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import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
@pytest.mark.parametrize("operation, expected", [("min", "a"), ("max", "b")])
def test_reductions_series_strings(operation, expected):
# GH#31746
ser = Series(["a", "b"], dtype="string")
res_operation_serie = getattr(ser, operation)()
assert res_operation_serie == expected
@pytest.mark.parametrize("as_period", [True, False])
def test_mode_extension_dtype(as_period):
# GH#41927 preserve dt64tz dtype
ser = Series([pd.Timestamp(1979, 4, n) for n in range(1, 5)])
if as_period:
ser = ser.dt.to_period("D")
else:
ser = ser.dt.tz_localize("US/Central")
res = ser.mode()
assert res.dtype == ser.dtype
tm.assert_series_equal(res, ser)
def test_mode_nullable_dtype(any_numeric_ea_dtype):
# GH#55340
ser = Series([1, 3, 2, pd.NA, 3, 2, pd.NA], dtype=any_numeric_ea_dtype)
result = ser.mode(dropna=False)
expected = Series([2, 3, pd.NA], dtype=any_numeric_ea_dtype)
tm.assert_series_equal(result, expected)
result = ser.mode(dropna=True)
expected = Series([2, 3], dtype=any_numeric_ea_dtype)
tm.assert_series_equal(result, expected)
ser[-1] = pd.NA
result = ser.mode(dropna=True)
expected = Series([2, 3], dtype=any_numeric_ea_dtype)
tm.assert_series_equal(result, expected)
result = ser.mode(dropna=False)
expected = Series([pd.NA], dtype=any_numeric_ea_dtype)
tm.assert_series_equal(result, expected)
def test_mode_infer_string():
# GH#56183
pytest.importorskip("pyarrow")
ser = Series(["a", "b"], dtype=object)
with pd.option_context("future.infer_string", True):
result = ser.mode()
expected = Series(["a", "b"], dtype=object)
tm.assert_series_equal(result, expected)
def test_reductions_td64_with_nat():
# GH#8617
ser = Series([0, pd.NaT], dtype="m8[ns]")
exp = ser[0]
assert ser.median() == exp
assert ser.min() == exp
assert ser.max() == exp
@pytest.mark.parametrize("skipna", [True, False])
def test_td64_sum_empty(skipna):
# GH#37151
ser = Series([], dtype="timedelta64[ns]")
result = ser.sum(skipna=skipna)
assert isinstance(result, pd.Timedelta)
assert result == pd.Timedelta(0)
def test_td64_summation_overflow():
# GH#9442
ser = Series(pd.date_range("20130101", periods=100000, freq="h"))
ser[0] += pd.Timedelta("1s 1ms")
# mean
result = (ser - ser.min()).mean()
expected = pd.Timedelta((pd.TimedeltaIndex(ser - ser.min()).asi8 / len(ser)).sum())
# the computation is converted to float so
# might be some loss of precision
assert np.allclose(result._value / 1000, expected._value / 1000)
# sum
msg = "overflow in timedelta operation"
with pytest.raises(ValueError, match=msg):
(ser - ser.min()).sum()
s1 = ser[0:10000]
with pytest.raises(ValueError, match=msg):
(s1 - s1.min()).sum()
s2 = ser[0:1000]
(s2 - s2.min()).sum()
def test_prod_numpy16_bug():
ser = Series([1.0, 1.0, 1.0], index=range(3))
result = ser.prod()
assert not isinstance(result, Series)
@pytest.mark.parametrize("func", [np.any, np.all])
@pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}])
def test_validate_any_all_out_keepdims_raises(kwargs, func):
ser = Series([1, 2])
param = next(iter(kwargs))
name = func.__name__
msg = (
f"the '{param}' parameter is not "
"supported in the pandas "
rf"implementation of {name}\(\)"
)
with pytest.raises(ValueError, match=msg):
func(ser, **kwargs)
def test_validate_sum_initial():
ser = Series([1, 2])
msg = (
r"the 'initial' parameter is not "
r"supported in the pandas "
r"implementation of sum\(\)"
)
with pytest.raises(ValueError, match=msg):
np.sum(ser, initial=10)
def test_validate_median_initial():
ser = Series([1, 2])
msg = (
r"the 'overwrite_input' parameter is not "
r"supported in the pandas "
r"implementation of median\(\)"
)
with pytest.raises(ValueError, match=msg):
# It seems like np.median doesn't dispatch, so we use the
# method instead of the ufunc.
ser.median(overwrite_input=True)
def test_validate_stat_keepdims():
ser = Series([1, 2])
msg = (
r"the 'keepdims' parameter is not "
r"supported in the pandas "
r"implementation of sum\(\)"
)
with pytest.raises(ValueError, match=msg):
np.sum(ser, keepdims=True)
def test_mean_with_convertible_string_raises(using_array_manager, using_infer_string):
# GH#44008
ser = Series(["1", "2"])
if using_infer_string:
msg = "does not support"
with pytest.raises(TypeError, match=msg):
ser.sum()
else:
assert ser.sum() == "12"
msg = "Could not convert string '12' to numeric|does not support"
with pytest.raises(TypeError, match=msg):
ser.mean()
df = ser.to_frame()
if not using_array_manager:
msg = r"Could not convert \['12'\] to numeric|does not support"
with pytest.raises(TypeError, match=msg):
df.mean()
def test_mean_dont_convert_j_to_complex(using_array_manager):
# GH#36703
df = pd.DataFrame([{"db": "J", "numeric": 123}])
if using_array_manager:
msg = "Could not convert string 'J' to numeric"
else:
msg = r"Could not convert \['J'\] to numeric|does not support"
with pytest.raises(TypeError, match=msg):
df.mean()
with pytest.raises(TypeError, match=msg):
df.agg("mean")
msg = "Could not convert string 'J' to numeric|does not support"
with pytest.raises(TypeError, match=msg):
df["db"].mean()
msg = "Could not convert string 'J' to numeric|ufunc 'divide'"
with pytest.raises(TypeError, match=msg):
np.mean(df["db"].astype("string").array)
def test_median_with_convertible_string_raises(using_array_manager):
# GH#34671 this _could_ return a string "2", but definitely not float 2.0
msg = r"Cannot convert \['1' '2' '3'\] to numeric|does not support"
ser = Series(["1", "2", "3"])
with pytest.raises(TypeError, match=msg):
ser.median()
if not using_array_manager:
msg = r"Cannot convert \[\['1' '2' '3'\]\] to numeric|does not support"
df = ser.to_frame()
with pytest.raises(TypeError, match=msg):
df.median()
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