manyi/venv/Lib/site-packages/pandas/core/computation/align.py

"""Core eval alignment algorithms
"""

from functools import partial, wraps
import warnings

import numpy as np

from pandas.compat import range, zip
from pandas.errors import PerformanceWarning

import pandas as pd
from pandas import compat
import pandas.core.common as com
from pandas.core.computation.common import _result_type_many


def _align_core_single_unary_op(term):
    if isinstance(term.value, np.ndarray):
        typ = partial(np.asanyarray, dtype=term.value.dtype)
    else:
        typ = type(term.value)
    ret = typ,

    if not hasattr(term.value, 'axes'):
        ret += None,
    else:
        ret += _zip_axes_from_type(typ, term.value.axes),
    return ret


def _zip_axes_from_type(typ, new_axes):
    axes = {ax_name: new_axes[ax_ind]
            for ax_ind, ax_name in compat.iteritems(typ._AXIS_NAMES)}
    return axes


def _any_pandas_objects(terms):
    """Check a sequence of terms for instances of PandasObject."""
    return any(isinstance(term.value, pd.core.generic.PandasObject)
               for term in terms)


def _filter_special_cases(f):
    @wraps(f)
    def wrapper(terms):
        # single unary operand
        if len(terms) == 1:
            return _align_core_single_unary_op(terms[0])

        term_values = (term.value for term in terms)

        # we don't have any pandas objects
        if not _any_pandas_objects(terms):
            return _result_type_many(*term_values), None

        return f(terms)
    return wrapper


@_filter_special_cases
def _align_core(terms):
    term_index = [i for i, term in enumerate(terms)
                  if hasattr(term.value, 'axes')]
    term_dims = [terms[i].value.ndim for i in term_index]
    ndims = pd.Series(dict(zip(term_index, term_dims)))

    # initial axes are the axes of the largest-axis'd term
    biggest = terms[ndims.idxmax()].value
    typ = biggest._constructor
    axes = biggest.axes
    naxes = len(axes)
    gt_than_one_axis = naxes > 1

    for value in (terms[i].value for i in term_index):
        is_series = isinstance(value, pd.Series)
        is_series_and_gt_one_axis = is_series and gt_than_one_axis

        for axis, items in enumerate(value.axes):
            if is_series_and_gt_one_axis:
                ax, itm = naxes - 1, value.index
            else:
                ax, itm = axis, items

            if not axes[ax].is_(itm):
                axes[ax] = axes[ax].join(itm, how='outer')

    for i, ndim in compat.iteritems(ndims):
        for axis, items in zip(range(ndim), axes):
            ti = terms[i].value

            if hasattr(ti, 'reindex'):
                transpose = isinstance(ti, pd.Series) and naxes > 1
                reindexer = axes[naxes - 1] if transpose else items

                term_axis_size = len(ti.axes[axis])
                reindexer_size = len(reindexer)

                ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
                if ordm >= 1 and reindexer_size >= 10000:
                    w = ('Alignment difference on axis {axis} is larger '
                         'than an order of magnitude on term {term!r}, by '
                         'more than {ordm:.4g}; performance may suffer'
                         ).format(axis=axis, term=terms[i].name, ordm=ordm)
                    warnings.warn(w, category=PerformanceWarning, stacklevel=6)

                f = partial(ti.reindex, reindexer, axis=axis, copy=False)

                terms[i].update(f())

        terms[i].update(terms[i].value.values)

    return typ, _zip_axes_from_type(typ, axes)


def _align(terms):
    """Align a set of terms"""
    try:
        # flatten the parse tree (a nested list, really)
        terms = list(com.flatten(terms))
    except TypeError:
        # can't iterate so it must just be a constant or single variable
        if isinstance(terms.value, pd.core.generic.NDFrame):
            typ = type(terms.value)
            return typ, _zip_axes_from_type(typ, terms.value.axes)
        return np.result_type(terms.type), None

    # if all resolved variables are numeric scalars
    if all(term.is_scalar for term in terms):
        return _result_type_many(*(term.value for term in terms)).type, None

    # perform the main alignment
    typ, axes = _align_core(terms)
    return typ, axes


def _reconstruct_object(typ, obj, axes, dtype):
    """Reconstruct an object given its type, raw value, and possibly empty
    (None) axes.

    Parameters
    ----------
    typ : object
        A type
    obj : object
        The value to use in the type constructor
    axes : dict
        The axes to use to construct the resulting pandas object

    Returns
    -------
    ret : typ
        An object of type ``typ`` with the value `obj` and possible axes
        `axes`.
    """
    try:
        typ = typ.type
    except AttributeError:
        pass

    res_t = np.result_type(obj.dtype, dtype)

    if (not isinstance(typ, partial) and
            issubclass(typ, pd.core.generic.PandasObject)):
        return typ(obj, dtype=res_t, **axes)

    # special case for pathological things like ~True/~False
    if hasattr(res_t, 'type') and typ == np.bool_ and res_t != np.bool_:
        ret_value = res_t.type(obj)
    else:
        ret_value = typ(obj).astype(res_t)
        # The condition is to distinguish 0-dim array (returned in case of
        # scalar) and 1 element array
        # e.g. np.array(0) and np.array([0])
        if len(obj.shape) == 1 and len(obj) == 1:
            if not isinstance(ret_value, np.ndarray):
                ret_value = np.array([ret_value]).astype(res_t)

    return ret_value