306 lines
10 KiB
Python
306 lines
10 KiB
Python
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import inspect
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import pydoc
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import numpy as np
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import pytest
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from pandas.util._test_decorators import skip_if_no
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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date_range,
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)
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import pandas._testing as tm
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class TestSeriesMisc:
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def test_tab_completion(self):
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# GH 9910
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s = Series(list("abcd"))
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# Series of str values should have .str but not .dt/.cat in __dir__
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assert "str" in dir(s)
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assert "dt" not in dir(s)
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assert "cat" not in dir(s)
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def test_tab_completion_dt(self):
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# similarly for .dt
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s = Series(date_range("1/1/2015", periods=5))
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assert "dt" in dir(s)
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assert "str" not in dir(s)
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assert "cat" not in dir(s)
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def test_tab_completion_cat(self):
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# Similarly for .cat, but with the twist that str and dt should be
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# there if the categories are of that type first cat and str.
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s = Series(list("abbcd"), dtype="category")
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assert "cat" in dir(s)
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assert "str" in dir(s) # as it is a string categorical
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assert "dt" not in dir(s)
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def test_tab_completion_cat_str(self):
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# similar to cat and str
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s = Series(date_range("1/1/2015", periods=5)).astype("category")
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assert "cat" in dir(s)
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assert "str" not in dir(s)
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assert "dt" in dir(s) # as it is a datetime categorical
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def test_tab_completion_with_categorical(self):
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# test the tab completion display
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ok_for_cat = [
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"categories",
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"codes",
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"ordered",
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"set_categories",
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"add_categories",
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"remove_categories",
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"rename_categories",
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"reorder_categories",
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"remove_unused_categories",
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"as_ordered",
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"as_unordered",
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]
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s = Series(list("aabbcde")).astype("category")
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results = sorted({r for r in s.cat.__dir__() if not r.startswith("_")})
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tm.assert_almost_equal(results, sorted(set(ok_for_cat)))
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@pytest.mark.parametrize(
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"index",
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[
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tm.makeStringIndex(10),
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tm.makeCategoricalIndex(10),
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Index(["foo", "bar", "baz"] * 2),
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tm.makeDateIndex(10),
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tm.makePeriodIndex(10),
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tm.makeTimedeltaIndex(10),
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tm.makeIntIndex(10),
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tm.makeUIntIndex(10),
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tm.makeIntIndex(10),
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tm.makeFloatIndex(10),
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Index([True, False]),
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Index([f"a{i}" for i in range(101)]),
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pd.MultiIndex.from_tuples(zip("ABCD", "EFGH")),
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pd.MultiIndex.from_tuples(zip([0, 1, 2, 3], "EFGH")),
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],
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)
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def test_index_tab_completion(self, index):
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# dir contains string-like values of the Index.
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s = Series(index=index, dtype=object)
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dir_s = dir(s)
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for i, x in enumerate(s.index.unique(level=0)):
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if i < 100:
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assert not isinstance(x, str) or not x.isidentifier() or x in dir_s
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else:
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assert x not in dir_s
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@pytest.mark.parametrize("ser", [Series(dtype=object), Series([1])])
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def test_not_hashable(self, ser):
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msg = "unhashable type: 'Series'"
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with pytest.raises(TypeError, match=msg):
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hash(ser)
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def test_contains(self, datetime_series):
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tm.assert_contains_all(datetime_series.index, datetime_series)
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def test_axis_alias(self):
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s = Series([1, 2, np.nan])
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tm.assert_series_equal(s.dropna(axis="rows"), s.dropna(axis="index"))
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assert s.dropna().sum("rows") == 3
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assert s._get_axis_number("rows") == 0
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assert s._get_axis_name("rows") == "index"
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def test_class_axis(self):
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# https://github.com/pandas-dev/pandas/issues/18147
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# no exception and no empty docstring
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assert pydoc.getdoc(Series.index)
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def test_ndarray_compat(self):
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# test numpy compat with Series as sub-class of NDFrame
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tsdf = DataFrame(
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np.random.randn(1000, 3),
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columns=["A", "B", "C"],
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index=date_range("1/1/2000", periods=1000),
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)
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def f(x):
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return x[x.idxmax()]
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result = tsdf.apply(f)
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expected = tsdf.max()
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tm.assert_series_equal(result, expected)
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def test_ndarray_compat_like_func(self):
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# using an ndarray like function
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s = Series(np.random.randn(10))
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result = Series(np.ones_like(s))
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expected = Series(1, index=range(10), dtype="float64")
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tm.assert_series_equal(result, expected)
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def test_ndarray_compat_ravel(self):
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# ravel
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s = Series(np.random.randn(10))
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tm.assert_almost_equal(s.ravel(order="F"), s.values.ravel(order="F"))
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def test_empty_method(self):
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s_empty = Series(dtype=object)
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assert s_empty.empty
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@pytest.mark.parametrize("dtype", ["int64", object])
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def test_empty_method_full_series(self, dtype):
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full_series = Series(index=[1], dtype=dtype)
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assert not full_series.empty
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@pytest.mark.parametrize("dtype", [None, "Int64"])
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def test_integer_series_size(self, dtype):
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# GH 25580
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s = Series(range(9), dtype=dtype)
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assert s.size == 9
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def test_attrs(self):
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s = Series([0, 1], name="abc")
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assert s.attrs == {}
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s.attrs["version"] = 1
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result = s + 1
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assert result.attrs == {"version": 1}
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@skip_if_no("jinja2")
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def test_inspect_getmembers(self):
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# GH38782
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ser = Series(dtype=object)
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# TODO(2.0): Change to None once is_monotonic deprecation
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# is enforced
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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inspect.getmembers(ser)
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def test_unknown_attribute(self):
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# GH#9680
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tdi = pd.timedelta_range(start=0, periods=10, freq="1s")
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ser = Series(np.random.normal(size=10), index=tdi)
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assert "foo" not in ser.__dict__.keys()
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msg = "'Series' object has no attribute 'foo'"
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with pytest.raises(AttributeError, match=msg):
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ser.foo
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@pytest.mark.parametrize("op", ["year", "day", "second", "weekday"])
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def test_datetime_series_no_datelike_attrs(self, op, datetime_series):
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# GH#7206
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msg = f"'Series' object has no attribute '{op}'"
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with pytest.raises(AttributeError, match=msg):
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getattr(datetime_series, op)
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def test_series_datetimelike_attribute_access(self):
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# attribute access should still work!
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ser = Series({"year": 2000, "month": 1, "day": 10})
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assert ser.year == 2000
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assert ser.month == 1
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assert ser.day == 10
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def test_series_datetimelike_attribute_access_invalid(self):
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ser = Series({"year": 2000, "month": 1, "day": 10})
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msg = "'Series' object has no attribute 'weekday'"
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with pytest.raises(AttributeError, match=msg):
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ser.weekday
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def test_series_iteritems_deprecated(self):
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ser = Series([1])
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with tm.assert_produces_warning(FutureWarning):
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next(ser.iteritems())
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@pytest.mark.parametrize(
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"kernel, has_numeric_only",
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[
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("skew", True),
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("var", True),
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("all", False),
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("prod", True),
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("any", False),
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("idxmin", False),
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("quantile", False),
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("idxmax", False),
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("min", True),
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("sem", True),
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("mean", True),
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("nunique", False),
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("max", True),
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("sum", True),
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("count", False),
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("median", True),
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("std", True),
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("backfill", False),
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("rank", True),
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("pct_change", False),
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("cummax", False),
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("shift", False),
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("diff", False),
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("cumsum", False),
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("cummin", False),
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("cumprod", False),
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("fillna", False),
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("ffill", False),
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("pad", False),
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("bfill", False),
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("sample", False),
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("tail", False),
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("take", False),
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("head", False),
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("cov", False),
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("corr", False),
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],
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)
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@pytest.mark.parametrize("dtype", [bool, int, float, object])
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def test_numeric_only(self, kernel, has_numeric_only, dtype):
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# GH#47500
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ser = Series([0, 1, 1], dtype=dtype)
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if kernel == "corrwith":
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args = (ser,)
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elif kernel == "corr":
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args = (ser,)
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elif kernel == "cov":
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args = (ser,)
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elif kernel == "nth":
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args = (0,)
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elif kernel == "fillna":
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args = (True,)
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elif kernel == "fillna":
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args = ("ffill",)
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elif kernel == "take":
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args = ([0],)
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elif kernel == "quantile":
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args = (0.5,)
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else:
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args = ()
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method = getattr(ser, kernel)
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if not has_numeric_only:
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msg = (
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"(got an unexpected keyword argument 'numeric_only'"
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"|too many arguments passed in)"
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)
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with pytest.raises(TypeError, match=msg):
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method(*args, numeric_only=True)
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elif dtype is object:
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if kernel == "rank":
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msg = "Calling Series.rank with numeric_only=True and dtype object"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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method(*args, numeric_only=True)
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else:
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warn_msg = (
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f"Calling Series.{kernel} with numeric_only=True and dtype object"
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)
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err_msg = f"Series.{kernel} does not implement numeric_only"
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with tm.assert_produces_warning(FutureWarning, match=warn_msg):
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with pytest.raises(NotImplementedError, match=err_msg):
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method(*args, numeric_only=True)
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else:
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result = method(*args, numeric_only=True)
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expected = method(*args, numeric_only=False)
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if isinstance(expected, Series):
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# transformer
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tm.assert_series_equal(result, expected)
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else:
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# reducer
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assert result == expected
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