Python numpy.flexible() Examples

The following are 12 code examples of numpy.flexible(). 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 numpy , or try the search function .
Example #1
Source File: _dtype.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name
Example #2
Source File: _dtype.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name
Example #3
Source File: _dtype.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name
Example #4
Source File: _dtype.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name
Example #5
Source File: _dtype.py    From Carnets with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name
Example #6
Source File: _dtype.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def __str__(dtype):
    if dtype.fields is not None:
        return _struct_str(dtype, include_align=True)
    elif dtype.subdtype:
        return _subarray_str(dtype)
    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
        return dtype.str
    else:
        return dtype.name
Example #7
Source File: _dtype.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def __str__(dtype):
    if dtype.fields is not None:
        return _struct_str(dtype, include_align=True)
    elif dtype.subdtype:
        return _subarray_str(dtype)
    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
        return dtype.str
    else:
        return dtype.name
Example #8
Source File: _dtype.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def __str__(dtype):
    if dtype.fields is not None:
        return _struct_str(dtype, include_align=True)
    elif dtype.subdtype:
        return _subarray_str(dtype)
    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
        return dtype.str
    else:
        return dtype.name
Example #9
Source File: _dtype.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def __str__(dtype):
    if dtype.fields is not None:
        return _struct_str(dtype, include_align=True)
    elif dtype.subdtype:
        return _subarray_str(dtype)
    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
        return dtype.str
    else:
        return dtype.name
Example #10
Source File: _dtype.py    From Carnets with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def __str__(dtype):
    if dtype.fields is not None:
        return _struct_str(dtype, include_align=True)
    elif dtype.subdtype:
        return _subarray_str(dtype)
    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
        return dtype.str
    else:
        return dtype.name
Example #11
Source File: construction.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def init_dict(data, index, columns, dtype=None):
    """
    Segregate Series based on type and coerce into matrices.
    Needs to handle a lot of exceptional cases.
    """
    if columns is not None:
        from pandas.core.series import Series
        arrays = Series(data, index=columns, dtype=object)
        data_names = arrays.index

        missing = arrays.isnull()
        if index is None:
            # GH10856
            # raise ValueError if only scalars in dict
            index = extract_index(arrays[~missing])
        else:
            index = ensure_index(index)

        # no obvious "empty" int column
        if missing.any() and not is_integer_dtype(dtype):
            if dtype is None or np.issubdtype(dtype, np.flexible):
                # GH#1783
                nan_dtype = object
            else:
                nan_dtype = dtype
            val = construct_1d_arraylike_from_scalar(np.nan, len(index),
                                                     nan_dtype)
            arrays.loc[missing] = [val] * missing.sum()

    else:

        for key in data:
            if (isinstance(data[key], ABCDatetimeIndex) and
                    data[key].tz is not None):
                # GH#24096 need copy to be deep for datetime64tz case
                # TODO: See if we can avoid these copies
                data[key] = data[key].copy(deep=True)

        keys = com.dict_keys_to_ordered_list(data)
        columns = data_names = Index(keys)
        arrays = [data[k] for k in keys]

    return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)


# ---------------------------------------------------------------------
Example #12
Source File: construction.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def init_dict(data, index, columns, dtype=None):
    """
    Segregate Series based on type and coerce into matrices.
    Needs to handle a lot of exceptional cases.
    """
    if columns is not None:
        from pandas.core.series import Series
        arrays = Series(data, index=columns, dtype=object)
        data_names = arrays.index

        missing = arrays.isnull()
        if index is None:
            # GH10856
            # raise ValueError if only scalars in dict
            index = extract_index(arrays[~missing])
        else:
            index = ensure_index(index)

        # no obvious "empty" int column
        if missing.any() and not is_integer_dtype(dtype):
            if dtype is None or np.issubdtype(dtype, np.flexible):
                # GH#1783
                nan_dtype = object
            else:
                nan_dtype = dtype
            val = construct_1d_arraylike_from_scalar(np.nan, len(index),
                                                     nan_dtype)
            arrays.loc[missing] = [val] * missing.sum()

    else:

        for key in data:
            if (isinstance(data[key], ABCDatetimeIndex) and
                    data[key].tz is not None):
                # GH#24096 need copy to be deep for datetime64tz case
                # TODO: See if we can avoid these copies
                data[key] = data[key].copy(deep=True)

        keys = com.dict_keys_to_ordered_list(data)
        columns = data_names = Index(keys)
        arrays = [data[k] for k in keys]

    return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)


# ---------------------------------------------------------------------