{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 数据类型\n", "\n", "## 常见数据类型\n", "\n", "Python 原生的数据类型相对较少, bool、int、float、str等。这在不需要关心数据在计算机中表示的所有方式的应用中是方便的。然而,对于科学计算,通常需要更多的控制。为了加以区分 numpy 在这些类型名称末尾都加了“_”。\n", "\n", "下表列举了常用 numpy 基本类型。\n", "\n", "\n", "类型 | 备注 | 说明 \n", "---|---|---\n", "bool_ = bool8 | 8位 | 布尔类型\n", "int8 = byte | 8位 | 整型\n", "int16 = short |\t16位| 整型\n", "int32 = intc | 32位| 整型\n", "int_ = int64 = long = int0 = intp | 64位| 整型\n", "uint8 = ubyte |8位 | 无符号整型\n", "uint16 = ushort|16位| 无符号整型\n", "uint32 = uintc|32位| 无符号整型\n", "uint64 = uintp = uint0 = uint| 64位| 无符号整型\n", "float16 = half|16位 | 浮点型\n", "float32 = single| 32位| 浮点型\n", "float_ = float64 = double| 64位| 浮点型\n", "str_ = unicode_ = str0 = unicode| |Unicode 字符串\n", "datetime64| |日期时间类型\n", "timedelta64| |表示两个时间之间的间隔\n", "\n", "\n", "\n", "## 创建数据类型\n", "\n", "numpy 的数值类型实际上是 dtype 对象的实例。\n", "\n", "```python\n", "class dtype(object):\n", " def __init__(self, obj, align=False, copy=False):\n", " pass\n", "```\n", "\n", "\n", "\n", "\n", "每个内建类型都有一个唯一定义它的字符代码,如下:\n", "\n", "字符 | \t对应类型|备注\n", "---|---|---\n", "b\t|boolean | 'b1'\n", "i\t|signed integer | 'i1', 'i2', 'i4', 'i8'\n", "u\t|unsigned integer | 'u1', 'u2' ,'u4' ,'u8'\n", "f\t|floating-point | 'f2', 'f4', 'f8'\n", "c\t|complex floating-point |\n", "m\t|timedelta64 |表示两个时间之间的间隔\n", "M\t|datetime64 |日期时间类型\n", "O\t|object |\n", "S\t|(byte-)string | S3表示长度为3的字符串\n", "U\t|Unicode | Unicode 字符串\n", "V\t|void\n", "\n", "【例】\n", "```python\n", "import numpy as np\n", "\n", "a = np.dtype('b1')\n", "print(a.type) # \n", "print(a.itemsize) # 1\n", "\n", "a = np.dtype('i1')\n", "print(a.type) # \n", "print(a.itemsize) # 1\n", "a = np.dtype('i2')\n", "print(a.type) # \n", "print(a.itemsize) # 2\n", "a = np.dtype('i4')\n", "print(a.type) # \n", "print(a.itemsize) # 4\n", "a = np.dtype('i8')\n", "print(a.type) # \n", "print(a.itemsize) # 8\n", "\n", "a = np.dtype('u1')\n", "print(a.type) # \n", "print(a.itemsize) # 1\n", "a = np.dtype('u2')\n", "print(a.type) # \n", "print(a.itemsize) # 2\n", "a = np.dtype('u4')\n", "print(a.type) # \n", "print(a.itemsize) # 4\n", "a = np.dtype('u8')\n", "print(a.type) # \n", "print(a.itemsize) # 8\n", "\n", "a = np.dtype('f2')\n", "print(a.type) # \n", "print(a.itemsize) # 2\n", "a = np.dtype('f4')\n", "print(a.type) # \n", "print(a.itemsize) # 4\n", "a = np.dtype('f8')\n", "print(a.type) # \n", "print(a.itemsize) # 8\n", "\n", "a = np.dtype('S')\n", "print(a.type) # \n", "print(a.itemsize) # 0\n", "a = np.dtype('S3')\n", "print(a.type) # \n", "print(a.itemsize) # 3\n", "\n", "a = np.dtype('U3')\n", "print(a.type) # \n", "print(a.itemsize) # 12\n", "```\n", "\n", "## 数据类型信息\n", "\n", "Python 的浮点数通常是64位浮点数,几乎等同于 `np.float64`。\n", "\n", "NumPy和Python整数类型的行为在整数溢出方面存在显着差异,与 NumPy 不同,Python 的`int` 是灵活的。这意味着Python整数可以扩展以容纳任何整数并且不会溢出。\n", "\n", "Machine limits for integer types.\n", "```python\n", "class iinfo(object):\n", " def __init__(self, int_type):\n", " pass\n", " def min(self):\n", " pass\n", " def max(self):\n", " pass\n", "```\n", "【例】\n", "\n", "```python\n", "import numpy as np\n", "\n", "ii16 = np.iinfo(np.int16)\n", "print(ii16.min) # -32768\n", "print(ii16.max) # 32767\n", "\n", "ii32 = np.iinfo(np.int32)\n", "print(ii32.min) # -2147483648\n", "print(ii32.max) # 2147483647\n", "```\n", "\n", "Machine limits for floating point types.\n", "```python\n", "class finfo(object):\n", " def _init(self, dtype):\n", "```\n", "【例】\n", "```python\n", "import numpy as np\n", "\n", "ff16 = np.finfo(np.float16)\n", "print(ff16.bits) # 16\n", "print(ff16.min) # -65500.0\n", "print(ff16.max) # 65500.0\n", "print(ff16.eps) # 0.000977\n", "\n", "ff32 = np.finfo(np.float32)\n", "print(ff32.bits) # 32\n", "print(ff32.min) # -3.4028235e+38\n", "print(ff32.max) # 3.4028235e+38\n", "print(ff32.eps) # 1.1920929e-07\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 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