225 lines
6.6 KiB
Python
225 lines
6.6 KiB
Python
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""" common utilities """
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import itertools
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from warnings import catch_warnings
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import numpy as np
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from pandas.core.dtypes.common import is_scalar
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from pandas import DataFrame, Float64Index, MultiIndex, Series, UInt64Index, date_range
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import pandas._testing as tm
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def _mklbl(prefix, n):
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return ["{prefix}{i}".format(prefix=prefix, i=i) for i in range(n)]
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def _axify(obj, key, axis):
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# create a tuple accessor
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axes = [slice(None)] * obj.ndim
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axes[axis] = key
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return tuple(axes)
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class Base:
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""" indexing comprehensive base class """
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_kinds = {"series", "frame"}
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_typs = {
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"ints",
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"uints",
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"labels",
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"mixed",
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"ts",
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"floats",
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"empty",
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"ts_rev",
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"multi",
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}
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def setup_method(self, method):
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self.series_ints = Series(np.random.rand(4), index=np.arange(0, 8, 2))
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self.frame_ints = DataFrame(
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np.random.randn(4, 4), index=np.arange(0, 8, 2), columns=np.arange(0, 12, 3)
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)
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self.series_uints = Series(
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np.random.rand(4), index=UInt64Index(np.arange(0, 8, 2))
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)
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self.frame_uints = DataFrame(
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np.random.randn(4, 4),
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index=UInt64Index(range(0, 8, 2)),
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columns=UInt64Index(range(0, 12, 3)),
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)
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self.series_floats = Series(
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np.random.rand(4), index=Float64Index(range(0, 8, 2))
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)
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self.frame_floats = DataFrame(
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np.random.randn(4, 4),
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index=Float64Index(range(0, 8, 2)),
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columns=Float64Index(range(0, 12, 3)),
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)
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m_idces = [
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MultiIndex.from_product([[1, 2], [3, 4]]),
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MultiIndex.from_product([[5, 6], [7, 8]]),
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MultiIndex.from_product([[9, 10], [11, 12]]),
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]
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self.series_multi = Series(np.random.rand(4), index=m_idces[0])
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self.frame_multi = DataFrame(
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np.random.randn(4, 4), index=m_idces[0], columns=m_idces[1]
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)
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self.series_labels = Series(np.random.randn(4), index=list("abcd"))
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self.frame_labels = DataFrame(
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np.random.randn(4, 4), index=list("abcd"), columns=list("ABCD")
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)
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self.series_mixed = Series(np.random.randn(4), index=[2, 4, "null", 8])
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self.frame_mixed = DataFrame(np.random.randn(4, 4), index=[2, 4, "null", 8])
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self.series_ts = Series(
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np.random.randn(4), index=date_range("20130101", periods=4)
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)
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self.frame_ts = DataFrame(
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np.random.randn(4, 4), index=date_range("20130101", periods=4)
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)
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dates_rev = date_range("20130101", periods=4).sort_values(ascending=False)
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self.series_ts_rev = Series(np.random.randn(4), index=dates_rev)
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self.frame_ts_rev = DataFrame(np.random.randn(4, 4), index=dates_rev)
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self.frame_empty = DataFrame()
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self.series_empty = Series(dtype=object)
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# form agglomerates
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for kind in self._kinds:
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d = dict()
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for typ in self._typs:
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d[typ] = getattr(self, "{kind}_{typ}".format(kind=kind, typ=typ))
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setattr(self, kind, d)
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def generate_indices(self, f, values=False):
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""" generate the indices
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if values is True , use the axis values
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is False, use the range
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"""
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axes = f.axes
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if values:
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axes = (list(range(len(ax))) for ax in axes)
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return itertools.product(*axes)
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def get_result(self, obj, method, key, axis):
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""" return the result for this obj with this key and this axis """
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if isinstance(key, dict):
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key = key[axis]
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# use an artificial conversion to map the key as integers to the labels
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# so ix can work for comparisons
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if method == "indexer":
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method = "ix"
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key = obj._get_axis(axis)[key]
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# in case we actually want 0 index slicing
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with catch_warnings(record=True):
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try:
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xp = getattr(obj, method).__getitem__(_axify(obj, key, axis))
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except AttributeError:
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xp = getattr(obj, method).__getitem__(key)
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return xp
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def get_value(self, name, f, i, values=False):
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""" return the value for the location i """
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# check against values
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if values:
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return f.values[i]
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elif name == "iat":
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return f.iloc[i]
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else:
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assert name == "at"
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return f.loc[i]
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def check_values(self, f, func, values=False):
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if f is None:
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return
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axes = f.axes
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indicies = itertools.product(*axes)
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for i in indicies:
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result = getattr(f, func)[i]
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# check against values
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if values:
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expected = f.values[i]
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else:
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expected = f
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for a in reversed(i):
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expected = expected.__getitem__(a)
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tm.assert_almost_equal(result, expected)
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def check_result(
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self, method1, key1, method2, key2, typs=None, axes=None, fails=None,
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):
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def _eq(axis, obj, key1, key2):
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""" compare equal for these 2 keys """
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if axis > obj.ndim - 1:
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return
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try:
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rs = getattr(obj, method1).__getitem__(_axify(obj, key1, axis))
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try:
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xp = self.get_result(obj=obj, method=method2, key=key2, axis=axis)
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except (KeyError, IndexError):
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# TODO: why is this allowed?
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return
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if is_scalar(rs) and is_scalar(xp):
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assert rs == xp
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else:
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tm.assert_equal(rs, xp)
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except (IndexError, TypeError, KeyError) as detail:
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# if we are in fails, the ok, otherwise raise it
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if fails is not None:
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if isinstance(detail, fails):
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result = f"ok ({type(detail).__name__})"
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return
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result = type(detail).__name__
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raise AssertionError(result, detail)
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if typs is None:
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typs = self._typs
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if axes is None:
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axes = [0, 1]
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elif not isinstance(axes, (tuple, list)):
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assert isinstance(axes, int)
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axes = [axes]
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# check
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for kind in self._kinds:
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d = getattr(self, kind)
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for ax in axes:
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for typ in typs:
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if typ not in self._typs:
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continue
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obj = d[typ]
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_eq(axis=ax, obj=obj, key1=key1, key2=key2)
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