from collections import OrderedDict from datetime import datetime, timedelta import numpy as np import numpy.ma as ma import pytest from pandas._libs import iNaT, lib from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype from pandas.core.dtypes.dtypes import CategoricalDtype import pandas as pd from pandas import ( Categorical, DataFrame, Index, IntervalIndex, MultiIndex, NaT, Series, Timestamp, date_range, isna, period_range, timedelta_range, ) import pandas._testing as tm from pandas.core.arrays import IntervalArray, period_array class TestSeriesConstructors: @pytest.mark.parametrize( "constructor,check_index_type", [ # 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: Series(), True), (lambda: Series(None), True), (lambda: Series({}), True), (lambda: Series(()), False), # creates a RangeIndex (lambda: Series([]), False), # creates a RangeIndex (lambda: Series((_ for _ in [])), False), # creates a RangeIndex (lambda: Series(data=None), True), (lambda: Series(data={}), True), (lambda: Series(data=()), False), # creates a RangeIndex (lambda: Series(data=[]), False), # creates a RangeIndex (lambda: Series(data=(_ for _ in [])), False), # creates a RangeIndex ], ) def test_empty_constructor(self, constructor, check_index_type): with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): expected = Series() result = constructor() assert len(result.index) == 0 tm.assert_series_equal(result, expected, check_index_type=check_index_type) def test_invalid_dtype(self): # GH15520 msg = "not understood" invalid_list = [pd.Timestamp, "pd.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) # Coercion assert float(Series([1.0])) == 1.0 assert int(Series([1.0])) == 1 def test_constructor(self, datetime_series): with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): empty_series = Series() assert datetime_series.index.is_all_dates # Pass in Series derived = Series(datetime_series) assert derived.index.is_all_dates assert tm.equalContents(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_ assert mixed[1] is np.NaN assert not empty_series.index.is_all_dates with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): assert not Series().index.is_all_dates # exception raised is of type Exception with pytest.raises(Exception, match="Data must be 1-dimensional"): Series(np.random.randn(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) @pytest.mark.parametrize("input_class", [list, dict, OrderedDict]) def test_constructor_empty(self, input_class): with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): 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: with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): 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)) 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, pd.Index([])]) def test_constructor_dtype_only(self, dtype, index): # GH-20865 result = pd.Series(dtype=dtype, index=index) assert result.dtype == dtype assert len(result) == 0 def test_constructor_no_data_index_order(self): with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): result = pd.Series(index=["b", "a", "c"]) assert result.index.tolist() == ["b", "a", "c"] def test_constructor_no_data_string_type(self): # GH 22477 result = pd.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 = pd.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): for i in range(10): yield i 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) @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(pd.date_range("1/1/2011", periods=2, freq="H"))), (list(pd.date_range("1/1/2011", periods=2, freq="H", tz="US/Eastern"))), ([pd.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) 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 = map(lambda x: x, range(10)) result = Series(m) exp = Series(range(10)) tm.assert_series_equal(result, exp) m = map(lambda x: x, 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 = pd.Categorical([0, 1, 2, 0, 1, 2], ["a", "b", "c"], fastpath=True) res = Series(cat) tm.assert_categorical_equal(res.values, cat) # can cast to a new dtype result = Series(pd.Categorical([1, 2, 3]), dtype="int64") expected = pd.Series([1, 2, 3], dtype="int64") tm.assert_series_equal(result, expected) # GH12574 cat = Series(pd.Categorical([1, 2, 3]), dtype="category") assert is_categorical_dtype(cat) assert is_categorical_dtype(cat.dtype) s = Series([1, 2, 3], dtype="category") assert is_categorical_dtype(s) assert is_categorical_dtype(s.dtype) 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) str(s.values) str(s) # 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) str(df.values) str(df) df = DataFrame({"A": s}) result = df["A"] tm.assert_series_equal(result, s) assert len(df) == len(factor) str(df.values) str(df) # 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) str(df.values) str(df) # 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_categorical_dtype(self): result = pd.Series( ["a", "b"], dtype=CategoricalDtype(["a", "b", "c"], ordered=True) ) assert is_categorical_dtype(result.dtype) is True tm.assert_index_equal(result.cat.categories, pd.Index(["a", "b", "c"])) assert result.cat.ordered result = pd.Series(["a", "b"], dtype=CategoricalDtype(["b", "a"])) assert is_categorical_dtype(result.dtype) tm.assert_index_equal(result.cat.categories, pd.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.cat.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) assert s.values is cat s.cat.categories = [1, 2, 3] exp_s = np.array([1, 2, 3, 1], dtype=np.int64) tm.assert_numpy_array_equal(s.__array__(), exp_s) tm.assert_numpy_array_equal(cat.__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) tm.assert_numpy_array_equal(cat.__array__(), exp_s2) def test_unordered_compare_equal(self): left = pd.Series(["a", "b", "c"], dtype=CategoricalDtype(["a", "b"])) right = pd.Series(pd.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) 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) 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 = pd.Series(data) expected = pd.Series([np.nan, np.nan, np.nan]) tm.assert_series_equal(result, expected) def test_series_ctor_plus_datetimeindex(self): rng = date_range("20090415", "20090519", freq="B") data = {k: 1 for k in rng} result = Series(data, index=rng) assert result.index is rng def test_constructor_default_index(self): s = Series([0, 1, 2]) tm.assert_index_equal(s.index, pd.Index(np.arange(3))) @pytest.mark.parametrize( "input", [ [1, 2, 3], (1, 2, 3), list(range(3)), pd.Categorical(["a", "b", "a"]), (i for i in range(3)), map(lambda x: x, 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 = "Length of passed values is 3, index implies 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 = "Length of passed values is 1, index implies 3" with pytest.raises(ValueError, match=msg): Series(["foo"], index=["a", "b", "c"]) def test_constructor_corner(self): df = tm.makeTimeDataFrame() 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") s = Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8") assert s.dtype == np.dtype("f8") 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 = pd.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 @pytest.mark.parametrize( "index", [ pd.date_range("20170101", periods=3, tz="US/Eastern"), pd.date_range("20170101", periods=3), pd.timedelta_range("1 day", periods=3), pd.period_range("2012Q1", periods=3, freq="Q"), pd.Index(list("abc")), pd.Int64Index([1, 2, 3]), pd.RangeIndex(0, 3), ], ids=lambda x: type(x).__name__, ) def test_constructor_limit_copies(self, index): # GH 17449 # limit copies of input s = pd.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_pass_none(self): with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): 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 with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False): 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([pd.NaT, pd.NaT]) assert exp.dtype == "datetime64[ns]" tm.assert_series_equal(Series([pd.NaT, pd.NaT]), exp) tm.assert_series_equal(Series(np.array([pd.NaT, pd.NaT])), exp) tm.assert_series_equal(Series([pd.NaT, np.nan]), exp) tm.assert_series_equal(Series(np.array([pd.NaT, np.nan])), exp) tm.assert_series_equal(Series([np.nan, pd.NaT]), exp) tm.assert_series_equal(Series(np.array([np.nan, pd.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_unsigned_dtype_overflow(self, uint_dtype): # see gh-15832 msg = "Trying to coerce negative values to unsigned integers" with pytest.raises(OverflowError, match=msg): Series([-1], dtype=uint_dtype) def test_constructor_coerce_float_fail(self, any_int_dtype): # see gh-15832 msg = "Trying to coerce float values to integers" with pytest.raises(ValueError, match=msg): Series([1, 2, 3.5], dtype=any_int_dtype) def test_constructor_coerce_float_valid(self, float_dtype): s = Series([1, 2, 3.5], dtype=float_dtype) expected = Series([1, 2, 3.5]).astype(float_dtype) tm.assert_series_equal(s, expected) def test_constructor_dtype_no_cast(self): # see gh-1572 s = Series([1, 2, 3]) s2 = Series(s, dtype=np.int64) s2[1] = 5 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 # 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 = pd.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_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]" # GH3416 dates = [ np.datetime64(datetime(2013, 1, 1)), np.datetime64(datetime(2013, 1, 2)), np.datetime64(datetime(2013, 1, 3)), ] s = Series(dates) assert s.dtype == "M8[ns]" s.iloc[0] = np.nan assert s.dtype == "M8[ns]" # GH3414 related expected = Series( [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]", ) result = Series(Series(dates).astype(np.int64) / 1000000, dtype="M8[ms]") tm.assert_series_equal(result, expected) result = Series(dates, dtype="datetime64[ns]") tm.assert_series_equal(result, expected) expected = Series( [pd.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) dts = Series(dates, dtype="datetime64[ns]") # valid astype dts.astype("int64") # invalid casting msg = r"cannot astype a datetimelike from \[datetime64\[ns\]\] to \[int32\]" 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) # 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) # don't mix types result = Series([Timestamp("20130101"), 1], index=["a", "b"]) assert result["a"] == Timestamp("20130101") assert result["b"] == 1 # GH6529 # coerce datetime64 non-ns properly dates = date_range("01-Jan-2015", "01-Dec-2015", freq="M") values2 = dates.view(np.ndarray).astype("datetime64[ns]") expected = Series(values2, index=dates) for dtype in ["s", "D", "ms", "us", "ns"]: values1 = dates.view(np.ndarray).astype(f"M8[{dtype}]") result = Series(values1, dates) tm.assert_series_equal(result, expected) # 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 # these will correctly infer a datetime s = Series([None, pd.NaT, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" s = Series([np.nan, pd.NaT, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" s = Series([pd.NaT, None, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" s = Series([pd.NaT, np.nan, "2013-08-05 15:30:00.000001"]) assert s.dtype == "datetime64[ns]" # 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" # non-convertible s = Series([1479596223000, -1479590, pd.NaT]) assert s.dtype == "object" assert s[2] is pd.NaT assert "NaT" in str(s) # if we passed a NaT it remains s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), pd.NaT]) assert s.dtype == "object" assert s[2] is pd.NaT assert "NaT" in str(s) # if we passed a nan it remains s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan]) assert s.dtype == "object" assert s[2] is np.nan assert "NaN" in str(s) 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 is_datetime64tz_dtype(s.dtype) assert "datetime64[ns, US/Eastern]" in str(s) # export result = s.values assert isinstance(result, np.ndarray) assert result.dtype == "datetime64[ns]" exp = pd.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", freq="D" ) result = s[0] assert result == Timestamp( "2013-01-01 00:00:00-0500", tz="US/Eastern", freq="D" ) 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) # long str t = Series(date_range("20130101", periods=1000, tz="US/Eastern")) assert "datetime64[ns, US/Eastern]" in str(t) result = pd.DatetimeIndex(s, freq="infer") tm.assert_index_equal(result, dr) # inference s = Series( [ pd.Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"), pd.Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"), ] ) assert s.dtype == "datetime64[ns, US/Pacific]" assert lib.infer_dtype(s, skipna=True) == "datetime64" s = Series( [ pd.Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"), pd.Timestamp("2013-01-02 14:00:00-0800", tz="US/Eastern"), ] ) assert s.dtype == "object" assert lib.infer_dtype(s, skipna=True) == "datetime" # with all NaT s = Series(pd.NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]") expected = Series(pd.DatetimeIndex(["NaT", "NaT"], tz="US/Eastern")) tm.assert_series_equal(s, expected) @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64]) @pytest.mark.parametrize("dtype", ["M8", "m8"]) @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"]) def test_construction_to_datetimelike_unit(self, arr_dtype, dtype, unit): # tests all units # gh-19223 dtype = f"{dtype}[{unit}]" arr = np.array([1, 2, 3], dtype=arr_dtype) s = Series(arr) result = s.astype(dtype) expected = Series(arr.astype(dtype)) tm.assert_series_equal(result, expected) @pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", pd.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(pd.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)]) 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]" 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 = [pd.Interval(0, 1), pd.Interval(0, 2), None] result = pd.Series(data_constructor(data)) expected = pd.Series(IntervalArray(data)) assert result.dtype == "interval[float64]" 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 = [pd.Interval(0, 1, closed="both"), pd.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 s = Series(pd.date_range("20130101", periods=3, tz="US/Eastern")) result = Series(s, dtype=s.dtype) tm.assert_series_equal(result, s) result = Series(s.dt.tz_convert("UTC"), dtype=s.dtype) tm.assert_series_equal(result, s) result = Series(s.values, dtype=s.dtype) tm.assert_series_equal(result, s) @pytest.mark.parametrize( "data_constructor", [list, np.array], ids=["list", "ndarray[object]"] ) def test_constructor_infer_period(self, data_constructor): data = [pd.Period("2000", "D"), pd.Period("2001", "D"), None] result = pd.Series(data_constructor(data)) expected = pd.Series(period_array(data)) tm.assert_series_equal(result, expected) assert result.dtype == "Period[D]" def test_constructor_period_incompatible_frequency(self): data = [pd.Period("2000", "D"), pd.Period("2001", "A")] result = pd.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]" 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, index=["b", "c", "d", "a"]) expected = Series([1, 2, np.nan, 0], index=["b", "c", "d", "a"]) tm.assert_series_equal(result, expected) pidx = tm.makePeriodIndex(100) 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 if python>= 3.6, else # order by value d = {"b": 1, "a": 0, "c": 2} result = Series(d) expected = Series([1, 0, 2], index=list("bac")) 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) def test_constructor_set(self): values = {1, 2, 3, 4, 5} with pytest.raises(TypeError, match="'set' type is unordered"): Series(values) values = frozenset(values) with pytest.raises(TypeError, match="'frozenset' type is unordered"): Series(values) # https://github.com/pandas-dev/pandas/issues/22698 @pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning") def test_fromDict(self): 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_ data = {"a": "0", "b": "1"} series = Series(data, dtype=float) assert series.dtype == np.float64 def test_fromValue(self, datetime_series): nans = Series(np.NaN, index=datetime_series.index, dtype=np.float64) assert nans.dtype == np.float_ assert len(nans) == len(datetime_series) strings = Series("foo", index=datetime_series.index) assert strings.dtype == np.object_ assert len(strings) == len(datetime_series) d = datetime.now() dates = Series(d, index=datetime_series.index) assert dates.dtype == "M8[ns]" 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), pd.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([pd.NaT, np.timedelta64(300000000)]) assert td.dtype == "timedelta64[ns]" td = Series([np.timedelta64(1, "s")]) assert td.dtype == "timedelta64[ns]" # these are frequency conversion astypes # for t in ['s', 'D', 'us', 'ms']: # with pytest.raises(TypeError): # td.astype('m8[%s]' % t) # valid astype td.astype("int64") # invalid casting msg = r"cannot astype a timedelta from \[timedelta64\[ns\]\] to \[int32\]" with pytest.raises(TypeError, match=msg): td.astype("int32") # this is an invalid casting msg = "Could not convert object 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" # these will correctly infer a timedelta s = Series([None, pd.NaT, "1 Day"]) assert s.dtype == "timedelta64[ns]" s = Series([np.nan, pd.NaT, "1 Day"]) assert s.dtype == "timedelta64[ns]" s = Series([pd.NaT, None, "1 Day"]) assert s.dtype == "timedelta64[ns]" s = Series([pd.NaT, np.nan, "1 Day"]) assert s.dtype == "timedelta64[ns]" # 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]) 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]") s = Series(arr) expected = Series(pd.timedelta_range("00:00:01", periods=3, freq="s")) tm.assert_series_equal(s, expected) # convert from a numpy array of non-ns datetime64 # note that creating a numpy datetime64 is in LOCAL time!!!! # seems to work for M8[D], but not for M8[s] # TODO: is the above comment still accurate/needed? 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")) 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")) 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(pd.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.node.add_marker(mark) with pytest.raises(ValueError, match=msg): Series([], dtype=dtype) @pytest.mark.parametrize( "dtype,msg", [ ("m8[ps]", "cannot convert timedeltalike"), ("M8[ps]", "cannot convert datetimelike"), ], ) def test_constructor_generic_timestamp_bad_frequency(self, dtype, msg): # see gh-15524, gh-15987 with pytest.raises(TypeError, match=msg): Series([], 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_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) def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture): # GH#25843 tz = tz_aware_fixture result = Series([Timestamp("2019", tz=tz)], dtype="datetime64[ns]") expected = Series([Timestamp("2019")]) tm.assert_series_equal(result, expected) 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): # https://github.com/pandas-dev/pandas/pull/33846 result = Series("M", index=[1, 2, 3], dtype="string") expected = pd.Series(["M", "M", "M"], index=[1, 2, 3], dtype="string") 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 = pd.Series(values, dtype=dtype) arr = pd.arrays.SparseArray(values, dtype=dtype) expected = pd.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)