How to use numpy_dtype method in pandera

Best Python code snippet using pandera_python

types.py

Source:types.py

1# Copyright 2018 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the 'License');4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an 'AS IS' BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ======================================15"""Utilities for XLA-specific Python types."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import collections20import numpy as np21from tensorflow.compiler.xla import xla_data_pb222# Records corresponsence between a XLA primitive type and Python/Numpy types.23#24# primitive_type: value of type xla_data_pb2.PrimitiveType25# numpy_dtype: corresponsing Numpy "dtype" (like np.float32)26# literal_field_name: name of the field in the LiteralProto message elements27# of this type go into.28# literal_field_type: type of the field named 'literal_field_name'.29#30# TODO(eliben): figure out how to avoid knowing the extra Python type and the31# astype cast when writing into Literals.32TypeConversionRecord = collections.namedtuple('TypeConversionRecord', [33    'primitive_type', 'numpy_dtype', 'literal_field_name', 'literal_field_type'34])35# Maps from XLA primitive types to TypeConversionRecord.36MAP_XLA_TYPE_TO_RECORD = {37    xla_data_pb2.F16:38        TypeConversionRecord(39            primitive_type=xla_data_pb2.F16,40            numpy_dtype=np.float16,41            literal_field_name='f16s',42            literal_field_type=float),43    xla_data_pb2.F32:44        TypeConversionRecord(45            primitive_type=xla_data_pb2.F32,46            numpy_dtype=np.float32,47            literal_field_name='f32s',48            literal_field_type=float),49    xla_data_pb2.F64:50        TypeConversionRecord(51            primitive_type=xla_data_pb2.F64,52            numpy_dtype=np.float64,53            literal_field_name='f64s',54            literal_field_type=float),55    xla_data_pb2.S8:56        TypeConversionRecord(57            primitive_type=xla_data_pb2.S8,58            numpy_dtype=np.int8,59            literal_field_name='s8s',60            literal_field_type=int),61    xla_data_pb2.S16:62        TypeConversionRecord(63            primitive_type=xla_data_pb2.S16,64            numpy_dtype=np.int16,65            literal_field_name='s16s',66            literal_field_type=int),67    xla_data_pb2.S32:68        TypeConversionRecord(69            primitive_type=xla_data_pb2.S32,70            numpy_dtype=np.int32,71            literal_field_name='s32s',72            literal_field_type=int),73    xla_data_pb2.S64:74        TypeConversionRecord(75            primitive_type=xla_data_pb2.S64,76            numpy_dtype=np.int64,77            literal_field_name='s64s',78            literal_field_type=int),79    xla_data_pb2.U8:80        TypeConversionRecord(81            primitive_type=xla_data_pb2.U8,82            numpy_dtype=np.uint8,83            literal_field_name='s8s',84            literal_field_type=int),85    xla_data_pb2.U16:86        TypeConversionRecord(87            primitive_type=xla_data_pb2.U16,88            numpy_dtype=np.uint16,89            literal_field_name='s16s',90            literal_field_type=int),91    xla_data_pb2.U32:92        TypeConversionRecord(93            primitive_type=xla_data_pb2.U32,94            numpy_dtype=np.uint32,95            literal_field_name='s32s',96            literal_field_type=int),97    xla_data_pb2.U64:98        TypeConversionRecord(99            primitive_type=xla_data_pb2.U64,100            numpy_dtype=np.uint64,101            literal_field_name='s64s',102            literal_field_type=int),103    xla_data_pb2.PRED:104        TypeConversionRecord(105            primitive_type=xla_data_pb2.PRED,106            numpy_dtype=np.bool,107            literal_field_name='preds',108            literal_field_type=bool)109}110# Maps from Numpy dtypes to TypeConversionRecord.111# Note the conversion on the key. Numpy has a known issue wherein dtype hashing112# doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus,113# when keying by dtype in this dict, we use the string form of dtypes.114MAP_DTYPE_TO_RECORD = {115    str(np.dtype(record.numpy_dtype)): record116    for record in MAP_XLA_TYPE_TO_RECORD.values()...

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