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Inzynierka/Lib/site-packages/pandas/tests/frame/test_constructors.py

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first commit
2023-06-02 12:51:02 +02:00
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
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