Sindbad~EG File Manager
from collections import OrderedDict
from collections.abc import Iterator
from datetime import (
datetime,
timedelta,
)
from dateutil.tz import tzoffset
import numpy as np
from numpy import ma
import pytest
from pandas._libs import (
iNaT,
lib,
)
from pandas.compat.numpy import np_version_gt2
from pandas.errors import IntCastingNaNError
import pandas.util._test_decorators as td
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
DatetimeTZDtype,
Index,
Interval,
IntervalIndex,
MultiIndex,
NaT,
Period,
RangeIndex,
Series,
Timestamp,
date_range,
isna,
period_range,
timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays import (
IntegerArray,
IntervalArray,
period_array,
)
from pandas.core.internals.blocks import NumpyBlock
class TestSeriesConstructors:
def test_from_ints_with_non_nano_dt64_dtype(self, index_or_series):
values = np.arange(10)
res = index_or_series(values, dtype="M8[s]")
expected = index_or_series(values.astype("M8[s]"))
tm.assert_equal(res, expected)
res = index_or_series(list(values), dtype="M8[s]")
tm.assert_equal(res, expected)
def test_from_na_value_and_interval_of_datetime_dtype(self):
# GH#41805
ser = Series([None], dtype="interval[datetime64[ns]]")
assert ser.isna().all()
assert ser.dtype == "interval[datetime64[ns], right]"
def test_infer_with_date_and_datetime(self):
# GH#49341 pre-2.0 we inferred datetime-and-date to datetime64, which
# was inconsistent with Index behavior
ts = Timestamp(2016, 1, 1)
vals = [ts.to_pydatetime(), ts.date()]
ser = Series(vals)
expected = Series(vals, dtype=object)
tm.assert_series_equal(ser, expected)
idx = Index(vals)
expected = Index(vals, dtype=object)
tm.assert_index_equal(idx, expected)
def test_unparsable_strings_with_dt64_dtype(self):
# pre-2.0 these would be silently ignored and come back with object dtype
vals = ["aa"]
msg = "^Unknown datetime string format, unable to parse: aa, at position 0$"
with pytest.raises(ValueError, match=msg):
Series(vals, dtype="datetime64[ns]")
with pytest.raises(ValueError, match=msg):
Series(np.array(vals, dtype=object), dtype="datetime64[ns]")
@pytest.mark.parametrize(
"constructor",
[
# NOTE: some overlap with test_constructor_empty but that test does not
# test for None or an empty generator.
# test_constructor_pass_none tests None but only with the index also
# passed.
(lambda idx: Series(index=idx)),
(lambda idx: Series(None, index=idx)),
(lambda idx: Series({}, index=idx)),
(lambda idx: Series((), index=idx)),
(lambda idx: Series([], index=idx)),
(lambda idx: Series((_ for _ in []), index=idx)),
(lambda idx: Series(data=None, index=idx)),
(lambda idx: Series(data={}, index=idx)),
(lambda idx: Series(data=(), index=idx)),
(lambda idx: Series(data=[], index=idx)),
(lambda idx: Series(data=(_ for _ in []), index=idx)),
],
)
@pytest.mark.parametrize("empty_index", [None, []])
def test_empty_constructor(self, constructor, empty_index):
# GH 49573 (addition of empty_index parameter)
expected = Series(index=empty_index)
result = constructor(empty_index)
assert result.dtype == object
assert len(result.index) == 0
tm.assert_series_equal(result, expected, check_index_type=True)
def test_invalid_dtype(self):
# GH15520
msg = "not understood"
invalid_list = [Timestamp, "Timestamp", list]
for dtype in invalid_list:
with pytest.raises(TypeError, match=msg):
Series([], name="time", dtype=dtype)
def test_invalid_compound_dtype(self):
# GH#13296
c_dtype = np.dtype([("a", "i8"), ("b", "f4")])
cdt_arr = np.array([(1, 0.4), (256, -13)], dtype=c_dtype)
with pytest.raises(ValueError, match="Use DataFrame instead"):
Series(cdt_arr, index=["A", "B"])
def test_scalar_conversion(self):
# Pass in scalar is disabled
scalar = Series(0.5)
assert not isinstance(scalar, float)
def test_scalar_extension_dtype(self, ea_scalar_and_dtype):
# GH 28401
ea_scalar, ea_dtype = ea_scalar_and_dtype
ser = Series(ea_scalar, index=range(3))
expected = Series([ea_scalar] * 3, dtype=ea_dtype)
assert ser.dtype == ea_dtype
tm.assert_series_equal(ser, expected)
def test_constructor(self, datetime_series, using_infer_string):
empty_series = Series()
assert datetime_series.index._is_all_dates
# Pass in Series
derived = Series(datetime_series)
assert derived.index._is_all_dates
tm.assert_index_equal(derived.index, datetime_series.index)
# Ensure new index is not created
assert id(datetime_series.index) == id(derived.index)
# Mixed type Series
mixed = Series(["hello", np.nan], index=[0, 1])
assert mixed.dtype == np.object_ if not using_infer_string else "string"
assert np.isnan(mixed[1])
assert not empty_series.index._is_all_dates
assert not Series().index._is_all_dates
# exception raised is of type ValueError GH35744
with pytest.raises(
ValueError,
match=r"Data must be 1-dimensional, got ndarray of shape \(3, 3\) instead",
):
Series(np.random.default_rng(2).standard_normal((3, 3)), index=np.arange(3))
mixed.name = "Series"
rs = Series(mixed).name
xp = "Series"
assert rs == xp
# raise on MultiIndex GH4187
m = MultiIndex.from_arrays([[1, 2], [3, 4]])
msg = "initializing a Series from a MultiIndex is not supported"
with pytest.raises(NotImplementedError, match=msg):
Series(m)
def test_constructor_index_ndim_gt_1_raises(self):
# GH#18579
df = DataFrame([[1, 2], [3, 4], [5, 6]], index=[3, 6, 9])
with pytest.raises(ValueError, match="Index data must be 1-dimensional"):
Series([1, 3, 2], index=df)
@pytest.mark.parametrize("input_class", [list, dict, OrderedDict])
def test_constructor_empty(self, input_class, using_infer_string):
empty = Series()
empty2 = Series(input_class())
# these are Index() and RangeIndex() which don't compare type equal
# but are just .equals
tm.assert_series_equal(empty, empty2, check_index_type=False)
# With explicit dtype:
empty = Series(dtype="float64")
empty2 = Series(input_class(), dtype="float64")
tm.assert_series_equal(empty, empty2, check_index_type=False)
# GH 18515 : with dtype=category:
empty = Series(dtype="category")
empty2 = Series(input_class(), dtype="category")
tm.assert_series_equal(empty, empty2, check_index_type=False)
if input_class is not list:
# With index:
empty = Series(index=range(10))
empty2 = Series(input_class(), index=range(10))
tm.assert_series_equal(empty, empty2)
# With index and dtype float64:
empty = Series(np.nan, index=range(10))
empty2 = Series(input_class(), index=range(10), dtype="float64")
tm.assert_series_equal(empty, empty2)
# GH 19853 : with empty string, index and dtype str
empty = Series("", dtype=str, index=range(3))
if using_infer_string:
empty2 = Series("", index=range(3), dtype=object)
else:
empty2 = Series("", index=range(3))
tm.assert_series_equal(empty, empty2)
@pytest.mark.parametrize("input_arg", [np.nan, float("nan")])
def test_constructor_nan(self, input_arg):
empty = Series(dtype="float64", index=range(10))
empty2 = Series(input_arg, index=range(10))
tm.assert_series_equal(empty, empty2, check_index_type=False)
@pytest.mark.parametrize(
"dtype",
["f8", "i8", "M8[ns]", "m8[ns]", "category", "object", "datetime64[ns, UTC]"],
)
@pytest.mark.parametrize("index", [None, Index([])])
def test_constructor_dtype_only(self, dtype, index):
# GH-20865
result = Series(dtype=dtype, index=index)
assert result.dtype == dtype
assert len(result) == 0
def test_constructor_no_data_index_order(self):
result = Series(index=["b", "a", "c"])
assert result.index.tolist() == ["b", "a", "c"]
def test_constructor_no_data_string_type(self):
# GH 22477
result = Series(index=[1], dtype=str)
assert np.isnan(result.iloc[0])
@pytest.mark.parametrize("item", ["entry", "ѐ", 13])
def test_constructor_string_element_string_type(self, item):
# GH 22477
result = Series(item, index=[1], dtype=str)
assert result.iloc[0] == str(item)
def test_constructor_dtype_str_na_values(self, string_dtype):
# https://github.com/pandas-dev/pandas/issues/21083
ser = Series(["x", None], dtype=string_dtype)
result = ser.isna()
expected = Series([False, True])
tm.assert_series_equal(result, expected)
assert ser.iloc[1] is None
ser = Series(["x", np.nan], dtype=string_dtype)
assert np.isnan(ser.iloc[1])
def test_constructor_series(self):
index1 = ["d", "b", "a", "c"]
index2 = sorted(index1)
s1 = Series([4, 7, -5, 3], index=index1)
s2 = Series(s1, index=index2)
tm.assert_series_equal(s2, s1.sort_index())
def test_constructor_iterable(self):
# GH 21987
class Iter:
def __iter__(self) -> Iterator:
yield from range(10)
expected = Series(list(range(10)), dtype="int64")
result = Series(Iter(), dtype="int64")
tm.assert_series_equal(result, expected)
def test_constructor_sequence(self):
# GH 21987
expected = Series(list(range(10)), dtype="int64")
result = Series(range(10), dtype="int64")
tm.assert_series_equal(result, expected)
def test_constructor_single_str(self):
# GH 21987
expected = Series(["abc"])
result = Series("abc")
tm.assert_series_equal(result, expected)
def test_constructor_list_like(self):
# make sure that we are coercing different
# list-likes to standard dtypes and not
# platform specific
expected = Series([1, 2, 3], dtype="int64")
for obj in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype="int64")]:
result = Series(obj, index=[0, 1, 2])
tm.assert_series_equal(result, expected)
def test_constructor_boolean_index(self):
# GH#18579
s1 = Series([1, 2, 3], index=[4, 5, 6])
index = s1 == 2
result = Series([1, 3, 2], index=index)
expected = Series([1, 3, 2], index=[False, True, False])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", ["bool", "int32", "int64", "float64"])
def test_constructor_index_dtype(self, dtype):
# GH 17088
s = Series(Index([0, 2, 4]), dtype=dtype)
assert s.dtype == dtype
@pytest.mark.parametrize(
"input_vals",
[
([1, 2]),
(["1", "2"]),
(list(date_range("1/1/2011", periods=2, freq="h"))),
(list(date_range("1/1/2011", periods=2, freq="h", tz="US/Eastern"))),
([Interval(left=0, right=5)]),
],
)
def test_constructor_list_str(self, input_vals, string_dtype):
# GH 16605
# Ensure that data elements from a list are converted to strings
# when dtype is str, 'str', or 'U'
result = Series(input_vals, dtype=string_dtype)
expected = Series(input_vals).astype(string_dtype)
tm.assert_series_equal(result, expected)
def test_constructor_list_str_na(self, string_dtype):
result = Series([1.0, 2.0, np.nan], dtype=string_dtype)
expected = Series(["1.0", "2.0", np.nan], dtype=object)
tm.assert_series_equal(result, expected)
assert np.isnan(result[2])
def test_constructor_generator(self):
gen = (i for i in range(10))
result = Series(gen)
exp = Series(range(10))
tm.assert_series_equal(result, exp)
# same but with non-default index
gen = (i for i in range(10))
result = Series(gen, index=range(10, 20))
exp.index = range(10, 20)
tm.assert_series_equal(result, exp)
def test_constructor_map(self):
# GH8909
m = (x for x in range(10))
result = Series(m)
exp = Series(range(10))
tm.assert_series_equal(result, exp)
# same but with non-default index
m = (x for x in range(10))
result = Series(m, index=range(10, 20))
exp.index = range(10, 20)
tm.assert_series_equal(result, exp)
def test_constructor_categorical(self):
cat = Categorical([0, 1, 2, 0, 1, 2], ["a", "b", "c"])
res = Series(cat)
tm.assert_categorical_equal(res.values, cat)
# can cast to a new dtype
result = Series(Categorical([1, 2, 3]), dtype="int64")
expected = Series([1, 2, 3], dtype="int64")
tm.assert_series_equal(result, expected)
def test_construct_from_categorical_with_dtype(self):
# GH12574
ser = Series(Categorical([1, 2, 3]), dtype="category")
assert isinstance(ser.dtype, CategoricalDtype)
def test_construct_intlist_values_category_dtype(self):
ser = Series([1, 2, 3], dtype="category")
assert isinstance(ser.dtype, CategoricalDtype)
def test_constructor_categorical_with_coercion(self):
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
# test basic creation / coercion of categoricals
s = Series(factor, name="A")
assert s.dtype == "category"
assert len(s) == len(factor)
# in a frame
df = DataFrame({"A": factor})
result = df["A"]
tm.assert_series_equal(result, s)
result = df.iloc[:, 0]
tm.assert_series_equal(result, s)
assert len(df) == len(factor)
df = DataFrame({"A": s})
result = df["A"]
tm.assert_series_equal(result, s)
assert len(df) == len(factor)
# multiples
df = DataFrame({"A": s, "B": s, "C": 1})
result1 = df["A"]
result2 = df["B"]
tm.assert_series_equal(result1, s)
tm.assert_series_equal(result2, s, check_names=False)
assert result2.name == "B"
assert len(df) == len(factor)
def test_constructor_categorical_with_coercion2(self):
# GH8623
x = DataFrame(
[[1, "John P. Doe"], [2, "Jane Dove"], [1, "John P. Doe"]],
columns=["person_id", "person_name"],
)
x["person_name"] = Categorical(x.person_name) # doing this breaks transform
expected = x.iloc[0].person_name
result = x.person_name.iloc[0]
assert result == expected
result = x.person_name[0]
assert result == expected
result = x.person_name.loc[0]
assert result == expected
def test_constructor_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_constructor_categorical_dtype(self):
result = Series(
["a", "b"], dtype=CategoricalDtype(["a", "b", "c"], ordered=True)
)
assert isinstance(result.dtype, CategoricalDtype)
tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))
assert result.cat.ordered
result = Series(["a", "b"], dtype=CategoricalDtype(["b", "a"]))
assert isinstance(result.dtype, CategoricalDtype)
tm.assert_index_equal(result.cat.categories, Index(["b", "a"]))
assert result.cat.ordered is False
# GH 19565 - Check broadcasting of scalar with Categorical dtype
result = Series(
"a", index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
)
expected = Series(
["a", "a"], index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
)
tm.assert_series_equal(result, expected)
def test_constructor_categorical_string(self):
# GH 26336: the string 'category' maintains existing CategoricalDtype
cdt = CategoricalDtype(categories=list("dabc"), ordered=True)
expected = Series(list("abcabc"), dtype=cdt)
# Series(Categorical, dtype='category') keeps existing dtype
cat = Categorical(list("abcabc"), dtype=cdt)
result = Series(cat, dtype="category")
tm.assert_series_equal(result, expected)
# Series(Series[Categorical], dtype='category') keeps existing dtype
result = Series(result, dtype="category")
tm.assert_series_equal(result, expected)
def test_categorical_sideeffects_free(self):
# Passing a categorical to a Series and then changing values in either
# the series or the categorical should not change the values in the
# other one, IF you specify copy!
cat = Categorical(["a", "b", "c", "a"])
s = Series(cat, copy=True)
assert s.cat is not cat
s = s.cat.rename_categories([1, 2, 3])
exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
tm.assert_numpy_array_equal(s.__array__(), exp_s)
tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
# setting
s[0] = 2
exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
tm.assert_numpy_array_equal(s.__array__(), exp_s2)
tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
# however, copy is False by default
# so this WILL change values
cat = Categorical(["a", "b", "c", "a"])
s = Series(cat, copy=False)
assert s.values is cat
s = s.cat.rename_categories([1, 2, 3])
assert s.values is not cat
exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
tm.assert_numpy_array_equal(s.__array__(), exp_s)
s[0] = 2
exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
tm.assert_numpy_array_equal(s.__array__(), exp_s2)
def test_unordered_compare_equal(self):
left = Series(["a", "b", "c"], dtype=CategoricalDtype(["a", "b"]))
right = Series(Categorical(["a", "b", np.nan], categories=["a", "b"]))
tm.assert_series_equal(left, right)
def test_constructor_maskedarray(self):
data = ma.masked_all((3,), dtype=float)
result = Series(data)
expected = Series([np.nan, np.nan, np.nan])
tm.assert_series_equal(result, expected)
data[0] = 0.0
data[2] = 2.0
index = ["a", "b", "c"]
result = Series(data, index=index)
expected = Series([0.0, np.nan, 2.0], index=index)
tm.assert_series_equal(result, expected)
data[1] = 1.0
result = Series(data, index=index)
expected = Series([0.0, 1.0, 2.0], index=index)
tm.assert_series_equal(result, expected)
data = ma.masked_all((3,), dtype=int)
result = Series(data)
expected = Series([np.nan, np.nan, np.nan], dtype=float)
tm.assert_series_equal(result, expected)
data[0] = 0
data[2] = 2
index = ["a", "b", "c"]
result = Series(data, index=index)
expected = Series([0, np.nan, 2], index=index, dtype=float)
tm.assert_series_equal(result, expected)
data[1] = 1
result = Series(data, index=index)
expected = Series([0, 1, 2], index=index, dtype=int)
with pytest.raises(AssertionError, match="Series classes are different"):
# TODO should this be raising at all?
# https://github.com/pandas-dev/pandas/issues/56131
tm.assert_series_equal(result, expected)
data = ma.masked_all((3,), dtype=bool)
result = Series(data)
expected = Series([np.nan, np.nan, np.nan], dtype=object)
tm.assert_series_equal(result, expected)
data[0] = True
data[2] = False
index = ["a", "b", "c"]
result = Series(data, index=index)
expected = Series([True, np.nan, False], index=index, dtype=object)
tm.assert_series_equal(result, expected)
data[1] = True
result = Series(data, index=index)
expected = Series([True, True, False], index=index, dtype=bool)
with pytest.raises(AssertionError, match="Series classes are different"):
# TODO should this be raising at all?
# https://github.com/pandas-dev/pandas/issues/56131
tm.assert_series_equal(result, expected)
data = ma.masked_all((3,), dtype="M8[ns]")
result = Series(data)
expected = Series([iNaT, iNaT, iNaT], dtype="M8[ns]")
tm.assert_series_equal(result, expected)
data[0] = datetime(2001, 1, 1)
data[2] = datetime(2001, 1, 3)
index = ["a", "b", "c"]
result = Series(data, index=index)
expected = Series(
[datetime(2001, 1, 1), iNaT, datetime(2001, 1, 3)],
index=index,
dtype="M8[ns]",
)
tm.assert_series_equal(result, expected)
data[1] = datetime(2001, 1, 2)
result = Series(data, index=index)
expected = Series(
[datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 3)],
index=index,
dtype="M8[ns]",
)
tm.assert_series_equal(result, expected)
def test_constructor_maskedarray_hardened(self):
# Check numpy masked arrays with hard masks -- from GH24574
data = ma.masked_all((3,), dtype=float).harden_mask()
result = Series(data)
expected = Series([np.nan, np.nan, np.nan])
tm.assert_series_equal(result, expected)
def test_series_ctor_plus_datetimeindex(self, using_copy_on_write):
rng = date_range("20090415", "20090519", freq="B")
data = {k: 1 for k in rng}
result = Series(data, index=rng)
if using_copy_on_write:
assert result.index.is_(rng)
else:
assert result.index is rng
def test_constructor_default_index(self):
s = Series([0, 1, 2])
tm.assert_index_equal(s.index, Index(range(3)), exact=True)
@pytest.mark.parametrize(
"input",
[
[1, 2, 3],
(1, 2, 3),
list(range(3)),
Categorical(["a", "b", "a"]),
(i for i in range(3)),
(x for x in range(3)),
],
)
def test_constructor_index_mismatch(self, input):
# GH 19342
# test that construction of a Series with an index of different length
# raises an error
msg = r"Length of values \(3\) does not match length of index \(4\)"
with pytest.raises(ValueError, match=msg):
Series(input, index=np.arange(4))
def test_constructor_numpy_scalar(self):
# GH 19342
# construction with a numpy scalar
# should not raise
result = Series(np.array(100), index=np.arange(4), dtype="int64")
expected = Series(100, index=np.arange(4), dtype="int64")
tm.assert_series_equal(result, expected)
def test_constructor_broadcast_list(self):
# GH 19342
# construction with single-element container and index
# should raise
msg = r"Length of values \(1\) does not match length of index \(3\)"
with pytest.raises(ValueError, match=msg):
Series(["foo"], index=["a", "b", "c"])
def test_constructor_corner(self):
df = DataFrame(range(5), index=date_range("2020-01-01", periods=5))
objs = [df, df]
s = Series(objs, index=[0, 1])
assert isinstance(s, Series)
def test_constructor_sanitize(self):
s = Series(np.array([1.0, 1.0, 8.0]), dtype="i8")
assert s.dtype == np.dtype("i8")
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
with pytest.raises(IntCastingNaNError, match=msg):
Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8")
def test_constructor_copy(self):
# GH15125
# test dtype parameter has no side effects on copy=True
for data in [[1.0], np.array([1.0])]:
x = Series(data)
y = Series(x, copy=True, dtype=float)
# copy=True maintains original data in Series
tm.assert_series_equal(x, y)
# changes to origin of copy does not affect the copy
x[0] = 2.0
assert not x.equals(y)
assert x[0] == 2.0
assert y[0] == 1.0
@td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite test
@pytest.mark.parametrize(
"index",
[
date_range("20170101", periods=3, tz="US/Eastern"),
date_range("20170101", periods=3),
timedelta_range("1 day", periods=3),
period_range("2012Q1", periods=3, freq="Q"),
Index(list("abc")),
Index([1, 2, 3]),
RangeIndex(0, 3),
],
ids=lambda x: type(x).__name__,
)
def test_constructor_limit_copies(self, index):
# GH 17449
# limit copies of input
s = Series(index)
# we make 1 copy; this is just a smoke test here
assert s._mgr.blocks[0].values is not index
def test_constructor_shallow_copy(self):
# constructing a Series from Series with copy=False should still
# give a "shallow" copy (share data, not attributes)
# https://github.com/pandas-dev/pandas/issues/49523
s = Series([1, 2, 3])
s_orig = s.copy()
s2 = Series(s)
assert s2._mgr is not s._mgr
# Overwriting index of s2 doesn't change s
s2.index = ["a", "b", "c"]
tm.assert_series_equal(s, s_orig)
def test_constructor_pass_none(self):
s = Series(None, index=range(5))
assert s.dtype == np.float64
s = Series(None, index=range(5), dtype=object)
assert s.dtype == np.object_
# GH 7431
# inference on the index
s = Series(index=np.array([None]))
expected = Series(index=Index([None]))
tm.assert_series_equal(s, expected)
def test_constructor_pass_nan_nat(self):
# GH 13467
exp = Series([np.nan, np.nan], dtype=np.float64)
assert exp.dtype == np.float64
tm.assert_series_equal(Series([np.nan, np.nan]), exp)
tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp)
exp = Series([NaT, NaT])
assert exp.dtype == "datetime64[ns]"
tm.assert_series_equal(Series([NaT, NaT]), exp)
tm.assert_series_equal(Series(np.array([NaT, NaT])), exp)
tm.assert_series_equal(Series([NaT, np.nan]), exp)
tm.assert_series_equal(Series(np.array([NaT, np.nan])), exp)
tm.assert_series_equal(Series([np.nan, NaT]), exp)
tm.assert_series_equal(Series(np.array([np.nan, NaT])), exp)
def test_constructor_cast(self):
msg = "could not convert string to float"
with pytest.raises(ValueError, match=msg):
Series(["a", "b", "c"], dtype=float)
def test_constructor_signed_int_overflow_raises(self):
# GH#41734 disallow silent overflow, enforced in 2.0
if np_version_gt2:
msg = "The elements provided in the data cannot all be casted to the dtype"
err = OverflowError
else:
msg = "Values are too large to be losslessly converted"
err = ValueError
with pytest.raises(err, match=msg):
Series([1, 200, 923442], dtype="int8")
with pytest.raises(err, match=msg):
Series([1, 200, 923442], dtype="uint8")
@pytest.mark.parametrize(
"values",
[
np.array([1], dtype=np.uint16),
np.array([1], dtype=np.uint32),
np.array([1], dtype=np.uint64),
[np.uint16(1)],
[np.uint32(1)],
[np.uint64(1)],
],
)
def test_constructor_numpy_uints(self, values):
# GH#47294
value = values[0]
result = Series(values)
assert result[0].dtype == value.dtype
assert result[0] == value
def test_constructor_unsigned_dtype_overflow(self, any_unsigned_int_numpy_dtype):
# see gh-15832
if np_version_gt2:
msg = (
f"The elements provided in the data cannot "
f"all be casted to the dtype {any_unsigned_int_numpy_dtype}"
)
else:
msg = "Trying to coerce negative values to unsigned integers"
with pytest.raises(OverflowError, match=msg):
Series([-1], dtype=any_unsigned_int_numpy_dtype)
def test_constructor_floating_data_int_dtype(self, frame_or_series):
# GH#40110
arr = np.random.default_rng(2).standard_normal(2)
# Long-standing behavior (for Series, new in 2.0 for DataFrame)
# has been to ignore the dtype on these;
# not clear if this is what we want long-term
# expected = frame_or_series(arr)
# GH#49599 as of 2.0 we raise instead of silently retaining float dtype
msg = "Trying to coerce float values to integer"
with pytest.raises(ValueError, match=msg):
frame_or_series(arr, dtype="i8")
with pytest.raises(ValueError, match=msg):
frame_or_series(list(arr), dtype="i8")
# pre-2.0, when we had NaNs, we silently ignored the integer dtype
arr[0] = np.nan
# expected = frame_or_series(arr)
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
with pytest.raises(IntCastingNaNError, match=msg):
frame_or_series(arr, dtype="i8")
exc = IntCastingNaNError
if frame_or_series is Series:
# TODO: try to align these
exc = ValueError
msg = "cannot convert float NaN to integer"
with pytest.raises(exc, match=msg):
# same behavior if we pass list instead of the ndarray
frame_or_series(list(arr), dtype="i8")
# float array that can be losslessly cast to integers
arr = np.array([1.0, 2.0], dtype="float64")
expected = frame_or_series(arr.astype("i8"))
obj = frame_or_series(arr, dtype="i8")
tm.assert_equal(obj, expected)
obj = frame_or_series(list(arr), dtype="i8")
tm.assert_equal(obj, expected)
def test_constructor_coerce_float_fail(self, any_int_numpy_dtype):
# see gh-15832
# Updated: make sure we treat this list the same as we would treat
# the equivalent ndarray
# GH#49599 pre-2.0 we silently retained float dtype, in 2.0 we raise
vals = [1, 2, 3.5]
msg = "Trying to coerce float values to integer"
with pytest.raises(ValueError, match=msg):
Series(vals, dtype=any_int_numpy_dtype)
with pytest.raises(ValueError, match=msg):
Series(np.array(vals), dtype=any_int_numpy_dtype)
def test_constructor_coerce_float_valid(self, float_numpy_dtype):
s = Series([1, 2, 3.5], dtype=float_numpy_dtype)
expected = Series([1, 2, 3.5]).astype(float_numpy_dtype)
tm.assert_series_equal(s, expected)
def test_constructor_invalid_coerce_ints_with_float_nan(self, any_int_numpy_dtype):
# GH 22585
# Updated: make sure we treat this list the same as we would treat the
# equivalent ndarray
vals = [1, 2, np.nan]
# pre-2.0 this would return with a float dtype, in 2.0 we raise
msg = "cannot convert float NaN to integer"
with pytest.raises(ValueError, match=msg):
Series(vals, dtype=any_int_numpy_dtype)
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
with pytest.raises(IntCastingNaNError, match=msg):
Series(np.array(vals), dtype=any_int_numpy_dtype)
def test_constructor_dtype_no_cast(self, using_copy_on_write, warn_copy_on_write):
# see gh-1572
s = Series([1, 2, 3])
s2 = Series(s, dtype=np.int64)
warn = FutureWarning if warn_copy_on_write else None
with tm.assert_produces_warning(warn):
s2[1] = 5
if using_copy_on_write:
assert s[1] == 2
else:
assert s[1] == 5
def test_constructor_datelike_coercion(self):
# GH 9477
# incorrectly inferring on dateimelike looking when object dtype is
# specified
s = Series([Timestamp("20130101"), "NOV"], dtype=object)
assert s.iloc[0] == Timestamp("20130101")
assert s.iloc[1] == "NOV"
assert s.dtype == object
def test_constructor_datelike_coercion2(self):
# the dtype was being reset on the slicing and re-inferred to datetime
# even thought the blocks are mixed
belly = "216 3T19".split()
wing1 = "2T15 4H19".split()
wing2 = "416 4T20".split()
mat = pd.to_datetime("2016-01-22 2019-09-07".split())
df = DataFrame({"wing1": wing1, "wing2": wing2, "mat": mat}, index=belly)
result = df.loc["3T19"]
assert result.dtype == object
result = df.loc["216"]
assert result.dtype == object
def test_constructor_mixed_int_and_timestamp(self, frame_or_series):
# specifically Timestamp with nanos, not datetimes
objs = [Timestamp(9), 10, NaT._value]
result = frame_or_series(objs, dtype="M8[ns]")
expected = frame_or_series([Timestamp(9), Timestamp(10), NaT])
tm.assert_equal(result, expected)
def test_constructor_datetimes_with_nulls(self):
# gh-15869
for arr in [
np.array([None, None, None, None, datetime.now(), None]),
np.array([None, None, datetime.now(), None]),
]:
result = Series(arr)
assert result.dtype == "M8[ns]"
def test_constructor_dtype_datetime64(self):
s = Series(iNaT, dtype="M8[ns]", index=range(5))
assert isna(s).all()
# in theory this should be all nulls, but since
# we are not specifying a dtype is ambiguous
s = Series(iNaT, index=range(5))
assert not isna(s).all()
s = Series(np.nan, dtype="M8[ns]", index=range(5))
assert isna(s).all()
s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype="M8[ns]")
assert isna(s[1])
assert s.dtype == "M8[ns]"
s = Series([datetime(2001, 1, 2, 0, 0), np.nan], dtype="M8[ns]")
assert isna(s[1])
assert s.dtype == "M8[ns]"
def test_constructor_dtype_datetime64_10(self):
# GH3416
pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
dates = [np.datetime64(x) for x in pydates]
ser = Series(dates)
assert ser.dtype == "M8[ns]"
ser.iloc[0] = np.nan
assert ser.dtype == "M8[ns]"
# GH3414 related
expected = Series(pydates, dtype="datetime64[ms]")
result = Series(Series(dates).astype(np.int64) / 1000000, dtype="M8[ms]")
tm.assert_series_equal(result, expected)
result = Series(dates, dtype="datetime64[ms]")
tm.assert_series_equal(result, expected)
expected = Series(
[NaT, datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]"
)
result = Series([np.nan] + dates[1:], dtype="datetime64[ns]")
tm.assert_series_equal(result, expected)
def test_constructor_dtype_datetime64_11(self):
pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
dates = [np.datetime64(x) for x in pydates]
dts = Series(dates, dtype="datetime64[ns]")
# valid astype
dts.astype("int64")
# invalid casting
msg = r"Converting from datetime64\[ns\] to int32 is not supported"
with pytest.raises(TypeError, match=msg):
dts.astype("int32")
# ints are ok
# we test with np.int64 to get similar results on
# windows / 32-bit platforms
result = Series(dts, dtype=np.int64)
expected = Series(dts.astype(np.int64))
tm.assert_series_equal(result, expected)
def test_constructor_dtype_datetime64_9(self):
# invalid dates can be help as object
result = Series([datetime(2, 1, 1)])
assert result[0] == datetime(2, 1, 1, 0, 0)
result = Series([datetime(3000, 1, 1)])
assert result[0] == datetime(3000, 1, 1, 0, 0)
def test_constructor_dtype_datetime64_8(self):
# don't mix types
result = Series([Timestamp("20130101"), 1], index=["a", "b"])
assert result["a"] == Timestamp("20130101")
assert result["b"] == 1
def test_constructor_dtype_datetime64_7(self):
# GH6529
# coerce datetime64 non-ns properly
dates = date_range("01-Jan-2015", "01-Dec-2015", freq="ME")
values2 = dates.view(np.ndarray).astype("datetime64[ns]")
expected = Series(values2, index=dates)
for unit in ["s", "D", "ms", "us", "ns"]:
dtype = np.dtype(f"M8[{unit}]")
values1 = dates.view(np.ndarray).astype(dtype)
result = Series(values1, dates)
if unit == "D":
# for unit="D" we cast to nearest-supported reso, i.e. "s"
dtype = np.dtype("M8[s]")
assert result.dtype == dtype
tm.assert_series_equal(result, expected.astype(dtype))
# GH 13876
# coerce to non-ns to object properly
expected = Series(values2, index=dates, dtype=object)
for dtype in ["s", "D", "ms", "us", "ns"]:
values1 = dates.view(np.ndarray).astype(f"M8[{dtype}]")
result = Series(values1, index=dates, dtype=object)
tm.assert_series_equal(result, expected)
# leave datetime.date alone
dates2 = np.array([d.date() for d in dates.to_pydatetime()], dtype=object)
series1 = Series(dates2, dates)
tm.assert_numpy_array_equal(series1.values, dates2)
assert series1.dtype == object
def test_constructor_dtype_datetime64_6(self):
# as of 2.0, these no longer infer datetime64 based on the strings,
# matching the Index behavior
ser = Series([None, NaT, "2013-08-05 15:30:00.000001"])
assert ser.dtype == object
ser = Series([np.nan, NaT, "2013-08-05 15:30:00.000001"])
assert ser.dtype == object
ser = Series([NaT, None, "2013-08-05 15:30:00.000001"])
assert ser.dtype == object
ser = Series([NaT, np.nan, "2013-08-05 15:30:00.000001"])
assert ser.dtype == object
def test_constructor_dtype_datetime64_5(self):
# tz-aware (UTC and other tz's)
# GH 8411
dr = date_range("20130101", periods=3)
assert Series(dr).iloc[0].tz is None
dr = date_range("20130101", periods=3, tz="UTC")
assert str(Series(dr).iloc[0].tz) == "UTC"
dr = date_range("20130101", periods=3, tz="US/Eastern")
assert str(Series(dr).iloc[0].tz) == "US/Eastern"
def test_constructor_dtype_datetime64_4(self):
# non-convertible
ser = Series([1479596223000, -1479590, NaT])
assert ser.dtype == "object"
assert ser[2] is NaT
assert "NaT" in str(ser)
def test_constructor_dtype_datetime64_3(self):
# if we passed a NaT it remains
ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), NaT])
assert ser.dtype == "object"
assert ser[2] is NaT
assert "NaT" in str(ser)
def test_constructor_dtype_datetime64_2(self):
# if we passed a nan it remains
ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan])
assert ser.dtype == "object"
assert ser[2] is np.nan
assert "NaN" in str(ser)
def test_constructor_with_datetime_tz(self):
# 8260
# support datetime64 with tz
dr = date_range("20130101", periods=3, tz="US/Eastern")
s = Series(dr)
assert s.dtype.name == "datetime64[ns, US/Eastern]"
assert s.dtype == "datetime64[ns, US/Eastern]"
assert isinstance(s.dtype, DatetimeTZDtype)
assert "datetime64[ns, US/Eastern]" in str(s)
# export
result = s.values
assert isinstance(result, np.ndarray)
assert result.dtype == "datetime64[ns]"
exp = DatetimeIndex(result)
exp = exp.tz_localize("UTC").tz_convert(tz=s.dt.tz)
tm.assert_index_equal(dr, exp)
# indexing
result = s.iloc[0]
assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
result = s[0]
assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
result = s[Series([True, True, False], index=s.index)]
tm.assert_series_equal(result, s[0:2])
result = s.iloc[0:1]
tm.assert_series_equal(result, Series(dr[0:1]))
# concat
result = pd.concat([s.iloc[0:1], s.iloc[1:]])
tm.assert_series_equal(result, s)
# short str
assert "datetime64[ns, US/Eastern]" in str(s)
# formatting with NaT
result = s.shift()
assert "datetime64[ns, US/Eastern]" in str(result)
assert "NaT" in str(result)
result = DatetimeIndex(s, freq="infer")
tm.assert_index_equal(result, dr)
def test_constructor_with_datetime_tz5(self):
# long str
ser = Series(date_range("20130101", periods=1000, tz="US/Eastern"))
assert "datetime64[ns, US/Eastern]" in str(ser)
def test_constructor_with_datetime_tz4(self):
# inference
ser = Series(
[
Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
]
)
assert ser.dtype == "datetime64[ns, US/Pacific]"
assert lib.infer_dtype(ser, skipna=True) == "datetime64"
def test_constructor_with_datetime_tz3(self):
ser = Series(
[
Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
Timestamp("2013-01-02 14:00:00-0800", tz="US/Eastern"),
]
)
assert ser.dtype == "object"
assert lib.infer_dtype(ser, skipna=True) == "datetime"
def test_constructor_with_datetime_tz2(self):
# with all NaT
ser = Series(NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
dti = DatetimeIndex(["NaT", "NaT"], tz="US/Eastern").as_unit("ns")
expected = Series(dti)
tm.assert_series_equal(ser, expected)
def test_constructor_no_partial_datetime_casting(self):
# GH#40111
vals = [
"nan",
Timestamp("1990-01-01"),
"2015-03-14T16:15:14.123-08:00",
"2019-03-04T21:56:32.620-07:00",
None,
]
ser = Series(vals)
assert all(ser[i] is vals[i] for i in range(len(vals)))
@pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
@pytest.mark.parametrize("kind", ["M", "m"])
@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
def test_construction_to_datetimelike_unit(self, arr_dtype, kind, unit):
# tests all units
# gh-19223
# TODO: GH#19223 was about .astype, doesn't belong here
dtype = f"{kind}8[{unit}]"
arr = np.array([1, 2, 3], dtype=arr_dtype)
ser = Series(arr)
result = ser.astype(dtype)
expected = Series(arr.astype(dtype))
if unit in ["ns", "us", "ms", "s"]:
assert result.dtype == dtype
assert expected.dtype == dtype
else:
# Otherwise we cast to nearest-supported unit, i.e. seconds
assert result.dtype == f"{kind}8[s]"
assert expected.dtype == f"{kind}8[s]"
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", NaT, np.nan, None])
def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg):
# GH 17415: With naive string
result = Series([arg], dtype="datetime64[ns, CET]")
expected = Series(Timestamp(arg)).dt.tz_localize("CET")
tm.assert_series_equal(result, expected)
def test_constructor_datetime64_bigendian(self):
# GH#30976
ms = np.datetime64(1, "ms")
arr = np.array([np.datetime64(1, "ms")], dtype=">M8[ms]")
result = Series(arr)
expected = Series([Timestamp(ms)]).astype("M8[ms]")
assert expected.dtype == "M8[ms]"
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("interval_constructor", [IntervalIndex, IntervalArray])
def test_construction_interval(self, interval_constructor):
# construction from interval & array of intervals
intervals = interval_constructor.from_breaks(np.arange(3), closed="right")
result = Series(intervals)
assert result.dtype == "interval[int64, right]"
tm.assert_index_equal(Index(result.values), Index(intervals))
@pytest.mark.parametrize(
"data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
)
def test_constructor_infer_interval(self, data_constructor):
# GH 23563: consistent closed results in interval dtype
data = [Interval(0, 1), Interval(0, 2), None]
result = Series(data_constructor(data))
expected = Series(IntervalArray(data))
assert result.dtype == "interval[float64, right]"
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
)
def test_constructor_interval_mixed_closed(self, data_constructor):
# GH 23563: mixed closed results in object dtype (not interval dtype)
data = [Interval(0, 1, closed="both"), Interval(0, 2, closed="neither")]
result = Series(data_constructor(data))
assert result.dtype == object
assert result.tolist() == data
def test_construction_consistency(self):
# make sure that we are not re-localizing upon construction
# GH 14928
ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
result = Series(ser, dtype=ser.dtype)
tm.assert_series_equal(result, ser)
result = Series(ser.dt.tz_convert("UTC"), dtype=ser.dtype)
tm.assert_series_equal(result, ser)
# Pre-2.0 dt64 values were treated as utc, which was inconsistent
# with DatetimeIndex, which treats them as wall times, see GH#33401
result = Series(ser.values, dtype=ser.dtype)
expected = Series(ser.values).dt.tz_localize(ser.dtype.tz)
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(None):
# one suggested alternative to the deprecated (changed in 2.0) usage
middle = Series(ser.values).dt.tz_localize("UTC")
result = middle.dt.tz_convert(ser.dtype.tz)
tm.assert_series_equal(result, ser)
with tm.assert_produces_warning(None):
# the other suggested alternative to the deprecated usage
result = Series(ser.values.view("int64"), dtype=ser.dtype)
tm.assert_series_equal(result, ser)
@pytest.mark.parametrize(
"data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
)
def test_constructor_infer_period(self, data_constructor):
data = [Period("2000", "D"), Period("2001", "D"), None]
result = Series(data_constructor(data))
expected = Series(period_array(data))
tm.assert_series_equal(result, expected)
assert result.dtype == "Period[D]"
@pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
def test_construct_from_ints_including_iNaT_scalar_period_dtype(self):
series = Series([0, 1000, 2000, pd._libs.iNaT], dtype="period[D]")
val = series[3]
assert isna(val)
series[2] = val
assert isna(series[2])
def test_constructor_period_incompatible_frequency(self):
data = [Period("2000", "D"), Period("2001", "Y")]
result = Series(data)
assert result.dtype == object
assert result.tolist() == data
def test_constructor_periodindex(self):
# GH7932
# converting a PeriodIndex when put in a Series
pi = period_range("20130101", periods=5, freq="D")
s = Series(pi)
assert s.dtype == "Period[D]"
with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
expected = Series(pi.astype(object))
tm.assert_series_equal(s, expected)
def test_constructor_dict(self):
d = {"a": 0.0, "b": 1.0, "c": 2.0}
result = Series(d)
expected = Series(d, index=sorted(d.keys()))
tm.assert_series_equal(result, expected)
result = Series(d, index=["b", "c", "d", "a"])
expected = Series([1, 2, np.nan, 0], index=["b", "c", "d", "a"])
tm.assert_series_equal(result, expected)
pidx = period_range("2020-01-01", periods=10, freq="D")
d = {pidx[0]: 0, pidx[1]: 1}
result = Series(d, index=pidx)
expected = Series(np.nan, pidx, dtype=np.float64)
expected.iloc[0] = 0
expected.iloc[1] = 1
tm.assert_series_equal(result, expected)
def test_constructor_dict_list_value_explicit_dtype(self):
# GH 18625
d = {"a": [[2], [3], [4]]}
result = Series(d, index=["a"], dtype="object")
expected = Series(d, index=["a"])
tm.assert_series_equal(result, expected)
def test_constructor_dict_order(self):
# GH19018
# initialization ordering: by insertion order
d = {"b": 1, "a": 0, "c": 2}
result = Series(d)
expected = Series([1, 0, 2], index=list("bac"))
tm.assert_series_equal(result, expected)
def test_constructor_dict_extension(self, ea_scalar_and_dtype, request):
ea_scalar, ea_dtype = ea_scalar_and_dtype
if isinstance(ea_scalar, Timestamp):
mark = pytest.mark.xfail(
reason="Construction from dict goes through "
"maybe_convert_objects which casts to nano"
)
request.applymarker(mark)
d = {"a": ea_scalar}
result = Series(d, index=["a"])
expected = Series(ea_scalar, index=["a"], dtype=ea_dtype)
assert result.dtype == ea_dtype
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
def test_constructor_dict_nan_key(self, value):
# GH 18480
d = {1: "a", value: "b", float("nan"): "c", 4: "d"}
result = Series(d).sort_values()
expected = Series(["a", "b", "c", "d"], index=[1, value, np.nan, 4])
tm.assert_series_equal(result, expected)
# MultiIndex:
d = {(1, 1): "a", (2, np.nan): "b", (3, value): "c"}
result = Series(d).sort_values()
expected = Series(
["a", "b", "c"], index=Index([(1, 1), (2, np.nan), (3, value)])
)
tm.assert_series_equal(result, expected)
def test_constructor_dict_datetime64_index(self):
# GH 9456
dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"]
values = [42544017.198965244, 1234565, 40512335.181958228, -1]
def create_data(constructor):
return dict(zip((constructor(x) for x in dates_as_str), values))
data_datetime64 = create_data(np.datetime64)
data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d"))
data_Timestamp = create_data(Timestamp)
expected = Series(values, (Timestamp(x) for x in dates_as_str))
result_datetime64 = Series(data_datetime64)
result_datetime = Series(data_datetime)
result_Timestamp = Series(data_Timestamp)
tm.assert_series_equal(result_datetime64, expected)
tm.assert_series_equal(result_datetime, expected)
tm.assert_series_equal(result_Timestamp, expected)
def test_constructor_dict_tuple_indexer(self):
# GH 12948
data = {(1, 1, None): -1.0}
result = Series(data)
expected = Series(
-1.0, index=MultiIndex(levels=[[1], [1], [np.nan]], codes=[[0], [0], [-1]])
)
tm.assert_series_equal(result, expected)
def test_constructor_mapping(self, non_dict_mapping_subclass):
# GH 29788
ndm = non_dict_mapping_subclass({3: "three"})
result = Series(ndm)
expected = Series(["three"], index=[3])
tm.assert_series_equal(result, expected)
def test_constructor_list_of_tuples(self):
data = [(1, 1), (2, 2), (2, 3)]
s = Series(data)
assert list(s) == data
def test_constructor_tuple_of_tuples(self):
data = ((1, 1), (2, 2), (2, 3))
s = Series(data)
assert tuple(s) == data
def test_constructor_dict_of_tuples(self):
data = {(1, 2): 3, (None, 5): 6}
result = Series(data).sort_values()
expected = Series([3, 6], index=MultiIndex.from_tuples([(1, 2), (None, 5)]))
tm.assert_series_equal(result, expected)
# https://github.com/pandas-dev/pandas/issues/22698
@pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning")
def test_fromDict(self, using_infer_string):
data = {"a": 0, "b": 1, "c": 2, "d": 3}
series = Series(data)
tm.assert_is_sorted(series.index)
data = {"a": 0, "b": "1", "c": "2", "d": datetime.now()}
series = Series(data)
assert series.dtype == np.object_
data = {"a": 0, "b": "1", "c": "2", "d": "3"}
series = Series(data)
assert series.dtype == np.object_ if not using_infer_string else "string"
data = {"a": "0", "b": "1"}
series = Series(data, dtype=float)
assert series.dtype == np.float64
def test_fromValue(self, datetime_series, using_infer_string):
nans = Series(np.nan, index=datetime_series.index, dtype=np.float64)
assert nans.dtype == np.float64
assert len(nans) == len(datetime_series)
strings = Series("foo", index=datetime_series.index)
assert strings.dtype == np.object_ if not using_infer_string else "string"
assert len(strings) == len(datetime_series)
d = datetime.now()
dates = Series(d, index=datetime_series.index)
assert dates.dtype == "M8[us]"
assert len(dates) == len(datetime_series)
# GH12336
# Test construction of categorical series from value
categorical = Series(0, index=datetime_series.index, dtype="category")
expected = Series(0, index=datetime_series.index).astype("category")
assert categorical.dtype == "category"
assert len(categorical) == len(datetime_series)
tm.assert_series_equal(categorical, expected)
def test_constructor_dtype_timedelta64(self):
# basic
td = Series([timedelta(days=i) for i in range(3)])
assert td.dtype == "timedelta64[ns]"
td = Series([timedelta(days=1)])
assert td.dtype == "timedelta64[ns]"
td = Series([timedelta(days=1), timedelta(days=2), np.timedelta64(1, "s")])
assert td.dtype == "timedelta64[ns]"
# mixed with NaT
td = Series([timedelta(days=1), NaT], dtype="m8[ns]")
assert td.dtype == "timedelta64[ns]"
td = Series([timedelta(days=1), np.nan], dtype="m8[ns]")
assert td.dtype == "timedelta64[ns]"
td = Series([np.timedelta64(300000000), NaT], dtype="m8[ns]")
assert td.dtype == "timedelta64[ns]"
# improved inference
# GH5689
td = Series([np.timedelta64(300000000), NaT])
assert td.dtype == "timedelta64[ns]"
# because iNaT is int, not coerced to timedelta
td = Series([np.timedelta64(300000000), iNaT])
assert td.dtype == "object"
td = Series([np.timedelta64(300000000), np.nan])
assert td.dtype == "timedelta64[ns]"
td = Series([NaT, np.timedelta64(300000000)])
assert td.dtype == "timedelta64[ns]"
td = Series([np.timedelta64(1, "s")])
assert td.dtype == "timedelta64[ns]"
# valid astype
td.astype("int64")
# invalid casting
msg = r"Converting from timedelta64\[ns\] to int32 is not supported"
with pytest.raises(TypeError, match=msg):
td.astype("int32")
# this is an invalid casting
msg = "|".join(
[
"Could not convert object to NumPy timedelta",
"Could not convert 'foo' to NumPy timedelta",
]
)
with pytest.raises(ValueError, match=msg):
Series([timedelta(days=1), "foo"], dtype="m8[ns]")
# leave as object here
td = Series([timedelta(days=i) for i in range(3)] + ["foo"])
assert td.dtype == "object"
# as of 2.0, these no longer infer timedelta64 based on the strings,
# matching Index behavior
ser = Series([None, NaT, "1 Day"])
assert ser.dtype == object
ser = Series([np.nan, NaT, "1 Day"])
assert ser.dtype == object
ser = Series([NaT, None, "1 Day"])
assert ser.dtype == object
ser = Series([NaT, np.nan, "1 Day"])
assert ser.dtype == object
# GH 16406
def test_constructor_mixed_tz(self):
s = Series([Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")])
expected = Series(
[Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")],
dtype="object",
)
tm.assert_series_equal(s, expected)
def test_NaT_scalar(self):
series = Series([0, 1000, 2000, iNaT], dtype="M8[ns]")
val = series[3]
assert isna(val)
series[2] = val
assert isna(series[2])
def test_NaT_cast(self):
# GH10747
result = Series([np.nan]).astype("M8[ns]")
expected = Series([NaT], dtype="M8[ns]")
tm.assert_series_equal(result, expected)
def test_constructor_name_hashable(self):
for n in [777, 777.0, "name", datetime(2001, 11, 11), (1,), "\u05D0"]:
for data in [[1, 2, 3], np.ones(3), {"a": 0, "b": 1}]:
s = Series(data, name=n)
assert s.name == n
def test_constructor_name_unhashable(self):
msg = r"Series\.name must be a hashable type"
for n in [["name_list"], np.ones(2), {1: 2}]:
for data in [["name_list"], np.ones(2), {1: 2}]:
with pytest.raises(TypeError, match=msg):
Series(data, name=n)
def test_auto_conversion(self):
series = Series(list(date_range("1/1/2000", periods=10)))
assert series.dtype == "M8[ns]"
def test_convert_non_ns(self):
# convert from a numpy array of non-ns timedelta64
arr = np.array([1, 2, 3], dtype="timedelta64[s]")
ser = Series(arr)
assert ser.dtype == arr.dtype
tdi = timedelta_range("00:00:01", periods=3, freq="s").as_unit("s")
expected = Series(tdi)
assert expected.dtype == arr.dtype
tm.assert_series_equal(ser, expected)
# convert from a numpy array of non-ns datetime64
arr = np.array(
["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]"
)
ser = Series(arr)
expected = Series(date_range("20130101", periods=3, freq="D"), dtype="M8[s]")
assert expected.dtype == "M8[s]"
tm.assert_series_equal(ser, expected)
arr = np.array(
["2013-01-01 00:00:01", "2013-01-01 00:00:02", "2013-01-01 00:00:03"],
dtype="datetime64[s]",
)
ser = Series(arr)
expected = Series(
date_range("20130101 00:00:01", periods=3, freq="s"), dtype="M8[s]"
)
assert expected.dtype == "M8[s]"
tm.assert_series_equal(ser, expected)
@pytest.mark.parametrize(
"index",
[
date_range("1/1/2000", periods=10),
timedelta_range("1 day", periods=10),
period_range("2000-Q1", periods=10, freq="Q"),
],
ids=lambda x: type(x).__name__,
)
def test_constructor_cant_cast_datetimelike(self, index):
# floats are not ok
# strip Index to convert PeriodIndex -> Period
# We don't care whether the error message says
# PeriodIndex or PeriodArray
msg = f"Cannot cast {type(index).__name__.rstrip('Index')}.*? to "
with pytest.raises(TypeError, match=msg):
Series(index, dtype=float)
# ints are ok
# we test with np.int64 to get similar results on
# windows / 32-bit platforms
result = Series(index, dtype=np.int64)
expected = Series(index.astype(np.int64))
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
date_range("1/1/2000", periods=10),
timedelta_range("1 day", periods=10),
period_range("2000-Q1", periods=10, freq="Q"),
],
ids=lambda x: type(x).__name__,
)
def test_constructor_cast_object(self, index):
s = Series(index, dtype=object)
exp = Series(index).astype(object)
tm.assert_series_equal(s, exp)
s = Series(Index(index, dtype=object), dtype=object)
exp = Series(index).astype(object)
tm.assert_series_equal(s, exp)
s = Series(index.astype(object), dtype=object)
exp = Series(index).astype(object)
tm.assert_series_equal(s, exp)
@pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
def test_constructor_generic_timestamp_no_frequency(self, dtype, request):
# see gh-15524, gh-15987
msg = "dtype has no unit. Please pass in"
if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
request.applymarker(mark)
with pytest.raises(ValueError, match=msg):
Series([], dtype=dtype)
@pytest.mark.parametrize("unit", ["ps", "as", "fs", "Y", "M", "W", "D", "h", "m"])
@pytest.mark.parametrize("kind", ["m", "M"])
def test_constructor_generic_timestamp_bad_frequency(self, kind, unit):
# see gh-15524, gh-15987
# as of 2.0 we raise on any non-supported unit rather than silently
# cast to nanos; previously we only raised for frequencies higher
# than ns
dtype = f"{kind}8[{unit}]"
msg = "dtype=.* is not supported. Supported resolutions are"
with pytest.raises(TypeError, match=msg):
Series([], dtype=dtype)
with pytest.raises(TypeError, match=msg):
# pre-2.0 the DataFrame cast raised but the Series case did not
DataFrame([[0]], dtype=dtype)
@pytest.mark.parametrize("dtype", [None, "uint8", "category"])
def test_constructor_range_dtype(self, dtype):
# GH 16804
expected = Series([0, 1, 2, 3, 4], dtype=dtype or "int64")
result = Series(range(5), dtype=dtype)
tm.assert_series_equal(result, expected)
def test_constructor_range_overflows(self):
# GH#30173 range objects that overflow int64
rng = range(2**63, 2**63 + 4)
ser = Series(rng)
expected = Series(list(rng))
tm.assert_series_equal(ser, expected)
assert list(ser) == list(rng)
assert ser.dtype == np.uint64
rng2 = range(2**63 + 4, 2**63, -1)
ser2 = Series(rng2)
expected2 = Series(list(rng2))
tm.assert_series_equal(ser2, expected2)
assert list(ser2) == list(rng2)
assert ser2.dtype == np.uint64
rng3 = range(-(2**63), -(2**63) - 4, -1)
ser3 = Series(rng3)
expected3 = Series(list(rng3))
tm.assert_series_equal(ser3, expected3)
assert list(ser3) == list(rng3)
assert ser3.dtype == object
rng4 = range(2**73, 2**73 + 4)
ser4 = Series(rng4)
expected4 = Series(list(rng4))
tm.assert_series_equal(ser4, expected4)
assert list(ser4) == list(rng4)
assert ser4.dtype == object
def test_constructor_tz_mixed_data(self):
# GH 13051
dt_list = [
Timestamp("2016-05-01 02:03:37"),
Timestamp("2016-04-30 19:03:37-0700", tz="US/Pacific"),
]
result = Series(dt_list)
expected = Series(dt_list, dtype=object)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("pydt", [True, False])
def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture, pydt):
# GH#25843, GH#41555, GH#33401
tz = tz_aware_fixture
ts = Timestamp("2019", tz=tz)
if pydt:
ts = ts.to_pydatetime()
msg = (
"Cannot convert timezone-aware data to timezone-naive dtype. "
r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
)
with pytest.raises(ValueError, match=msg):
Series([ts], dtype="datetime64[ns]")
with pytest.raises(ValueError, match=msg):
Series(np.array([ts], dtype=object), dtype="datetime64[ns]")
with pytest.raises(ValueError, match=msg):
Series({0: ts}, dtype="datetime64[ns]")
msg = "Cannot unbox tzaware Timestamp to tznaive dtype"
with pytest.raises(TypeError, match=msg):
Series(ts, index=[0], dtype="datetime64[ns]")
def test_constructor_datetime64(self):
rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
dates = np.asarray(rng)
series = Series(dates)
assert np.issubdtype(series.dtype, np.dtype("M8[ns]"))
def test_constructor_datetimelike_scalar_to_string_dtype(
self, nullable_string_dtype
):
# https://github.com/pandas-dev/pandas/pull/33846
result = Series("M", index=[1, 2, 3], dtype=nullable_string_dtype)
expected = Series(["M", "M", "M"], index=[1, 2, 3], dtype=nullable_string_dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"values",
[
[np.datetime64("2012-01-01"), np.datetime64("2013-01-01")],
["2012-01-01", "2013-01-01"],
],
)
def test_constructor_sparse_datetime64(self, values):
# https://github.com/pandas-dev/pandas/issues/35762
dtype = pd.SparseDtype("datetime64[ns]")
result = Series(values, dtype=dtype)
arr = pd.arrays.SparseArray(values, dtype=dtype)
expected = Series(arr)
tm.assert_series_equal(result, expected)
def test_construction_from_ordered_collection(self):
# https://github.com/pandas-dev/pandas/issues/36044
result = Series({"a": 1, "b": 2}.keys())
expected = Series(["a", "b"])
tm.assert_series_equal(result, expected)
result = Series({"a": 1, "b": 2}.values())
expected = Series([1, 2])
tm.assert_series_equal(result, expected)
def test_construction_from_large_int_scalar_no_overflow(self):
# https://github.com/pandas-dev/pandas/issues/36291
n = 1_000_000_000_000_000_000_000
result = Series(n, index=[0])
expected = Series(n)
tm.assert_series_equal(result, expected)
def test_constructor_list_of_periods_infers_period_dtype(self):
series = Series(list(period_range("2000-01-01", periods=10, freq="D")))
assert series.dtype == "Period[D]"
series = Series(
[Period("2011-01-01", freq="D"), Period("2011-02-01", freq="D")]
)
assert series.dtype == "Period[D]"
def test_constructor_subclass_dict(self, dict_subclass):
data = dict_subclass((x, 10.0 * x) for x in range(10))
series = Series(data)
expected = Series(dict(data.items()))
tm.assert_series_equal(series, expected)
def test_constructor_ordereddict(self):
# GH3283
data = OrderedDict(
(f"col{i}", np.random.default_rng(2).random()) for i in range(12)
)
series = Series(data)
expected = Series(list(data.values()), list(data.keys()))
tm.assert_series_equal(series, expected)
# Test with subclass
class A(OrderedDict):
pass
series = Series(A(data))
tm.assert_series_equal(series, expected)
def test_constructor_dict_multiindex(self):
d = {("a", "a"): 0.0, ("b", "a"): 1.0, ("b", "c"): 2.0}
_d = sorted(d.items())
result = Series(d)
expected = Series(
[x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])
)
tm.assert_series_equal(result, expected)
d["z"] = 111.0
_d.insert(0, ("z", d["z"]))
result = Series(d)
expected = Series(
[x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)
)
result = result.reindex(index=expected.index)
tm.assert_series_equal(result, expected)
def test_constructor_dict_multiindex_reindex_flat(self):
# construction involves reindexing with a MultiIndex corner case
data = {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2, "j": np.nan}
expected = Series(data)
result = Series(expected[:-1].to_dict(), index=expected.index)
tm.assert_series_equal(result, expected)
def test_constructor_dict_timedelta_index(self):
# GH #12169 : Resample category data with timedelta index
# construct Series from dict as data and TimedeltaIndex as index
# will result NaN in result Series data
expected = Series(
data=["A", "B", "C"], index=pd.to_timedelta([0, 10, 20], unit="s")
)
result = Series(
data={
pd.to_timedelta(0, unit="s"): "A",
pd.to_timedelta(10, unit="s"): "B",
pd.to_timedelta(20, unit="s"): "C",
},
index=pd.to_timedelta([0, 10, 20], unit="s"),
)
tm.assert_series_equal(result, expected)
def test_constructor_infer_index_tz(self):
values = [188.5, 328.25]
tzinfo = tzoffset(None, 7200)
index = [
datetime(2012, 5, 11, 11, tzinfo=tzinfo),
datetime(2012, 5, 11, 12, tzinfo=tzinfo),
]
series = Series(data=values, index=index)
assert series.index.tz == tzinfo
# it works! GH#2443
repr(series.index[0])
def test_constructor_with_pandas_dtype(self):
# going through 2D->1D path
vals = [(1,), (2,), (3,)]
ser = Series(vals)
dtype = ser.array.dtype # NumpyEADtype
ser2 = Series(vals, dtype=dtype)
tm.assert_series_equal(ser, ser2)
def test_constructor_int_dtype_missing_values(self):
# GH#43017
result = Series(index=[0], dtype="int64")
expected = Series(np.nan, index=[0], dtype="float64")
tm.assert_series_equal(result, expected)
def test_constructor_bool_dtype_missing_values(self):
# GH#43018
result = Series(index=[0], dtype="bool")
expected = Series(True, index=[0], dtype="bool")
tm.assert_series_equal(result, expected)
def test_constructor_int64_dtype(self, any_int_dtype):
# GH#44923
result = Series(["0", "1", "2"], dtype=any_int_dtype)
expected = Series([0, 1, 2], dtype=any_int_dtype)
tm.assert_series_equal(result, expected)
def test_constructor_raise_on_lossy_conversion_of_strings(self):
# GH#44923
if not np_version_gt2:
raises = pytest.raises(
ValueError, match="string values cannot be losslessly cast to int8"
)
else:
raises = pytest.raises(
OverflowError, match="The elements provided in the data"
)
with raises:
Series(["128"], dtype="int8")
def test_constructor_dtype_timedelta_alternative_construct(self):
# GH#35465
result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
expected = Series(pd.to_timedelta([1000000, 200000, 3000000], unit="ns"))
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(
reason="Not clear what the correct expected behavior should be with "
"integers now that we support non-nano. ATM (2022-10-08) we treat ints "
"as nanoseconds, then cast to the requested dtype. xref #48312"
)
def test_constructor_dtype_timedelta_ns_s(self):
# GH#35465
result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]")
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(
reason="Not clear what the correct expected behavior should be with "
"integers now that we support non-nano. ATM (2022-10-08) we treat ints "
"as nanoseconds, then cast to the requested dtype. xref #48312"
)
def test_constructor_dtype_timedelta_ns_s_astype_int64(self):
# GH#35465
result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]").astype(
"int64"
)
expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]").astype(
"int64"
)
tm.assert_series_equal(result, expected)
@pytest.mark.filterwarnings(
"ignore:elementwise comparison failed:DeprecationWarning"
)
@pytest.mark.parametrize("func", [Series, DataFrame, Index, pd.array])
def test_constructor_mismatched_null_nullable_dtype(
self, func, any_numeric_ea_dtype
):
# GH#44514
msg = "|".join(
[
"cannot safely cast non-equivalent object",
r"int\(\) argument must be a string, a bytes-like object "
"or a (real )?number",
r"Cannot cast array data from dtype\('O'\) to dtype\('float64'\) "
"according to the rule 'safe'",
"object cannot be converted to a FloatingDtype",
"'values' contains non-numeric NA",
]
)
for null in tm.NP_NAT_OBJECTS + [NaT]:
with pytest.raises(TypeError, match=msg):
func([null, 1.0, 3.0], dtype=any_numeric_ea_dtype)
def test_series_constructor_ea_int_from_bool(self):
# GH#42137
result = Series([True, False, True, pd.NA], dtype="Int64")
expected = Series([1, 0, 1, pd.NA], dtype="Int64")
tm.assert_series_equal(result, expected)
result = Series([True, False, True], dtype="Int64")
expected = Series([1, 0, 1], dtype="Int64")
tm.assert_series_equal(result, expected)
def test_series_constructor_ea_int_from_string_bool(self):
# GH#42137
with pytest.raises(ValueError, match="invalid literal"):
Series(["True", "False", "True", pd.NA], dtype="Int64")
@pytest.mark.parametrize("val", [1, 1.0])
def test_series_constructor_overflow_uint_ea(self, val):
# GH#38798
max_val = np.iinfo(np.uint64).max - 1
result = Series([max_val, val], dtype="UInt64")
expected = Series(np.array([max_val, 1], dtype="uint64"), dtype="UInt64")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("val", [1, 1.0])
def test_series_constructor_overflow_uint_ea_with_na(self, val):
# GH#38798
max_val = np.iinfo(np.uint64).max - 1
result = Series([max_val, val, pd.NA], dtype="UInt64")
expected = Series(
IntegerArray(
np.array([max_val, 1, 0], dtype="uint64"),
np.array([0, 0, 1], dtype=np.bool_),
)
)
tm.assert_series_equal(result, expected)
def test_series_constructor_overflow_uint_with_nan(self):
# GH#38798
max_val = np.iinfo(np.uint64).max - 1
result = Series([max_val, np.nan], dtype="UInt64")
expected = Series(
IntegerArray(
np.array([max_val, 1], dtype="uint64"),
np.array([0, 1], dtype=np.bool_),
)
)
tm.assert_series_equal(result, expected)
def test_series_constructor_ea_all_na(self):
# GH#38798
result = Series([np.nan, np.nan], dtype="UInt64")
expected = Series(
IntegerArray(
np.array([1, 1], dtype="uint64"),
np.array([1, 1], dtype=np.bool_),
)
)
tm.assert_series_equal(result, expected)
def test_series_from_index_dtype_equal_does_not_copy(self):
# GH#52008
idx = Index([1, 2, 3])
expected = idx.copy(deep=True)
ser = Series(idx, dtype="int64")
ser.iloc[0] = 100
tm.assert_index_equal(idx, expected)
def test_series_string_inference(self):
# GH#54430
pytest.importorskip("pyarrow")
dtype = "string[pyarrow_numpy]"
expected = Series(["a", "b"], dtype=dtype)
with pd.option_context("future.infer_string", True):
ser = Series(["a", "b"])
tm.assert_series_equal(ser, expected)
expected = Series(["a", 1], dtype="object")
with pd.option_context("future.infer_string", True):
ser = Series(["a", 1])
tm.assert_series_equal(ser, expected)
@pytest.mark.parametrize("na_value", [None, np.nan, pd.NA])
def test_series_string_with_na_inference(self, na_value):
# GH#54430
pytest.importorskip("pyarrow")
dtype = "string[pyarrow_numpy]"
expected = Series(["a", na_value], dtype=dtype)
with pd.option_context("future.infer_string", True):
ser = Series(["a", na_value])
tm.assert_series_equal(ser, expected)
def test_series_string_inference_scalar(self):
# GH#54430
pytest.importorskip("pyarrow")
expected = Series("a", index=[1], dtype="string[pyarrow_numpy]")
with pd.option_context("future.infer_string", True):
ser = Series("a", index=[1])
tm.assert_series_equal(ser, expected)
def test_series_string_inference_array_string_dtype(self):
# GH#54496
pytest.importorskip("pyarrow")
expected = Series(["a", "b"], dtype="string[pyarrow_numpy]")
with pd.option_context("future.infer_string", True):
ser = Series(np.array(["a", "b"]))
tm.assert_series_equal(ser, expected)
def test_series_string_inference_storage_definition(self):
# GH#54793
pytest.importorskip("pyarrow")
expected = Series(["a", "b"], dtype="string[pyarrow_numpy]")
with pd.option_context("future.infer_string", True):
result = Series(["a", "b"], dtype="string")
tm.assert_series_equal(result, expected)
def test_series_constructor_infer_string_scalar(self):
# GH#55537
with pd.option_context("future.infer_string", True):
ser = Series("a", index=[1, 2], dtype="string[python]")
expected = Series(["a", "a"], index=[1, 2], dtype="string[python]")
tm.assert_series_equal(ser, expected)
assert ser.dtype.storage == "python"
def test_series_string_inference_na_first(self):
# GH#55655
pytest.importorskip("pyarrow")
expected = Series([pd.NA, "b"], dtype="string[pyarrow_numpy]")
with pd.option_context("future.infer_string", True):
result = Series([pd.NA, "b"])
tm.assert_series_equal(result, expected)
def test_inference_on_pandas_objects(self):
# GH#56012
ser = Series([Timestamp("2019-12-31")], dtype=object)
with tm.assert_produces_warning(None):
# This doesn't do inference
result = Series(ser)
assert result.dtype == np.object_
idx = Index([Timestamp("2019-12-31")], dtype=object)
with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
result = Series(idx)
assert result.dtype != np.object_
class TestSeriesConstructorIndexCoercion:
def test_series_constructor_datetimelike_index_coercion(self):
idx = date_range("2020-01-01", periods=5)
ser = Series(
np.random.default_rng(2).standard_normal(len(idx)), idx.astype(object)
)
# as of 2.0, we no longer silently cast the object-dtype index
# to DatetimeIndex GH#39307, GH#23598
assert not isinstance(ser.index, DatetimeIndex)
@pytest.mark.parametrize("container", [None, np.array, Series, Index])
@pytest.mark.parametrize("data", [1.0, range(4)])
def test_series_constructor_infer_multiindex(self, container, data):
indexes = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
if container is not None:
indexes = [container(ind) for ind in indexes]
multi = Series(data, index=indexes)
assert isinstance(multi.index, MultiIndex)
# TODO: make this not cast to object in pandas 3.0
@pytest.mark.skipif(
not np_version_gt2, reason="StringDType only available in numpy 2 and above"
)
@pytest.mark.parametrize(
"data",
[
["a", "b", "c"],
["a", "b", np.nan],
],
)
def test_np_string_array_object_cast(self, data):
from numpy.dtypes import StringDType
arr = np.array(data, dtype=StringDType())
res = Series(arr)
assert res.dtype == np.object_
assert (res == data).all()
class TestSeriesConstructorInternals:
def test_constructor_no_pandas_array(self, using_array_manager):
ser = Series([1, 2, 3])
result = Series(ser.array)
tm.assert_series_equal(ser, result)
if not using_array_manager:
assert isinstance(result._mgr.blocks[0], NumpyBlock)
assert result._mgr.blocks[0].is_numeric
@td.skip_array_manager_invalid_test
def test_from_array(self):
result = Series(pd.array(["1h", "2h"], dtype="timedelta64[ns]"))
assert result._mgr.blocks[0].is_extension is False
result = Series(pd.array(["2015"], dtype="datetime64[ns]"))
assert result._mgr.blocks[0].is_extension is False
@td.skip_array_manager_invalid_test
def test_from_list_dtype(self):
result = Series(["1h", "2h"], dtype="timedelta64[ns]")
assert result._mgr.blocks[0].is_extension is False
result = Series(["2015"], dtype="datetime64[ns]")
assert result._mgr.blocks[0].is_extension is False
def test_constructor(rand_series_with_duplicate_datetimeindex):
dups = rand_series_with_duplicate_datetimeindex
assert isinstance(dups, Series)
assert isinstance(dups.index, DatetimeIndex)
@pytest.mark.parametrize(
"input_dict,expected",
[
({0: 0}, np.array([[0]], dtype=np.int64)),
({"a": "a"}, np.array([["a"]], dtype=object)),
({1: 1}, np.array([[1]], dtype=np.int64)),
],
)
def test_numpy_array(input_dict, expected):
result = np.array([Series(input_dict)])
tm.assert_numpy_array_equal(result, expected)
def test_index_ordered_dict_keys():
# GH 22077
param_index = OrderedDict(
[
((("a", "b"), ("c", "d")), 1),
((("a", None), ("c", "d")), 2),
]
)
series = Series([1, 2], index=param_index.keys())
expected = Series(
[1, 2],
index=MultiIndex.from_tuples(
[(("a", "b"), ("c", "d")), (("a", None), ("c", "d"))]
),
)
tm.assert_series_equal(series, expected)
@pytest.mark.parametrize(
"input_list",
[
[1, complex("nan"), 2],
[1 + 1j, complex("nan"), 2 + 2j],
],
)
def test_series_with_complex_nan(input_list):
# GH#53627
ser = Series(input_list)
result = Series(ser.array)
assert ser.dtype == "complex128"
tm.assert_series_equal(ser, result)
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