mirror of
https://github.com/PiBrewing/craftbeerpi4.git
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204 lines
6.8 KiB
Python
204 lines
6.8 KiB
Python
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import operator
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import DataFrame, Series
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import pandas._testing as tm
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class TestSeriesAnalytics:
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def test_prod_numpy16_bug(self):
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s = Series([1.0, 1.0, 1.0], index=range(3))
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result = s.prod()
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assert not isinstance(result, Series)
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def test_matmul(self):
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# matmul test is for GH #10259
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a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
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b = DataFrame(
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np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
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).T
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# Series @ DataFrame -> Series
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result = operator.matmul(a, b)
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expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"])
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tm.assert_series_equal(result, expected)
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# DataFrame @ Series -> Series
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result = operator.matmul(b.T, a)
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expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
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tm.assert_series_equal(result, expected)
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# Series @ Series -> scalar
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result = operator.matmul(a, a)
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expected = np.dot(a.values, a.values)
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tm.assert_almost_equal(result, expected)
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# GH 21530
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# vector (1D np.array) @ Series (__rmatmul__)
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result = operator.matmul(a.values, a)
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expected = np.dot(a.values, a.values)
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tm.assert_almost_equal(result, expected)
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# GH 21530
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# vector (1D list) @ Series (__rmatmul__)
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result = operator.matmul(a.values.tolist(), a)
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expected = np.dot(a.values, a.values)
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tm.assert_almost_equal(result, expected)
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# GH 21530
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# matrix (2D np.array) @ Series (__rmatmul__)
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result = operator.matmul(b.T.values, a)
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expected = np.dot(b.T.values, a.values)
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tm.assert_almost_equal(result, expected)
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# GH 21530
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# matrix (2D nested lists) @ Series (__rmatmul__)
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result = operator.matmul(b.T.values.tolist(), a)
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expected = np.dot(b.T.values, a.values)
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tm.assert_almost_equal(result, expected)
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# mixed dtype DataFrame @ Series
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a["p"] = int(a.p)
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result = operator.matmul(b.T, a)
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expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
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tm.assert_series_equal(result, expected)
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# different dtypes DataFrame @ Series
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a = a.astype(int)
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result = operator.matmul(b.T, a)
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expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
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tm.assert_series_equal(result, expected)
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msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"
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# exception raised is of type Exception
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with pytest.raises(Exception, match=msg):
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a.dot(a.values[:3])
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msg = "matrices are not aligned"
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with pytest.raises(ValueError, match=msg):
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a.dot(b.T)
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def test_ptp(self):
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# GH21614
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N = 1000
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arr = np.random.randn(N)
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ser = Series(arr)
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assert np.ptp(ser) == np.ptp(arr)
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def test_repeat(self):
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s = Series(np.random.randn(3), index=["a", "b", "c"])
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reps = s.repeat(5)
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exp = Series(s.values.repeat(5), index=s.index.values.repeat(5))
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tm.assert_series_equal(reps, exp)
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to_rep = [2, 3, 4]
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reps = s.repeat(to_rep)
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exp = Series(s.values.repeat(to_rep), index=s.index.values.repeat(to_rep))
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tm.assert_series_equal(reps, exp)
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def test_numpy_repeat(self):
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s = Series(np.arange(3), name="x")
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expected = Series(s.values.repeat(2), name="x", index=s.index.values.repeat(2))
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tm.assert_series_equal(np.repeat(s, 2), expected)
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msg = "the 'axis' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.repeat(s, 2, axis=0)
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def test_is_monotonic(self):
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s = Series(np.random.randint(0, 10, size=1000))
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assert not s.is_monotonic
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s = Series(np.arange(1000))
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assert s.is_monotonic is True
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assert s.is_monotonic_increasing is True
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s = Series(np.arange(1000, 0, -1))
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assert s.is_monotonic_decreasing is True
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s = Series(pd.date_range("20130101", periods=10))
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assert s.is_monotonic is True
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assert s.is_monotonic_increasing is True
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s = Series(list(reversed(s.tolist())))
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assert s.is_monotonic is False
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assert s.is_monotonic_decreasing is True
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@pytest.mark.parametrize("func", [np.any, np.all])
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@pytest.mark.parametrize("kwargs", [dict(keepdims=True), dict(out=object())])
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@td.skip_if_np_lt("1.15")
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def test_validate_any_all_out_keepdims_raises(self, kwargs, func):
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s = pd.Series([1, 2])
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param = list(kwargs)[0]
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name = func.__name__
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msg = (
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f"the '{param}' parameter is not "
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"supported in the pandas "
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fr"implementation of {name}\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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func(s, **kwargs)
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@td.skip_if_np_lt("1.15")
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def test_validate_sum_initial(self):
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s = pd.Series([1, 2])
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msg = (
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r"the 'initial' parameter is not "
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r"supported in the pandas "
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r"implementation of sum\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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np.sum(s, initial=10)
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def test_validate_median_initial(self):
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s = pd.Series([1, 2])
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msg = (
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r"the 'overwrite_input' parameter is not "
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r"supported in the pandas "
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r"implementation of median\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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# It seems like np.median doesn't dispatch, so we use the
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# method instead of the ufunc.
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s.median(overwrite_input=True)
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@td.skip_if_np_lt("1.15")
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def test_validate_stat_keepdims(self):
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s = pd.Series([1, 2])
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msg = (
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r"the 'keepdims' parameter is not "
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r"supported in the pandas "
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r"implementation of sum\(\)"
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)
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with pytest.raises(ValueError, match=msg):
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np.sum(s, keepdims=True)
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def test_td64_summation_overflow(self):
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# GH 9442
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s = pd.Series(pd.date_range("20130101", periods=100000, freq="H"))
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s[0] += pd.Timedelta("1s 1ms")
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# mean
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result = (s - s.min()).mean()
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expected = pd.Timedelta((pd.TimedeltaIndex((s - s.min())).asi8 / len(s)).sum())
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# the computation is converted to float so
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# might be some loss of precision
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assert np.allclose(result.value / 1000, expected.value / 1000)
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# sum
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msg = "overflow in timedelta operation"
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with pytest.raises(ValueError, match=msg):
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(s - s.min()).sum()
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s1 = s[0:10000]
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with pytest.raises(ValueError, match=msg):
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(s1 - s1.min()).sum()
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s2 = s[0:1000]
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(s2 - s2.min()).sum()
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