Python compat.filter方法代码示例

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def _ensure_term(where, scope_level):
    """
    ensure that the where is a Term or a list of Term
    this makes sure that we are capturing the scope of variables
    that are passed
    create the terms here with a frame_level=2 (we are 2 levels down)
    """

    # only consider list/tuple here as an ndarray is automatically a coordinate
    # list
    level = scope_level + 1
    if isinstance(where, (list, tuple)):
        wlist = []
        for w in filter(lambda x: x is not None, where):
            if not maybe_expression(w):
                wlist.append(w)
            else:
                wlist.append(Term(w, scope_level=level))
        where = wlist
    elif maybe_expression(where):
        where = Term(where, scope_level=level)
    return where 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def read_coordinates(self, where=None, start=None, stop=None, **kwargs):
        """select coordinates (row numbers) from a table; return the
        coordinates object
        """

        # validate the version
        self.validate_version(where)

        # infer the data kind
        if not self.infer_axes():
            return False

        # create the selection
        self.selection = Selection(
            self, where=where, start=start, stop=stop, **kwargs)
        coords = self.selection.select_coords()
        if self.selection.filter is not None:
            for field, op, filt in self.selection.filter.format():
                data = self.read_column(
                    field, start=coords.min(), stop=coords.max() + 1)
                coords = coords[
                    op(data.iloc[coords - coords.min()], filt).values]

        return Index(coords) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def _ensure_term(where, scope_level):
    """
    ensure that the where is a Term or a list of Term
    this makes sure that we are capturing the scope of variables
    that are passed
    create the terms here with a frame_level=2 (we are 2 levels down)
    """

    # only consider list/tuple here as an ndarray is automaticaly a coordinate
    # list
    level = scope_level + 1
    if isinstance(where, (list, tuple)):
        wlist = []
        for w in filter(lambda x: x is not None, where):
            if not maybe_expression(w):
                wlist.append(w)
            else:
                wlist.append(Term(w, scope_level=level))
        where = wlist
    elif maybe_expression(where):
        where = Term(where, scope_level=level)
    return where 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def _get_join_keys(llab, rlab, shape, sort):

    # how many levels can be done without overflow
    pred = lambda i: not is_int64_overflow_possible(shape[:i])
    nlev = next(filter(pred, range(len(shape), 0, -1)))

    # get keys for the first `nlev` levels
    stride = np.prod(shape[1:nlev], dtype='i8')
    lkey = stride * llab[0].astype('i8', subok=False, copy=False)
    rkey = stride * rlab[0].astype('i8', subok=False, copy=False)

    for i in range(1, nlev):
        with np.errstate(divide='ignore'):
            stride //= shape[i]
        lkey += llab[i] * stride
        rkey += rlab[i] * stride

    if nlev == len(shape):  # all done!
        return lkey, rkey

    # densify current keys to avoid overflow
    lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)

    llab = [lkey] + llab[nlev:]
    rlab = [rkey] + rlab[nlev:]
    shape = [count] + shape[nlev:]

    return _get_join_keys(llab, rlab, shape, sort) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def _get_join_keys(llab, rlab, shape, sort):

    # how many levels can be done without overflow
    pred = lambda i: not is_int64_overflow_possible(shape[:i])
    nlev = next(filter(pred, range(len(shape), 0, -1)))

    # get keys for the first `nlev` levels
    stride = np.prod(shape[1:nlev], dtype='i8')
    lkey = stride * llab[0].astype('i8', subok=False, copy=False)
    rkey = stride * rlab[0].astype('i8', subok=False, copy=False)

    for i in range(1, nlev):
        stride //= shape[i]
        lkey += llab[i] * stride
        rkey += rlab[i] * stride

    if nlev == len(shape):  # all done!
        return lkey, rkey

    # densify current keys to avoid overflow
    lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)

    llab = [lkey] + llab[nlev:]
    rlab = [rkey] + rlab[nlev:]
    shape = [count] + shape[nlev:]

    return _get_join_keys(llab, rlab, shape, sort) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def process_axes(self, obj, columns=None):
        """ process axes filters """

        # make a copy to avoid side effects
        if columns is not None:
            columns = list(columns)

        # make sure to include levels if we have them
        if columns is not None and self.is_multi_index:
            for n in self.levels:
                if n not in columns:
                    columns.insert(0, n)

        # reorder by any non_index_axes & limit to the select columns
        for axis, labels in self.non_index_axes:
            obj = _reindex_axis(obj, axis, labels, columns)

        # apply the selection filters (but keep in the same order)
        if self.selection.filter is not None:
            for field, op, filt in self.selection.filter.format():

                def process_filter(field, filt):

                    for axis_name in obj._AXIS_NAMES.values():
                        axis_number = obj._get_axis_number(axis_name)
                        axis_values = obj._get_axis(axis_name)

                        # see if the field is the name of an axis
                        if field == axis_name:

                            # if we have a multi-index, then need to include
                            # the levels
                            if self.is_multi_index:
                                filt = filt.union(Index(self.levels))

                            takers = op(axis_values, filt)
                            return obj.loc._getitem_axis(takers,
                                                         axis=axis_number)

                        # this might be the name of a file IN an axis
                        elif field in axis_values:

                            # we need to filter on this dimension
                            values = ensure_index(getattr(obj, field).values)
                            filt = ensure_index(filt)

                            # hack until we support reversed dim flags
                            if isinstance(obj, DataFrame):
                                axis_number = 1 - axis_number
                            takers = op(values, filt)
                            return obj.loc._getitem_axis(takers,
                                                         axis=axis_number)

                    raise ValueError("cannot find the field [{field}] for "
                                     "filtering!".format(field=field))

                obj = process_filter(field, filt)

        return obj 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def __init__(self, table, where=None, start=None, stop=None):
        self.table = table
        self.where = where
        self.start = start
        self.stop = stop
        self.condition = None
        self.filter = None
        self.terms = None
        self.coordinates = None

        if is_list_like(where):

            # see if we have a passed coordinate like
            try:
                inferred = lib.infer_dtype(where, skipna=False)
                if inferred == 'integer' or inferred == 'boolean':
                    where = np.asarray(where)
                    if where.dtype == np.bool_:
                        start, stop = self.start, self.stop
                        if start is None:
                            start = 0
                        if stop is None:
                            stop = self.table.nrows
                        self.coordinates = np.arange(start, stop)[where]
                    elif issubclass(where.dtype.type, np.integer):
                        if ((self.start is not None and
                                (where < self.start).any()) or
                            (self.stop is not None and
                                (where >= self.stop).any())):
                            raise ValueError(
                                "where must have index locations >= start and "
                                "< stop"
                            )
                        self.coordinates = where

            except ValueError:
                pass

        if self.coordinates is None:

            self.terms = self.generate(where)

            # create the numexpr & the filter
            if self.terms is not None:
                self.condition, self.filter = self.terms.evaluate() 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def process_axes(self, obj, columns=None):
        """ process axes filters """

        # make a copy to avoid side effects
        if columns is not None:
            columns = list(columns)

        # make sure to include levels if we have them
        if columns is not None and self.is_multi_index:
            for n in self.levels:
                if n not in columns:
                    columns.insert(0, n)

        # reorder by any non_index_axes & limit to the select columns
        for axis, labels in self.non_index_axes:
            obj = _reindex_axis(obj, axis, labels, columns)

        # apply the selection filters (but keep in the same order)
        if self.selection.filter is not None:
            for field, op, filt in self.selection.filter.format():

                def process_filter(field, filt):

                    for axis_name in obj._AXIS_NAMES.values():
                        axis_number = obj._get_axis_number(axis_name)
                        axis_values = obj._get_axis(axis_name)

                        # see if the field is the name of an axis
                        if field == axis_name:

                            # if we have a multi-index, then need to include
                            # the levels
                            if self.is_multi_index:
                                filt = filt.union(Index(self.levels))

                            takers = op(axis_values, filt)
                            return obj.loc._getitem_axis(takers,
                                                         axis=axis_number)

                        # this might be the name of a file IN an axis
                        elif field in axis_values:

                            # we need to filter on this dimension
                            values = _ensure_index(getattr(obj, field).values)
                            filt = _ensure_index(filt)

                            # hack until we support reversed dim flags
                            if isinstance(obj, DataFrame):
                                axis_number = 1 - axis_number
                            takers = op(values, filt)
                            return obj.loc._getitem_axis(takers,
                                                         axis=axis_number)

                    raise ValueError(
                        "cannot find the field [%s] for filtering!" % field)

                obj = process_filter(field, filt)

        return obj 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import filter [as 别名]
def __init__(self, table, where=None, start=None, stop=None, **kwargs):
        self.table = table
        self.where = where
        self.start = start
        self.stop = stop
        self.condition = None
        self.filter = None
        self.terms = None
        self.coordinates = None

        if is_list_like(where):

            # see if we have a passed coordinate like
            try:
                inferred = lib.infer_dtype(where)
                if inferred == 'integer' or inferred == 'boolean':
                    where = np.asarray(where)
                    if where.dtype == np.bool_:
                        start, stop = self.start, self.stop
                        if start is None:
                            start = 0
                        if stop is None:
                            stop = self.table.nrows
                        self.coordinates = np.arange(start, stop)[where]
                    elif issubclass(where.dtype.type, np.integer):
                        if ((self.start is not None and
                                (where < self.start).any()) or
                            (self.stop is not None and
                                (where >= self.stop).any())):
                            raise ValueError(
                                "where must have index locations >= start and "
                                "< stop"
                            )
                        self.coordinates = where

            except:
                pass

        if self.coordinates is None:

            self.terms = self.generate(where)

            # create the numexpr & the filter
            if self.terms is not None:
                self.condition, self.filter = self.terms.evaluate()