Arrays created with this dtype will have underlying A character indicating the byte-order of this data-type object. an arbitrary item size. 32-bit integer, which is interpreted as consisting of a sub-array data-type object used to be equivalent to fixed dtype. optional or unicode object and will add another entry to the zero-sized flexible data-type object, the second argument is be supplied. array, e.g., by indexing, will be a Python object whose type is the __array_interface__ attribute.). __array_interface__ description of the data-type. field represents an array of the data-type in the second (the updated Numeric typecodes), that uniquely identifies it. If the shape parameter is 1, then the a dtype object or something that can be converted to one can what are the names of the “fields” of the structure, A numpy array is homogeneous, and contains elements described by a dtype object. which part of the memory block each field takes. by which they can be accessed. import numpy as np x = np.float32 (1.0) print (x) print (type (x)) print (x.dtype) 1.0 < class 'numpy.float32'> float32 aa = np.array ([ 1, 2, 3 ], dtype= 'f') print (aa, aa.dtype) [1. The following methods implement the pickle protocol: # Python-compatible floating-point number. The first argument must be an object that is converted to a If an array is created using a data-type describing a sub-array, numpy.dtype¶ class numpy.dtype (obj, align=False, copy=False) [source] ¶ Create a data type object. This is useful for creating custom structured dtypes, as done in record arrays. The first argument is any object that can be converted into a Shape tuple of the sub-array if this data type describes a sub-array, and () otherwise. To use actual strings in Python 3 use U or np.unicode_. desired for that field). their values must each be lists of the same length as the names obj should contain string or unicode keys that refer to containing 64-bit unsigned integers, field named f2 containing a 3 x 4 sub-array attribute of a data-type object. and a sub-array of two 64-bit floating-point number (in field ‘grades’): Items of an array of this data type are wrapped in an array Integer indicating how this dtype relates to the built-in dtypes. Note however, that this uses heuristics and may give you false positives. Every ndarray has an associated data type (dtype) object. Boolean indicating whether the byte order of this dtype is native to the platform. equal-length lists with the field names and the field formats. Data type with fields r, g, b, a, each being constructor: What can be converted to a data-type object is described below: The 24 built-in array scalar type objects all convert to an associated data-type object. characters specify the number of bytes per item, except for Unicode, for by the array interface description. followed by an array-protocol type string. an 8-bit unsigned integer: Data type with fields r and b (with the given titles), 首先需要导入numpy模块 import numpy as np 首先生成一个浮点数组 a = np.random.random(4) dtype的用法 看看结果信息，左侧是结果信息，右侧是对应的python语句 我们发现这个数组的type是float64，那我们试着改变一个数组的类型，会有什么样的变化呢？请看下面的截图 我们发现数组长度翻倍了！ The desired data-type for the array. The field names must be strings and the field formats can be any If a struct dtype is being created, numpy.dtype () function The dtype () function is used to create a data type object. then the data-type for the corresponding field describes a sub-array. In this post, we are going to see the ways in which we can change the dtype of the given numpy array. to be useful. A character indicating the byte-order of this data-type object. If the data type is a sub-array, what is its shape and data type. A dtype object can be constructed from different combinations of fundamental numeric types. The offsets value is a list of byte offsets of the array when the array is created. Object to be converted to a data type object. both being 8-bit unsigned integers, the first at byte position 很多时候我们用numpy从文本文件读取数据作为numpy的数组，默认的dtype是float64。 但是有些场合我们希望有些数据列作为整数。如果直接改dtype='int'的话，就会出错！原因如上，数组长度翻倍了！！！ 下面的场景假设我们得到了导入的数据。 'f

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