mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-10 09:17:48 +01:00
890 lines
30 KiB
Python
890 lines
30 KiB
Python
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from copy import copy, deepcopy
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import numpy as np
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import pytest
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from pandas.compat.numpy import _np_version_under1p17
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from pandas.core.dtypes.common import is_scalar
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import pandas as pd
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from pandas import DataFrame, Series, date_range
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import pandas._testing as tm
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import pandas.core.common as com
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# ----------------------------------------------------------------------
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# Generic types test cases
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class Generic:
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@property
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def _ndim(self):
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return self._typ._AXIS_LEN
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def _axes(self):
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""" return the axes for my object typ """
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return self._typ._AXIS_ORDERS
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def _construct(self, shape, value=None, dtype=None, **kwargs):
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"""
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construct an object for the given shape
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if value is specified use that if its a scalar
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if value is an array, repeat it as needed
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"""
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if isinstance(shape, int):
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shape = tuple([shape] * self._ndim)
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if value is not None:
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if is_scalar(value):
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if value == "empty":
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arr = None
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dtype = np.float64
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# remove the info axis
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kwargs.pop(self._typ._info_axis_name, None)
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else:
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arr = np.empty(shape, dtype=dtype)
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arr.fill(value)
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else:
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fshape = np.prod(shape)
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arr = value.ravel()
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new_shape = fshape / arr.shape[0]
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if fshape % arr.shape[0] != 0:
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raise Exception("invalid value passed in _construct")
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arr = np.repeat(arr, new_shape).reshape(shape)
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else:
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arr = np.random.randn(*shape)
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return self._typ(arr, dtype=dtype, **kwargs)
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def _compare(self, result, expected):
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self._comparator(result, expected)
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def test_rename(self):
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# single axis
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idx = list("ABCD")
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# relabeling values passed into self.rename
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args = [
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str.lower,
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{x: x.lower() for x in idx},
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Series({x: x.lower() for x in idx}),
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]
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for axis in self._axes():
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kwargs = {axis: idx}
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obj = self._construct(4, **kwargs)
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for arg in args:
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# rename a single axis
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result = obj.rename(**{axis: arg})
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expected = obj.copy()
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setattr(expected, axis, list("abcd"))
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self._compare(result, expected)
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# multiple axes at once
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def test_get_numeric_data(self):
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n = 4
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kwargs = {
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self._typ._get_axis_name(i): list(range(n)) for i in range(self._ndim)
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}
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# get the numeric data
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o = self._construct(n, **kwargs)
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result = o._get_numeric_data()
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self._compare(result, o)
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# non-inclusion
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result = o._get_bool_data()
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expected = self._construct(n, value="empty", **kwargs)
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self._compare(result, expected)
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# get the bool data
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arr = np.array([True, True, False, True])
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o = self._construct(n, value=arr, **kwargs)
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result = o._get_numeric_data()
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self._compare(result, o)
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# _get_numeric_data is includes _get_bool_data, so can't test for
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# non-inclusion
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def test_nonzero(self):
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# GH 4633
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# look at the boolean/nonzero behavior for objects
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obj = self._construct(shape=4)
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msg = f"The truth value of a {self._typ.__name__} is ambiguous"
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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obj = self._construct(shape=4, value=1)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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obj = self._construct(shape=4, value=np.nan)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 0)
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with pytest.raises(ValueError, match=msg):
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bool(obj == 1)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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# empty
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obj = self._construct(shape=0)
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with pytest.raises(ValueError, match=msg):
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bool(obj)
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# invalid behaviors
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obj1 = self._construct(shape=4, value=1)
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obj2 = self._construct(shape=4, value=1)
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with pytest.raises(ValueError, match=msg):
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if obj1:
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pass
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with pytest.raises(ValueError, match=msg):
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obj1 and obj2
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with pytest.raises(ValueError, match=msg):
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obj1 or obj2
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with pytest.raises(ValueError, match=msg):
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not obj1
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def test_downcast(self):
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# test close downcasting
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o = self._construct(shape=4, value=9, dtype=np.int64)
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result = o.copy()
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result._mgr = o._mgr.downcast()
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self._compare(result, o)
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o = self._construct(shape=4, value=9.5)
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result = o.copy()
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result._mgr = o._mgr.downcast()
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self._compare(result, o)
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def test_constructor_compound_dtypes(self):
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# see gh-5191
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# Compound dtypes should raise NotImplementedError.
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def f(dtype):
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return self._construct(shape=3, value=1, dtype=dtype)
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msg = (
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"compound dtypes are not implemented "
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f"in the {self._typ.__name__} constructor"
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)
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with pytest.raises(NotImplementedError, match=msg):
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f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")])
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# these work (though results may be unexpected)
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f("int64")
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f("float64")
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f("M8[ns]")
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def check_metadata(self, x, y=None):
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for m in x._metadata:
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v = getattr(x, m, None)
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if y is None:
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assert v is None
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else:
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assert v == getattr(y, m, None)
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def test_metadata_propagation(self):
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# check that the metadata matches up on the resulting ops
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o = self._construct(shape=3)
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o.name = "foo"
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o2 = self._construct(shape=3)
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o2.name = "bar"
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# ----------
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# preserving
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# ----------
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# simple ops with scalars
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for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
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result = getattr(o, op)(1)
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self.check_metadata(o, result)
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# ops with like
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for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
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result = getattr(o, op)(o)
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self.check_metadata(o, result)
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# simple boolean
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for op in ["__eq__", "__le__", "__ge__"]:
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v1 = getattr(o, op)(o)
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self.check_metadata(o, v1)
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self.check_metadata(o, v1 & v1)
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self.check_metadata(o, v1 | v1)
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# combine_first
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result = o.combine_first(o2)
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self.check_metadata(o, result)
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# ---------------------------
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# non-preserving (by default)
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# ---------------------------
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# add non-like
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result = o + o2
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self.check_metadata(result)
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# simple boolean
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for op in ["__eq__", "__le__", "__ge__"]:
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# this is a name matching op
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v1 = getattr(o, op)(o)
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v2 = getattr(o, op)(o2)
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self.check_metadata(v2)
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self.check_metadata(v1 & v2)
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self.check_metadata(v1 | v2)
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def test_head_tail(self, index):
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# GH5370
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o = self._construct(shape=len(index))
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axis = o._get_axis_name(0)
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setattr(o, axis, index)
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o.head()
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self._compare(o.head(), o.iloc[:5])
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self._compare(o.tail(), o.iloc[-5:])
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# 0-len
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self._compare(o.head(0), o.iloc[0:0])
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self._compare(o.tail(0), o.iloc[0:0])
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# bounded
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self._compare(o.head(len(o) + 1), o)
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self._compare(o.tail(len(o) + 1), o)
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# neg index
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self._compare(o.head(-3), o.head(len(index) - 3))
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self._compare(o.tail(-3), o.tail(len(index) - 3))
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def test_sample(self):
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# Fixes issue: 2419
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o = self._construct(shape=10)
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###
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# Check behavior of random_state argument
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###
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# Check for stability when receives seed or random state -- run 10
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# times.
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for test in range(10):
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seed = np.random.randint(0, 100)
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self._compare(
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o.sample(n=4, random_state=seed), o.sample(n=4, random_state=seed)
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)
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self._compare(
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o.sample(frac=0.7, random_state=seed),
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o.sample(frac=0.7, random_state=seed),
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)
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self._compare(
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o.sample(n=4, random_state=np.random.RandomState(test)),
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o.sample(n=4, random_state=np.random.RandomState(test)),
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)
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self._compare(
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o.sample(frac=0.7, random_state=np.random.RandomState(test)),
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o.sample(frac=0.7, random_state=np.random.RandomState(test)),
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)
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self._compare(
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o.sample(
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frac=2, replace=True, random_state=np.random.RandomState(test)
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),
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o.sample(
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frac=2, replace=True, random_state=np.random.RandomState(test)
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),
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)
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os1, os2 = [], []
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for _ in range(2):
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np.random.seed(test)
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os1.append(o.sample(n=4))
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os2.append(o.sample(frac=0.7))
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self._compare(*os1)
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self._compare(*os2)
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# Check for error when random_state argument invalid.
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with pytest.raises(ValueError):
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o.sample(random_state="astring!")
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###
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# Check behavior of `frac` and `N`
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###
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# Giving both frac and N throws error
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with pytest.raises(ValueError):
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o.sample(n=3, frac=0.3)
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# Check that raises right error for negative lengths
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with pytest.raises(ValueError):
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o.sample(n=-3)
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with pytest.raises(ValueError):
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o.sample(frac=-0.3)
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# Make sure float values of `n` give error
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with pytest.raises(ValueError):
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o.sample(n=3.2)
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# Check lengths are right
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assert len(o.sample(n=4) == 4)
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assert len(o.sample(frac=0.34) == 3)
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assert len(o.sample(frac=0.36) == 4)
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###
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# Check weights
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###
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# Weight length must be right
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with pytest.raises(ValueError):
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o.sample(n=3, weights=[0, 1])
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with pytest.raises(ValueError):
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bad_weights = [0.5] * 11
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o.sample(n=3, weights=bad_weights)
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with pytest.raises(ValueError):
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bad_weight_series = Series([0, 0, 0.2])
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o.sample(n=4, weights=bad_weight_series)
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# Check won't accept negative weights
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with pytest.raises(ValueError):
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bad_weights = [-0.1] * 10
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o.sample(n=3, weights=bad_weights)
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# Check inf and -inf throw errors:
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with pytest.raises(ValueError):
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weights_with_inf = [0.1] * 10
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weights_with_inf[0] = np.inf
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o.sample(n=3, weights=weights_with_inf)
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with pytest.raises(ValueError):
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weights_with_ninf = [0.1] * 10
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weights_with_ninf[0] = -np.inf
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o.sample(n=3, weights=weights_with_ninf)
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||
|
|
# All zeros raises errors
|
||
|
|
zero_weights = [0] * 10
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
o.sample(n=3, weights=zero_weights)
|
||
|
|
|
||
|
|
# All missing weights
|
||
|
|
nan_weights = [np.nan] * 10
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
o.sample(n=3, weights=nan_weights)
|
||
|
|
|
||
|
|
# Check np.nan are replaced by zeros.
|
||
|
|
weights_with_nan = [np.nan] * 10
|
||
|
|
weights_with_nan[5] = 0.5
|
||
|
|
self._compare(o.sample(n=1, axis=0, weights=weights_with_nan), o.iloc[5:6])
|
||
|
|
|
||
|
|
# Check None are also replaced by zeros.
|
||
|
|
weights_with_None = [None] * 10
|
||
|
|
weights_with_None[5] = 0.5
|
||
|
|
self._compare(o.sample(n=1, axis=0, weights=weights_with_None), o.iloc[5:6])
|
||
|
|
|
||
|
|
def test_sample_upsampling_without_replacement(self):
|
||
|
|
# GH27451
|
||
|
|
|
||
|
|
df = pd.DataFrame({"A": list("abc")})
|
||
|
|
msg = (
|
||
|
|
"Replace has to be set to `True` when "
|
||
|
|
"upsampling the population `frac` > 1."
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.sample(frac=2, replace=False)
|
||
|
|
|
||
|
|
def test_sample_is_copy(self):
|
||
|
|
# GH-27357, GH-30784: ensure the result of sample is an actual copy and
|
||
|
|
# doesn't track the parent dataframe / doesn't give SettingWithCopy warnings
|
||
|
|
df = pd.DataFrame(np.random.randn(10, 3), columns=["a", "b", "c"])
|
||
|
|
df2 = df.sample(3)
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
df2["d"] = 1
|
||
|
|
|
||
|
|
def test_size_compat(self):
|
||
|
|
# GH8846
|
||
|
|
# size property should be defined
|
||
|
|
|
||
|
|
o = self._construct(shape=10)
|
||
|
|
assert o.size == np.prod(o.shape)
|
||
|
|
assert o.size == 10 ** len(o.axes)
|
||
|
|
|
||
|
|
def test_split_compat(self):
|
||
|
|
# xref GH8846
|
||
|
|
o = self._construct(shape=10)
|
||
|
|
assert len(np.array_split(o, 5)) == 5
|
||
|
|
assert len(np.array_split(o, 2)) == 2
|
||
|
|
|
||
|
|
# See gh-12301
|
||
|
|
def test_stat_unexpected_keyword(self):
|
||
|
|
obj = self._construct(5)
|
||
|
|
starwars = "Star Wars"
|
||
|
|
errmsg = "unexpected keyword"
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match=errmsg):
|
||
|
|
obj.max(epic=starwars) # stat_function
|
||
|
|
with pytest.raises(TypeError, match=errmsg):
|
||
|
|
obj.var(epic=starwars) # stat_function_ddof
|
||
|
|
with pytest.raises(TypeError, match=errmsg):
|
||
|
|
obj.sum(epic=starwars) # cum_function
|
||
|
|
with pytest.raises(TypeError, match=errmsg):
|
||
|
|
obj.any(epic=starwars) # logical_function
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("func", ["sum", "cumsum", "any", "var"])
|
||
|
|
def test_api_compat(self, func):
|
||
|
|
|
||
|
|
# GH 12021
|
||
|
|
# compat for __name__, __qualname__
|
||
|
|
|
||
|
|
obj = self._construct(5)
|
||
|
|
f = getattr(obj, func)
|
||
|
|
assert f.__name__ == func
|
||
|
|
assert f.__qualname__.endswith(func)
|
||
|
|
|
||
|
|
def test_stat_non_defaults_args(self):
|
||
|
|
obj = self._construct(5)
|
||
|
|
out = np.array([0])
|
||
|
|
errmsg = "the 'out' parameter is not supported"
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=errmsg):
|
||
|
|
obj.max(out=out) # stat_function
|
||
|
|
with pytest.raises(ValueError, match=errmsg):
|
||
|
|
obj.var(out=out) # stat_function_ddof
|
||
|
|
with pytest.raises(ValueError, match=errmsg):
|
||
|
|
obj.sum(out=out) # cum_function
|
||
|
|
with pytest.raises(ValueError, match=errmsg):
|
||
|
|
obj.any(out=out) # logical_function
|
||
|
|
|
||
|
|
def test_truncate_out_of_bounds(self):
|
||
|
|
# GH11382
|
||
|
|
|
||
|
|
# small
|
||
|
|
shape = [int(2e3)] + ([1] * (self._ndim - 1))
|
||
|
|
small = self._construct(shape, dtype="int8", value=1)
|
||
|
|
self._compare(small.truncate(), small)
|
||
|
|
self._compare(small.truncate(before=0, after=3e3), small)
|
||
|
|
self._compare(small.truncate(before=-1, after=2e3), small)
|
||
|
|
|
||
|
|
# big
|
||
|
|
shape = [int(2e6)] + ([1] * (self._ndim - 1))
|
||
|
|
big = self._construct(shape, dtype="int8", value=1)
|
||
|
|
self._compare(big.truncate(), big)
|
||
|
|
self._compare(big.truncate(before=0, after=3e6), big)
|
||
|
|
self._compare(big.truncate(before=-1, after=2e6), big)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"func",
|
||
|
|
[copy, deepcopy, lambda x: x.copy(deep=False), lambda x: x.copy(deep=True)],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("shape", [0, 1, 2])
|
||
|
|
def test_copy_and_deepcopy(self, shape, func):
|
||
|
|
# GH 15444
|
||
|
|
obj = self._construct(shape)
|
||
|
|
obj_copy = func(obj)
|
||
|
|
assert obj_copy is not obj
|
||
|
|
self._compare(obj_copy, obj)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"periods,fill_method,limit,exp",
|
||
|
|
[
|
||
|
|
(1, "ffill", None, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, 0]),
|
||
|
|
(1, "ffill", 1, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, np.nan]),
|
||
|
|
(1, "bfill", None, [np.nan, 0, 0, 1, 1, 1.5, np.nan, np.nan]),
|
||
|
|
(1, "bfill", 1, [np.nan, np.nan, 0, 1, 1, 1.5, np.nan, np.nan]),
|
||
|
|
(-1, "ffill", None, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, 0, np.nan]),
|
||
|
|
(-1, "ffill", 1, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, np.nan, np.nan]),
|
||
|
|
(-1, "bfill", None, [0, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]),
|
||
|
|
(-1, "bfill", 1, [np.nan, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_pct_change(self, periods, fill_method, limit, exp):
|
||
|
|
vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan]
|
||
|
|
obj = self._typ(vals)
|
||
|
|
func = getattr(obj, "pct_change")
|
||
|
|
res = func(periods=periods, fill_method=fill_method, limit=limit)
|
||
|
|
if type(obj) is DataFrame:
|
||
|
|
tm.assert_frame_equal(res, DataFrame(exp))
|
||
|
|
else:
|
||
|
|
tm.assert_series_equal(res, Series(exp))
|
||
|
|
|
||
|
|
|
||
|
|
class TestNDFrame:
|
||
|
|
# tests that don't fit elsewhere
|
||
|
|
|
||
|
|
def test_sample(sel):
|
||
|
|
# Fixes issue: 2419
|
||
|
|
# additional specific object based tests
|
||
|
|
|
||
|
|
# A few dataframe test with degenerate weights.
|
||
|
|
easy_weight_list = [0] * 10
|
||
|
|
easy_weight_list[5] = 1
|
||
|
|
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"col1": range(10, 20),
|
||
|
|
"col2": range(20, 30),
|
||
|
|
"colString": ["a"] * 10,
|
||
|
|
"easyweights": easy_weight_list,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
sample1 = df.sample(n=1, weights="easyweights")
|
||
|
|
tm.assert_frame_equal(sample1, df.iloc[5:6])
|
||
|
|
|
||
|
|
# Ensure proper error if string given as weight for Series or
|
||
|
|
# DataFrame with axis = 1.
|
||
|
|
s = Series(range(10))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
s.sample(n=3, weights="weight_column")
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.sample(n=1, weights="weight_column", axis=1)
|
||
|
|
|
||
|
|
# Check weighting key error
|
||
|
|
with pytest.raises(
|
||
|
|
KeyError, match="'String passed to weights not a valid column'"
|
||
|
|
):
|
||
|
|
df.sample(n=3, weights="not_a_real_column_name")
|
||
|
|
|
||
|
|
# Check that re-normalizes weights that don't sum to one.
|
||
|
|
weights_less_than_1 = [0] * 10
|
||
|
|
weights_less_than_1[0] = 0.5
|
||
|
|
tm.assert_frame_equal(df.sample(n=1, weights=weights_less_than_1), df.iloc[:1])
|
||
|
|
|
||
|
|
###
|
||
|
|
# Test axis argument
|
||
|
|
###
|
||
|
|
|
||
|
|
# Test axis argument
|
||
|
|
df = pd.DataFrame({"col1": range(10), "col2": ["a"] * 10})
|
||
|
|
second_column_weight = [0, 1]
|
||
|
|
tm.assert_frame_equal(
|
||
|
|
df.sample(n=1, axis=1, weights=second_column_weight), df[["col2"]]
|
||
|
|
)
|
||
|
|
|
||
|
|
# Different axis arg types
|
||
|
|
tm.assert_frame_equal(
|
||
|
|
df.sample(n=1, axis="columns", weights=second_column_weight), df[["col2"]]
|
||
|
|
)
|
||
|
|
|
||
|
|
weight = [0] * 10
|
||
|
|
weight[5] = 0.5
|
||
|
|
tm.assert_frame_equal(df.sample(n=1, axis="rows", weights=weight), df.iloc[5:6])
|
||
|
|
tm.assert_frame_equal(
|
||
|
|
df.sample(n=1, axis="index", weights=weight), df.iloc[5:6]
|
||
|
|
)
|
||
|
|
|
||
|
|
# Check out of range axis values
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.sample(n=1, axis=2)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.sample(n=1, axis="not_a_name")
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
s = pd.Series(range(10))
|
||
|
|
s.sample(n=1, axis=1)
|
||
|
|
|
||
|
|
# Test weight length compared to correct axis
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.sample(n=1, axis=1, weights=[0.5] * 10)
|
||
|
|
|
||
|
|
# Check weights with axis = 1
|
||
|
|
easy_weight_list = [0] * 3
|
||
|
|
easy_weight_list[2] = 1
|
||
|
|
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10}
|
||
|
|
)
|
||
|
|
sample1 = df.sample(n=1, axis=1, weights=easy_weight_list)
|
||
|
|
tm.assert_frame_equal(sample1, df[["colString"]])
|
||
|
|
|
||
|
|
# Test default axes
|
||
|
|
tm.assert_frame_equal(
|
||
|
|
df.sample(n=3, random_state=42), df.sample(n=3, axis=0, random_state=42)
|
||
|
|
)
|
||
|
|
|
||
|
|
# Test that function aligns weights with frame
|
||
|
|
df = DataFrame({"col1": [5, 6, 7], "col2": ["a", "b", "c"]}, index=[9, 5, 3])
|
||
|
|
s = Series([1, 0, 0], index=[3, 5, 9])
|
||
|
|
tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=s))
|
||
|
|
|
||
|
|
# Weights have index values to be dropped because not in
|
||
|
|
# sampled DataFrame
|
||
|
|
s2 = Series([0.001, 0, 10000], index=[3, 5, 10])
|
||
|
|
tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=s2))
|
||
|
|
|
||
|
|
# Weights have empty values to be filed with zeros
|
||
|
|
s3 = Series([0.01, 0], index=[3, 5])
|
||
|
|
tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=s3))
|
||
|
|
|
||
|
|
# No overlap in weight and sampled DataFrame indices
|
||
|
|
s4 = Series([1, 0], index=[1, 2])
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.sample(1, weights=s4)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"func_str,arg",
|
||
|
|
[
|
||
|
|
("np.array", [2, 3, 1, 0]),
|
||
|
|
pytest.param(
|
||
|
|
"np.random.MT19937",
|
||
|
|
3,
|
||
|
|
marks=pytest.mark.skipif(_np_version_under1p17, reason="NumPy<1.17"),
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
"np.random.PCG64",
|
||
|
|
11,
|
||
|
|
marks=pytest.mark.skipif(_np_version_under1p17, reason="NumPy<1.17"),
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_sample_random_state(self, func_str, arg):
|
||
|
|
# GH32503
|
||
|
|
df = pd.DataFrame({"col1": range(10, 20), "col2": range(20, 30)})
|
||
|
|
result = df.sample(n=3, random_state=eval(func_str)(arg))
|
||
|
|
expected = df.sample(n=3, random_state=com.random_state(eval(func_str)(arg)))
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_squeeze(self):
|
||
|
|
# noop
|
||
|
|
for s in [tm.makeFloatSeries(), tm.makeStringSeries(), tm.makeObjectSeries()]:
|
||
|
|
tm.assert_series_equal(s.squeeze(), s)
|
||
|
|
for df in [tm.makeTimeDataFrame()]:
|
||
|
|
tm.assert_frame_equal(df.squeeze(), df)
|
||
|
|
|
||
|
|
# squeezing
|
||
|
|
df = tm.makeTimeDataFrame().reindex(columns=["A"])
|
||
|
|
tm.assert_series_equal(df.squeeze(), df["A"])
|
||
|
|
|
||
|
|
# don't fail with 0 length dimensions GH11229 & GH8999
|
||
|
|
empty_series = Series([], name="five", dtype=np.float64)
|
||
|
|
empty_frame = DataFrame([empty_series])
|
||
|
|
tm.assert_series_equal(empty_series, empty_series.squeeze())
|
||
|
|
tm.assert_series_equal(empty_series, empty_frame.squeeze())
|
||
|
|
|
||
|
|
# axis argument
|
||
|
|
df = tm.makeTimeDataFrame(nper=1).iloc[:, :1]
|
||
|
|
assert df.shape == (1, 1)
|
||
|
|
tm.assert_series_equal(df.squeeze(axis=0), df.iloc[0])
|
||
|
|
tm.assert_series_equal(df.squeeze(axis="index"), df.iloc[0])
|
||
|
|
tm.assert_series_equal(df.squeeze(axis=1), df.iloc[:, 0])
|
||
|
|
tm.assert_series_equal(df.squeeze(axis="columns"), df.iloc[:, 0])
|
||
|
|
assert df.squeeze() == df.iloc[0, 0]
|
||
|
|
msg = "No axis named 2 for object type DataFrame"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.squeeze(axis=2)
|
||
|
|
msg = "No axis named x for object type DataFrame"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.squeeze(axis="x")
|
||
|
|
|
||
|
|
df = tm.makeTimeDataFrame(3)
|
||
|
|
tm.assert_frame_equal(df.squeeze(axis=0), df)
|
||
|
|
|
||
|
|
def test_numpy_squeeze(self):
|
||
|
|
s = tm.makeFloatSeries()
|
||
|
|
tm.assert_series_equal(np.squeeze(s), s)
|
||
|
|
|
||
|
|
df = tm.makeTimeDataFrame().reindex(columns=["A"])
|
||
|
|
tm.assert_series_equal(np.squeeze(df), df["A"])
|
||
|
|
|
||
|
|
def test_transpose(self):
|
||
|
|
for s in [tm.makeFloatSeries(), tm.makeStringSeries(), tm.makeObjectSeries()]:
|
||
|
|
# calls implementation in pandas/core/base.py
|
||
|
|
tm.assert_series_equal(s.transpose(), s)
|
||
|
|
for df in [tm.makeTimeDataFrame()]:
|
||
|
|
tm.assert_frame_equal(df.transpose().transpose(), df)
|
||
|
|
|
||
|
|
def test_numpy_transpose(self):
|
||
|
|
msg = "the 'axes' parameter is not supported"
|
||
|
|
|
||
|
|
s = tm.makeFloatSeries()
|
||
|
|
tm.assert_series_equal(np.transpose(s), s)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
np.transpose(s, axes=1)
|
||
|
|
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
tm.assert_frame_equal(np.transpose(np.transpose(df)), df)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
np.transpose(df, axes=1)
|
||
|
|
|
||
|
|
def test_take(self):
|
||
|
|
indices = [1, 5, -2, 6, 3, -1]
|
||
|
|
for s in [tm.makeFloatSeries(), tm.makeStringSeries(), tm.makeObjectSeries()]:
|
||
|
|
out = s.take(indices)
|
||
|
|
expected = Series(
|
||
|
|
data=s.values.take(indices), index=s.index.take(indices), dtype=s.dtype
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(out, expected)
|
||
|
|
for df in [tm.makeTimeDataFrame()]:
|
||
|
|
out = df.take(indices)
|
||
|
|
expected = DataFrame(
|
||
|
|
data=df.values.take(indices, axis=0),
|
||
|
|
index=df.index.take(indices),
|
||
|
|
columns=df.columns,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(out, expected)
|
||
|
|
|
||
|
|
def test_take_invalid_kwargs(self):
|
||
|
|
indices = [-3, 2, 0, 1]
|
||
|
|
s = tm.makeFloatSeries()
|
||
|
|
df = tm.makeTimeDataFrame()
|
||
|
|
|
||
|
|
for obj in (s, df):
|
||
|
|
msg = r"take\(\) got an unexpected keyword argument 'foo'"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
obj.take(indices, foo=2)
|
||
|
|
|
||
|
|
msg = "the 'out' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
obj.take(indices, out=indices)
|
||
|
|
|
||
|
|
msg = "the 'mode' parameter is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
obj.take(indices, mode="clip")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("is_copy", [True, False])
|
||
|
|
def test_depr_take_kwarg_is_copy(self, is_copy):
|
||
|
|
# GH 27357
|
||
|
|
df = DataFrame({"A": [1, 2, 3]})
|
||
|
|
msg = (
|
||
|
|
"is_copy is deprecated and will be removed in a future version. "
|
||
|
|
"'take' always returns a copy, so there is no need to specify this."
|
||
|
|
)
|
||
|
|
with tm.assert_produces_warning(FutureWarning) as w:
|
||
|
|
df.take([0, 1], is_copy=is_copy)
|
||
|
|
|
||
|
|
assert w[0].message.args[0] == msg
|
||
|
|
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
s.take([0, 1], is_copy=is_copy)
|
||
|
|
|
||
|
|
def test_equals(self):
|
||
|
|
# Add object dtype column with nans
|
||
|
|
index = np.random.random(10)
|
||
|
|
df1 = DataFrame(np.random.random(10), index=index, columns=["floats"])
|
||
|
|
df1["text"] = "the sky is so blue. we could use more chocolate.".split()
|
||
|
|
df1["start"] = date_range("2000-1-1", periods=10, freq="T")
|
||
|
|
df1["end"] = date_range("2000-1-1", periods=10, freq="D")
|
||
|
|
df1["diff"] = df1["end"] - df1["start"]
|
||
|
|
df1["bool"] = np.arange(10) % 3 == 0
|
||
|
|
df1.loc[::2] = np.nan
|
||
|
|
df2 = df1.copy()
|
||
|
|
assert df1["text"].equals(df2["text"])
|
||
|
|
assert df1["start"].equals(df2["start"])
|
||
|
|
assert df1["end"].equals(df2["end"])
|
||
|
|
assert df1["diff"].equals(df2["diff"])
|
||
|
|
assert df1["bool"].equals(df2["bool"])
|
||
|
|
assert df1.equals(df2)
|
||
|
|
assert not df1.equals(object)
|
||
|
|
|
||
|
|
# different dtype
|
||
|
|
different = df1.copy()
|
||
|
|
different["floats"] = different["floats"].astype("float32")
|
||
|
|
assert not df1.equals(different)
|
||
|
|
|
||
|
|
# different index
|
||
|
|
different_index = -index
|
||
|
|
different = df2.set_index(different_index)
|
||
|
|
assert not df1.equals(different)
|
||
|
|
|
||
|
|
# different columns
|
||
|
|
different = df2.copy()
|
||
|
|
different.columns = df2.columns[::-1]
|
||
|
|
assert not df1.equals(different)
|
||
|
|
|
||
|
|
# DatetimeIndex
|
||
|
|
index = pd.date_range("2000-1-1", periods=10, freq="T")
|
||
|
|
df1 = df1.set_index(index)
|
||
|
|
df2 = df1.copy()
|
||
|
|
assert df1.equals(df2)
|
||
|
|
|
||
|
|
# MultiIndex
|
||
|
|
df3 = df1.set_index(["text"], append=True)
|
||
|
|
df2 = df1.set_index(["text"], append=True)
|
||
|
|
assert df3.equals(df2)
|
||
|
|
|
||
|
|
df2 = df1.set_index(["floats"], append=True)
|
||
|
|
assert not df3.equals(df2)
|
||
|
|
|
||
|
|
# NaN in index
|
||
|
|
df3 = df1.set_index(["floats"], append=True)
|
||
|
|
df2 = df1.set_index(["floats"], append=True)
|
||
|
|
assert df3.equals(df2)
|
||
|
|
|
||
|
|
def test_pipe(self):
|
||
|
|
df = DataFrame({"A": [1, 2, 3]})
|
||
|
|
f = lambda x, y: x ** y
|
||
|
|
result = df.pipe(f, 2)
|
||
|
|
expected = DataFrame({"A": [1, 4, 9]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.A.pipe(f, 2)
|
||
|
|
tm.assert_series_equal(result, expected.A)
|
||
|
|
|
||
|
|
def test_pipe_tuple(self):
|
||
|
|
df = DataFrame({"A": [1, 2, 3]})
|
||
|
|
f = lambda x, y: y
|
||
|
|
result = df.pipe((f, "y"), 0)
|
||
|
|
tm.assert_frame_equal(result, df)
|
||
|
|
|
||
|
|
result = df.A.pipe((f, "y"), 0)
|
||
|
|
tm.assert_series_equal(result, df.A)
|
||
|
|
|
||
|
|
def test_pipe_tuple_error(self):
|
||
|
|
df = DataFrame({"A": [1, 2, 3]})
|
||
|
|
f = lambda x, y: y
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.pipe((f, "y"), x=1, y=0)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.A.pipe((f, "y"), x=1, y=0)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("box", [pd.Series, pd.DataFrame])
|
||
|
|
def test_axis_classmethods(self, box):
|
||
|
|
obj = box(dtype=object)
|
||
|
|
values = box._AXIS_TO_AXIS_NUMBER.keys()
|
||
|
|
for v in values:
|
||
|
|
assert obj._get_axis_number(v) == box._get_axis_number(v)
|
||
|
|
assert obj._get_axis_name(v) == box._get_axis_name(v)
|
||
|
|
assert obj._get_block_manager_axis(v) == box._get_block_manager_axis(v)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("box", [pd.Series, pd.DataFrame])
|
||
|
|
def test_axis_names_deprecated(self, box):
|
||
|
|
# GH33637
|
||
|
|
obj = box(dtype=object)
|
||
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
||
|
|
obj._AXIS_NAMES
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("box", [pd.Series, pd.DataFrame])
|
||
|
|
def test_axis_numbers_deprecated(self, box):
|
||
|
|
# GH33637
|
||
|
|
obj = box(dtype=object)
|
||
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
||
|
|
obj._AXIS_NUMBERS
|