import array
|
from collections import (
|
OrderedDict,
|
abc,
|
defaultdict,
|
namedtuple,
|
)
|
from dataclasses import make_dataclass
|
from datetime import (
|
date,
|
datetime,
|
timedelta,
|
)
|
import functools
|
import random
|
import re
|
from typing import Iterator
|
import warnings
|
|
import numpy as np
|
from numpy import ma
|
from numpy.ma import mrecords
|
import pytest
|
import pytz
|
|
from pandas.errors import IntCastingNaNError
|
import pandas.util._test_decorators as td
|
|
from pandas.core.dtypes.common import is_integer_dtype
|
from pandas.core.dtypes.dtypes import (
|
DatetimeTZDtype,
|
IntervalDtype,
|
PandasDtype,
|
PeriodDtype,
|
)
|
|
import pandas as pd
|
from pandas import (
|
Categorical,
|
CategoricalIndex,
|
DataFrame,
|
DatetimeIndex,
|
Index,
|
Interval,
|
MultiIndex,
|
Period,
|
RangeIndex,
|
Series,
|
Timedelta,
|
Timestamp,
|
cut,
|
date_range,
|
isna,
|
)
|
import pandas._testing as tm
|
from pandas.arrays import (
|
DatetimeArray,
|
IntervalArray,
|
PeriodArray,
|
SparseArray,
|
TimedeltaArray,
|
)
|
|
MIXED_FLOAT_DTYPES = ["float16", "float32", "float64"]
|
MIXED_INT_DTYPES = [
|
"uint8",
|
"uint16",
|
"uint32",
|
"uint64",
|
"int8",
|
"int16",
|
"int32",
|
"int64",
|
]
|
|
|
class TestDataFrameConstructors:
|
def test_constructor_from_ndarray_with_str_dtype(self):
|
# If we don't ravel/reshape around ensure_str_array, we end up
|
# with an array of strings each of which is e.g. "[0 1 2]"
|
arr = np.arange(12).reshape(4, 3)
|
df = DataFrame(arr, dtype=str)
|
expected = DataFrame(arr.astype(str))
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_from_2d_datetimearray(self, using_array_manager):
|
dti = date_range("2016-01-01", periods=6, tz="US/Pacific")
|
dta = dti._data.reshape(3, 2)
|
|
df = DataFrame(dta)
|
expected = DataFrame({0: dta[:, 0], 1: dta[:, 1]})
|
tm.assert_frame_equal(df, expected)
|
if not using_array_manager:
|
# GH#44724 big performance hit if we de-consolidate
|
assert len(df._mgr.blocks) == 1
|
|
def test_constructor_dict_with_tzaware_scalar(self):
|
# GH#42505
|
dt = Timestamp("2019-11-03 01:00:00-0700").tz_convert("America/Los_Angeles")
|
|
df = DataFrame({"dt": dt}, index=[0])
|
expected = DataFrame({"dt": [dt]})
|
tm.assert_frame_equal(df, expected)
|
|
# Non-homogeneous
|
df = DataFrame({"dt": dt, "value": [1]})
|
expected = DataFrame({"dt": [dt], "value": [1]})
|
tm.assert_frame_equal(df, expected)
|
|
def test_construct_ndarray_with_nas_and_int_dtype(self):
|
# GH#26919 match Series by not casting np.nan to meaningless int
|
arr = np.array([[1, np.nan], [2, 3]])
|
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
|
with pytest.raises(IntCastingNaNError, match=msg):
|
DataFrame(arr, dtype="i8")
|
|
# check this matches Series behavior
|
with pytest.raises(IntCastingNaNError, match=msg):
|
Series(arr[0], dtype="i8", name=0)
|
|
def test_construct_from_list_of_datetimes(self):
|
df = DataFrame([datetime.now(), datetime.now()])
|
assert df[0].dtype == np.dtype("M8[ns]")
|
|
def test_constructor_from_tzaware_datetimeindex(self):
|
# don't cast a DatetimeIndex WITH a tz, leave as object
|
# GH#6032
|
naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B")
|
idx = naive.tz_localize("US/Pacific")
|
|
expected = Series(np.array(idx.tolist(), dtype="object"), name="B")
|
assert expected.dtype == idx.dtype
|
|
# convert index to series
|
result = Series(idx)
|
tm.assert_series_equal(result, expected)
|
|
def test_array_of_dt64_nat_with_td64dtype_raises(self, frame_or_series):
|
# GH#39462
|
nat = np.datetime64("NaT", "ns")
|
arr = np.array([nat], dtype=object)
|
if frame_or_series is DataFrame:
|
arr = arr.reshape(1, 1)
|
|
msg = "Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
|
with pytest.raises(TypeError, match=msg):
|
frame_or_series(arr, dtype="m8[ns]")
|
|
@pytest.mark.parametrize("kind", ["m", "M"])
|
def test_datetimelike_values_with_object_dtype(self, kind, frame_or_series):
|
# with dtype=object, we should cast dt64 values to Timestamps, not pydatetimes
|
if kind == "M":
|
dtype = "M8[ns]"
|
scalar_type = Timestamp
|
else:
|
dtype = "m8[ns]"
|
scalar_type = Timedelta
|
|
arr = np.arange(6, dtype="i8").view(dtype).reshape(3, 2)
|
if frame_or_series is Series:
|
arr = arr[:, 0]
|
|
obj = frame_or_series(arr, dtype=object)
|
assert obj._mgr.arrays[0].dtype == object
|
assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
|
|
# go through a different path in internals.construction
|
obj = frame_or_series(frame_or_series(arr), dtype=object)
|
assert obj._mgr.arrays[0].dtype == object
|
assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
|
|
obj = frame_or_series(frame_or_series(arr), dtype=PandasDtype(object))
|
assert obj._mgr.arrays[0].dtype == object
|
assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
|
|
if frame_or_series is DataFrame:
|
# other paths through internals.construction
|
sers = [Series(x) for x in arr]
|
obj = frame_or_series(sers, dtype=object)
|
assert obj._mgr.arrays[0].dtype == object
|
assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type)
|
|
def test_series_with_name_not_matching_column(self):
|
# GH#9232
|
x = Series(range(5), name=1)
|
y = Series(range(5), name=0)
|
|
result = DataFrame(x, columns=[0])
|
expected = DataFrame([], columns=[0])
|
tm.assert_frame_equal(result, expected)
|
|
result = DataFrame(y, columns=[1])
|
expected = DataFrame([], columns=[1])
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"constructor",
|
[
|
lambda: DataFrame(),
|
lambda: DataFrame(None),
|
lambda: DataFrame(()),
|
lambda: DataFrame([]),
|
lambda: DataFrame(_ for _ in []),
|
lambda: DataFrame(range(0)),
|
lambda: DataFrame(data=None),
|
lambda: DataFrame(data=()),
|
lambda: DataFrame(data=[]),
|
lambda: DataFrame(data=(_ for _ in [])),
|
lambda: DataFrame(data=range(0)),
|
],
|
)
|
def test_empty_constructor(self, constructor):
|
expected = DataFrame()
|
result = constructor()
|
assert len(result.index) == 0
|
assert len(result.columns) == 0
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"constructor",
|
[
|
lambda: DataFrame({}),
|
lambda: DataFrame(data={}),
|
],
|
)
|
def test_empty_constructor_object_index(self, constructor):
|
expected = DataFrame(index=RangeIndex(0), columns=RangeIndex(0))
|
result = constructor()
|
assert len(result.index) == 0
|
assert len(result.columns) == 0
|
tm.assert_frame_equal(result, expected, check_index_type=True)
|
|
@pytest.mark.parametrize(
|
"emptylike,expected_index,expected_columns",
|
[
|
([[]], RangeIndex(1), RangeIndex(0)),
|
([[], []], RangeIndex(2), RangeIndex(0)),
|
([(_ for _ in [])], RangeIndex(1), RangeIndex(0)),
|
],
|
)
|
def test_emptylike_constructor(self, emptylike, expected_index, expected_columns):
|
expected = DataFrame(index=expected_index, columns=expected_columns)
|
result = DataFrame(emptylike)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_mixed(self, float_string_frame):
|
index, data = tm.getMixedTypeDict()
|
|
# TODO(wesm), incomplete test?
|
indexed_frame = DataFrame(data, index=index) # noqa
|
unindexed_frame = DataFrame(data) # noqa
|
|
assert float_string_frame["foo"].dtype == np.object_
|
|
def test_constructor_cast_failure(self):
|
# as of 2.0, we raise if we can't respect "dtype", previously we
|
# silently ignored
|
msg = "could not convert string to float"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"a": ["a", "b", "c"]}, dtype=np.float64)
|
|
# GH 3010, constructing with odd arrays
|
df = DataFrame(np.ones((4, 2)))
|
|
# this is ok
|
df["foo"] = np.ones((4, 2)).tolist()
|
|
# this is not ok
|
msg = "Expected a 1D array, got an array with shape \\(4, 2\\)"
|
with pytest.raises(ValueError, match=msg):
|
df["test"] = np.ones((4, 2))
|
|
# this is ok
|
df["foo2"] = np.ones((4, 2)).tolist()
|
|
def test_constructor_dtype_copy(self):
|
orig_df = DataFrame({"col1": [1.0], "col2": [2.0], "col3": [3.0]})
|
|
new_df = DataFrame(orig_df, dtype=float, copy=True)
|
|
new_df["col1"] = 200.0
|
assert orig_df["col1"][0] == 1.0
|
|
def test_constructor_dtype_nocast_view_dataframe(self, using_copy_on_write):
|
df = DataFrame([[1, 2]])
|
should_be_view = DataFrame(df, dtype=df[0].dtype)
|
if using_copy_on_write:
|
should_be_view.iloc[0, 0] = 99
|
assert df.values[0, 0] == 1
|
else:
|
should_be_view[0][0] = 99
|
assert df.values[0, 0] == 99
|
|
def test_constructor_dtype_nocast_view_2d_array(
|
self, using_array_manager, using_copy_on_write
|
):
|
df = DataFrame([[1, 2], [3, 4]], dtype="int64")
|
if not using_array_manager and not using_copy_on_write:
|
should_be_view = DataFrame(df.values, dtype=df[0].dtype)
|
should_be_view[0][0] = 97
|
assert df.values[0, 0] == 97
|
else:
|
# INFO(ArrayManager) DataFrame(ndarray) doesn't necessarily preserve
|
# a view on the array to ensure contiguous 1D arrays
|
df2 = DataFrame(df.values, dtype=df[0].dtype)
|
assert df2._mgr.arrays[0].flags.c_contiguous
|
|
@td.skip_array_manager_invalid_test
|
def test_1d_object_array_does_not_copy(self):
|
# https://github.com/pandas-dev/pandas/issues/39272
|
arr = np.array(["a", "b"], dtype="object")
|
df = DataFrame(arr, copy=False)
|
assert np.shares_memory(df.values, arr)
|
|
@td.skip_array_manager_invalid_test
|
def test_2d_object_array_does_not_copy(self):
|
# https://github.com/pandas-dev/pandas/issues/39272
|
arr = np.array([["a", "b"], ["c", "d"]], dtype="object")
|
df = DataFrame(arr, copy=False)
|
assert np.shares_memory(df.values, arr)
|
|
def test_constructor_dtype_list_data(self):
|
df = DataFrame([[1, "2"], [None, "a"]], dtype=object)
|
assert df.loc[1, 0] is None
|
assert df.loc[0, 1] == "2"
|
|
def test_constructor_list_of_2d_raises(self):
|
# https://github.com/pandas-dev/pandas/issues/32289
|
a = DataFrame()
|
b = np.empty((0, 0))
|
with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"):
|
DataFrame([a])
|
|
with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"):
|
DataFrame([b])
|
|
a = DataFrame({"A": [1, 2]})
|
with pytest.raises(ValueError, match=r"shape=\(2, 2, 1\)"):
|
DataFrame([a, a])
|
|
@pytest.mark.parametrize(
|
"typ, ad",
|
[
|
# mixed floating and integer coexist in the same frame
|
["float", {}],
|
# add lots of types
|
["float", {"A": 1, "B": "foo", "C": "bar"}],
|
# GH 622
|
["int", {}],
|
],
|
)
|
def test_constructor_mixed_dtypes(self, typ, ad):
|
if typ == "int":
|
dtypes = MIXED_INT_DTYPES
|
arrays = [np.array(np.random.rand(10), dtype=d) for d in dtypes]
|
elif typ == "float":
|
dtypes = MIXED_FLOAT_DTYPES
|
arrays = [np.array(np.random.randint(10, size=10), dtype=d) for d in dtypes]
|
|
for d, a in zip(dtypes, arrays):
|
assert a.dtype == d
|
ad.update(dict(zip(dtypes, arrays)))
|
df = DataFrame(ad)
|
|
dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES
|
for d in dtypes:
|
if d in df:
|
assert df.dtypes[d] == d
|
|
def test_constructor_complex_dtypes(self):
|
# GH10952
|
a = np.random.rand(10).astype(np.complex64)
|
b = np.random.rand(10).astype(np.complex128)
|
|
df = DataFrame({"a": a, "b": b})
|
assert a.dtype == df.a.dtype
|
assert b.dtype == df.b.dtype
|
|
def test_constructor_dtype_str_na_values(self, string_dtype):
|
# https://github.com/pandas-dev/pandas/issues/21083
|
df = DataFrame({"A": ["x", None]}, dtype=string_dtype)
|
result = df.isna()
|
expected = DataFrame({"A": [False, True]})
|
tm.assert_frame_equal(result, expected)
|
assert df.iloc[1, 0] is None
|
|
df = DataFrame({"A": ["x", np.nan]}, dtype=string_dtype)
|
assert np.isnan(df.iloc[1, 0])
|
|
def test_constructor_rec(self, float_frame):
|
rec = float_frame.to_records(index=False)
|
rec.dtype.names = list(rec.dtype.names)[::-1]
|
|
index = float_frame.index
|
|
df = DataFrame(rec)
|
tm.assert_index_equal(df.columns, Index(rec.dtype.names))
|
|
df2 = DataFrame(rec, index=index)
|
tm.assert_index_equal(df2.columns, Index(rec.dtype.names))
|
tm.assert_index_equal(df2.index, index)
|
|
# case with columns != the ones we would infer from the data
|
rng = np.arange(len(rec))[::-1]
|
df3 = DataFrame(rec, index=rng, columns=["C", "B"])
|
expected = DataFrame(rec, index=rng).reindex(columns=["C", "B"])
|
tm.assert_frame_equal(df3, expected)
|
|
def test_constructor_bool(self):
|
df = DataFrame({0: np.ones(10, dtype=bool), 1: np.zeros(10, dtype=bool)})
|
assert df.values.dtype == np.bool_
|
|
def test_constructor_overflow_int64(self):
|
# see gh-14881
|
values = np.array([2**64 - i for i in range(1, 10)], dtype=np.uint64)
|
|
result = DataFrame({"a": values})
|
assert result["a"].dtype == np.uint64
|
|
# see gh-2355
|
data_scores = [
|
(6311132704823138710, 273),
|
(2685045978526272070, 23),
|
(8921811264899370420, 45),
|
(17019687244989530680, 270),
|
(9930107427299601010, 273),
|
]
|
dtype = [("uid", "u8"), ("score", "u8")]
|
data = np.zeros((len(data_scores),), dtype=dtype)
|
data[:] = data_scores
|
df_crawls = DataFrame(data)
|
assert df_crawls["uid"].dtype == np.uint64
|
|
@pytest.mark.parametrize(
|
"values",
|
[
|
np.array([2**64], dtype=object),
|
np.array([2**65]),
|
[2**64 + 1],
|
np.array([-(2**63) - 4], dtype=object),
|
np.array([-(2**64) - 1]),
|
[-(2**65) - 2],
|
],
|
)
|
def test_constructor_int_overflow(self, values):
|
# see gh-18584
|
value = values[0]
|
result = DataFrame(values)
|
|
assert result[0].dtype == object
|
assert result[0][0] == value
|
|
@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 = DataFrame(values)
|
|
assert result[0].dtype == value.dtype
|
assert result[0][0] == value
|
|
def test_constructor_ordereddict(self):
|
nitems = 100
|
nums = list(range(nitems))
|
random.shuffle(nums)
|
expected = [f"A{i:d}" for i in nums]
|
df = DataFrame(OrderedDict(zip(expected, [[0]] * nitems)))
|
assert expected == list(df.columns)
|
|
def test_constructor_dict(self):
|
datetime_series = tm.makeTimeSeries(nper=30)
|
# test expects index shifted by 5
|
datetime_series_short = tm.makeTimeSeries(nper=30)[5:]
|
|
frame = DataFrame({"col1": datetime_series, "col2": datetime_series_short})
|
|
# col2 is padded with NaN
|
assert len(datetime_series) == 30
|
assert len(datetime_series_short) == 25
|
|
tm.assert_series_equal(frame["col1"], datetime_series.rename("col1"))
|
|
exp = Series(
|
np.concatenate([[np.nan] * 5, datetime_series_short.values]),
|
index=datetime_series.index,
|
name="col2",
|
)
|
tm.assert_series_equal(exp, frame["col2"])
|
|
frame = DataFrame(
|
{"col1": datetime_series, "col2": datetime_series_short},
|
columns=["col2", "col3", "col4"],
|
)
|
|
assert len(frame) == len(datetime_series_short)
|
assert "col1" not in frame
|
assert isna(frame["col3"]).all()
|
|
# Corner cases
|
assert len(DataFrame()) == 0
|
|
# mix dict and array, wrong size - no spec for which error should raise
|
# first
|
msg = "Mixing dicts with non-Series may lead to ambiguous ordering."
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]})
|
|
def test_constructor_dict_length1(self):
|
# Length-one dict micro-optimization
|
frame = DataFrame({"A": {"1": 1, "2": 2}})
|
tm.assert_index_equal(frame.index, Index(["1", "2"]))
|
|
def test_constructor_dict_with_index(self):
|
# empty dict plus index
|
idx = Index([0, 1, 2])
|
frame = DataFrame({}, index=idx)
|
assert frame.index is idx
|
|
def test_constructor_dict_with_index_and_columns(self):
|
# empty dict with index and columns
|
idx = Index([0, 1, 2])
|
frame = DataFrame({}, index=idx, columns=idx)
|
assert frame.index is idx
|
assert frame.columns is idx
|
assert len(frame._series) == 3
|
|
def test_constructor_dict_of_empty_lists(self):
|
# with dict of empty list and Series
|
frame = DataFrame({"A": [], "B": []}, columns=["A", "B"])
|
tm.assert_index_equal(frame.index, RangeIndex(0), exact=True)
|
|
def test_constructor_dict_with_none(self):
|
# GH 14381
|
# Dict with None value
|
frame_none = DataFrame({"a": None}, index=[0])
|
frame_none_list = DataFrame({"a": [None]}, index=[0])
|
assert frame_none._get_value(0, "a") is None
|
assert frame_none_list._get_value(0, "a") is None
|
tm.assert_frame_equal(frame_none, frame_none_list)
|
|
def test_constructor_dict_errors(self):
|
# GH10856
|
# dict with scalar values should raise error, even if columns passed
|
msg = "If using all scalar values, you must pass an index"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"a": 0.7})
|
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"a": 0.7}, columns=["a"])
|
|
@pytest.mark.parametrize("scalar", [2, np.nan, None, "D"])
|
def test_constructor_invalid_items_unused(self, scalar):
|
# No error if invalid (scalar) value is in fact not used:
|
result = DataFrame({"a": scalar}, columns=["b"])
|
expected = DataFrame(columns=["b"])
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
|
def test_constructor_dict_nan_key(self, value):
|
# GH 18455
|
cols = [1, value, 3]
|
idx = ["a", value]
|
values = [[0, 3], [1, 4], [2, 5]]
|
data = {cols[c]: Series(values[c], index=idx) for c in range(3)}
|
result = DataFrame(data).sort_values(1).sort_values("a", axis=1)
|
expected = DataFrame(
|
np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols
|
)
|
tm.assert_frame_equal(result, expected)
|
|
result = DataFrame(data, index=idx).sort_values("a", axis=1)
|
tm.assert_frame_equal(result, expected)
|
|
result = DataFrame(data, index=idx, columns=cols)
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("value", [np.nan, None, float("nan")])
|
def test_constructor_dict_nan_tuple_key(self, value):
|
# GH 18455
|
cols = Index([(11, 21), (value, 22), (13, value)])
|
idx = Index([("a", value), (value, 2)])
|
values = [[0, 3], [1, 4], [2, 5]]
|
data = {cols[c]: Series(values[c], index=idx) for c in range(3)}
|
result = DataFrame(data).sort_values((11, 21)).sort_values(("a", value), axis=1)
|
expected = DataFrame(
|
np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols
|
)
|
tm.assert_frame_equal(result, expected)
|
|
result = DataFrame(data, index=idx).sort_values(("a", value), axis=1)
|
tm.assert_frame_equal(result, expected)
|
|
result = DataFrame(data, index=idx, columns=cols)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_dict_order_insertion(self):
|
datetime_series = tm.makeTimeSeries(nper=30)
|
datetime_series_short = tm.makeTimeSeries(nper=25)
|
|
# GH19018
|
# initialization ordering: by insertion order if python>= 3.6
|
d = {"b": datetime_series_short, "a": datetime_series}
|
frame = DataFrame(data=d)
|
expected = DataFrame(data=d, columns=list("ba"))
|
tm.assert_frame_equal(frame, expected)
|
|
def test_constructor_dict_nan_key_and_columns(self):
|
# GH 16894
|
result = DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
|
expected = DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_multi_index(self):
|
# GH 4078
|
# construction error with mi and all-nan frame
|
tuples = [(2, 3), (3, 3), (3, 3)]
|
mi = MultiIndex.from_tuples(tuples)
|
df = DataFrame(index=mi, columns=mi)
|
assert isna(df).values.ravel().all()
|
|
tuples = [(3, 3), (2, 3), (3, 3)]
|
mi = MultiIndex.from_tuples(tuples)
|
df = DataFrame(index=mi, columns=mi)
|
assert isna(df).values.ravel().all()
|
|
def test_constructor_2d_index(self):
|
# GH 25416
|
# handling of 2d index in construction
|
df = DataFrame([[1]], columns=[[1]], index=[1, 2])
|
expected = DataFrame(
|
[1, 1],
|
index=Index([1, 2], dtype="int64"),
|
columns=MultiIndex(levels=[[1]], codes=[[0]]),
|
)
|
tm.assert_frame_equal(df, expected)
|
|
df = DataFrame([[1]], columns=[[1]], index=[[1, 2]])
|
expected = DataFrame(
|
[1, 1],
|
index=MultiIndex(levels=[[1, 2]], codes=[[0, 1]]),
|
columns=MultiIndex(levels=[[1]], codes=[[0]]),
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_error_msgs(self):
|
msg = "Empty data passed with indices specified."
|
# passing an empty array with columns specified.
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(np.empty(0), columns=list("abc"))
|
|
msg = "Mixing dicts with non-Series may lead to ambiguous ordering."
|
# mix dict and array, wrong size
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]})
|
|
# wrong size ndarray, GH 3105
|
msg = r"Shape of passed values is \(4, 3\), indices imply \(3, 3\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(
|
np.arange(12).reshape((4, 3)),
|
columns=["foo", "bar", "baz"],
|
index=date_range("2000-01-01", periods=3),
|
)
|
|
arr = np.array([[4, 5, 6]])
|
msg = r"Shape of passed values is \(1, 3\), indices imply \(1, 4\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(index=[0], columns=range(0, 4), data=arr)
|
|
arr = np.array([4, 5, 6])
|
msg = r"Shape of passed values is \(3, 1\), indices imply \(1, 4\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(index=[0], columns=range(0, 4), data=arr)
|
|
# higher dim raise exception
|
with pytest.raises(ValueError, match="Must pass 2-d input"):
|
DataFrame(np.zeros((3, 3, 3)), columns=["A", "B", "C"], index=[1])
|
|
# wrong size axis labels
|
msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(np.random.rand(2, 3), columns=["A", "B", "C"], index=[1])
|
|
msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(np.random.rand(2, 3), columns=["A", "B"], index=[1, 2])
|
|
# gh-26429
|
msg = "2 columns passed, passed data had 10 columns"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame((range(10), range(10, 20)), columns=("ones", "twos"))
|
|
msg = "If using all scalar values, you must pass an index"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"a": False, "b": True})
|
|
def test_constructor_subclass_dict(self, dict_subclass):
|
# Test for passing dict subclass to constructor
|
data = {
|
"col1": dict_subclass((x, 10.0 * x) for x in range(10)),
|
"col2": dict_subclass((x, 20.0 * x) for x in range(10)),
|
}
|
df = DataFrame(data)
|
refdf = DataFrame({col: dict(val.items()) for col, val in data.items()})
|
tm.assert_frame_equal(refdf, df)
|
|
data = dict_subclass(data.items())
|
df = DataFrame(data)
|
tm.assert_frame_equal(refdf, df)
|
|
def test_constructor_defaultdict(self, float_frame):
|
# try with defaultdict
|
data = {}
|
float_frame.loc[: float_frame.index[10], "B"] = np.nan
|
|
for k, v in float_frame.items():
|
dct = defaultdict(dict)
|
dct.update(v.to_dict())
|
data[k] = dct
|
frame = DataFrame(data)
|
expected = frame.reindex(index=float_frame.index)
|
tm.assert_frame_equal(float_frame, expected)
|
|
def test_constructor_dict_block(self):
|
expected = np.array([[4.0, 3.0, 2.0, 1.0]])
|
df = DataFrame(
|
{"d": [4.0], "c": [3.0], "b": [2.0], "a": [1.0]},
|
columns=["d", "c", "b", "a"],
|
)
|
tm.assert_numpy_array_equal(df.values, expected)
|
|
def test_constructor_dict_cast(self):
|
# cast float tests
|
test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}}
|
frame = DataFrame(test_data, dtype=float)
|
assert len(frame) == 3
|
assert frame["B"].dtype == np.float64
|
assert frame["A"].dtype == np.float64
|
|
frame = DataFrame(test_data)
|
assert len(frame) == 3
|
assert frame["B"].dtype == np.object_
|
assert frame["A"].dtype == np.float64
|
|
def test_constructor_dict_cast2(self):
|
# can't cast to float
|
test_data = {
|
"A": dict(zip(range(20), tm.makeStringIndex(20))),
|
"B": dict(zip(range(15), np.random.randn(15))),
|
}
|
with pytest.raises(ValueError, match="could not convert string"):
|
DataFrame(test_data, dtype=float)
|
|
def test_constructor_dict_dont_upcast(self):
|
d = {"Col1": {"Row1": "A String", "Row2": np.nan}}
|
df = DataFrame(d)
|
assert isinstance(df["Col1"]["Row2"], float)
|
|
def test_constructor_dict_dont_upcast2(self):
|
dm = DataFrame([[1, 2], ["a", "b"]], index=[1, 2], columns=[1, 2])
|
assert isinstance(dm[1][1], int)
|
|
def test_constructor_dict_of_tuples(self):
|
# GH #1491
|
data = {"a": (1, 2, 3), "b": (4, 5, 6)}
|
|
result = DataFrame(data)
|
expected = DataFrame({k: list(v) for k, v in data.items()})
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
def test_constructor_dict_of_ranges(self):
|
# GH 26356
|
data = {"a": range(3), "b": range(3, 6)}
|
|
result = DataFrame(data)
|
expected = DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_dict_of_iterators(self):
|
# GH 26349
|
data = {"a": iter(range(3)), "b": reversed(range(3))}
|
|
result = DataFrame(data)
|
expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_dict_of_generators(self):
|
# GH 26349
|
data = {"a": (i for i in (range(3))), "b": (i for i in reversed(range(3)))}
|
result = DataFrame(data)
|
expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_dict_multiindex(self):
|
d = {
|
("a", "a"): {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2},
|
("b", "a"): {("i", "i"): 6, ("i", "j"): 5, ("j", "i"): 4},
|
("b", "c"): {("i", "i"): 7, ("i", "j"): 8, ("j", "i"): 9},
|
}
|
_d = sorted(d.items())
|
df = DataFrame(d)
|
expected = DataFrame(
|
[x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])
|
).T
|
expected.index = MultiIndex.from_tuples(expected.index)
|
tm.assert_frame_equal(
|
df,
|
expected,
|
)
|
|
d["z"] = {"y": 123.0, ("i", "i"): 111, ("i", "j"): 111, ("j", "i"): 111}
|
_d.insert(0, ("z", d["z"]))
|
expected = DataFrame(
|
[x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)
|
).T
|
expected.index = Index(expected.index, tupleize_cols=False)
|
df = DataFrame(d)
|
df = df.reindex(columns=expected.columns, index=expected.index)
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_dict_datetime64_index(self):
|
# GH 10160
|
dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"]
|
|
def create_data(constructor):
|
return {i: {constructor(s): 2 * i} for i, s in enumerate(dates_as_str)}
|
|
data_datetime64 = create_data(np.datetime64)
|
data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d"))
|
data_Timestamp = create_data(Timestamp)
|
|
expected = DataFrame(
|
[
|
{0: 0, 1: None, 2: None, 3: None},
|
{0: None, 1: 2, 2: None, 3: None},
|
{0: None, 1: None, 2: 4, 3: None},
|
{0: None, 1: None, 2: None, 3: 6},
|
],
|
index=[Timestamp(dt) for dt in dates_as_str],
|
)
|
|
result_datetime64 = DataFrame(data_datetime64)
|
result_datetime = DataFrame(data_datetime)
|
result_Timestamp = DataFrame(data_Timestamp)
|
tm.assert_frame_equal(result_datetime64, expected)
|
tm.assert_frame_equal(result_datetime, expected)
|
tm.assert_frame_equal(result_Timestamp, expected)
|
|
@pytest.mark.parametrize(
|
"klass,name",
|
[
|
(lambda x: np.timedelta64(x, "D"), "timedelta64"),
|
(lambda x: timedelta(days=x), "pytimedelta"),
|
(lambda x: Timedelta(x, "D"), "Timedelta[ns]"),
|
(lambda x: Timedelta(x, "D").as_unit("s"), "Timedelta[s]"),
|
],
|
)
|
def test_constructor_dict_timedelta64_index(self, klass, name):
|
# GH 10160
|
td_as_int = [1, 2, 3, 4]
|
|
data = {i: {klass(s): 2 * i} for i, s in enumerate(td_as_int)}
|
|
expected = DataFrame(
|
[
|
{0: 0, 1: None, 2: None, 3: None},
|
{0: None, 1: 2, 2: None, 3: None},
|
{0: None, 1: None, 2: 4, 3: None},
|
{0: None, 1: None, 2: None, 3: 6},
|
],
|
index=[Timedelta(td, "D") for td in td_as_int],
|
)
|
|
result = DataFrame(data)
|
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_period_dict(self):
|
# PeriodIndex
|
a = pd.PeriodIndex(["2012-01", "NaT", "2012-04"], freq="M")
|
b = pd.PeriodIndex(["2012-02-01", "2012-03-01", "NaT"], freq="D")
|
df = DataFrame({"a": a, "b": b})
|
assert df["a"].dtype == a.dtype
|
assert df["b"].dtype == b.dtype
|
|
# list of periods
|
df = DataFrame({"a": a.astype(object).tolist(), "b": b.astype(object).tolist()})
|
assert df["a"].dtype == a.dtype
|
assert df["b"].dtype == b.dtype
|
|
def test_constructor_dict_extension_scalar(self, ea_scalar_and_dtype):
|
ea_scalar, ea_dtype = ea_scalar_and_dtype
|
df = DataFrame({"a": ea_scalar}, index=[0])
|
assert df["a"].dtype == ea_dtype
|
|
expected = DataFrame(index=[0], columns=["a"], data=ea_scalar)
|
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize(
|
"data,dtype",
|
[
|
(Period("2020-01"), PeriodDtype("M")),
|
(Interval(left=0, right=5), IntervalDtype("int64", "right")),
|
(
|
Timestamp("2011-01-01", tz="US/Eastern"),
|
DatetimeTZDtype(tz="US/Eastern"),
|
),
|
],
|
)
|
def test_constructor_extension_scalar_data(self, data, dtype):
|
# GH 34832
|
df = DataFrame(index=[0, 1], columns=["a", "b"], data=data)
|
|
assert df["a"].dtype == dtype
|
assert df["b"].dtype == dtype
|
|
arr = pd.array([data] * 2, dtype=dtype)
|
expected = DataFrame({"a": arr, "b": arr})
|
|
tm.assert_frame_equal(df, expected)
|
|
def test_nested_dict_frame_constructor(self):
|
rng = pd.period_range("1/1/2000", periods=5)
|
df = DataFrame(np.random.randn(10, 5), columns=rng)
|
|
data = {}
|
for col in df.columns:
|
for row in df.index:
|
data.setdefault(col, {})[row] = df._get_value(row, col)
|
|
result = DataFrame(data, columns=rng)
|
tm.assert_frame_equal(result, df)
|
|
data = {}
|
for col in df.columns:
|
for row in df.index:
|
data.setdefault(row, {})[col] = df._get_value(row, col)
|
|
result = DataFrame(data, index=rng).T
|
tm.assert_frame_equal(result, df)
|
|
def _check_basic_constructor(self, empty):
|
# mat: 2d matrix with shape (3, 2) to input. empty - makes sized
|
# objects
|
mat = empty((2, 3), dtype=float)
|
# 2-D input
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
|
assert len(frame.index) == 2
|
assert len(frame.columns) == 3
|
|
# 1-D input
|
frame = DataFrame(empty((3,)), columns=["A"], index=[1, 2, 3])
|
assert len(frame.index) == 3
|
assert len(frame.columns) == 1
|
|
if empty is not np.ones:
|
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
|
with pytest.raises(IntCastingNaNError, match=msg):
|
DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64)
|
return
|
else:
|
frame = DataFrame(
|
mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64
|
)
|
assert frame.values.dtype == np.int64
|
|
# wrong size axis labels
|
msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(mat, columns=["A", "B", "C"], index=[1])
|
msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(mat, columns=["A", "B"], index=[1, 2])
|
|
# higher dim raise exception
|
with pytest.raises(ValueError, match="Must pass 2-d input"):
|
DataFrame(empty((3, 3, 3)), columns=["A", "B", "C"], index=[1])
|
|
# automatic labeling
|
frame = DataFrame(mat)
|
tm.assert_index_equal(frame.index, Index(range(2)), exact=True)
|
tm.assert_index_equal(frame.columns, Index(range(3)), exact=True)
|
|
frame = DataFrame(mat, index=[1, 2])
|
tm.assert_index_equal(frame.columns, Index(range(3)), exact=True)
|
|
frame = DataFrame(mat, columns=["A", "B", "C"])
|
tm.assert_index_equal(frame.index, Index(range(2)), exact=True)
|
|
# 0-length axis
|
frame = DataFrame(empty((0, 3)))
|
assert len(frame.index) == 0
|
|
frame = DataFrame(empty((3, 0)))
|
assert len(frame.columns) == 0
|
|
def test_constructor_ndarray(self):
|
self._check_basic_constructor(np.ones)
|
|
frame = DataFrame(["foo", "bar"], index=[0, 1], columns=["A"])
|
assert len(frame) == 2
|
|
def test_constructor_maskedarray(self):
|
self._check_basic_constructor(ma.masked_all)
|
|
# Check non-masked values
|
mat = ma.masked_all((2, 3), dtype=float)
|
mat[0, 0] = 1.0
|
mat[1, 2] = 2.0
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
assert 1.0 == frame["A"][1]
|
assert 2.0 == frame["C"][2]
|
|
# what is this even checking??
|
mat = ma.masked_all((2, 3), dtype=float)
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
assert np.all(~np.asarray(frame == frame))
|
|
def test_constructor_maskedarray_nonfloat(self):
|
# masked int promoted to float
|
mat = ma.masked_all((2, 3), dtype=int)
|
# 2-D input
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
|
assert len(frame.index) == 2
|
assert len(frame.columns) == 3
|
assert np.all(~np.asarray(frame == frame))
|
|
# cast type
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.float64)
|
assert frame.values.dtype == np.float64
|
|
# Check non-masked values
|
mat2 = ma.copy(mat)
|
mat2[0, 0] = 1
|
mat2[1, 2] = 2
|
frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
|
assert 1 == frame["A"][1]
|
assert 2 == frame["C"][2]
|
|
# masked np.datetime64 stays (use NaT as null)
|
mat = ma.masked_all((2, 3), dtype="M8[ns]")
|
# 2-D input
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
|
assert len(frame.index) == 2
|
assert len(frame.columns) == 3
|
assert isna(frame).values.all()
|
|
# cast type
|
msg = r"datetime64\[ns\] values and dtype=int64 is not supported"
|
with pytest.raises(TypeError, match=msg):
|
with warnings.catch_warnings():
|
warnings.filterwarnings(
|
"ignore",
|
category=DeprecationWarning,
|
message="elementwise comparison failed",
|
)
|
DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64)
|
|
# Check non-masked values
|
mat2 = ma.copy(mat)
|
mat2[0, 0] = 1
|
mat2[1, 2] = 2
|
frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
|
assert 1 == frame["A"].view("i8")[1]
|
assert 2 == frame["C"].view("i8")[2]
|
|
# masked bool promoted to object
|
mat = ma.masked_all((2, 3), dtype=bool)
|
# 2-D input
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2])
|
|
assert len(frame.index) == 2
|
assert len(frame.columns) == 3
|
assert np.all(~np.asarray(frame == frame))
|
|
# cast type
|
frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=object)
|
assert frame.values.dtype == object
|
|
# Check non-masked values
|
mat2 = ma.copy(mat)
|
mat2[0, 0] = True
|
mat2[1, 2] = False
|
frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2])
|
assert frame["A"][1] is True
|
assert frame["C"][2] is False
|
|
def test_constructor_maskedarray_hardened(self):
|
# Check numpy masked arrays with hard masks -- from GH24574
|
mat_hard = ma.masked_all((2, 2), dtype=float).harden_mask()
|
result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
|
expected = DataFrame(
|
{"A": [np.nan, np.nan], "B": [np.nan, np.nan]},
|
columns=["A", "B"],
|
index=[1, 2],
|
dtype=float,
|
)
|
tm.assert_frame_equal(result, expected)
|
# Check case where mask is hard but no data are masked
|
mat_hard = ma.ones((2, 2), dtype=float).harden_mask()
|
result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2])
|
expected = DataFrame(
|
{"A": [1.0, 1.0], "B": [1.0, 1.0]},
|
columns=["A", "B"],
|
index=[1, 2],
|
dtype=float,
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_maskedrecarray_dtype(self):
|
# Ensure constructor honors dtype
|
data = np.ma.array(
|
np.ma.zeros(5, dtype=[("date", "<f8"), ("price", "<f8")]), mask=[False] * 5
|
)
|
data = data.view(mrecords.mrecarray)
|
with pytest.raises(TypeError, match=r"Pass \{name: data\[name\]"):
|
# Support for MaskedRecords deprecated GH#40363
|
DataFrame(data, dtype=int)
|
|
def test_constructor_corner_shape(self):
|
df = DataFrame(index=[])
|
assert df.values.shape == (0, 0)
|
|
@pytest.mark.parametrize(
|
"data, index, columns, dtype, expected",
|
[
|
(None, list(range(10)), ["a", "b"], object, np.object_),
|
(None, None, ["a", "b"], "int64", np.dtype("int64")),
|
(None, list(range(10)), ["a", "b"], int, np.dtype("float64")),
|
({}, None, ["foo", "bar"], None, np.object_),
|
({"b": 1}, list(range(10)), list("abc"), int, np.dtype("float64")),
|
],
|
)
|
def test_constructor_dtype(self, data, index, columns, dtype, expected):
|
df = DataFrame(data, index, columns, dtype)
|
assert df.values.dtype == expected
|
|
@pytest.mark.parametrize(
|
"data,input_dtype,expected_dtype",
|
(
|
([True, False, None], "boolean", pd.BooleanDtype),
|
([1.0, 2.0, None], "Float64", pd.Float64Dtype),
|
([1, 2, None], "Int64", pd.Int64Dtype),
|
(["a", "b", "c"], "string", pd.StringDtype),
|
),
|
)
|
def test_constructor_dtype_nullable_extension_arrays(
|
self, data, input_dtype, expected_dtype
|
):
|
df = DataFrame({"a": data}, dtype=input_dtype)
|
assert df["a"].dtype == expected_dtype()
|
|
def test_constructor_scalar_inference(self):
|
data = {"int": 1, "bool": True, "float": 3.0, "complex": 4j, "object": "foo"}
|
df = DataFrame(data, index=np.arange(10))
|
|
assert df["int"].dtype == np.int64
|
assert df["bool"].dtype == np.bool_
|
assert df["float"].dtype == np.float64
|
assert df["complex"].dtype == np.complex128
|
assert df["object"].dtype == np.object_
|
|
def test_constructor_arrays_and_scalars(self):
|
df = DataFrame({"a": np.random.randn(10), "b": True})
|
exp = DataFrame({"a": df["a"].values, "b": [True] * 10})
|
|
tm.assert_frame_equal(df, exp)
|
with pytest.raises(ValueError, match="must pass an index"):
|
DataFrame({"a": False, "b": True})
|
|
def test_constructor_DataFrame(self, float_frame):
|
df = DataFrame(float_frame)
|
tm.assert_frame_equal(df, float_frame)
|
|
df_casted = DataFrame(float_frame, dtype=np.int64)
|
assert df_casted.values.dtype == np.int64
|
|
def test_constructor_empty_dataframe(self):
|
# GH 20624
|
actual = DataFrame(DataFrame(), dtype="object")
|
expected = DataFrame([], dtype="object")
|
tm.assert_frame_equal(actual, expected)
|
|
def test_constructor_more(self, float_frame):
|
# used to be in test_matrix.py
|
arr = np.random.randn(10)
|
dm = DataFrame(arr, columns=["A"], index=np.arange(10))
|
assert dm.values.ndim == 2
|
|
arr = np.random.randn(0)
|
dm = DataFrame(arr)
|
assert dm.values.ndim == 2
|
assert dm.values.ndim == 2
|
|
# no data specified
|
dm = DataFrame(columns=["A", "B"], index=np.arange(10))
|
assert dm.values.shape == (10, 2)
|
|
dm = DataFrame(columns=["A", "B"])
|
assert dm.values.shape == (0, 2)
|
|
dm = DataFrame(index=np.arange(10))
|
assert dm.values.shape == (10, 0)
|
|
# can't cast
|
mat = np.array(["foo", "bar"], dtype=object).reshape(2, 1)
|
msg = "could not convert string to float: 'foo'"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(mat, index=[0, 1], columns=[0], dtype=float)
|
|
dm = DataFrame(DataFrame(float_frame._series))
|
tm.assert_frame_equal(dm, float_frame)
|
|
# int cast
|
dm = DataFrame(
|
{"A": np.ones(10, dtype=int), "B": np.ones(10, dtype=np.float64)},
|
index=np.arange(10),
|
)
|
|
assert len(dm.columns) == 2
|
assert dm.values.dtype == np.float64
|
|
def test_constructor_empty_list(self):
|
df = DataFrame([], index=[])
|
expected = DataFrame(index=[])
|
tm.assert_frame_equal(df, expected)
|
|
# GH 9939
|
df = DataFrame([], columns=["A", "B"])
|
expected = DataFrame({}, columns=["A", "B"])
|
tm.assert_frame_equal(df, expected)
|
|
# Empty generator: list(empty_gen()) == []
|
def empty_gen():
|
yield from ()
|
|
df = DataFrame(empty_gen(), columns=["A", "B"])
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_list_of_lists(self):
|
# GH #484
|
df = DataFrame(data=[[1, "a"], [2, "b"]], columns=["num", "str"])
|
assert is_integer_dtype(df["num"])
|
assert df["str"].dtype == np.object_
|
|
# GH 4851
|
# list of 0-dim ndarrays
|
expected = DataFrame({0: np.arange(10)})
|
data = [np.array(x) for x in range(10)]
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, expected)
|
|
def test_nested_pandasarray_matches_nested_ndarray(self):
|
# GH#43986
|
ser = Series([1, 2])
|
|
arr = np.array([None, None], dtype=object)
|
arr[0] = ser
|
arr[1] = ser * 2
|
|
df = DataFrame(arr)
|
expected = DataFrame(pd.array(arr))
|
tm.assert_frame_equal(df, expected)
|
assert df.shape == (2, 1)
|
tm.assert_numpy_array_equal(df[0].values, arr)
|
|
def test_constructor_list_like_data_nested_list_column(self):
|
# GH 32173
|
arrays = [list("abcd"), list("cdef")]
|
result = DataFrame([[1, 2, 3, 4], [4, 5, 6, 7]], columns=arrays)
|
|
mi = MultiIndex.from_arrays(arrays)
|
expected = DataFrame([[1, 2, 3, 4], [4, 5, 6, 7]], columns=mi)
|
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_wrong_length_nested_list_column(self):
|
# GH 32173
|
arrays = [list("abc"), list("cde")]
|
|
msg = "3 columns passed, passed data had 4"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame([[1, 2, 3, 4], [4, 5, 6, 7]], columns=arrays)
|
|
def test_constructor_unequal_length_nested_list_column(self):
|
# GH 32173
|
arrays = [list("abcd"), list("cde")]
|
|
# exception raised inside MultiIndex constructor
|
msg = "all arrays must be same length"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame([[1, 2, 3, 4], [4, 5, 6, 7]], columns=arrays)
|
|
@pytest.mark.parametrize(
|
"data",
|
[
|
[[Timestamp("2021-01-01")]],
|
[{"x": Timestamp("2021-01-01")}],
|
{"x": [Timestamp("2021-01-01")]},
|
{"x": Timestamp("2021-01-01")},
|
],
|
)
|
def test_constructor_one_element_data_list(self, data):
|
# GH#42810
|
result = DataFrame(data, index=[0, 1, 2], columns=["x"])
|
expected = DataFrame({"x": [Timestamp("2021-01-01")] * 3})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_sequence_like(self):
|
# GH 3783
|
# collections.Sequence like
|
|
class DummyContainer(abc.Sequence):
|
def __init__(self, lst) -> None:
|
self._lst = lst
|
|
def __getitem__(self, n):
|
return self._lst.__getitem__(n)
|
|
def __len__(self) -> int:
|
return self._lst.__len__()
|
|
lst_containers = [DummyContainer([1, "a"]), DummyContainer([2, "b"])]
|
columns = ["num", "str"]
|
result = DataFrame(lst_containers, columns=columns)
|
expected = DataFrame([[1, "a"], [2, "b"]], columns=columns)
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
def test_constructor_stdlib_array(self):
|
# GH 4297
|
# support Array
|
result = DataFrame({"A": array.array("i", range(10))})
|
expected = DataFrame({"A": list(range(10))})
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
expected = DataFrame([list(range(10)), list(range(10))])
|
result = DataFrame([array.array("i", range(10)), array.array("i", range(10))])
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
def test_constructor_range(self):
|
# GH26342
|
result = DataFrame(range(10))
|
expected = DataFrame(list(range(10)))
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_ranges(self):
|
result = DataFrame([range(10), range(10)])
|
expected = DataFrame([list(range(10)), list(range(10))])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_iterable(self):
|
# GH 21987
|
class Iter:
|
def __iter__(self) -> Iterator:
|
for i in range(10):
|
yield [1, 2, 3]
|
|
expected = DataFrame([[1, 2, 3]] * 10)
|
result = DataFrame(Iter())
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_iterator(self):
|
result = DataFrame(iter(range(10)))
|
expected = DataFrame(list(range(10)))
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_iterators(self):
|
result = DataFrame([iter(range(10)), iter(range(10))])
|
expected = DataFrame([list(range(10)), list(range(10))])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_generator(self):
|
# related #2305
|
|
gen1 = (i for i in range(10))
|
gen2 = (i for i in range(10))
|
|
expected = DataFrame([list(range(10)), list(range(10))])
|
result = DataFrame([gen1, gen2])
|
tm.assert_frame_equal(result, expected)
|
|
gen = ([i, "a"] for i in range(10))
|
result = DataFrame(gen)
|
expected = DataFrame({0: range(10), 1: "a"})
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
def test_constructor_list_of_dicts(self):
|
result = DataFrame([{}])
|
expected = DataFrame(index=RangeIndex(1), columns=[])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_ordered_dict_nested_preserve_order(self):
|
# see gh-18166
|
nested1 = OrderedDict([("b", 1), ("a", 2)])
|
nested2 = OrderedDict([("b", 2), ("a", 5)])
|
data = OrderedDict([("col2", nested1), ("col1", nested2)])
|
result = DataFrame(data)
|
data = {"col2": [1, 2], "col1": [2, 5]}
|
expected = DataFrame(data=data, index=["b", "a"])
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("dict_type", [dict, OrderedDict])
|
def test_constructor_ordered_dict_preserve_order(self, dict_type):
|
# see gh-13304
|
expected = DataFrame([[2, 1]], columns=["b", "a"])
|
|
data = dict_type()
|
data["b"] = [2]
|
data["a"] = [1]
|
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, expected)
|
|
data = dict_type()
|
data["b"] = 2
|
data["a"] = 1
|
|
result = DataFrame([data])
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("dict_type", [dict, OrderedDict])
|
def test_constructor_ordered_dict_conflicting_orders(self, dict_type):
|
# the first dict element sets the ordering for the DataFrame,
|
# even if there are conflicting orders from subsequent ones
|
row_one = dict_type()
|
row_one["b"] = 2
|
row_one["a"] = 1
|
|
row_two = dict_type()
|
row_two["a"] = 1
|
row_two["b"] = 2
|
|
row_three = {"b": 2, "a": 1}
|
|
expected = DataFrame([[2, 1], [2, 1]], columns=["b", "a"])
|
result = DataFrame([row_one, row_two])
|
tm.assert_frame_equal(result, expected)
|
|
expected = DataFrame([[2, 1], [2, 1], [2, 1]], columns=["b", "a"])
|
result = DataFrame([row_one, row_two, row_three])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_series_aligned_index(self):
|
series = [Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)]
|
result = DataFrame(series)
|
expected = DataFrame(
|
{"b": [0, 1, 2], "a": [0, 1, 2], "c": [0, 1, 2]},
|
columns=["b", "a", "c"],
|
index=["0", "1", "2"],
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_derived_dicts(self):
|
class CustomDict(dict):
|
pass
|
|
d = {"a": 1.5, "b": 3}
|
|
data_custom = [CustomDict(d)]
|
data = [d]
|
|
result_custom = DataFrame(data_custom)
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, result_custom)
|
|
def test_constructor_ragged(self):
|
data = {"A": np.random.randn(10), "B": np.random.randn(8)}
|
with pytest.raises(ValueError, match="All arrays must be of the same length"):
|
DataFrame(data)
|
|
def test_constructor_scalar(self):
|
idx = Index(range(3))
|
df = DataFrame({"a": 0}, index=idx)
|
expected = DataFrame({"a": [0, 0, 0]}, index=idx)
|
tm.assert_frame_equal(df, expected, check_dtype=False)
|
|
def test_constructor_Series_copy_bug(self, float_frame):
|
df = DataFrame(float_frame["A"], index=float_frame.index, columns=["A"])
|
df.copy()
|
|
def test_constructor_mixed_dict_and_Series(self):
|
data = {}
|
data["A"] = {"foo": 1, "bar": 2, "baz": 3}
|
data["B"] = Series([4, 3, 2, 1], index=["bar", "qux", "baz", "foo"])
|
|
result = DataFrame(data)
|
assert result.index.is_monotonic_increasing
|
|
# ordering ambiguous, raise exception
|
with pytest.raises(ValueError, match="ambiguous ordering"):
|
DataFrame({"A": ["a", "b"], "B": {"a": "a", "b": "b"}})
|
|
# this is OK though
|
result = DataFrame({"A": ["a", "b"], "B": Series(["a", "b"], index=["a", "b"])})
|
expected = DataFrame({"A": ["a", "b"], "B": ["a", "b"]}, index=["a", "b"])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_mixed_type_rows(self):
|
# Issue 25075
|
data = [[1, 2], (3, 4)]
|
result = DataFrame(data)
|
expected = DataFrame([[1, 2], [3, 4]])
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"tuples,lists",
|
[
|
((), []),
|
((()), []),
|
(((), ()), [(), ()]),
|
(((), ()), [[], []]),
|
(([], []), [[], []]),
|
(([1], [2]), [[1], [2]]), # GH 32776
|
(([1, 2, 3], [4, 5, 6]), [[1, 2, 3], [4, 5, 6]]),
|
],
|
)
|
def test_constructor_tuple(self, tuples, lists):
|
# GH 25691
|
result = DataFrame(tuples)
|
expected = DataFrame(lists)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_tuples(self):
|
result = DataFrame({"A": [(1, 2), (3, 4)]})
|
expected = DataFrame({"A": Series([(1, 2), (3, 4)])})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_namedtuples(self):
|
# GH11181
|
named_tuple = namedtuple("Pandas", list("ab"))
|
tuples = [named_tuple(1, 3), named_tuple(2, 4)]
|
expected = DataFrame({"a": [1, 2], "b": [3, 4]})
|
result = DataFrame(tuples)
|
tm.assert_frame_equal(result, expected)
|
|
# with columns
|
expected = DataFrame({"y": [1, 2], "z": [3, 4]})
|
result = DataFrame(tuples, columns=["y", "z"])
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_dataclasses(self):
|
# GH21910
|
Point = make_dataclass("Point", [("x", int), ("y", int)])
|
|
data = [Point(0, 3), Point(1, 3)]
|
expected = DataFrame({"x": [0, 1], "y": [3, 3]})
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_dataclasses_with_varying_types(self):
|
# GH21910
|
# varying types
|
Point = make_dataclass("Point", [("x", int), ("y", int)])
|
HLine = make_dataclass("HLine", [("x0", int), ("x1", int), ("y", int)])
|
|
data = [Point(0, 3), HLine(1, 3, 3)]
|
|
expected = DataFrame(
|
{"x": [0, np.nan], "y": [3, 3], "x0": [np.nan, 1], "x1": [np.nan, 3]}
|
)
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_of_dataclasses_error_thrown(self):
|
# GH21910
|
Point = make_dataclass("Point", [("x", int), ("y", int)])
|
|
# expect TypeError
|
msg = "asdict() should be called on dataclass instances"
|
with pytest.raises(TypeError, match=re.escape(msg)):
|
DataFrame([Point(0, 0), {"x": 1, "y": 0}])
|
|
def test_constructor_list_of_dict_order(self):
|
# GH10056
|
data = [
|
{"First": 1, "Second": 4, "Third": 7, "Fourth": 10},
|
{"Second": 5, "First": 2, "Fourth": 11, "Third": 8},
|
{"Second": 6, "First": 3, "Fourth": 12, "Third": 9, "YYY": 14, "XXX": 13},
|
]
|
expected = DataFrame(
|
{
|
"First": [1, 2, 3],
|
"Second": [4, 5, 6],
|
"Third": [7, 8, 9],
|
"Fourth": [10, 11, 12],
|
"YYY": [None, None, 14],
|
"XXX": [None, None, 13],
|
}
|
)
|
result = DataFrame(data)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_Series_named(self):
|
a = Series([1, 2, 3], index=["a", "b", "c"], name="x")
|
df = DataFrame(a)
|
assert df.columns[0] == "x"
|
tm.assert_index_equal(df.index, a.index)
|
|
# ndarray like
|
arr = np.random.randn(10)
|
s = Series(arr, name="x")
|
df = DataFrame(s)
|
expected = DataFrame({"x": s})
|
tm.assert_frame_equal(df, expected)
|
|
s = Series(arr, index=range(3, 13))
|
df = DataFrame(s)
|
expected = DataFrame({0: s})
|
tm.assert_frame_equal(df, expected)
|
|
msg = r"Shape of passed values is \(10, 1\), indices imply \(10, 2\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(s, columns=[1, 2])
|
|
# #2234
|
a = Series([], name="x", dtype=object)
|
df = DataFrame(a)
|
assert df.columns[0] == "x"
|
|
# series with name and w/o
|
s1 = Series(arr, name="x")
|
df = DataFrame([s1, arr]).T
|
expected = DataFrame({"x": s1, "Unnamed 0": arr}, columns=["x", "Unnamed 0"])
|
tm.assert_frame_equal(df, expected)
|
|
# this is a bit non-intuitive here; the series collapse down to arrays
|
df = DataFrame([arr, s1]).T
|
expected = DataFrame({1: s1, 0: arr}, columns=[0, 1])
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_Series_named_and_columns(self):
|
# GH 9232 validation
|
|
s0 = Series(range(5), name=0)
|
s1 = Series(range(5), name=1)
|
|
# matching name and column gives standard frame
|
tm.assert_frame_equal(DataFrame(s0, columns=[0]), s0.to_frame())
|
tm.assert_frame_equal(DataFrame(s1, columns=[1]), s1.to_frame())
|
|
# non-matching produces empty frame
|
assert DataFrame(s0, columns=[1]).empty
|
assert DataFrame(s1, columns=[0]).empty
|
|
def test_constructor_Series_differently_indexed(self):
|
# name
|
s1 = Series([1, 2, 3], index=["a", "b", "c"], name="x")
|
|
# no name
|
s2 = Series([1, 2, 3], index=["a", "b", "c"])
|
|
other_index = Index(["a", "b"])
|
|
df1 = DataFrame(s1, index=other_index)
|
exp1 = DataFrame(s1.reindex(other_index))
|
assert df1.columns[0] == "x"
|
tm.assert_frame_equal(df1, exp1)
|
|
df2 = DataFrame(s2, index=other_index)
|
exp2 = DataFrame(s2.reindex(other_index))
|
assert df2.columns[0] == 0
|
tm.assert_index_equal(df2.index, other_index)
|
tm.assert_frame_equal(df2, exp2)
|
|
@pytest.mark.parametrize(
|
"name_in1,name_in2,name_in3,name_out",
|
[
|
("idx", "idx", "idx", "idx"),
|
("idx", "idx", None, None),
|
("idx", None, None, None),
|
("idx1", "idx2", None, None),
|
("idx1", "idx1", "idx2", None),
|
("idx1", "idx2", "idx3", None),
|
(None, None, None, None),
|
],
|
)
|
def test_constructor_index_names(self, name_in1, name_in2, name_in3, name_out):
|
# GH13475
|
indices = [
|
Index(["a", "b", "c"], name=name_in1),
|
Index(["b", "c", "d"], name=name_in2),
|
Index(["c", "d", "e"], name=name_in3),
|
]
|
series = {
|
c: Series([0, 1, 2], index=i) for i, c in zip(indices, ["x", "y", "z"])
|
}
|
result = DataFrame(series)
|
|
exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
|
expected = DataFrame(
|
{
|
"x": [0, 1, 2, np.nan, np.nan],
|
"y": [np.nan, 0, 1, 2, np.nan],
|
"z": [np.nan, np.nan, 0, 1, 2],
|
},
|
index=exp_ind,
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_manager_resize(self, float_frame):
|
index = list(float_frame.index[:5])
|
columns = list(float_frame.columns[:3])
|
|
result = DataFrame(float_frame._mgr, index=index, columns=columns)
|
tm.assert_index_equal(result.index, Index(index))
|
tm.assert_index_equal(result.columns, Index(columns))
|
|
def test_constructor_mix_series_nonseries(self, float_frame):
|
df = DataFrame(
|
{"A": float_frame["A"], "B": list(float_frame["B"])}, columns=["A", "B"]
|
)
|
tm.assert_frame_equal(df, float_frame.loc[:, ["A", "B"]])
|
|
msg = "does not match index length"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"A": float_frame["A"], "B": list(float_frame["B"])[:-2]})
|
|
def test_constructor_miscast_na_int_dtype(self):
|
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
|
|
with pytest.raises(IntCastingNaNError, match=msg):
|
DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
|
|
def test_constructor_column_duplicates(self):
|
# it works! #2079
|
df = DataFrame([[8, 5]], columns=["a", "a"])
|
edf = DataFrame([[8, 5]])
|
edf.columns = ["a", "a"]
|
|
tm.assert_frame_equal(df, edf)
|
|
idf = DataFrame.from_records([(8, 5)], columns=["a", "a"])
|
|
tm.assert_frame_equal(idf, edf)
|
|
def test_constructor_empty_with_string_dtype(self):
|
# GH 9428
|
expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object)
|
|
df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str)
|
tm.assert_frame_equal(df, expected)
|
df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_)
|
tm.assert_frame_equal(df, expected)
|
df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.unicode_)
|
tm.assert_frame_equal(df, expected)
|
df = DataFrame(index=[0, 1], columns=[0, 1], dtype="U5")
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_empty_with_string_extension(self, nullable_string_dtype):
|
# GH 34915
|
expected = DataFrame(columns=["c1"], dtype=nullable_string_dtype)
|
df = DataFrame(columns=["c1"], dtype=nullable_string_dtype)
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_single_value(self):
|
# expecting single value upcasting here
|
df = DataFrame(0.0, index=[1, 2, 3], columns=["a", "b", "c"])
|
tm.assert_frame_equal(
|
df, DataFrame(np.zeros(df.shape).astype("float64"), df.index, df.columns)
|
)
|
|
df = DataFrame(0, index=[1, 2, 3], columns=["a", "b", "c"])
|
tm.assert_frame_equal(
|
df, DataFrame(np.zeros(df.shape).astype("int64"), df.index, df.columns)
|
)
|
|
df = DataFrame("a", index=[1, 2], columns=["a", "c"])
|
tm.assert_frame_equal(
|
df,
|
DataFrame(
|
np.array([["a", "a"], ["a", "a"]], dtype=object),
|
index=[1, 2],
|
columns=["a", "c"],
|
),
|
)
|
|
msg = "DataFrame constructor not properly called!"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame("a", [1, 2])
|
with pytest.raises(ValueError, match=msg):
|
DataFrame("a", columns=["a", "c"])
|
|
msg = "incompatible data and dtype"
|
with pytest.raises(TypeError, match=msg):
|
DataFrame("a", [1, 2], ["a", "c"], float)
|
|
def test_constructor_with_datetimes(self):
|
intname = np.dtype(np.int_).name
|
floatname = np.dtype(np.float_).name
|
datetime64name = np.dtype("M8[ns]").name
|
objectname = np.dtype(np.object_).name
|
|
# single item
|
df = DataFrame(
|
{
|
"A": 1,
|
"B": "foo",
|
"C": "bar",
|
"D": Timestamp("20010101"),
|
"E": datetime(2001, 1, 2, 0, 0),
|
},
|
index=np.arange(10),
|
)
|
result = df.dtypes
|
expected = Series(
|
[np.dtype("int64")]
|
+ [np.dtype(objectname)] * 2
|
+ [np.dtype(datetime64name)] * 2,
|
index=list("ABCDE"),
|
)
|
tm.assert_series_equal(result, expected)
|
|
# check with ndarray construction ndim==0 (e.g. we are passing a ndim 0
|
# ndarray with a dtype specified)
|
df = DataFrame(
|
{
|
"a": 1.0,
|
"b": 2,
|
"c": "foo",
|
floatname: np.array(1.0, dtype=floatname),
|
intname: np.array(1, dtype=intname),
|
},
|
index=np.arange(10),
|
)
|
result = df.dtypes
|
expected = Series(
|
[np.dtype("float64")]
|
+ [np.dtype("int64")]
|
+ [np.dtype("object")]
|
+ [np.dtype("float64")]
|
+ [np.dtype(intname)],
|
index=["a", "b", "c", floatname, intname],
|
)
|
tm.assert_series_equal(result, expected)
|
|
# check with ndarray construction ndim>0
|
df = DataFrame(
|
{
|
"a": 1.0,
|
"b": 2,
|
"c": "foo",
|
floatname: np.array([1.0] * 10, dtype=floatname),
|
intname: np.array([1] * 10, dtype=intname),
|
},
|
index=np.arange(10),
|
)
|
result = df.dtypes
|
expected = Series(
|
[np.dtype("float64")]
|
+ [np.dtype("int64")]
|
+ [np.dtype("object")]
|
+ [np.dtype("float64")]
|
+ [np.dtype(intname)],
|
index=["a", "b", "c", floatname, intname],
|
)
|
tm.assert_series_equal(result, expected)
|
|
def test_constructor_with_datetimes1(self):
|
# GH 2809
|
ind = date_range(start="2000-01-01", freq="D", periods=10)
|
datetimes = [ts.to_pydatetime() for ts in ind]
|
datetime_s = Series(datetimes)
|
assert datetime_s.dtype == "M8[ns]"
|
|
def test_constructor_with_datetimes2(self):
|
# GH 2810
|
ind = date_range(start="2000-01-01", freq="D", periods=10)
|
datetimes = [ts.to_pydatetime() for ts in ind]
|
dates = [ts.date() for ts in ind]
|
df = DataFrame(datetimes, columns=["datetimes"])
|
df["dates"] = dates
|
result = df.dtypes
|
expected = Series(
|
[np.dtype("datetime64[ns]"), np.dtype("object")],
|
index=["datetimes", "dates"],
|
)
|
tm.assert_series_equal(result, expected)
|
|
def test_constructor_with_datetimes3(self):
|
# GH 7594
|
# don't coerce tz-aware
|
tz = pytz.timezone("US/Eastern")
|
dt = tz.localize(datetime(2012, 1, 1))
|
|
df = DataFrame({"End Date": dt}, index=[0])
|
assert df.iat[0, 0] == dt
|
tm.assert_series_equal(
|
df.dtypes, Series({"End Date": "datetime64[ns, US/Eastern]"})
|
)
|
|
df = DataFrame([{"End Date": dt}])
|
assert df.iat[0, 0] == dt
|
tm.assert_series_equal(
|
df.dtypes, Series({"End Date": "datetime64[ns, US/Eastern]"})
|
)
|
|
def test_constructor_with_datetimes4(self):
|
# tz-aware (UTC and other tz's)
|
# GH 8411
|
dr = date_range("20130101", periods=3)
|
df = DataFrame({"value": dr})
|
assert df.iat[0, 0].tz is None
|
dr = date_range("20130101", periods=3, tz="UTC")
|
df = DataFrame({"value": dr})
|
assert str(df.iat[0, 0].tz) == "UTC"
|
dr = date_range("20130101", periods=3, tz="US/Eastern")
|
df = DataFrame({"value": dr})
|
assert str(df.iat[0, 0].tz) == "US/Eastern"
|
|
def test_constructor_with_datetimes5(self):
|
# GH 7822
|
# preserver an index with a tz on dict construction
|
i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern")
|
|
expected = DataFrame({"a": i.to_series().reset_index(drop=True)})
|
df = DataFrame()
|
df["a"] = i
|
tm.assert_frame_equal(df, expected)
|
|
df = DataFrame({"a": i})
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_with_datetimes6(self):
|
# multiples
|
i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern")
|
i_no_tz = date_range("1/1/2011", periods=5, freq="10s")
|
df = DataFrame({"a": i, "b": i_no_tz})
|
expected = DataFrame({"a": i.to_series().reset_index(drop=True), "b": i_no_tz})
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize(
|
"arr",
|
[
|
np.array([None, None, None, None, datetime.now(), None]),
|
np.array([None, None, datetime.now(), None]),
|
[[np.datetime64("NaT")], [None]],
|
[[np.datetime64("NaT")], [pd.NaT]],
|
[[None], [np.datetime64("NaT")]],
|
[[None], [pd.NaT]],
|
[[pd.NaT], [np.datetime64("NaT")]],
|
[[pd.NaT], [None]],
|
],
|
)
|
def test_constructor_datetimes_with_nulls(self, arr):
|
# gh-15869, GH#11220
|
result = DataFrame(arr).dtypes
|
expected = Series([np.dtype("datetime64[ns]")])
|
tm.assert_series_equal(result, expected)
|
|
@pytest.mark.parametrize("order", ["K", "A", "C", "F"])
|
@pytest.mark.parametrize(
|
"unit",
|
["M", "D", "h", "m", "s", "ms", "us", "ns"],
|
)
|
def test_constructor_datetimes_non_ns(self, order, unit):
|
dtype = f"datetime64[{unit}]"
|
na = np.array(
|
[
|
["2015-01-01", "2015-01-02", "2015-01-03"],
|
["2017-01-01", "2017-01-02", "2017-02-03"],
|
],
|
dtype=dtype,
|
order=order,
|
)
|
df = DataFrame(na)
|
expected = DataFrame(na.astype("M8[ns]"))
|
if unit in ["M", "D", "h", "m"]:
|
with pytest.raises(TypeError, match="Cannot cast"):
|
expected.astype(dtype)
|
|
# instead the constructor casts to the closest supported reso, i.e. "s"
|
expected = expected.astype("datetime64[s]")
|
else:
|
expected = expected.astype(dtype=dtype)
|
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("order", ["K", "A", "C", "F"])
|
@pytest.mark.parametrize(
|
"unit",
|
[
|
"D",
|
"h",
|
"m",
|
"s",
|
"ms",
|
"us",
|
"ns",
|
],
|
)
|
def test_constructor_timedelta_non_ns(self, order, unit):
|
dtype = f"timedelta64[{unit}]"
|
na = np.array(
|
[
|
[np.timedelta64(1, "D"), np.timedelta64(2, "D")],
|
[np.timedelta64(4, "D"), np.timedelta64(5, "D")],
|
],
|
dtype=dtype,
|
order=order,
|
)
|
df = DataFrame(na)
|
if unit in ["D", "h", "m"]:
|
# we get the nearest supported unit, i.e. "s"
|
exp_unit = "s"
|
else:
|
exp_unit = unit
|
exp_dtype = np.dtype(f"m8[{exp_unit}]")
|
expected = DataFrame(
|
[
|
[Timedelta(1, "D"), Timedelta(2, "D")],
|
[Timedelta(4, "D"), Timedelta(5, "D")],
|
],
|
dtype=exp_dtype,
|
)
|
# TODO(2.0): ideally we should get the same 'expected' without passing
|
# dtype=exp_dtype.
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_for_list_with_dtypes(self):
|
# test list of lists/ndarrays
|
df = DataFrame([np.arange(5) for x in range(5)])
|
result = df.dtypes
|
expected = Series([np.dtype("int")] * 5)
|
tm.assert_series_equal(result, expected)
|
|
df = DataFrame([np.array(np.arange(5), dtype="int32") for x in range(5)])
|
result = df.dtypes
|
expected = Series([np.dtype("int32")] * 5)
|
tm.assert_series_equal(result, expected)
|
|
# overflow issue? (we always expected int64 upcasting here)
|
df = DataFrame({"a": [2**31, 2**31 + 1]})
|
assert df.dtypes.iloc[0] == np.dtype("int64")
|
|
# GH #2751 (construction with no index specified), make sure we cast to
|
# platform values
|
df = DataFrame([1, 2])
|
assert df.dtypes.iloc[0] == np.dtype("int64")
|
|
df = DataFrame([1.0, 2.0])
|
assert df.dtypes.iloc[0] == np.dtype("float64")
|
|
df = DataFrame({"a": [1, 2]})
|
assert df.dtypes.iloc[0] == np.dtype("int64")
|
|
df = DataFrame({"a": [1.0, 2.0]})
|
assert df.dtypes.iloc[0] == np.dtype("float64")
|
|
df = DataFrame({"a": 1}, index=range(3))
|
assert df.dtypes.iloc[0] == np.dtype("int64")
|
|
df = DataFrame({"a": 1.0}, index=range(3))
|
assert df.dtypes.iloc[0] == np.dtype("float64")
|
|
# with object list
|
df = DataFrame(
|
{
|
"a": [1, 2, 4, 7],
|
"b": [1.2, 2.3, 5.1, 6.3],
|
"c": list("abcd"),
|
"d": [datetime(2000, 1, 1) for i in range(4)],
|
"e": [1.0, 2, 4.0, 7],
|
}
|
)
|
result = df.dtypes
|
expected = Series(
|
[
|
np.dtype("int64"),
|
np.dtype("float64"),
|
np.dtype("object"),
|
np.dtype("datetime64[ns]"),
|
np.dtype("float64"),
|
],
|
index=list("abcde"),
|
)
|
tm.assert_series_equal(result, expected)
|
|
def test_constructor_frame_copy(self, float_frame):
|
cop = DataFrame(float_frame, copy=True)
|
cop["A"] = 5
|
assert (cop["A"] == 5).all()
|
assert not (float_frame["A"] == 5).all()
|
|
def test_constructor_frame_shallow_copy(self, float_frame):
|
# constructing a DataFrame from DataFrame with copy=False should still
|
# give a "shallow" copy (share data, not attributes)
|
# https://github.com/pandas-dev/pandas/issues/49523
|
orig = float_frame.copy()
|
cop = DataFrame(float_frame)
|
assert cop._mgr is not float_frame._mgr
|
# Overwriting index of copy doesn't change original
|
cop.index = np.arange(len(cop))
|
tm.assert_frame_equal(float_frame, orig)
|
|
def test_constructor_ndarray_copy(
|
self, float_frame, using_array_manager, using_copy_on_write
|
):
|
if not using_array_manager:
|
arr = float_frame.values.copy()
|
df = DataFrame(arr)
|
|
arr[5] = 5
|
if using_copy_on_write:
|
assert not (df.values[5] == 5).all()
|
else:
|
assert (df.values[5] == 5).all()
|
|
df = DataFrame(arr, copy=True)
|
arr[6] = 6
|
assert not (df.values[6] == 6).all()
|
else:
|
arr = float_frame.values.copy()
|
# default: copy to ensure contiguous arrays
|
df = DataFrame(arr)
|
assert df._mgr.arrays[0].flags.c_contiguous
|
arr[0, 0] = 100
|
assert df.iloc[0, 0] != 100
|
|
# manually specify copy=False
|
df = DataFrame(arr, copy=False)
|
assert not df._mgr.arrays[0].flags.c_contiguous
|
arr[0, 0] = 1000
|
assert df.iloc[0, 0] == 1000
|
|
def test_constructor_series_copy(self, float_frame):
|
series = float_frame._series
|
|
df = DataFrame({"A": series["A"]}, copy=True)
|
# TODO can be replaced with `df.loc[:, "A"] = 5` after deprecation about
|
# inplace mutation is enforced
|
df.loc[df.index[0] : df.index[-1], "A"] = 5
|
|
assert not (series["A"] == 5).all()
|
|
@pytest.mark.parametrize(
|
"df",
|
[
|
DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan]),
|
DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1.1, 2.2, np.nan]),
|
DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]),
|
DataFrame(
|
[[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]
|
),
|
DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1, 2, 2]),
|
],
|
)
|
def test_constructor_with_nas(self, df):
|
# GH 5016
|
# na's in indices
|
# GH 21428 (non-unique columns)
|
|
for i in range(len(df.columns)):
|
df.iloc[:, i]
|
|
indexer = np.arange(len(df.columns))[isna(df.columns)]
|
|
# No NaN found -> error
|
if len(indexer) == 0:
|
with pytest.raises(KeyError, match="^nan$"):
|
df.loc[:, np.nan]
|
# single nan should result in Series
|
elif len(indexer) == 1:
|
tm.assert_series_equal(df.iloc[:, indexer[0]], df.loc[:, np.nan])
|
# multiple nans should result in DataFrame
|
else:
|
tm.assert_frame_equal(df.iloc[:, indexer], df.loc[:, np.nan])
|
|
def test_constructor_lists_to_object_dtype(self):
|
# from #1074
|
d = DataFrame({"a": [np.nan, False]})
|
assert d["a"].dtype == np.object_
|
assert not d["a"][1]
|
|
def test_constructor_ndarray_categorical_dtype(self):
|
cat = Categorical(["A", "B", "C"])
|
arr = np.array(cat).reshape(-1, 1)
|
arr = np.broadcast_to(arr, (3, 4))
|
|
result = DataFrame(arr, dtype=cat.dtype)
|
|
expected = DataFrame({0: cat, 1: cat, 2: cat, 3: cat})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_categorical(self):
|
# GH8626
|
|
# dict creation
|
df = DataFrame({"A": list("abc")}, dtype="category")
|
expected = Series(list("abc"), dtype="category", name="A")
|
tm.assert_series_equal(df["A"], expected)
|
|
# to_frame
|
s = Series(list("abc"), dtype="category")
|
result = s.to_frame()
|
expected = Series(list("abc"), dtype="category", name=0)
|
tm.assert_series_equal(result[0], expected)
|
result = s.to_frame(name="foo")
|
expected = Series(list("abc"), dtype="category", name="foo")
|
tm.assert_series_equal(result["foo"], expected)
|
|
# list-like creation
|
df = DataFrame(list("abc"), dtype="category")
|
expected = Series(list("abc"), dtype="category", name=0)
|
tm.assert_series_equal(df[0], expected)
|
|
def test_construct_from_1item_list_of_categorical(self):
|
# pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
|
# Categorical special case
|
# ndim != 1
|
cat = Categorical(list("abc"))
|
df = DataFrame([cat])
|
expected = DataFrame([cat.astype(object)])
|
tm.assert_frame_equal(df, expected)
|
|
def test_construct_from_list_of_categoricals(self):
|
# pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
|
# Categorical special case
|
|
df = DataFrame([Categorical(list("abc")), Categorical(list("abd"))])
|
expected = DataFrame([["a", "b", "c"], ["a", "b", "d"]])
|
tm.assert_frame_equal(df, expected)
|
|
def test_from_nested_listlike_mixed_types(self):
|
# pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove
|
# Categorical special case
|
# mixed
|
df = DataFrame([Categorical(list("abc")), list("def")])
|
expected = DataFrame([["a", "b", "c"], ["d", "e", "f"]])
|
tm.assert_frame_equal(df, expected)
|
|
def test_construct_from_listlikes_mismatched_lengths(self):
|
df = DataFrame([Categorical(list("abc")), Categorical(list("abdefg"))])
|
expected = DataFrame([list("abc"), list("abdefg")])
|
tm.assert_frame_equal(df, expected)
|
|
def test_constructor_categorical_series(self):
|
items = [1, 2, 3, 1]
|
exp = Series(items).astype("category")
|
res = Series(items, dtype="category")
|
tm.assert_series_equal(res, exp)
|
|
items = ["a", "b", "c", "a"]
|
exp = Series(items).astype("category")
|
res = Series(items, dtype="category")
|
tm.assert_series_equal(res, exp)
|
|
# insert into frame with different index
|
# GH 8076
|
index = date_range("20000101", periods=3)
|
expected = Series(
|
Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"])
|
)
|
expected.index = index
|
|
expected = DataFrame({"x": expected})
|
df = DataFrame({"x": Series(["a", "b", "c"], dtype="category")}, index=index)
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize(
|
"dtype",
|
tm.ALL_NUMERIC_DTYPES
|
+ tm.DATETIME64_DTYPES
|
+ tm.TIMEDELTA64_DTYPES
|
+ tm.BOOL_DTYPES,
|
)
|
def test_check_dtype_empty_numeric_column(self, dtype):
|
# GH24386: Ensure dtypes are set correctly for an empty DataFrame.
|
# Empty DataFrame is generated via dictionary data with non-overlapping columns.
|
data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype)
|
|
assert data.b.dtype == dtype
|
|
@pytest.mark.parametrize(
|
"dtype", tm.STRING_DTYPES + tm.BYTES_DTYPES + tm.OBJECT_DTYPES
|
)
|
def test_check_dtype_empty_string_column(self, request, dtype, using_array_manager):
|
# GH24386: Ensure dtypes are set correctly for an empty DataFrame.
|
# Empty DataFrame is generated via dictionary data with non-overlapping columns.
|
data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype)
|
|
if using_array_manager and dtype in tm.BYTES_DTYPES:
|
# TODO(ArrayManager) astype to bytes dtypes does not yet give object dtype
|
td.mark_array_manager_not_yet_implemented(request)
|
|
assert data.b.dtype.name == "object"
|
|
def test_to_frame_with_falsey_names(self):
|
# GH 16114
|
result = Series(name=0, dtype=object).to_frame().dtypes
|
expected = Series({0: object})
|
tm.assert_series_equal(result, expected)
|
|
result = DataFrame(Series(name=0, dtype=object)).dtypes
|
tm.assert_series_equal(result, expected)
|
|
@pytest.mark.arm_slow
|
@pytest.mark.parametrize("dtype", [None, "uint8", "category"])
|
def test_constructor_range_dtype(self, dtype):
|
expected = DataFrame({"A": [0, 1, 2, 3, 4]}, dtype=dtype or "int64")
|
|
# GH 26342
|
result = DataFrame(range(5), columns=["A"], dtype=dtype)
|
tm.assert_frame_equal(result, expected)
|
|
# GH 16804
|
result = DataFrame({"A": range(5)}, dtype=dtype)
|
tm.assert_frame_equal(result, expected)
|
|
def test_frame_from_list_subclass(self):
|
# GH21226
|
class List(list):
|
pass
|
|
expected = DataFrame([[1, 2, 3], [4, 5, 6]])
|
result = DataFrame(List([List([1, 2, 3]), List([4, 5, 6])]))
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize(
|
"extension_arr",
|
[
|
Categorical(list("aabbc")),
|
SparseArray([1, np.nan, np.nan, np.nan]),
|
IntervalArray([Interval(0, 1), Interval(1, 5)]),
|
PeriodArray(pd.period_range(start="1/1/2017", end="1/1/2018", freq="M")),
|
],
|
)
|
def test_constructor_with_extension_array(self, extension_arr):
|
# GH11363
|
expected = DataFrame(Series(extension_arr))
|
result = DataFrame(extension_arr)
|
tm.assert_frame_equal(result, expected)
|
|
def test_datetime_date_tuple_columns_from_dict(self):
|
# GH 10863
|
v = date.today()
|
tup = v, v
|
result = DataFrame({tup: Series(range(3), index=range(3))}, columns=[tup])
|
expected = DataFrame([0, 1, 2], columns=Index(Series([tup])))
|
tm.assert_frame_equal(result, expected)
|
|
def test_construct_with_two_categoricalindex_series(self):
|
# GH 14600
|
s1 = Series([39, 6, 4], index=CategoricalIndex(["female", "male", "unknown"]))
|
s2 = Series(
|
[2, 152, 2, 242, 150],
|
index=CategoricalIndex(["f", "female", "m", "male", "unknown"]),
|
)
|
result = DataFrame([s1, s2])
|
expected = DataFrame(
|
np.array([[39, 6, 4, np.nan, np.nan], [152.0, 242.0, 150.0, 2.0, 2.0]]),
|
columns=["female", "male", "unknown", "f", "m"],
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_series_nonexact_categoricalindex(self):
|
# GH 42424
|
ser = Series(range(0, 100))
|
ser1 = cut(ser, 10).value_counts().head(5)
|
ser2 = cut(ser, 10).value_counts().tail(5)
|
result = DataFrame({"1": ser1, "2": ser2})
|
index = CategoricalIndex(
|
[
|
Interval(-0.099, 9.9, closed="right"),
|
Interval(9.9, 19.8, closed="right"),
|
Interval(19.8, 29.7, closed="right"),
|
Interval(29.7, 39.6, closed="right"),
|
Interval(39.6, 49.5, closed="right"),
|
Interval(49.5, 59.4, closed="right"),
|
Interval(59.4, 69.3, closed="right"),
|
Interval(69.3, 79.2, closed="right"),
|
Interval(79.2, 89.1, closed="right"),
|
Interval(89.1, 99, closed="right"),
|
],
|
ordered=True,
|
)
|
expected = DataFrame(
|
{"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
|
)
|
tm.assert_frame_equal(expected, result)
|
|
def test_from_M8_structured(self):
|
dates = [(datetime(2012, 9, 9, 0, 0), datetime(2012, 9, 8, 15, 10))]
|
arr = np.array(dates, dtype=[("Date", "M8[us]"), ("Forecasting", "M8[us]")])
|
df = DataFrame(arr)
|
|
assert df["Date"][0] == dates[0][0]
|
assert df["Forecasting"][0] == dates[0][1]
|
|
s = Series(arr["Date"])
|
assert isinstance(s[0], Timestamp)
|
assert s[0] == dates[0][0]
|
|
def test_from_datetime_subclass(self):
|
# GH21142 Verify whether Datetime subclasses are also of dtype datetime
|
class DatetimeSubclass(datetime):
|
pass
|
|
data = DataFrame({"datetime": [DatetimeSubclass(2020, 1, 1, 1, 1)]})
|
assert data.datetime.dtype == "datetime64[ns]"
|
|
def test_with_mismatched_index_length_raises(self):
|
# GH#33437
|
dti = date_range("2016-01-01", periods=3, tz="US/Pacific")
|
msg = "Shape of passed values|Passed arrays should have the same length"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(dti, index=range(4))
|
|
def test_frame_ctor_datetime64_column(self):
|
rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
|
dates = np.asarray(rng)
|
|
df = DataFrame({"A": np.random.randn(len(rng)), "B": dates})
|
assert np.issubdtype(df["B"].dtype, np.dtype("M8[ns]"))
|
|
def test_dataframe_constructor_infer_multiindex(self):
|
index_lists = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
|
|
multi = DataFrame(
|
np.random.randn(4, 4),
|
index=[np.array(x) for x in index_lists],
|
)
|
assert isinstance(multi.index, MultiIndex)
|
assert not isinstance(multi.columns, MultiIndex)
|
|
multi = DataFrame(np.random.randn(4, 4), columns=index_lists)
|
assert isinstance(multi.columns, MultiIndex)
|
|
@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 are converted to strings when
|
# dtype is str, 'str', or 'U'
|
|
result = DataFrame({"A": input_vals}, dtype=string_dtype)
|
expected = DataFrame({"A": input_vals}).astype({"A": string_dtype})
|
tm.assert_frame_equal(result, expected)
|
|
def test_constructor_list_str_na(self, string_dtype):
|
result = DataFrame({"A": [1.0, 2.0, None]}, dtype=string_dtype)
|
expected = DataFrame({"A": ["1.0", "2.0", None]}, dtype=object)
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("copy", [False, True])
|
def test_dict_nocopy(
|
self,
|
request,
|
copy,
|
any_numeric_ea_dtype,
|
any_numpy_dtype,
|
using_array_manager,
|
using_copy_on_write,
|
):
|
if (
|
using_array_manager
|
and not copy
|
and any_numpy_dtype not in tm.STRING_DTYPES + tm.BYTES_DTYPES
|
):
|
# TODO(ArrayManager) properly honor copy keyword for dict input
|
td.mark_array_manager_not_yet_implemented(request)
|
|
a = np.array([1, 2], dtype=any_numpy_dtype)
|
b = np.array([3, 4], dtype=any_numpy_dtype)
|
if b.dtype.kind in ["S", "U"]:
|
# These get cast, making the checks below more cumbersome
|
return
|
|
c = pd.array([1, 2], dtype=any_numeric_ea_dtype)
|
c_orig = c.copy()
|
df = DataFrame({"a": a, "b": b, "c": c}, copy=copy)
|
|
def get_base(obj):
|
if isinstance(obj, np.ndarray):
|
return obj.base
|
elif isinstance(obj.dtype, np.dtype):
|
# i.e. DatetimeArray, TimedeltaArray
|
return obj._ndarray.base
|
else:
|
raise TypeError
|
|
def check_views(c_only: bool = False):
|
# written to work for either BlockManager or ArrayManager
|
|
# Check that the underlying data behind df["c"] is still `c`
|
# after setting with iloc. Since we don't know which entry in
|
# df._mgr.arrays corresponds to df["c"], we just check that exactly
|
# one of these arrays is `c`. GH#38939
|
assert sum(x is c for x in df._mgr.arrays) == 1
|
if c_only:
|
# If we ever stop consolidating in setitem_with_indexer,
|
# this will become unnecessary.
|
return
|
|
assert (
|
sum(
|
get_base(x) is a
|
for x in df._mgr.arrays
|
if isinstance(x.dtype, np.dtype)
|
)
|
== 1
|
)
|
assert (
|
sum(
|
get_base(x) is b
|
for x in df._mgr.arrays
|
if isinstance(x.dtype, np.dtype)
|
)
|
== 1
|
)
|
|
if not copy:
|
# constructor preserves views
|
check_views()
|
|
# TODO: most of the rest of this test belongs in indexing tests
|
df.iloc[0, 0] = 0
|
df.iloc[0, 1] = 0
|
if not copy:
|
check_views(True)
|
|
# FIXME(GH#35417): until GH#35417, iloc.setitem into EA values does not preserve
|
# view, so we have to check in the other direction
|
df.iloc[:, 2] = pd.array([45, 46], dtype=c.dtype)
|
assert df.dtypes.iloc[2] == c.dtype
|
if not copy and not using_copy_on_write:
|
check_views(True)
|
|
if copy:
|
if a.dtype.kind == "M":
|
assert a[0] == a.dtype.type(1, "ns")
|
assert b[0] == b.dtype.type(3, "ns")
|
else:
|
assert a[0] == a.dtype.type(1)
|
assert b[0] == b.dtype.type(3)
|
# FIXME(GH#35417): enable after GH#35417
|
assert c[0] == c_orig[0] # i.e. df.iloc[0, 2]=45 did *not* update c
|
elif not using_copy_on_write:
|
# TODO: we can call check_views if we stop consolidating
|
# in setitem_with_indexer
|
assert c[0] == 45 # i.e. df.iloc[0, 2]=45 *did* update c
|
# TODO: we can check b[0] == 0 if we stop consolidating in
|
# setitem_with_indexer (except for datetimelike?)
|
|
def test_from_series_with_name_with_columns(self):
|
# GH 7893
|
result = DataFrame(Series(1, name="foo"), columns=["bar"])
|
expected = DataFrame(columns=["bar"])
|
tm.assert_frame_equal(result, expected)
|
|
def test_nested_list_columns(self):
|
# GH 14467
|
result = DataFrame(
|
[[1, 2, 3], [4, 5, 6]], columns=[["A", "A", "A"], ["a", "b", "c"]]
|
)
|
expected = DataFrame(
|
[[1, 2, 3], [4, 5, 6]],
|
columns=MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")]),
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_from_2d_object_array_of_periods_or_intervals(self):
|
# Period analogue to GH#26825
|
pi = pd.period_range("2016-04-05", periods=3)
|
data = pi._data.astype(object).reshape(1, -1)
|
df = DataFrame(data)
|
assert df.shape == (1, 3)
|
assert (df.dtypes == pi.dtype).all()
|
assert (df == pi).all().all()
|
|
ii = pd.IntervalIndex.from_breaks([3, 4, 5, 6])
|
data2 = ii._data.astype(object).reshape(1, -1)
|
df2 = DataFrame(data2)
|
assert df2.shape == (1, 3)
|
assert (df2.dtypes == ii.dtype).all()
|
assert (df2 == ii).all().all()
|
|
# mixed
|
data3 = np.r_[data, data2, data, data2].T
|
df3 = DataFrame(data3)
|
expected = DataFrame({0: pi, 1: ii, 2: pi, 3: ii})
|
tm.assert_frame_equal(df3, expected)
|
|
@pytest.mark.parametrize(
|
"col_a, col_b",
|
[
|
([[1], [2]], np.array([[1], [2]])),
|
(np.array([[1], [2]]), [[1], [2]]),
|
(np.array([[1], [2]]), np.array([[1], [2]])),
|
],
|
)
|
def test_error_from_2darray(self, col_a, col_b):
|
msg = "Per-column arrays must each be 1-dimensional"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame({"a": col_a, "b": col_b})
|
|
def test_from_dict_with_missing_copy_false(self):
|
# GH#45369 filled columns should not be views of one another
|
df = DataFrame(index=[1, 2, 3], columns=["a", "b", "c"], copy=False)
|
assert not np.shares_memory(df["a"]._values, df["b"]._values)
|
|
df.iloc[0, 0] = 0
|
expected = DataFrame(
|
{
|
"a": [0, np.nan, np.nan],
|
"b": [np.nan, np.nan, np.nan],
|
"c": [np.nan, np.nan, np.nan],
|
},
|
index=[1, 2, 3],
|
dtype=object,
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_construction_empty_array_multi_column_raises(self):
|
# GH#46822
|
msg = "Empty data passed with indices specified."
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(data=np.array([]), columns=["a", "b"])
|
|
|
class TestDataFrameConstructorIndexInference:
|
def test_frame_from_dict_of_series_overlapping_monthly_period_indexes(self):
|
rng1 = pd.period_range("1/1/1999", "1/1/2012", freq="M")
|
s1 = Series(np.random.randn(len(rng1)), rng1)
|
|
rng2 = pd.period_range("1/1/1980", "12/1/2001", freq="M")
|
s2 = Series(np.random.randn(len(rng2)), rng2)
|
df = DataFrame({"s1": s1, "s2": s2})
|
|
exp = pd.period_range("1/1/1980", "1/1/2012", freq="M")
|
tm.assert_index_equal(df.index, exp)
|
|
def test_frame_from_dict_with_mixed_tzaware_indexes(self):
|
# GH#44091
|
dti = date_range("2016-01-01", periods=3)
|
|
ser1 = Series(range(3), index=dti)
|
ser2 = Series(range(3), index=dti.tz_localize("UTC"))
|
ser3 = Series(range(3), index=dti.tz_localize("US/Central"))
|
ser4 = Series(range(3))
|
|
# no tz-naive, but we do have mixed tzs and a non-DTI
|
df1 = DataFrame({"A": ser2, "B": ser3, "C": ser4})
|
exp_index = Index(
|
list(ser2.index) + list(ser3.index) + list(ser4.index), dtype=object
|
)
|
tm.assert_index_equal(df1.index, exp_index)
|
|
df2 = DataFrame({"A": ser2, "C": ser4, "B": ser3})
|
exp_index3 = Index(
|
list(ser2.index) + list(ser4.index) + list(ser3.index), dtype=object
|
)
|
tm.assert_index_equal(df2.index, exp_index3)
|
|
df3 = DataFrame({"B": ser3, "A": ser2, "C": ser4})
|
exp_index3 = Index(
|
list(ser3.index) + list(ser2.index) + list(ser4.index), dtype=object
|
)
|
tm.assert_index_equal(df3.index, exp_index3)
|
|
df4 = DataFrame({"C": ser4, "B": ser3, "A": ser2})
|
exp_index4 = Index(
|
list(ser4.index) + list(ser3.index) + list(ser2.index), dtype=object
|
)
|
tm.assert_index_equal(df4.index, exp_index4)
|
|
# TODO: not clear if these raising is desired (no extant tests),
|
# but this is de facto behavior 2021-12-22
|
msg = "Cannot join tz-naive with tz-aware DatetimeIndex"
|
with pytest.raises(TypeError, match=msg):
|
DataFrame({"A": ser2, "B": ser3, "C": ser4, "D": ser1})
|
with pytest.raises(TypeError, match=msg):
|
DataFrame({"A": ser2, "B": ser3, "D": ser1})
|
with pytest.raises(TypeError, match=msg):
|
DataFrame({"D": ser1, "A": ser2, "B": ser3})
|
|
|
class TestDataFrameConstructorWithDtypeCoercion:
|
def test_floating_values_integer_dtype(self):
|
# GH#40110 make DataFrame behavior with arraylike floating data and
|
# inty dtype match Series behavior
|
|
arr = np.random.randn(10, 5)
|
|
# GH#49599 in 2.0 we raise instead of either
|
# a) silently ignoring dtype and returningfloat (the old Series behavior) or
|
# b) rounding (the old DataFrame behavior)
|
msg = "Trying to coerce float values to integers"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(arr, dtype="i8")
|
|
df = DataFrame(arr.round(), dtype="i8")
|
assert (df.dtypes == "i8").all()
|
|
# with NaNs, we go through a different path with a different warning
|
arr[0, 0] = np.nan
|
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
|
with pytest.raises(IntCastingNaNError, match=msg):
|
DataFrame(arr, dtype="i8")
|
with pytest.raises(IntCastingNaNError, match=msg):
|
Series(arr[0], dtype="i8")
|
# The future (raising) behavior matches what we would get via astype:
|
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
|
with pytest.raises(IntCastingNaNError, match=msg):
|
DataFrame(arr).astype("i8")
|
with pytest.raises(IntCastingNaNError, match=msg):
|
Series(arr[0]).astype("i8")
|
|
|
class TestDataFrameConstructorWithDatetimeTZ:
|
@pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
|
def test_construction_preserves_tzaware_dtypes(self, tz):
|
# after GH#7822
|
# these retain the timezones on dict construction
|
dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
|
dr_tz = dr.tz_localize(tz)
|
df = DataFrame({"A": "foo", "B": dr_tz}, index=dr)
|
tz_expected = DatetimeTZDtype("ns", dr_tz.tzinfo)
|
assert df["B"].dtype == tz_expected
|
|
# GH#2810 (with timezones)
|
datetimes_naive = [ts.to_pydatetime() for ts in dr]
|
datetimes_with_tz = [ts.to_pydatetime() for ts in dr_tz]
|
df = DataFrame({"dr": dr})
|
df["dr_tz"] = dr_tz
|
df["datetimes_naive"] = datetimes_naive
|
df["datetimes_with_tz"] = datetimes_with_tz
|
result = df.dtypes
|
expected = Series(
|
[
|
np.dtype("datetime64[ns]"),
|
DatetimeTZDtype(tz=tz),
|
np.dtype("datetime64[ns]"),
|
DatetimeTZDtype(tz=tz),
|
],
|
index=["dr", "dr_tz", "datetimes_naive", "datetimes_with_tz"],
|
)
|
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):
|
DataFrame({0: [ts]}, dtype="datetime64[ns]")
|
|
msg2 = "Cannot unbox tzaware Timestamp to tznaive dtype"
|
with pytest.raises(TypeError, match=msg2):
|
DataFrame({0: ts}, index=[0], dtype="datetime64[ns]")
|
|
with pytest.raises(ValueError, match=msg):
|
DataFrame([ts], dtype="datetime64[ns]")
|
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(np.array([ts], dtype=object), dtype="datetime64[ns]")
|
|
with pytest.raises(TypeError, match=msg2):
|
DataFrame(ts, index=[0], columns=[0], dtype="datetime64[ns]")
|
|
with pytest.raises(ValueError, match=msg):
|
DataFrame([Series([ts])], dtype="datetime64[ns]")
|
|
with pytest.raises(ValueError, match=msg):
|
DataFrame([[ts]], columns=[0], dtype="datetime64[ns]")
|
|
def test_from_dict(self):
|
# 8260
|
# support datetime64 with tz
|
|
idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo")
|
dr = date_range("20130110", periods=3)
|
|
# construction
|
df = DataFrame({"A": idx, "B": dr})
|
assert df["A"].dtype, "M8[ns, US/Eastern"
|
assert df["A"].name == "A"
|
tm.assert_series_equal(df["A"], Series(idx, name="A"))
|
tm.assert_series_equal(df["B"], Series(dr, name="B"))
|
|
def test_from_index(self):
|
# from index
|
idx2 = date_range("20130101", periods=3, tz="US/Eastern", name="foo")
|
df2 = DataFrame(idx2)
|
tm.assert_series_equal(df2["foo"], Series(idx2, name="foo"))
|
df2 = DataFrame(Series(idx2))
|
tm.assert_series_equal(df2["foo"], Series(idx2, name="foo"))
|
|
idx2 = date_range("20130101", periods=3, tz="US/Eastern")
|
df2 = DataFrame(idx2)
|
tm.assert_series_equal(df2[0], Series(idx2, name=0))
|
df2 = DataFrame(Series(idx2))
|
tm.assert_series_equal(df2[0], Series(idx2, name=0))
|
|
def test_frame_dict_constructor_datetime64_1680(self):
|
dr = date_range("1/1/2012", periods=10)
|
s = Series(dr, index=dr)
|
|
# it works!
|
DataFrame({"a": "foo", "b": s}, index=dr)
|
DataFrame({"a": "foo", "b": s.values}, index=dr)
|
|
def test_frame_datetime64_mixed_index_ctor_1681(self):
|
dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
|
ts = Series(dr)
|
|
# it works!
|
d = DataFrame({"A": "foo", "B": ts}, index=dr)
|
assert d["B"].isna().all()
|
|
def test_frame_timeseries_column(self):
|
# GH19157
|
dr = date_range(start="20130101T10:00:00", periods=3, freq="T", tz="US/Eastern")
|
result = DataFrame(dr, columns=["timestamps"])
|
expected = DataFrame(
|
{
|
"timestamps": [
|
Timestamp("20130101T10:00:00", tz="US/Eastern"),
|
Timestamp("20130101T10:01:00", tz="US/Eastern"),
|
Timestamp("20130101T10:02:00", tz="US/Eastern"),
|
]
|
}
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_nested_dict_construction(self):
|
# GH22227
|
columns = ["Nevada", "Ohio"]
|
pop = {
|
"Nevada": {2001: 2.4, 2002: 2.9},
|
"Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6},
|
}
|
result = DataFrame(pop, index=[2001, 2002, 2003], columns=columns)
|
expected = DataFrame(
|
[(2.4, 1.7), (2.9, 3.6), (np.nan, np.nan)],
|
columns=columns,
|
index=Index([2001, 2002, 2003]),
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_from_tzaware_object_array(self):
|
# GH#26825 2D object array of tzaware timestamps should not raise
|
dti = date_range("2016-04-05 04:30", periods=3, tz="UTC")
|
data = dti._data.astype(object).reshape(1, -1)
|
df = DataFrame(data)
|
assert df.shape == (1, 3)
|
assert (df.dtypes == dti.dtype).all()
|
assert (df == dti).all().all()
|
|
def test_from_tzaware_mixed_object_array(self):
|
# GH#26825
|
arr = np.array(
|
[
|
[
|
Timestamp("2013-01-01 00:00:00"),
|
Timestamp("2013-01-02 00:00:00"),
|
Timestamp("2013-01-03 00:00:00"),
|
],
|
[
|
Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
|
pd.NaT,
|
Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
|
],
|
[
|
Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
|
pd.NaT,
|
Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
|
],
|
],
|
dtype=object,
|
).T
|
res = DataFrame(arr, columns=["A", "B", "C"])
|
|
expected_dtypes = [
|
"datetime64[ns]",
|
"datetime64[ns, US/Eastern]",
|
"datetime64[ns, CET]",
|
]
|
assert (res.dtypes == expected_dtypes).all()
|
|
def test_from_2d_ndarray_with_dtype(self):
|
# GH#12513
|
array_dim2 = np.arange(10).reshape((5, 2))
|
df = DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
|
|
expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]")
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("typ", [set, frozenset])
|
def test_construction_from_set_raises(self, typ):
|
# https://github.com/pandas-dev/pandas/issues/32582
|
values = typ({1, 2, 3})
|
msg = f"'{typ.__name__}' type is unordered"
|
with pytest.raises(TypeError, match=msg):
|
DataFrame({"a": values})
|
|
with pytest.raises(TypeError, match=msg):
|
Series(values)
|
|
def test_construction_from_ndarray_datetimelike(self):
|
# ensure the underlying arrays are properly wrapped as EA when
|
# constructed from 2D ndarray
|
arr = np.arange(0, 12, dtype="datetime64[ns]").reshape(4, 3)
|
df = DataFrame(arr)
|
assert all(isinstance(arr, DatetimeArray) for arr in df._mgr.arrays)
|
|
def test_construction_from_ndarray_with_eadtype_mismatched_columns(self):
|
arr = np.random.randn(10, 2)
|
dtype = pd.array([2.0]).dtype
|
msg = r"len\(arrays\) must match len\(columns\)"
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(arr, columns=["foo"], dtype=dtype)
|
|
arr2 = pd.array([2.0, 3.0, 4.0])
|
with pytest.raises(ValueError, match=msg):
|
DataFrame(arr2, columns=["foo", "bar"])
|
|
def test_columns_indexes_raise_on_sets(self):
|
# GH 47215
|
data = [[1, 2, 3], [4, 5, 6]]
|
with pytest.raises(ValueError, match="index cannot be a set"):
|
DataFrame(data, index={"a", "b"})
|
with pytest.raises(ValueError, match="columns cannot be a set"):
|
DataFrame(data, columns={"a", "b", "c"})
|
|
|
def get1(obj): # TODO: make a helper in tm?
|
if isinstance(obj, Series):
|
return obj.iloc[0]
|
else:
|
return obj.iloc[0, 0]
|
|
|
class TestFromScalar:
|
@pytest.fixture(params=[list, dict, None])
|
def box(self, request):
|
return request.param
|
|
@pytest.fixture
|
def constructor(self, frame_or_series, box):
|
extra = {"index": range(2)}
|
if frame_or_series is DataFrame:
|
extra["columns"] = ["A"]
|
|
if box is None:
|
return functools.partial(frame_or_series, **extra)
|
|
elif box is dict:
|
if frame_or_series is Series:
|
return lambda x, **kwargs: frame_or_series(
|
{0: x, 1: x}, **extra, **kwargs
|
)
|
else:
|
return lambda x, **kwargs: frame_or_series({"A": x}, **extra, **kwargs)
|
else:
|
if frame_or_series is Series:
|
return lambda x, **kwargs: frame_or_series([x, x], **extra, **kwargs)
|
else:
|
return lambda x, **kwargs: frame_or_series(
|
{"A": [x, x]}, **extra, **kwargs
|
)
|
|
@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
|
def test_from_nat_scalar(self, dtype, constructor):
|
obj = constructor(pd.NaT, dtype=dtype)
|
assert np.all(obj.dtypes == dtype)
|
assert np.all(obj.isna())
|
|
def test_from_timedelta_scalar_preserves_nanos(self, constructor):
|
td = Timedelta(1)
|
|
obj = constructor(td, dtype="m8[ns]")
|
assert get1(obj) == td
|
|
def test_from_timestamp_scalar_preserves_nanos(self, constructor, fixed_now_ts):
|
ts = fixed_now_ts + Timedelta(1)
|
|
obj = constructor(ts, dtype="M8[ns]")
|
assert get1(obj) == ts
|
|
def test_from_timedelta64_scalar_object(self, constructor):
|
td = Timedelta(1)
|
td64 = td.to_timedelta64()
|
|
obj = constructor(td64, dtype=object)
|
assert isinstance(get1(obj), np.timedelta64)
|
|
@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
|
def test_from_scalar_datetimelike_mismatched(self, constructor, cls):
|
scalar = cls("NaT", "ns")
|
dtype = {np.datetime64: "m8[ns]", np.timedelta64: "M8[ns]"}[cls]
|
|
if cls is np.datetime64:
|
msg1 = r"dtype datetime64\[ns\] cannot be converted to timedelta64\[ns\]"
|
else:
|
msg1 = r"dtype timedelta64\[ns\] cannot be converted to datetime64\[ns\]"
|
msg = "|".join(["Cannot cast", msg1])
|
|
with pytest.raises(TypeError, match=msg):
|
constructor(scalar, dtype=dtype)
|
|
scalar = cls(4, "ns")
|
with pytest.raises(TypeError, match=msg):
|
constructor(scalar, dtype=dtype)
|
|
@pytest.mark.xfail(
|
reason="Timestamp constructor has been updated to cast dt64 to non-nano, "
|
"but DatetimeArray._from_sequence has not"
|
)
|
@pytest.mark.parametrize("cls", [datetime, np.datetime64])
|
def test_from_out_of_bounds_ns_datetime(self, constructor, cls):
|
# scalar that won't fit in nanosecond dt64, but will fit in microsecond
|
scalar = datetime(9999, 1, 1)
|
exp_dtype = "M8[us]" # pydatetime objects default to this reso
|
if cls is np.datetime64:
|
scalar = np.datetime64(scalar, "D")
|
exp_dtype = "M8[s]" # closest reso to input
|
result = constructor(scalar)
|
|
item = get1(result)
|
dtype = result.dtype if isinstance(result, Series) else result.dtypes.iloc[0]
|
|
assert type(item) is Timestamp
|
assert item.asm8.dtype == exp_dtype
|
assert dtype == exp_dtype
|
|
def test_out_of_s_bounds_datetime64(self, constructor):
|
scalar = np.datetime64(np.iinfo(np.int64).max, "D")
|
result = constructor(scalar)
|
item = get1(result)
|
assert type(item) is np.datetime64
|
dtype = result.dtype if isinstance(result, Series) else result.dtypes.iloc[0]
|
assert dtype == object
|
|
@pytest.mark.xfail(
|
reason="TimedeltaArray constructor has been updated to cast td64 to non-nano, "
|
"but TimedeltaArray._from_sequence has not"
|
)
|
@pytest.mark.parametrize("cls", [timedelta, np.timedelta64])
|
def test_from_out_of_bounds_ns_timedelta(self, constructor, cls):
|
# scalar that won't fit in nanosecond td64, but will fit in microsecond
|
scalar = datetime(9999, 1, 1) - datetime(1970, 1, 1)
|
exp_dtype = "m8[us]" # smallest reso that fits
|
if cls is np.timedelta64:
|
scalar = np.timedelta64(scalar, "D")
|
exp_dtype = "m8[s]" # closest reso to input
|
result = constructor(scalar)
|
|
item = get1(result)
|
dtype = result.dtype if isinstance(result, Series) else result.dtypes.iloc[0]
|
|
assert type(item) is Timedelta
|
assert item.asm8.dtype == exp_dtype
|
assert dtype == exp_dtype
|
|
@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
|
def test_out_of_s_bounds_timedelta64(self, constructor, cls):
|
scalar = cls(np.iinfo(np.int64).max, "D")
|
result = constructor(scalar)
|
item = get1(result)
|
assert type(item) is cls
|
dtype = result.dtype if isinstance(result, Series) else result.dtypes.iloc[0]
|
assert dtype == object
|
|
def test_tzaware_data_tznaive_dtype(self, constructor, box, frame_or_series):
|
tz = "US/Eastern"
|
ts = Timestamp("2019", tz=tz)
|
|
if box is None or (frame_or_series is DataFrame and box is dict):
|
msg = "Cannot unbox tzaware Timestamp to tznaive dtype"
|
err = TypeError
|
else:
|
msg = (
|
"Cannot convert timezone-aware data to timezone-naive dtype. "
|
r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
|
)
|
err = ValueError
|
|
with pytest.raises(err, match=msg):
|
constructor(ts, dtype="M8[ns]")
|
|
|
# TODO: better location for this test?
|
class TestAllowNonNano:
|
# Until 2.0, we do not preserve non-nano dt64/td64 when passed as ndarray,
|
# but do preserve it when passed as DTA/TDA
|
|
@pytest.fixture(params=[True, False])
|
def as_td(self, request):
|
return request.param
|
|
@pytest.fixture
|
def arr(self, as_td):
|
values = np.arange(5).astype(np.int64).view("M8[s]")
|
if as_td:
|
values = values - values[0]
|
return TimedeltaArray._simple_new(values, dtype=values.dtype)
|
else:
|
return DatetimeArray._simple_new(values, dtype=values.dtype)
|
|
def test_index_allow_non_nano(self, arr):
|
idx = Index(arr)
|
assert idx.dtype == arr.dtype
|
|
def test_dti_tdi_allow_non_nano(self, arr, as_td):
|
if as_td:
|
idx = pd.TimedeltaIndex(arr)
|
else:
|
idx = DatetimeIndex(arr)
|
assert idx.dtype == arr.dtype
|
|
def test_series_allow_non_nano(self, arr):
|
ser = Series(arr)
|
assert ser.dtype == arr.dtype
|
|
def test_frame_allow_non_nano(self, arr):
|
df = DataFrame(arr)
|
assert df.dtypes[0] == arr.dtype
|
|
def test_frame_from_dict_allow_non_nano(self, arr):
|
df = DataFrame({0: arr})
|
assert df.dtypes[0] == arr.dtype
|
