951 lines
31 KiB
Python
951 lines
31 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.core.dtypes.common import is_scalar
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import pandas as pd
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from pandas import DataFrame, MultiIndex, Series, date_range
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import pandas.util.testing as tm
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from pandas.util.testing import assert_frame_equal, assert_series_equal
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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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""" 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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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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# 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 = {self._typ._AXIS_NAMES[i]: list(range(n)) for i in range(self._ndim)}
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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_get_default(self):
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# GH 7725
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d0 = "a", "b", "c", "d"
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d1 = np.arange(4, dtype="int64")
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others = "e", 10
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for data, index in ((d0, d1), (d1, d0)):
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s = Series(data, index=index)
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for i, d in zip(index, data):
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assert s.get(i) == d
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assert s.get(i, d) == d
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assert s.get(i, "z") == d
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for other in others:
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assert s.get(other, "z") == "z"
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assert s.get(other, other) == other
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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 = "The truth value of a {} is ambiguous".format(self._typ.__name__)
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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._data = o._data.downcast(dtypes="infer")
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self._compare(result, o)
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o = self._construct(shape=4, value=9.0)
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expected = o.astype(np.int64)
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result = o.copy()
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result._data = o._data.downcast(dtypes="infer")
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self._compare(result, expected)
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o = self._construct(shape=4, value=9.5)
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result = o.copy()
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result._data = o._data.downcast(dtypes="infer")
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self._compare(result, o)
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# are close
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o = self._construct(shape=4, value=9.000000000005)
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result = o.copy()
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result._data = o._data.downcast(dtypes="infer")
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expected = o.astype(np.int64)
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self._compare(result, expected)
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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 = "compound dtypes are not implemented in the {} constructor".format(
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self._typ.__name__
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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):
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# GH5370
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o = self._construct(shape=10)
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# check all index types
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for index in [
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tm.makeFloatIndex,
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tm.makeIntIndex,
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tm.makeStringIndex,
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tm.makeUnicodeIndex,
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tm.makeDateIndex,
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tm.makePeriodIndex,
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]:
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axis = o._get_axis_name(0)
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setattr(o, axis, index(len(getattr(o, axis))))
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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(7))
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self._compare(o.tail(-3), o.tail(7))
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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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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)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
bad_weight_series = Series([0, 0, 0.2])
|
||
|
|
o.sample(n=4, weights=bad_weight_series)
|
||
|
|
|
||
|
|
# Check won't accept negative weights
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
bad_weights = [-0.1] * 10
|
||
|
|
o.sample(n=3, weights=bad_weights)
|
||
|
|
|
||
|
|
# Check inf and -inf throw errors:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
weights_with_inf = [0.1] * 10
|
||
|
|
weights_with_inf[0] = np.inf
|
||
|
|
o.sample(n=3, weights=weights_with_inf)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
weights_with_ninf = [0.1] * 10
|
||
|
|
weights_with_ninf[0] = -np.inf
|
||
|
|
o.sample(n=3, weights=weights_with_ninf)
|
||
|
|
|
||
|
|
# 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_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
|
||
|
|
|
||
|
|
def test_unexpected_keyword(self): # GH8597
|
||
|
|
df = DataFrame(np.random.randn(5, 2), columns=["jim", "joe"])
|
||
|
|
ca = pd.Categorical([0, 0, 2, 2, 3, np.nan])
|
||
|
|
ts = df["joe"].copy()
|
||
|
|
ts[2] = np.nan
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match="unexpected keyword"):
|
||
|
|
df.drop("joe", axis=1, in_place=True)
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match="unexpected keyword"):
|
||
|
|
df.reindex([1, 0], inplace=True)
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match="unexpected keyword"):
|
||
|
|
ca.fillna(0, inplace=True)
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match="unexpected keyword"):
|
||
|
|
ts.fillna(0, in_place=True)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
def test_api_compat(self):
|
||
|
|
|
||
|
|
# GH 12021
|
||
|
|
# compat for __name__, __qualname__
|
||
|
|
|
||
|
|
obj = self._construct(5)
|
||
|
|
for func in ["sum", "cumsum", "any", "var"]:
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_validate_bool_args(self):
|
||
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
||
|
|
invalid_values = [1, "True", [1, 2, 3], 5.0]
|
||
|
|
|
||
|
|
for value in invalid_values:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).rename_axis(
|
||
|
|
mapper={"a": "x", "b": "y"}, axis=1, inplace=value
|
||
|
|
)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).drop("a", axis=1, inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).sort_index(inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df)._consolidate(inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).fillna(value=0, inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).replace(to_replace=1, value=7, inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).interpolate(inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df)._where(cond=df.a > 2, inplace=value)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
super(DataFrame, df).mask(cond=df.a > 2, inplace=value)
|
||
|
|
|
||
|
|
def test_copy_and_deepcopy(self):
|
||
|
|
# GH 15444
|
||
|
|
for shape in [0, 1, 2]:
|
||
|
|
obj = self._construct(shape)
|
||
|
|
for func in [
|
||
|
|
copy,
|
||
|
|
deepcopy,
|
||
|
|
lambda x: x.copy(deep=False),
|
||
|
|
lambda x: x.copy(deep=True),
|
||
|
|
]:
|
||
|
|
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")
|
||
|
|
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]
|
||
|
|
assert_frame_equal(
|
||
|
|
df.sample(n=1, axis=1, weights=second_column_weight), df[["col2"]]
|
||
|
|
)
|
||
|
|
|
||
|
|
# Different axis arg types
|
||
|
|
assert_frame_equal(
|
||
|
|
df.sample(n=1, axis="columns", weights=second_column_weight), df[["col2"]]
|
||
|
|
)
|
||
|
|
|
||
|
|
weight = [0] * 10
|
||
|
|
weight[5] = 0.5
|
||
|
|
assert_frame_equal(df.sample(n=1, axis="rows", weights=weight), df.iloc[5:6])
|
||
|
|
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)
|
||
|
|
assert_frame_equal(sample1, df[["colString"]])
|
||
|
|
|
||
|
|
# Test default axes
|
||
|
|
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])
|
||
|
|
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])
|
||
|
|
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])
|
||
|
|
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)
|
||
|
|
|
||
|
|
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")
|
||
|
|
empty_frame = DataFrame([empty_series])
|
||
|
|
|
||
|
|
[
|
||
|
|
tm.assert_series_equal(empty_series, higher_dim.squeeze())
|
||
|
|
for higher_dim in [empty_series, empty_frame]
|
||
|
|
]
|
||
|
|
|
||
|
|
# 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 <class 'pandas.core.frame.DataFrame'>"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.squeeze(axis=2)
|
||
|
|
msg = "No axis named x for object type <class 'pandas.core.frame.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")
|
||
|
|
|
||
|
|
def test_equals(self):
|
||
|
|
s1 = pd.Series([1, 2, 3], index=[0, 2, 1])
|
||
|
|
s2 = s1.copy()
|
||
|
|
assert s1.equals(s2)
|
||
|
|
|
||
|
|
s1[1] = 99
|
||
|
|
assert not s1.equals(s2)
|
||
|
|
|
||
|
|
# NaNs compare as equal
|
||
|
|
s1 = pd.Series([1, np.nan, 3, np.nan], index=[0, 2, 1, 3])
|
||
|
|
s2 = s1.copy()
|
||
|
|
assert s1.equals(s2)
|
||
|
|
|
||
|
|
s2[0] = 9.9
|
||
|
|
assert not s1.equals(s2)
|
||
|
|
|
||
|
|
idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
|
||
|
|
s1 = Series([1, 2, np.nan], index=idx)
|
||
|
|
s2 = s1.copy()
|
||
|
|
assert s1.equals(s2)
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
# GH 8437
|
||
|
|
a = pd.Series([False, np.nan])
|
||
|
|
b = pd.Series([False, np.nan])
|
||
|
|
c = pd.Series(index=range(2))
|
||
|
|
d = pd.Series(index=range(2))
|
||
|
|
e = pd.Series(index=range(2))
|
||
|
|
f = pd.Series(index=range(2))
|
||
|
|
c[:-1] = d[:-1] = e[0] = f[0] = False
|
||
|
|
assert a.equals(a)
|
||
|
|
assert a.equals(b)
|
||
|
|
assert a.equals(c)
|
||
|
|
assert a.equals(d)
|
||
|
|
assert a.equals(e)
|
||
|
|
assert e.equals(f)
|
||
|
|
|
||
|
|
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]})
|
||
|
|
assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.A.pipe(f, 2)
|
||
|
|
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)
|
||
|
|
assert_frame_equal(result, df)
|
||
|
|
|
||
|
|
result = df.A.pipe((f, "y"), 0)
|
||
|
|
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()
|
||
|
|
values = (
|
||
|
|
list(box._AXIS_NAMES.keys())
|
||
|
|
+ list(box._AXIS_NUMBERS.keys())
|
||
|
|
+ list(box._AXIS_ALIASES.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)
|
||
|
|
|
||
|
|
def test_deprecated_to_dense(self):
|
||
|
|
# GH 26557: DEPR
|
||
|
|
# Deprecated 0.25.0
|
||
|
|
|
||
|
|
df = pd.DataFrame({"A": [1, 2, 3]})
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
result = df.to_dense()
|
||
|
|
tm.assert_frame_equal(result, df)
|
||
|
|
|
||
|
|
ser = pd.Series([1, 2, 3])
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
result = ser.to_dense()
|
||
|
|
tm.assert_series_equal(result, ser)
|
||
|
|
|
||
|
|
def test_deprecated_get_dtype_counts(self):
|
||
|
|
# GH 18262
|
||
|
|
df = DataFrame([1])
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
df.get_dtype_counts()
|