Python _validators.validate_bool_kwarg方法代碼示例

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
                               limit=None, fill_value=None, coerce=False,
                               downcast=None):
        """ fillna but using the interpolate machinery """

        inplace = validate_bool_kwarg(inplace, 'inplace')

        # if we are coercing, then don't force the conversion
        # if the block can't hold the type
        if coerce:
            if not self._can_hold_na:
                if inplace:
                    return [self]
                else:
                    return [self.copy()]

        values = self.values if inplace else self.values.copy()
        values, fill_value = self._try_coerce_args(values, fill_value)
        values = missing.interpolate_2d(values, method=method, axis=axis,
                                        limit=limit, fill_value=fill_value,
                                        dtype=self.dtype)
        values = self._try_coerce_result(values)

        blocks = [self.make_block_same_class(values, ndim=self.ndim)]
        return self._maybe_downcast(blocks, downcast) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def set_ordered(self, value, inplace=False):
        """
        Sets the ordered attribute to the boolean value

        Parameters
        ----------
        value : boolean to set whether this categorical is ordered (True) or
           not (False)
        inplace : boolean (default: False)
           Whether or not to set the ordered attribute inplace or return a copy
           of this categorical with ordered set to the value
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        new_dtype = CategoricalDtype(self.categories, ordered=value)
        cat = self if inplace else self.copy()
        cat._dtype = new_dtype
        if not inplace:
            return cat 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def _set_name(self, name, inplace=False):
        """
        Set the Series name.

        Parameters
        ----------
        name : str
        inplace : bool
            whether to modify `self` directly or return a copy
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        ser = self if inplace else self.copy()
        ser.name = name
        return ser

    # ----------------------------------------------------------------------
    # Statistics, overridden ndarray methods

    # TODO: integrate bottleneck 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
                               limit=None, fill_value=None, coerce=False,
                               downcast=None, mgr=None):
        """ fillna but using the interpolate machinery """

        inplace = validate_bool_kwarg(inplace, 'inplace')

        # if we are coercing, then don't force the conversion
        # if the block can't hold the type
        if coerce:
            if not self._can_hold_na:
                if inplace:
                    return [self]
                else:
                    return [self.copy()]

        values = self.values if inplace else self.values.copy()
        values, _, fill_value, _ = self._try_coerce_args(values, fill_value)
        values = missing.interpolate_2d(values, method=method, axis=axis,
                                        limit=limit, fill_value=fill_value,
                                        dtype=self.dtype)
        values = self._try_coerce_result(values)

        blocks = [self.make_block_same_class(values, ndim=self.ndim)]
        return self._maybe_downcast(blocks, downcast) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
                               limit=None, fill_value=None, coerce=False,
                               downcast=None, mgr=None):
        """ fillna but using the interpolate machinery """

        inplace = validate_bool_kwarg(inplace, 'inplace')

        # if we are coercing, then don't force the conversion
        # if the block can't hold the type
        if coerce:
            if not self._can_hold_na:
                if inplace:
                    return [self]
                else:
                    return [self.copy()]

        values = self.values if inplace else self.values.copy()
        values, _, fill_value, _ = self._try_coerce_args(values, fill_value)
        values = missing.interpolate_2d(values, method=method, axis=axis,
                                        limit=limit, fill_value=fill_value,
                                        dtype=self.dtype)
        values = self._try_coerce_result(values)

        blocks = [self.make_block(values, klass=self.__class__, fastpath=True)]
        return self._maybe_downcast(blocks, downcast) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def test_validate_bool_kwarg_fail(name, value):
    msg = ("For argument \"%s\" expected type bool, received type %s" %
           (name, type(value).__name__))

    with pytest.raises(ValueError, match=msg):
        validate_bool_kwarg(value, name) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def test_validate_bool_kwarg(name, value):
    assert validate_bool_kwarg(value, name) == value 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def replace(self, to_replace, value, inplace=False, filter=None,
                regex=False, convert=True):
        """replace the to_replace value with value, possible to create new
        blocks here this is just a call to putmask. regex is not used here.
        It is used in ObjectBlocks.  It is here for API compatibility.
        """

        inplace = validate_bool_kwarg(inplace, 'inplace')
        original_to_replace = to_replace

        # try to replace, if we raise an error, convert to ObjectBlock and
        # retry
        try:
            values, to_replace = self._try_coerce_args(self.values,
                                                       to_replace)
            mask = missing.mask_missing(values, to_replace)
            if filter is not None:
                filtered_out = ~self.mgr_locs.isin(filter)
                mask[filtered_out.nonzero()[0]] = False

            blocks = self.putmask(mask, value, inplace=inplace)
            if convert:
                blocks = [b.convert(by_item=True, numeric=False,
                                    copy=not inplace) for b in blocks]
            return blocks
        except (TypeError, ValueError):
            # GH 22083, TypeError or ValueError occurred within error handling
            # causes infinite loop. Cast and retry only if not objectblock.
            if is_object_dtype(self):
                raise

            # try again with a compatible block
            block = self.astype(object)
            return block.replace(to_replace=original_to_replace,
                                 value=value,
                                 inplace=inplace,
                                 filter=filter,
                                 regex=regex,
                                 convert=convert) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def _interpolate(self, method=None, index=None, values=None,
                     fill_value=None, axis=0, limit=None,
                     limit_direction='forward', limit_area=None,
                     inplace=False, downcast=None, **kwargs):
        """ interpolate using scipy wrappers """

        inplace = validate_bool_kwarg(inplace, 'inplace')
        data = self.values if inplace else self.values.copy()

        # only deal with floats
        if not self.is_float:
            if not self.is_integer:
                return self
            data = data.astype(np.float64)

        if fill_value is None:
            fill_value = self.fill_value

        if method in ('krogh', 'piecewise_polynomial', 'pchip'):
            if not index.is_monotonic:
                raise ValueError("{0} interpolation requires that the "
                                 "index be monotonic.".format(method))
        # process 1-d slices in the axis direction

        def func(x):

            # process a 1-d slice, returning it
            # should the axis argument be handled below in apply_along_axis?
            # i.e. not an arg to missing.interpolate_1d
            return missing.interpolate_1d(index, x, method=method, limit=limit,
                                          limit_direction=limit_direction,
                                          limit_area=limit_area,
                                          fill_value=fill_value,
                                          bounds_error=False, **kwargs)

        # interp each column independently
        interp_values = np.apply_along_axis(func, axis, data)

        blocks = [self.make_block_same_class(interp_values)]
        return self._maybe_downcast(blocks, downcast) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def putmask(self, mask, new, align=True, inplace=False, axis=0,
                transpose=False):
        """
        putmask the data to the block; we must be a single block and not
        generate other blocks

        return the resulting block

        Parameters
        ----------
        mask  : the condition to respect
        new : a ndarray/object
        align : boolean, perform alignment on other/cond, default is True
        inplace : perform inplace modification, default is False

        Returns
        -------
        a new block, the result of the putmask
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')

        # use block's copy logic.
        # .values may be an Index which does shallow copy by default
        new_values = self.values if inplace else self.copy().values
        new_values, new = self._try_coerce_args(new_values, new)

        if isinstance(new, np.ndarray) and len(new) == len(mask):
            new = new[mask]

        mask = _safe_reshape(mask, new_values.shape)

        new_values[mask] = new
        new_values = self._try_coerce_result(new_values)
        return [self.make_block(values=new_values)] 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def as_unordered(self, inplace=False):
        """
        Set the Categorical to be unordered.

        Parameters
        ----------
        inplace : boolean (default: False)
           Whether or not to set the ordered attribute inplace or return a copy
           of this categorical with ordered set to False
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        return self.set_ordered(False, inplace=inplace) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def remove_unused_categories(self, inplace=False):
        """
        Removes categories which are not used.

        Parameters
        ----------
        inplace : boolean (default: False)
           Whether or not to drop unused categories inplace or return a copy of
           this categorical with unused categories dropped.

        Returns
        -------
        cat : Categorical with unused categories dropped or None if inplace.

        See Also
        --------
        rename_categories
        reorder_categories
        add_categories
        remove_categories
        set_categories
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        cat = self if inplace else self.copy()
        idx, inv = np.unique(cat._codes, return_inverse=True)

        if idx.size != 0 and idx[0] == -1:  # na sentinel
            idx, inv = idx[1:], inv - 1

        new_categories = cat.dtype.categories.take(idx)
        new_dtype = CategoricalDtype._from_fastpath(new_categories,
                                                    ordered=self.ordered)
        cat._dtype = new_dtype
        cat._codes = coerce_indexer_dtype(inv, new_dtype.categories)

        if not inplace:
            return cat 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def as_ordered(self, inplace=False):
        """
        Sets the Categorical to be ordered

        Parameters
        ----------
        inplace : boolean (default: False)
           Whether or not to set the ordered attribute inplace or return a copy
           of this categorical with ordered set to True
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        return self.set_ordered(True, inplace=inplace) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def as_unordered(self, inplace=False):
        """
        Sets the Categorical to be unordered

        Parameters
        ----------
        inplace : boolean (default: False)
           Whether or not to set the ordered attribute inplace or return a copy
           of this categorical with ordered set to False
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        return self.set_ordered(False, inplace=inplace) 

# 需要導入模塊: from pandas.util import _validators [as 別名]
# 或者: from pandas.util._validators import validate_bool_kwarg [as 別名]
def reorder_categories(self, new_categories, ordered=None, inplace=False):
        """ Reorders categories as specified in new_categories.

        `new_categories` need to include all old categories and no new category
        items.

        Raises
        ------
        ValueError
            If the new categories do not contain all old category items or any
            new ones

        Parameters
        ----------
        new_categories : Index-like
           The categories in new order.
        ordered : boolean, optional
           Whether or not the categorical is treated as a ordered categorical.
           If not given, do not change the ordered information.
        inplace : boolean (default: False)
           Whether or not to reorder the categories inplace or return a copy of
           this categorical with reordered categories.

        Returns
        -------
        cat : Categorical with reordered categories or None if inplace.

        See also
        --------
        rename_categories
        add_categories
        remove_categories
        remove_unused_categories
        set_categories
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        if set(self.dtype.categories) != set(new_categories):
            raise ValueError("items in new_categories are not the same as in "
                             "old categories")
        return self.set_categories(new_categories, ordered=ordered,
                                   inplace=inplace)