series(Categorical(items))
res = s.astype("category")
tm.assert_series_equal(res, exp)
items = [1, 2, 3, 1]
s = Series(items)
exp = Series(Categorical(items))
res = s.astype("category")
tm.assert_series_equal(res, exp)
df = DataFrame({"cats": [1, 2, 3, 4, 5, 6], "vals": [1, 2, 3, 4, 5, 6]})
cats = Categorical([1, 2, 3, 4, 5, 6])
exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
df["cats"] = df["cats"].astype("category")
tm.assert_frame_equal(exp_df, df)
df = DataFrame(
{"cats": ["a", "b", "b", "a", "a", "d"], "vals": [1, 2, 3, 4, 5, 6]}
)
cats = Categorical(["a", "b", "b", "a", "a", "d"])
exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
df["cats"] = df["cats"].astype("category")
tm.assert_frame_equal(exp_df, df)
# with keywords
lst = ["a", "b", "c", "a"]
s = Series(lst)
exp = Series(Categorical(lst, ordered=True))
res = s.astype(CategoricalDtype(None, ordered=True))
tm.assert_series_equal(res, exp)
exp = Series(Categorical(lst, categories=list("abcdef"), ordered=True))
res = s.astype(CategoricalDtype(list("abcdef"), ordered=True))
tm.assert_series_equal(res, exp)
def test_astype_categorical_to_other(self):
value = np.random.RandomState(0).randint(0, 10000, 100)
df = DataFrame({"value": value})
labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
cat_labels = Categorical(labels, labels)
df = df.sort_values(by=["value"], ascending=True)
df["value_group"] = pd.cut(
df.value, range(0, 10500, 500), right=False, labels=cat_labels
)
s = df["value_group"]
expected = s
tm.assert_series_equal(s.astype("category"), expected)
tm.assert_series_equal(s.astype(CategoricalDtype()), expected)
msg = r"could not convert string to float|invalid literal for float\(\)"
with pytest.raises(ValueError, match=msg):
s.astype("float64")
cat = Series(Categorical(["a", "b", "b", "a", "a", "c", "c", "c"]))
exp = Series(["a", "b", "b", "a", "a", "c", "c", "c"])
tm.assert_series_equal(cat.astype("str"), exp)
s2 = Series(Categorical(["1", "2", "3", "4"]))
exp2 = Series([1, 2, 3, 4]).astype(int)
tm.assert_series_equal(s2.astype("int"), exp2)
# object don't sort correctly, so just compare that we have the same
# values
def cmp(a, b):
tm.assert_almost_equal(np.sort(np.unique(a)), np.sort(np.unique(b)))
expected = Series(np.array(s.values), name="value_group")
cmp(s.astype("object"), expected)
cmp(s.astype(np.object_), expected)
# array conversion
tm.assert_almost_equal(np.array(s), np.array(s.values))
tm.assert_series_equal(s.astype("category"), s)
tm.assert_series_equal(s.astype(CategoricalDtype()), s)
roundtrip_expected = s.cat.set_categories(
s.cat.categories.sort_values()
).cat.remove_unused_categories()
tm.assert_series_equal(
s.astype("object").astype("category"), roundtrip_expected
)
tm.assert_series_equal(
s.astype("object").astype(CategoricalDtype()), roundtrip_expected
)
# invalid conversion (these are NOT a dtype)
msg = (
"dtype '<class 'pandas.core.arrays.categorical.Categorical'>' "
"not understood"
)
for invalid in [
lambda x: x.astype(Categorical),
lambda x: x.astype("object").astype(Categorical),
]:
with pytest.raises(TypeError, match=msg):
invalid(s)
@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 10696/18593
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_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("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)
@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)
def test_arg_for_errors_in_astype(self):
# see gh-14878
s = 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):
s.astype(np.float64, errors=False)
s.astype(np.int8, errors="raise")
def test_intercept_astype_object(self):
series = Series(date_range("1/1/2000", periods=10))
# This test no longer makes sense, as
# Series is by default already M8[ns].
expected = series.astype("object")
df = DataFrame({"a": series, "b": np.random.randn(len(series))})
exp_dtypes = Series(
[np.dtype("datetime64[ns]"), np.dtype("float64")], index=["a", "b"]
)
tm.assert_series_equal(df.dtypes, exp_dtypes)
result = df.values.squeeze()
assert (result[:, 0] == expected.values).all()
df = DataFrame({"a": series, "b": ["foo"] * len(series)})
result = df.values.squeeze()
assert (result[:, 0] == expected.values).all()
def test_series_to_categorical(self):
# see gh-16524: test conversion of Series to Categorical
series = Series(["a", "b", "c"])
result = Series(series, dtype="category")
expected = Series(["a", "b", "c"], dtype="category")
tm.assert_series_equal(result, expected)
def test_infer_objects_series(self):
# GH 11221
actual = Series(np.array([1, 2, 3], dtype="O")).infer_objects()
expected = Series([1, 2, 3])
tm.assert_series_equal(actual, expected)
actual = Series(np.array([1, 2, 3, None], dtype="O")).infer_objects()
expected = Series([1.0, 2.0, 3.0, np.nan])
tm.assert_series_equal(actual, expected)
# only soft conversions, unconvertable pass thru unchanged
actual = Series(np.array([1, 2, 3, None, "a"], dtype="O")).infer_objects()
expected = Series([1, 2, 3, None, "a"])
assert actual.dtype == "object"
tm.assert_series_equal(actual, expected)
@pytest.mark.parametrize(
"data",
[
pd.period_range("2000", periods=4),
pd.IntervalIndex.from_breaks([1, 2, 3, 4]),
],
)
def test_values_compatibility(self, data):
# https://github.com/pandas-dev/pandas/issues/23995
result = pd.Series(data).values
expected = np.array(data.astype(object))
tm.assert_numpy_array_equal(result, expected)
def test_reindex_astype_order_consistency(self):
# GH 17444
s = Series([1, 2, 3], index=[2, 0, 1])
new_index = [0, 1, 2]
temp_dtype = "category"
new_dtype = str
s1 = s.reindex(new_index).astype(temp_dtype).astype(new_dtype)
s2 = s.astype(temp_dtype).reindex(new_index).astype(new_dtype)
tm.assert_series_equal(s1, s2)
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