common.py
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""" common utilities """
import itertools
import numpy as np
from pandas import DataFrame, Float64Index, MultiIndex, Series, UInt64Index, date_range
import pandas._testing as tm
def _mklbl(prefix, n):
return [f"{prefix}{i}" for i in range(n)]
def _axify(obj, key, axis):
# create a tuple accessor
axes = [slice(None)] * obj.ndim
axes[axis] = key
return tuple(axes)
class Base:
""" indexing comprehensive base class """
_kinds = {"series", "frame"}
_typs = {
"ints",
"uints",
"labels",
"mixed",
"ts",
"floats",
"empty",
"ts_rev",
"multi",
}
def setup_method(self, method):
self.series_ints = Series(np.random.rand(4), index=np.arange(0, 8, 2))
self.frame_ints = DataFrame(
np.random.randn(4, 4), index=np.arange(0, 8, 2), columns=np.arange(0, 12, 3)
)
self.series_uints = Series(
np.random.rand(4), index=UInt64Index(np.arange(0, 8, 2))
)
self.frame_uints = DataFrame(
np.random.randn(4, 4),
index=UInt64Index(range(0, 8, 2)),
columns=UInt64Index(range(0, 12, 3)),
)
self.series_floats = Series(
np.random.rand(4), index=Float64Index(range(0, 8, 2))
)
self.frame_floats = DataFrame(
np.random.randn(4, 4),
index=Float64Index(range(0, 8, 2)),
columns=Float64Index(range(0, 12, 3)),
)
m_idces = [
MultiIndex.from_product([[1, 2], [3, 4]]),
MultiIndex.from_product([[5, 6], [7, 8]]),
MultiIndex.from_product([[9, 10], [11, 12]]),
]
self.series_multi = Series(np.random.rand(4), index=m_idces[0])
self.frame_multi = DataFrame(
np.random.randn(4, 4), index=m_idces[0], columns=m_idces[1]
)
self.series_labels = Series(np.random.randn(4), index=list("abcd"))
self.frame_labels = DataFrame(
np.random.randn(4, 4), index=list("abcd"), columns=list("ABCD")
)
self.series_mixed = Series(np.random.randn(4), index=[2, 4, "null", 8])
self.frame_mixed = DataFrame(np.random.randn(4, 4), index=[2, 4, "null", 8])
self.series_ts = Series(
np.random.randn(4), index=date_range("20130101", periods=4)
)
self.frame_ts = DataFrame(
np.random.randn(4, 4), index=date_range("20130101", periods=4)
)
dates_rev = date_range("20130101", periods=4).sort_values(ascending=False)
self.series_ts_rev = Series(np.random.randn(4), index=dates_rev)
self.frame_ts_rev = DataFrame(np.random.randn(4, 4), index=dates_rev)
self.frame_empty = DataFrame()
self.series_empty = Series(dtype=object)
# form agglomerates
for kind in self._kinds:
d = dict()
for typ in self._typs:
d[typ] = getattr(self, f"{kind}_{typ}")
setattr(self, kind, d)
def generate_indices(self, f, values=False):
"""
generate the indices
if values is True , use the axis values
is False, use the range
"""
axes = f.axes
if values:
axes = (list(range(len(ax))) for ax in axes)
return itertools.product(*axes)
def get_value(self, name, f, i, values=False):
""" return the value for the location i """
# check against values
if values:
return f.values[i]
elif name == "iat":
return f.iloc[i]
else:
assert name == "at"
return f.loc[i]
def check_values(self, f, func, values=False):
if f is None:
return
axes = f.axes
indicies = itertools.product(*axes)
for i in indicies:
result = getattr(f, func)[i]
# check against values
if values:
expected = f.values[i]
else:
expected = f
for a in reversed(i):
expected = expected.__getitem__(a)
tm.assert_almost_equal(result, expected)
def check_result(
self, method, key, typs=None, axes=None, fails=None,
):
def _eq(axis, obj, key):
""" compare equal for these 2 keys """
axified = _axify(obj, key, axis)
try:
getattr(obj, method).__getitem__(axified)
except (IndexError, TypeError, KeyError) as detail:
# if we are in fails, the ok, otherwise raise it
if fails is not None:
if isinstance(detail, fails):
return
raise
if typs is None:
typs = self._typs
if axes is None:
axes = [0, 1]
else:
assert axes in [0, 1]
axes = [axes]
# check
for kind in self._kinds:
d = getattr(self, kind)
for ax in axes:
for typ in typs:
assert typ in self._typs
obj = d[typ]
if ax < obj.ndim:
_eq(axis=ax, obj=obj, key=key)