106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
from datetime import timedelta
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import numpy as np
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import pytest
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from pandas._libs import iNaT
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import pandas as pd
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from pandas import (
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Categorical,
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Index,
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NaT,
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Series,
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isna,
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)
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import pandas._testing as tm
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class TestSeriesMissingData:
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def test_categorical_nan_handling(self):
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# NaNs are represented as -1 in labels
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s = Series(Categorical(["a", "b", np.nan, "a"]))
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tm.assert_index_equal(s.cat.categories, Index(["a", "b"]))
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tm.assert_numpy_array_equal(
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s.values.codes, np.array([0, 1, -1, 0], dtype=np.int8)
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)
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def test_isna_for_inf(self):
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s = Series(["a", np.inf, np.nan, pd.NA, 1.0])
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msg = "use_inf_as_na option is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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with pd.option_context("mode.use_inf_as_na", True):
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r = s.isna()
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dr = s.dropna()
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e = Series([False, True, True, True, False])
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de = Series(["a", 1.0], index=[0, 4])
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tm.assert_series_equal(r, e)
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tm.assert_series_equal(dr, de)
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def test_timedelta64_nan(self):
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td = Series([timedelta(days=i) for i in range(10)])
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# nan ops on timedeltas
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td1 = td.copy()
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td1[0] = np.nan
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assert isna(td1[0])
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assert td1[0]._value == iNaT
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td1[0] = td[0]
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assert not isna(td1[0])
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# GH#16674 iNaT is treated as an integer when given by the user
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with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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td1[1] = iNaT
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assert not isna(td1[1])
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assert td1.dtype == np.object_
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assert td1[1] == iNaT
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td1[1] = td[1]
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assert not isna(td1[1])
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td1[2] = NaT
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assert isna(td1[2])
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assert td1[2]._value == iNaT
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td1[2] = td[2]
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assert not isna(td1[2])
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# boolean setting
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# GH#2899 boolean setting
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td3 = np.timedelta64(timedelta(days=3))
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td7 = np.timedelta64(timedelta(days=7))
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td[(td > td3) & (td < td7)] = np.nan
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assert isna(td).sum() == 3
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@pytest.mark.xfail(
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reason="Chained inequality raises when trying to define 'selector'"
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)
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def test_logical_range_select(self, datetime_series):
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# NumPy limitation =(
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# https://github.com/pandas-dev/pandas/commit/9030dc021f07c76809848925cb34828f6c8484f3
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selector = -0.5 <= datetime_series <= 0.5
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expected = (datetime_series >= -0.5) & (datetime_series <= 0.5)
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tm.assert_series_equal(selector, expected)
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def test_valid(self, datetime_series):
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ts = datetime_series.copy()
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ts.index = ts.index._with_freq(None)
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ts[::2] = np.nan
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result = ts.dropna()
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assert len(result) == ts.count()
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tm.assert_series_equal(result, ts[1::2])
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tm.assert_series_equal(result, ts[pd.notna(ts)])
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def test_hasnans_uncached_for_series():
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# GH#19700
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# set float64 dtype to avoid upcast when setting nan
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idx = Index([0, 1], dtype="float64")
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assert idx.hasnans is False
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assert "hasnans" in idx._cache
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ser = idx.to_series()
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assert ser.hasnans is False
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assert not hasattr(ser, "_cache")
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ser.iloc[-1] = np.nan
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assert ser.hasnans is True
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