Python pandas.core.dtypes.cast.maybe_upcast_putmask() Examples

The following are 7 code examples of pandas.core.dtypes.cast.maybe_upcast_putmask(). 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.dtypes.cast , or try the search function .
Example #1
Source File: ops.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _arith_method_PANEL(op, name, str_rep=None, fill_zeros=None,
                        default_axis=None, **eval_kwargs):
    # copied from Series na_op above, but without unnecessary branch for
    # non-scalar
    def na_op(x, y):
        import pandas.core.computation.expressions as expressions

        try:
            result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
        except TypeError:

            # TODO: might need to find_common_type here?
            result = np.empty(len(x), dtype=x.dtype)
            mask = notna(x)
            result[mask] = op(x[mask], y)
            result, changed = maybe_upcast_putmask(result, ~mask, np.nan)

        result = missing.fill_zeros(result, x, y, name, fill_zeros)
        return result

    # work only for scalars
    def f(self, other):
        if not is_scalar(other):
            raise ValueError('Simple arithmetic with {name} can only be '
                             'done with scalar values'
                             .format(name=self._constructor.__name__))

        return self._combine(other, op)

    f.__name__ = name
    return f
Example #2
Source File: ops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _arith_method_PANEL(op, name, str_rep=None, fill_zeros=None,
                        default_axis=None, **eval_kwargs):
    # copied from Series na_op above, but without unnecessary branch for
    # non-scalar
    def na_op(x, y):
        import pandas.core.computation.expressions as expressions

        try:
            result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
        except TypeError:

            # TODO: might need to find_common_type here?
            result = np.empty(len(x), dtype=x.dtype)
            mask = notna(x)
            result[mask] = op(x[mask], y)
            result, changed = maybe_upcast_putmask(result, ~mask, np.nan)

        result = missing.fill_zeros(result, x, y, name, fill_zeros)
        return result

    # work only for scalars
    def f(self, other):
        if not is_scalar(other):
            raise ValueError('Simple arithmetic with {name} can only be '
                             'done with scalar values'
                             .format(name=self._constructor.__name__))

        return self._combine(other, op)

    f.__name__ = name
    return f
Example #3
Source File: nanops.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True, mask=None):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """

    if is_datetime64tz_dtype(values):
        # com.values_from_object returns M8[ns] dtype instead of tz-aware,
        #  so this case must be handled separately from the rest
        dtype = values.dtype
        values = getattr(values, "_values", values)
    else:
        values = com.values_from_object(values)
        dtype = values.dtype

    if mask is None:
        if isfinite:
            mask = _isfinite(values)
        else:
            mask = isna(values)

    if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
        # changing timedelta64/datetime64 to int64 needs to happen after
        #  finding `mask` above
        values = getattr(values, "asi8", values)
        values = values.view(np.int64)

    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max, fill_value
Example #4
Source File: nanops.py    From vnpy_crypto with MIT License 4 votes vote down vote up 
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """
    values = com._values_from_object(values)
    if isfinite:
        mask = _isfinite(values)
    else:
        mask = isna(values)

    dtype = values.dtype
    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    values = _view_if_needed(values)

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max
Example #5
Source File: nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True, mask=None):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """

    if is_datetime64tz_dtype(values):
        # com.values_from_object returns M8[ns] dtype instead of tz-aware,
        #  so this case must be handled separately from the rest
        dtype = values.dtype
        values = getattr(values, "_values", values)
    else:
        values = com.values_from_object(values)
        dtype = values.dtype

    if mask is None:
        if isfinite:
            mask = _isfinite(values)
        else:
            mask = isna(values)

    if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
        # changing timedelta64/datetime64 to int64 needs to happen after
        #  finding `mask` above
        values = getattr(values, "asi8", values)
        values = values.view(np.int64)

    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max, fill_value
Example #6
Source File: nanops.py    From Splunking-Crime with GNU Affero General Public License v3.0 4 votes vote down vote up 
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """
    values = _values_from_object(values)
    if isfinite:
        mask = _isfinite(values)
    else:
        mask = isna(values)

    dtype = values.dtype
    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    values = _view_if_needed(values)

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max
Example #7
Source File: nanops.py    From elasticintel with GNU General Public License v3.0 4 votes vote down vote up 
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """
    values = _values_from_object(values)
    if isfinite:
        mask = _isfinite(values)
    else:
        mask = isna(values)

    dtype = values.dtype
    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    values = _view_if_needed(values)

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max