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import decimal
import numpy as np
from numpy import iinfo
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
ArrowDtype,
DataFrame,
Index,
Series,
option_context,
to_numeric,
)
import pandas._testing as tm
@pytest.fixture(params=[None, "ignore", "raise", "coerce"])
def errors(request):
return request.param
@pytest.fixture(params=[True, False])
def signed(request):
return request.param
@pytest.fixture(params=[lambda x: x, str], ids=["identity", "str"])
def transform(request):
return request.param
@pytest.fixture(params=[47393996303418497800, 100000000000000000000])
def large_val(request):
return request.param
@pytest.fixture(params=[True, False])
def multiple_elts(request):
return request.param
@pytest.fixture(
params=[
(lambda x: Index(x, name="idx"), tm.assert_index_equal),
(lambda x: Series(x, name="ser"), tm.assert_series_equal),
(lambda x: np.array(Index(x).values), tm.assert_numpy_array_equal),
]
)
def transform_assert_equal(request):
return request.param
@pytest.mark.parametrize(
"input_kwargs,result_kwargs",
[
({}, {"dtype": np.int64}),
({"errors": "coerce", "downcast": "integer"}, {"dtype": np.int8}),
],
)
def test_empty(input_kwargs, result_kwargs):
# see gh-16302
ser = Series([], dtype=object)
result = to_numeric(ser, **input_kwargs)
expected = Series([], **result_kwargs)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
)
@pytest.mark.parametrize("last_val", ["7", 7])
def test_series(last_val, infer_string):
with option_context("future.infer_string", infer_string):
ser = Series(["1", "-3.14", last_val])
result = to_numeric(ser)
expected = Series([1, -3.14, 7])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data",
[
[1, 3, 4, 5],
[1.0, 3.0, 4.0, 5.0],
# Bool is regarded as numeric.
[True, False, True, True],
],
)
def test_series_numeric(data):
ser = Series(data, index=list("ABCD"), name="EFG")
result = to_numeric(ser)
tm.assert_series_equal(result, ser)
@pytest.mark.parametrize(
"data,msg",
[
([1, -3.14, "apple"], 'Unable to parse string "apple" at position 2'),
(
["orange", 1, -3.14, "apple"],
'Unable to parse string "orange" at position 0',
),
],
)
def test_error(data, msg):
ser = Series(data)
with pytest.raises(ValueError, match=msg):
to_numeric(ser, errors="raise")
@pytest.mark.parametrize(
"errors,exp_data", [("ignore", [1, -3.14, "apple"]), ("coerce", [1, -3.14, np.nan])]
)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_ignore_error(errors, exp_data):
ser = Series([1, -3.14, "apple"])
result = to_numeric(ser, errors=errors)
expected = Series(exp_data)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"errors,exp",
[
("raise", 'Unable to parse string "apple" at position 2'),
("ignore", [True, False, "apple"]),
# Coerces to float.
("coerce", [1.0, 0.0, np.nan]),
],
)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_bool_handling(errors, exp):
ser = Series([True, False, "apple"])
if isinstance(exp, str):
with pytest.raises(ValueError, match=exp):
to_numeric(ser, errors=errors)
else:
result = to_numeric(ser, errors=errors)
expected = Series(exp)
tm.assert_series_equal(result, expected)
def test_list():
ser = ["1", "-3.14", "7"]
res = to_numeric(ser)
expected = np.array([1, -3.14, 7])
tm.assert_numpy_array_equal(res, expected)
@pytest.mark.parametrize(
"data,arr_kwargs",
[
([1, 3, 4, 5], {"dtype": np.int64}),
([1.0, 3.0, 4.0, 5.0], {}),
# Boolean is regarded as numeric.
([True, False, True, True], {}),
],
)
def test_list_numeric(data, arr_kwargs):
result = to_numeric(data)
expected = np.array(data, **arr_kwargs)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("kwargs", [{"dtype": "O"}, {}])
def test_numeric(kwargs):
data = [1, -3.14, 7]
ser = Series(data, **kwargs)
result = to_numeric(ser)
expected = Series(data)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"columns",
[
# One column.
"a",
# Multiple columns.
["a", "b"],
],
)
def test_numeric_df_columns(columns):
# see gh-14827
df = DataFrame(
{
"a": [1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), "0.1"],
"b": [1.0, 2.0, 3.0, 4.0],
}
)
expected = DataFrame({"a": [1.2, 3.14, np.inf, 0.1], "b": [1.0, 2.0, 3.0, 4.0]})
df_copy = df.copy()
df_copy[columns] = df_copy[columns].apply(to_numeric)
tm.assert_frame_equal(df_copy, expected)
@pytest.mark.parametrize(
"data,exp_data",
[
(
[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1],
[[3.14, 1.0], 1.6, 0.1],
),
([np.array([decimal.Decimal(3.14), 1.0]), 0.1], [[3.14, 1.0], 0.1]),
],
)
def test_numeric_embedded_arr_likes(data, exp_data):
# Test to_numeric with embedded lists and arrays
df = DataFrame({"a": data})
df["a"] = df["a"].apply(to_numeric)
expected = DataFrame({"a": exp_data})
tm.assert_frame_equal(df, expected)
def test_all_nan():
ser = Series(["a", "b", "c"])
result = to_numeric(ser, errors="coerce")
expected = Series([np.nan, np.nan, np.nan])
tm.assert_series_equal(result, expected)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_type_check(errors):
# see gh-11776
df = DataFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
kwargs = {"errors": errors} if errors is not None else {}
with pytest.raises(TypeError, match="1-d array"):
to_numeric(df, **kwargs)
@pytest.mark.parametrize("val", [1, 1.1, 20001])
def test_scalar(val, signed, transform):
val = -val if signed else val
assert to_numeric(transform(val)) == float(val)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_really_large_scalar(large_val, signed, transform, errors):
# see gh-24910
kwargs = {"errors": errors} if errors is not None else {}
val = -large_val if signed else large_val
val = transform(val)
val_is_string = isinstance(val, str)
if val_is_string and errors in (None, "raise"):
msg = "Integer out of range. at position 0"
with pytest.raises(ValueError, match=msg):
to_numeric(val, **kwargs)
else:
expected = float(val) if (errors == "coerce" and val_is_string) else val
tm.assert_almost_equal(to_numeric(val, **kwargs), expected)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_really_large_in_arr(large_val, signed, transform, multiple_elts, errors):
# see gh-24910
kwargs = {"errors": errors} if errors is not None else {}
val = -large_val if signed else large_val
val = transform(val)
extra_elt = "string"
arr = [val] + multiple_elts * [extra_elt]
val_is_string = isinstance(val, str)
coercing = errors == "coerce"
if errors in (None, "raise") and (val_is_string or multiple_elts):
if val_is_string:
msg = "Integer out of range. at position 0"
else:
msg = 'Unable to parse string "string" at position 1'
with pytest.raises(ValueError, match=msg):
to_numeric(arr, **kwargs)
else:
result = to_numeric(arr, **kwargs)
exp_val = float(val) if (coercing and val_is_string) else val
expected = [exp_val]
if multiple_elts:
if coercing:
expected.append(np.nan)
exp_dtype = float
else:
expected.append(extra_elt)
exp_dtype = object
else:
exp_dtype = float if isinstance(exp_val, (int, float)) else object
tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors):
# see gh-24910
#
# Even if we discover that we have to hold float, does not mean
# we should be lenient on subsequent elements that fail to be integer.
kwargs = {"errors": errors} if errors is not None else {}
arr = [str(-large_val if signed else large_val)]
if multiple_elts:
arr.insert(0, large_val)
if errors in (None, "raise"):
index = int(multiple_elts)
msg = f"Integer out of range. at position {index}"
with pytest.raises(ValueError, match=msg):
to_numeric(arr, **kwargs)
else:
result = to_numeric(arr, **kwargs)
if errors == "coerce":
expected = [float(i) for i in arr]
exp_dtype = float
else:
expected = arr
exp_dtype = object
tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
@pytest.mark.parametrize(
"errors,checker",
[
("raise", 'Unable to parse string "fail" at position 0'),
("ignore", lambda x: x == "fail"),
("coerce", lambda x: np.isnan(x)),
],
)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_scalar_fail(errors, checker):
scalar = "fail"
if isinstance(checker, str):
with pytest.raises(ValueError, match=checker):
to_numeric(scalar, errors=errors)
else:
assert checker(to_numeric(scalar, errors=errors))
@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, np.nan, 3, np.nan]])
def test_numeric_dtypes(data, transform_assert_equal):
transform, assert_equal = transform_assert_equal
data = transform(data)
result = to_numeric(data)
assert_equal(result, data)
@pytest.mark.parametrize(
"data,exp",
[
(["1", "2", "3"], np.array([1, 2, 3], dtype="int64")),
(["1.5", "2.7", "3.4"], np.array([1.5, 2.7, 3.4])),
],
)
def test_str(data, exp, transform_assert_equal):
transform, assert_equal = transform_assert_equal
result = to_numeric(transform(data))
expected = transform(exp)
assert_equal(result, expected)
def test_datetime_like(tz_naive_fixture, transform_assert_equal):
transform, assert_equal = transform_assert_equal
idx = pd.date_range("20130101", periods=3, tz=tz_naive_fixture)
result = to_numeric(transform(idx))
expected = transform(idx.asi8)
assert_equal(result, expected)
def test_timedelta(transform_assert_equal):
transform, assert_equal = transform_assert_equal
idx = pd.timedelta_range("1 days", periods=3, freq="D")
result = to_numeric(transform(idx))
expected = transform(idx.asi8)
assert_equal(result, expected)
def test_period(request, transform_assert_equal):
transform, assert_equal = transform_assert_equal
idx = pd.period_range("2011-01", periods=3, freq="M", name="")
inp = transform(idx)
if not isinstance(inp, Index):
request.applymarker(
pytest.mark.xfail(reason="Missing PeriodDtype support in to_numeric")
)
result = to_numeric(inp)
expected = transform(idx.asi8)
assert_equal(result, expected)
@pytest.mark.parametrize(
"errors,expected",
[
("raise", "Invalid object type at position 0"),
("ignore", Series([[10.0, 2], 1.0, "apple"])),
("coerce", Series([np.nan, 1.0, np.nan])),
],
)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_non_hashable(errors, expected):
# see gh-13324
ser = Series([[10.0, 2], 1.0, "apple"])
if isinstance(expected, str):
with pytest.raises(TypeError, match=expected):
to_numeric(ser, errors=errors)
else:
result = to_numeric(ser, errors=errors)
tm.assert_series_equal(result, expected)
def test_downcast_invalid_cast():
# see gh-13352
data = ["1", 2, 3]
invalid_downcast = "unsigned-integer"
msg = "invalid downcasting method provided"
with pytest.raises(ValueError, match=msg):
to_numeric(data, downcast=invalid_downcast)
def test_errors_invalid_value():
# see gh-26466
data = ["1", 2, 3]
invalid_error_value = "invalid"
msg = "invalid error value specified"
with pytest.raises(ValueError, match=msg):
to_numeric(data, errors=invalid_error_value)
@pytest.mark.parametrize(
"data",
[
["1", 2, 3],
[1, 2, 3],
np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
],
)
@pytest.mark.parametrize(
"kwargs,exp_dtype",
[
# Basic function tests.
({}, np.int64),
({"downcast": None}, np.int64),
# Support below np.float32 is rare and far between.
({"downcast": "float"}, np.dtype(np.float32).char),
# Basic dtype support.
({"downcast": "unsigned"}, np.dtype(np.typecodes["UnsignedInteger"][0])),
],
)
def test_downcast_basic(data, kwargs, exp_dtype):
# see gh-13352
result = to_numeric(data, **kwargs)
expected = np.array([1, 2, 3], dtype=exp_dtype)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("signed_downcast", ["integer", "signed"])
@pytest.mark.parametrize(
"data",
[
["1", 2, 3],
[1, 2, 3],
np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
],
)
def test_signed_downcast(data, signed_downcast):
# see gh-13352
smallest_int_dtype = np.dtype(np.typecodes["Integer"][0])
expected = np.array([1, 2, 3], dtype=smallest_int_dtype)
res = to_numeric(data, downcast=signed_downcast)
tm.assert_numpy_array_equal(res, expected)
def test_ignore_downcast_invalid_data():
# If we can't successfully cast the given
# data to a numeric dtype, do not bother
# with the downcast parameter.
data = ["foo", 2, 3]
expected = np.array(data, dtype=object)
msg = "errors='ignore' is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
res = to_numeric(data, errors="ignore", downcast="unsigned")
tm.assert_numpy_array_equal(res, expected)
def test_ignore_downcast_neg_to_unsigned():
# Cannot cast to an unsigned integer
# because we have a negative number.
data = ["-1", 2, 3]
expected = np.array([-1, 2, 3], dtype=np.int64)
res = to_numeric(data, downcast="unsigned")
tm.assert_numpy_array_equal(res, expected)
# Warning in 32 bit platforms
@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning")
@pytest.mark.parametrize("downcast", ["integer", "signed", "unsigned"])
@pytest.mark.parametrize(
"data,expected",
[
(["1.1", 2, 3], np.array([1.1, 2, 3], dtype=np.float64)),
(
[10000.0, 20000, 3000, 40000.36, 50000, 50000.00],
np.array(
[10000.0, 20000, 3000, 40000.36, 50000, 50000.00], dtype=np.float64
),
),
],
)
def test_ignore_downcast_cannot_convert_float(data, expected, downcast):
# Cannot cast to an integer (signed or unsigned)
# because we have a float number.
res = to_numeric(data, downcast=downcast)
tm.assert_numpy_array_equal(res, expected)
@pytest.mark.parametrize(
"downcast,expected_dtype",
[("integer", np.int16), ("signed", np.int16), ("unsigned", np.uint16)],
)
def test_downcast_not8bit(downcast, expected_dtype):
# the smallest integer dtype need not be np.(u)int8
data = ["256", 257, 258]
expected = np.array([256, 257, 258], dtype=expected_dtype)
res = to_numeric(data, downcast=downcast)
tm.assert_numpy_array_equal(res, expected)
@pytest.mark.parametrize(
"dtype,downcast,min_max",
[
("int8", "integer", [iinfo(np.int8).min, iinfo(np.int8).max]),
("int16", "integer", [iinfo(np.int16).min, iinfo(np.int16).max]),
("int32", "integer", [iinfo(np.int32).min, iinfo(np.int32).max]),
("int64", "integer", [iinfo(np.int64).min, iinfo(np.int64).max]),
("uint8", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max]),
("uint16", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max]),
("uint32", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max]),
("uint64", "unsigned", [iinfo(np.uint64).min, iinfo(np.uint64).max]),
("int16", "integer", [iinfo(np.int8).min, iinfo(np.int8).max + 1]),
("int32", "integer", [iinfo(np.int16).min, iinfo(np.int16).max + 1]),
("int64", "integer", [iinfo(np.int32).min, iinfo(np.int32).max + 1]),
("int16", "integer", [iinfo(np.int8).min - 1, iinfo(np.int16).max]),
("int32", "integer", [iinfo(np.int16).min - 1, iinfo(np.int32).max]),
("int64", "integer", [iinfo(np.int32).min - 1, iinfo(np.int64).max]),
("uint16", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max + 1]),
("uint32", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max + 1]),
("uint64", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max + 1]),
],
)
def test_downcast_limits(dtype, downcast, min_max):
# see gh-14404: test the limits of each downcast.
series = to_numeric(Series(min_max), downcast=downcast)
assert series.dtype == dtype
def test_downcast_float64_to_float32():
# GH-43693: Check float64 preservation when >= 16,777,217
series = Series([16777217.0, np.finfo(np.float64).max, np.nan], dtype=np.float64)
result = to_numeric(series, downcast="float")
assert series.dtype == result.dtype
@pytest.mark.parametrize(
"ser,expected",
[
(
Series([0, 9223372036854775808]),
Series([0, 9223372036854775808], dtype=np.uint64),
)
],
)
def test_downcast_uint64(ser, expected):
# see gh-14422:
# BUG: to_numeric doesn't work uint64 numbers
result = to_numeric(ser, downcast="unsigned")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,exp_data",
[
(
[200, 300, "", "NaN", 30000000000000000000],
[200, 300, np.nan, np.nan, 30000000000000000000],
),
(
["12345678901234567890", "1234567890", "ITEM"],
[12345678901234567890, 1234567890, np.nan],
),
],
)
def test_coerce_uint64_conflict(data, exp_data):
# see gh-17007 and gh-17125
#
# Still returns float despite the uint64-nan conflict,
# which would normally force the casting to object.
result = to_numeric(Series(data), errors="coerce")
expected = Series(exp_data, dtype=float)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"errors,exp",
[
("ignore", Series(["12345678901234567890", "1234567890", "ITEM"])),
("raise", "Unable to parse string"),
],
)
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_non_coerce_uint64_conflict(errors, exp):
# see gh-17007 and gh-17125
#
# For completeness.
ser = Series(["12345678901234567890", "1234567890", "ITEM"])
if isinstance(exp, str):
with pytest.raises(ValueError, match=exp):
to_numeric(ser, errors=errors)
else:
result = to_numeric(ser, errors=errors)
tm.assert_series_equal(result, ser)
@pytest.mark.parametrize("dc1", ["integer", "float", "unsigned"])
@pytest.mark.parametrize("dc2", ["integer", "float", "unsigned"])
def test_downcast_empty(dc1, dc2):
# GH32493
tm.assert_numpy_array_equal(
to_numeric([], downcast=dc1),
to_numeric([], downcast=dc2),
check_dtype=False,
)
def test_failure_to_convert_uint64_string_to_NaN():
# GH 32394
result = to_numeric("uint64", errors="coerce")
assert np.isnan(result)
ser = Series([32, 64, np.nan])
result = to_numeric(Series(["32", "64", "uint64"]), errors="coerce")
tm.assert_series_equal(result, ser)
@pytest.mark.parametrize(
"strrep",
[
"243.164",
"245.968",
"249.585",
"259.745",
"265.742",
"272.567",
"279.196",
"280.366",
"275.034",
"271.351",
"272.889",
"270.627",
"280.828",
"290.383",
"308.153",
"319.945",
"336.0",
"344.09",
"351.385",
"356.178",
"359.82",
"361.03",
"367.701",
"380.812",
"387.98",
"391.749",
"391.171",
"385.97",
"385.345",
"386.121",
"390.996",
"399.734",
"413.073",
"421.532",
"430.221",
"437.092",
"439.746",
"446.01",
"451.191",
"460.463",
"469.779",
"472.025",
"479.49",
"474.864",
"467.54",
"471.978",
],
)
def test_precision_float_conversion(strrep):
# GH 31364
result = to_numeric(strrep)
assert result == float(strrep)
@pytest.mark.parametrize(
"values, expected",
[
(["1", "2", None], Series([1, 2, np.nan], dtype="Int64")),
(["1", "2", "3"], Series([1, 2, 3], dtype="Int64")),
(["1", "2", 3], Series([1, 2, 3], dtype="Int64")),
(["1", "2", 3.5], Series([1, 2, 3.5], dtype="Float64")),
(["1", None, 3.5], Series([1, np.nan, 3.5], dtype="Float64")),
(["1", "2", "3.5"], Series([1, 2, 3.5], dtype="Float64")),
],
)
def test_to_numeric_from_nullable_string(values, nullable_string_dtype, expected):
# https://github.com/pandas-dev/pandas/issues/37262
s = Series(values, dtype=nullable_string_dtype)
result = to_numeric(s)
tm.assert_series_equal(result, expected)
def test_to_numeric_from_nullable_string_coerce(nullable_string_dtype):
# GH#52146
values = ["a", "1"]
ser = Series(values, dtype=nullable_string_dtype)
result = to_numeric(ser, errors="coerce")
expected = Series([pd.NA, 1], dtype="Int64")
tm.assert_series_equal(result, expected)
def test_to_numeric_from_nullable_string_ignore(nullable_string_dtype):
# GH#52146
values = ["a", "1"]
ser = Series(values, dtype=nullable_string_dtype)
expected = ser.copy()
msg = "errors='ignore' is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_numeric(ser, errors="ignore")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data, input_dtype, downcast, expected_dtype",
(
([1, 1], "Int64", "integer", "Int8"),
([1.0, pd.NA], "Float64", "integer", "Int8"),
([1.0, 1.1], "Float64", "integer", "Float64"),
([1, pd.NA], "Int64", "integer", "Int8"),
([450, 300], "Int64", "integer", "Int16"),
([1, 1], "Float64", "integer", "Int8"),
([np.iinfo(np.int64).max - 1, 1], "Int64", "integer", "Int64"),
([1, 1], "Int64", "signed", "Int8"),
([1.0, 1.0], "Float32", "signed", "Int8"),
([1.0, 1.1], "Float64", "signed", "Float64"),
([1, pd.NA], "Int64", "signed", "Int8"),
([450, -300], "Int64", "signed", "Int16"),
([np.iinfo(np.uint64).max - 1, 1], "UInt64", "signed", "UInt64"),
([1, 1], "Int64", "unsigned", "UInt8"),
([1.0, 1.0], "Float32", "unsigned", "UInt8"),
([1.0, 1.1], "Float64", "unsigned", "Float64"),
([1, pd.NA], "Int64", "unsigned", "UInt8"),
([450, -300], "Int64", "unsigned", "Int64"),
([-1, -1], "Int32", "unsigned", "Int32"),
([1, 1], "Float64", "float", "Float32"),
([1, 1.1], "Float64", "float", "Float32"),
([1, 1], "Float32", "float", "Float32"),
([1, 1.1], "Float32", "float", "Float32"),
),
)
def test_downcast_nullable_numeric(data, input_dtype, downcast, expected_dtype):
arr = pd.array(data, dtype=input_dtype)
result = to_numeric(arr, downcast=downcast)
expected = pd.array(data, dtype=expected_dtype)
tm.assert_extension_array_equal(result, expected)
def test_downcast_nullable_mask_is_copied():
# GH38974
arr = pd.array([1, 2, pd.NA], dtype="Int64")
result = to_numeric(arr, downcast="integer")
expected = pd.array([1, 2, pd.NA], dtype="Int8")
tm.assert_extension_array_equal(result, expected)
arr[1] = pd.NA # should not modify result
tm.assert_extension_array_equal(result, expected)
def test_to_numeric_scientific_notation():
# GH 15898
result = to_numeric("1.7e+308")
expected = np.float64(1.7e308)
assert result == expected
@pytest.mark.parametrize("val", [9876543210.0, 2.0**128])
def test_to_numeric_large_float_not_downcast_to_float_32(val):
# GH 19729
expected = Series([val])
result = to_numeric(expected, downcast="float")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"val, dtype", [(1, "Int64"), (1.5, "Float64"), (True, "boolean")]
)
def test_to_numeric_dtype_backend(val, dtype):
# GH#50505
ser = Series([val], dtype=object)
result = to_numeric(ser, dtype_backend="numpy_nullable")
expected = Series([val], dtype=dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"val, dtype",
[
(1, "Int64"),
(1.5, "Float64"),
(True, "boolean"),
(1, "int64[pyarrow]"),
(1.5, "float64[pyarrow]"),
(True, "bool[pyarrow]"),
],
)
def test_to_numeric_dtype_backend_na(val, dtype):
# GH#50505
if "pyarrow" in dtype:
pytest.importorskip("pyarrow")
dtype_backend = "pyarrow"
else:
dtype_backend = "numpy_nullable"
ser = Series([val, None], dtype=object)
result = to_numeric(ser, dtype_backend=dtype_backend)
expected = Series([val, pd.NA], dtype=dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"val, dtype, downcast",
[
(1, "Int8", "integer"),
(1.5, "Float32", "float"),
(1, "Int8", "signed"),
(1, "int8[pyarrow]", "integer"),
(1.5, "float[pyarrow]", "float"),
(1, "int8[pyarrow]", "signed"),
],
)
def test_to_numeric_dtype_backend_downcasting(val, dtype, downcast):
# GH#50505
if "pyarrow" in dtype:
pytest.importorskip("pyarrow")
dtype_backend = "pyarrow"
else:
dtype_backend = "numpy_nullable"
ser = Series([val, None], dtype=object)
result = to_numeric(ser, dtype_backend=dtype_backend, downcast=downcast)
expected = Series([val, pd.NA], dtype=dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"smaller, dtype_backend",
[["UInt8", "numpy_nullable"], ["uint8[pyarrow]", "pyarrow"]],
)
def test_to_numeric_dtype_backend_downcasting_uint(smaller, dtype_backend):
# GH#50505
if dtype_backend == "pyarrow":
pytest.importorskip("pyarrow")
ser = Series([1, pd.NA], dtype="UInt64")
result = to_numeric(ser, dtype_backend=dtype_backend, downcast="unsigned")
expected = Series([1, pd.NA], dtype=smaller)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"dtype",
[
"Int64",
"UInt64",
"Float64",
"boolean",
"int64[pyarrow]",
"uint64[pyarrow]",
"float64[pyarrow]",
"bool[pyarrow]",
],
)
def test_to_numeric_dtype_backend_already_nullable(dtype):
# GH#50505
if "pyarrow" in dtype:
pytest.importorskip("pyarrow")
ser = Series([1, pd.NA], dtype=dtype)
result = to_numeric(ser, dtype_backend="numpy_nullable")
expected = Series([1, pd.NA], dtype=dtype)
tm.assert_series_equal(result, expected)
def test_to_numeric_dtype_backend_error(dtype_backend):
# GH#50505
ser = Series(["a", "b", ""])
expected = ser.copy()
with pytest.raises(ValueError, match="Unable to parse string"):
to_numeric(ser, dtype_backend=dtype_backend)
msg = "errors='ignore' is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_numeric(ser, dtype_backend=dtype_backend, errors="ignore")
tm.assert_series_equal(result, expected)
result = to_numeric(ser, dtype_backend=dtype_backend, errors="coerce")
if dtype_backend == "pyarrow":
dtype = "double[pyarrow]"
else:
dtype = "Float64"
expected = Series([np.nan, np.nan, np.nan], dtype=dtype)
tm.assert_series_equal(result, expected)
def test_invalid_dtype_backend():
ser = Series([1, 2, 3])
msg = (
"dtype_backend numpy is invalid, only 'numpy_nullable' and "
"'pyarrow' are allowed."
)
with pytest.raises(ValueError, match=msg):
to_numeric(ser, dtype_backend="numpy")
def test_coerce_pyarrow_backend():
# GH 52588
pa = pytest.importorskip("pyarrow")
ser = Series(list("12x"), dtype=ArrowDtype(pa.string()))
result = to_numeric(ser, errors="coerce", dtype_backend="pyarrow")
expected = Series([1, 2, None], dtype=ArrowDtype(pa.int64()))
tm.assert_series_equal(result, expected)

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from datetime import time
import locale
import numpy as np
import pytest
from pandas.compat import PY311
from pandas import Series
import pandas._testing as tm
from pandas.core.tools.times import to_time
# The tests marked with this are locale-dependent.
# They pass, except when the machine locale is zh_CN or it_IT.
fails_on_non_english = pytest.mark.xfail(
locale.getlocale()[0] in ("zh_CN", "it_IT"),
reason="fail on a CI build with LC_ALL=zh_CN.utf8/it_IT.utf8",
strict=False,
)
class TestToTime:
@pytest.mark.parametrize(
"time_string",
[
"14:15",
"1415",
pytest.param("2:15pm", marks=fails_on_non_english),
pytest.param("0215pm", marks=fails_on_non_english),
"14:15:00",
"141500",
pytest.param("2:15:00pm", marks=fails_on_non_english),
pytest.param("021500pm", marks=fails_on_non_english),
time(14, 15),
],
)
def test_parsers_time(self, time_string):
# GH#11818
assert to_time(time_string) == time(14, 15)
def test_odd_format(self):
new_string = "14.15"
msg = r"Cannot convert arg \['14\.15'\] to a time"
if not PY311:
with pytest.raises(ValueError, match=msg):
to_time(new_string)
assert to_time(new_string, format="%H.%M") == time(14, 15)
def test_arraylike(self):
arg = ["14:15", "20:20"]
expected_arr = [time(14, 15), time(20, 20)]
assert to_time(arg) == expected_arr
assert to_time(arg, format="%H:%M") == expected_arr
assert to_time(arg, infer_time_format=True) == expected_arr
assert to_time(arg, format="%I:%M%p", errors="coerce") == [None, None]
msg = "errors='ignore' is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
res = to_time(arg, format="%I:%M%p", errors="ignore")
tm.assert_numpy_array_equal(res, np.array(arg, dtype=np.object_))
msg = "Cannot convert.+to a time with given format"
with pytest.raises(ValueError, match=msg):
to_time(arg, format="%I:%M%p", errors="raise")
tm.assert_series_equal(
to_time(Series(arg, name="test")), Series(expected_arr, name="test")
)
res = to_time(np.array(arg))
assert isinstance(res, list)
assert res == expected_arr

340
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from datetime import (
time,
timedelta,
)
import numpy as np
import pytest
from pandas.compat import IS64
from pandas.errors import OutOfBoundsTimedelta
import pandas as pd
from pandas import (
Series,
TimedeltaIndex,
isna,
to_timedelta,
)
import pandas._testing as tm
from pandas.core.arrays import TimedeltaArray
class TestTimedeltas:
def test_to_timedelta_dt64_raises(self):
# Passing datetime64-dtype data to TimedeltaIndex is no longer
# supported GH#29794
msg = r"dtype datetime64\[ns\] cannot be converted to timedelta64\[ns\]"
ser = Series([pd.NaT])
with pytest.raises(TypeError, match=msg):
to_timedelta(ser)
with pytest.raises(TypeError, match=msg):
ser.to_frame().apply(to_timedelta)
@pytest.mark.parametrize("readonly", [True, False])
def test_to_timedelta_readonly(self, readonly):
# GH#34857
arr = np.array([], dtype=object)
if readonly:
arr.setflags(write=False)
result = to_timedelta(arr)
expected = to_timedelta([])
tm.assert_index_equal(result, expected)
def test_to_timedelta_null(self):
result = to_timedelta(["", ""])
assert isna(result).all()
def test_to_timedelta_same_np_timedelta64(self):
# pass thru
result = to_timedelta(np.array([np.timedelta64(1, "s")]))
expected = pd.Index(np.array([np.timedelta64(1, "s")]))
tm.assert_index_equal(result, expected)
def test_to_timedelta_series(self):
# Series
expected = Series([timedelta(days=1), timedelta(days=1, seconds=1)])
result = to_timedelta(Series(["1d", "1days 00:00:01"]))
tm.assert_series_equal(result, expected)
def test_to_timedelta_units(self):
# with units
result = TimedeltaIndex(
[np.timedelta64(0, "ns"), np.timedelta64(10, "s").astype("m8[ns]")]
)
expected = to_timedelta([0, 10], unit="s")
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"dtype, unit",
[
["int64", "s"],
["int64", "m"],
["int64", "h"],
["timedelta64[s]", "s"],
["timedelta64[D]", "D"],
],
)
def test_to_timedelta_units_dtypes(self, dtype, unit):
# arrays of various dtypes
arr = np.array([1] * 5, dtype=dtype)
result = to_timedelta(arr, unit=unit)
exp_dtype = "m8[ns]" if dtype == "int64" else "m8[s]"
expected = TimedeltaIndex([np.timedelta64(1, unit)] * 5, dtype=exp_dtype)
tm.assert_index_equal(result, expected)
def test_to_timedelta_oob_non_nano(self):
arr = np.array([pd.NaT._value + 1], dtype="timedelta64[m]")
msg = (
"Cannot convert -9223372036854775807 minutes to "
r"timedelta64\[s\] without overflow"
)
with pytest.raises(OutOfBoundsTimedelta, match=msg):
to_timedelta(arr)
with pytest.raises(OutOfBoundsTimedelta, match=msg):
TimedeltaIndex(arr)
with pytest.raises(OutOfBoundsTimedelta, match=msg):
TimedeltaArray._from_sequence(arr, dtype="m8[s]")
@pytest.mark.parametrize(
"arg", [np.arange(10).reshape(2, 5), pd.DataFrame(np.arange(10).reshape(2, 5))]
)
@pytest.mark.parametrize("errors", ["ignore", "raise", "coerce"])
@pytest.mark.filterwarnings("ignore:errors='ignore' is deprecated:FutureWarning")
def test_to_timedelta_dataframe(self, arg, errors):
# GH 11776
with pytest.raises(TypeError, match="1-d array"):
to_timedelta(arg, errors=errors)
def test_to_timedelta_invalid_errors(self):
# bad value for errors parameter
msg = "errors must be one of"
with pytest.raises(ValueError, match=msg):
to_timedelta(["foo"], errors="never")
@pytest.mark.parametrize("arg", [[1, 2], 1])
def test_to_timedelta_invalid_unit(self, arg):
# these will error
msg = "invalid unit abbreviation: foo"
with pytest.raises(ValueError, match=msg):
to_timedelta(arg, unit="foo")
def test_to_timedelta_time(self):
# time not supported ATM
msg = (
"Value must be Timedelta, string, integer, float, timedelta or convertible"
)
with pytest.raises(ValueError, match=msg):
to_timedelta(time(second=1))
assert to_timedelta(time(second=1), errors="coerce") is pd.NaT
def test_to_timedelta_bad_value(self):
msg = "Could not convert 'foo' to NumPy timedelta"
with pytest.raises(ValueError, match=msg):
to_timedelta(["foo", "bar"])
def test_to_timedelta_bad_value_coerce(self):
tm.assert_index_equal(
TimedeltaIndex([pd.NaT, pd.NaT]),
to_timedelta(["foo", "bar"], errors="coerce"),
)
tm.assert_index_equal(
TimedeltaIndex(["1 day", pd.NaT, "1 min"]),
to_timedelta(["1 day", "bar", "1 min"], errors="coerce"),
)
def test_to_timedelta_invalid_errors_ignore(self):
# gh-13613: these should not error because errors='ignore'
msg = "errors='ignore' is deprecated"
invalid_data = "apple"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_timedelta(invalid_data, errors="ignore")
assert invalid_data == result
invalid_data = ["apple", "1 days"]
expected = np.array(invalid_data, dtype=object)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_timedelta(invalid_data, errors="ignore")
tm.assert_numpy_array_equal(expected, result)
invalid_data = pd.Index(["apple", "1 days"])
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_timedelta(invalid_data, errors="ignore")
tm.assert_index_equal(invalid_data, result)
invalid_data = Series(["apple", "1 days"])
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_timedelta(invalid_data, errors="ignore")
tm.assert_series_equal(invalid_data, result)
@pytest.mark.parametrize(
"val, errors",
[
("1M", True),
("1 M", True),
("1Y", True),
("1 Y", True),
("1y", True),
("1 y", True),
("1m", False),
("1 m", False),
("1 day", False),
("2day", False),
],
)
def test_unambiguous_timedelta_values(self, val, errors):
# GH36666 Deprecate use of strings denoting units with 'M', 'Y', 'm' or 'y'
# in pd.to_timedelta
msg = "Units 'M', 'Y' and 'y' do not represent unambiguous timedelta"
if errors:
with pytest.raises(ValueError, match=msg):
to_timedelta(val)
else:
# check it doesn't raise
to_timedelta(val)
def test_to_timedelta_via_apply(self):
# GH 5458
expected = Series([np.timedelta64(1, "s")])
result = Series(["00:00:01"]).apply(to_timedelta)
tm.assert_series_equal(result, expected)
result = Series([to_timedelta("00:00:01")])
tm.assert_series_equal(result, expected)
def test_to_timedelta_inference_without_warning(self):
# GH#41731 inference produces a warning in the Series constructor,
# but _not_ in to_timedelta
vals = ["00:00:01", pd.NaT]
with tm.assert_produces_warning(None):
result = to_timedelta(vals)
expected = TimedeltaIndex([pd.Timedelta(seconds=1), pd.NaT])
tm.assert_index_equal(result, expected)
def test_to_timedelta_on_missing_values(self):
# GH5438
timedelta_NaT = np.timedelta64("NaT")
actual = to_timedelta(Series(["00:00:01", np.nan]))
expected = Series(
[np.timedelta64(1000000000, "ns"), timedelta_NaT],
dtype=f"{tm.ENDIAN}m8[ns]",
)
tm.assert_series_equal(actual, expected)
ser = Series(["00:00:01", pd.NaT], dtype="m8[ns]")
actual = to_timedelta(ser)
tm.assert_series_equal(actual, expected)
@pytest.mark.parametrize("val", [np.nan, pd.NaT, pd.NA])
def test_to_timedelta_on_missing_values_scalar(self, val):
actual = to_timedelta(val)
assert actual._value == np.timedelta64("NaT").astype("int64")
@pytest.mark.parametrize("val", [np.nan, pd.NaT, pd.NA])
def test_to_timedelta_on_missing_values_list(self, val):
actual = to_timedelta([val])
assert actual[0]._value == np.timedelta64("NaT").astype("int64")
@pytest.mark.xfail(not IS64, reason="Floating point error")
def test_to_timedelta_float(self):
# https://github.com/pandas-dev/pandas/issues/25077
arr = np.arange(0, 1, 1e-6)[-10:]
result = to_timedelta(arr, unit="s")
expected_asi8 = np.arange(999990000, 10**9, 1000, dtype="int64")
tm.assert_numpy_array_equal(result.asi8, expected_asi8)
def test_to_timedelta_coerce_strings_unit(self):
arr = np.array([1, 2, "error"], dtype=object)
result = to_timedelta(arr, unit="ns", errors="coerce")
expected = to_timedelta([1, 2, pd.NaT], unit="ns")
tm.assert_index_equal(result, expected)
def test_to_timedelta_ignore_strings_unit(self):
arr = np.array([1, 2, "error"], dtype=object)
msg = "errors='ignore' is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = to_timedelta(arr, unit="ns", errors="ignore")
tm.assert_numpy_array_equal(result, arr)
@pytest.mark.parametrize(
"expected_val, result_val", [[timedelta(days=2), 2], [None, None]]
)
def test_to_timedelta_nullable_int64_dtype(self, expected_val, result_val):
# GH 35574
expected = Series([timedelta(days=1), expected_val])
result = to_timedelta(Series([1, result_val], dtype="Int64"), unit="days")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
("input", "expected"),
[
("8:53:08.71800000001", "8:53:08.718"),
("8:53:08.718001", "8:53:08.718001"),
("8:53:08.7180000001", "8:53:08.7180000001"),
("-8:53:08.71800000001", "-8:53:08.718"),
("8:53:08.7180000089", "8:53:08.718000008"),
],
)
@pytest.mark.parametrize("func", [pd.Timedelta, to_timedelta])
def test_to_timedelta_precision_over_nanos(self, input, expected, func):
# GH: 36738
expected = pd.Timedelta(expected)
result = func(input)
assert result == expected
def test_to_timedelta_zerodim(self, fixed_now_ts):
# ndarray.item() incorrectly returns int for dt64[ns] and td64[ns]
dt64 = fixed_now_ts.to_datetime64()
arg = np.array(dt64)
msg = (
"Value must be Timedelta, string, integer, float, timedelta "
"or convertible, not datetime64"
)
with pytest.raises(ValueError, match=msg):
to_timedelta(arg)
arg2 = arg.view("m8[ns]")
result = to_timedelta(arg2)
assert isinstance(result, pd.Timedelta)
assert result._value == dt64.view("i8")
def test_to_timedelta_numeric_ea(self, any_numeric_ea_dtype):
# GH#48796
ser = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
result = to_timedelta(ser)
expected = Series([pd.Timedelta(1, unit="ns"), pd.NaT])
tm.assert_series_equal(result, expected)
def test_to_timedelta_fraction(self):
result = to_timedelta(1.0 / 3, unit="h")
expected = pd.Timedelta("0 days 00:19:59.999999998")
assert result == expected
def test_from_numeric_arrow_dtype(any_numeric_ea_dtype):
# GH 52425
pytest.importorskip("pyarrow")
ser = Series([1, 2], dtype=f"{any_numeric_ea_dtype.lower()}[pyarrow]")
result = to_timedelta(ser)
expected = Series([1, 2], dtype="timedelta64[ns]")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("unit", ["ns", "ms"])
def test_from_timedelta_arrow_dtype(unit):
# GH 54298
pytest.importorskip("pyarrow")
expected = Series([timedelta(1)], dtype=f"duration[{unit}][pyarrow]")
result = to_timedelta(expected)
tm.assert_series_equal(result, expected)