Python pandas.core.sorting.is_int64_overflow_possible() Examples

The following are 5 code examples of pandas.core.sorting.is_int64_overflow_possible(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.core.sorting , or try the search function .
Example #1
Source File: merge.py    From recruit with Apache License 2.0 5 votes vote down vote up 
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)
Example #2
Source File: merge.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
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)
Example #3
Source File: merge.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
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)
Example #4
Source File: merge.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
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)
Example #5
Source File: merge.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
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)