{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Numpy 基础"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://imgbed.momodel.cn/1200px_NumPy_logo.svg.png\" width=300>\n",
"\n",
"\n",
"\n",
"\n",
"在这节课中，我们将会学习到 Numpy 的相关内容。\n",
"\n",
"`NumPy（Numerical Python）`是一个开源的 **Python** 科学计算库，用于快速处理任意维度的数组。\n",
"\n",
"对于同样的数值计算任务，使用 `NumPy` 比直接使用 **Python** 要简洁的多。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `ndarray` 介绍"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`NumPy` 提供了一个`N` 维数组类型 `ndarray`，它描述了**相同类型**的 `items` 的集合。\n",
" \n",
"|语文|数学|英语|政治|体育|\n",
"|--|--|--|--|--|\n",
"|80|89|86|67|79|\n",
"|78|97|89|76|81|\n",
"\n",
"用 `ndarray` 进行存储："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建ndarray\n",
"score = np.array([[80, 89, 86, 67, 79],[78, 97, 89, 67, 81]])\n",
"\n",
"# 打印结果\n",
"score\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `ndarray` 的属性 \n",
"数组属性反映了数组本身固有的信息。\n",
"\n",
"|属性名字|\t属性解释|\n",
"|--|--|\n",
"|ndarray.shape|\t数组维度的元组|\n",
"|ndarray.ndim|\t数组维数|\n",
"|ndarray.size|\t数组中的元素数量|\n",
"|ndarray.dtype|\t数组元素的类型|\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ `shape`：数组形状"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建不同形状的数组\n",
"a = np.array([[1, 2, 3],[4, 5, 6]])\n",
"b = np.array([1, 2, 3, 4])\n",
"c = np.array([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6.0]]])\n",
"\n",
"# 分别打印出形状\n",
"print(a.shape)\n",
"print(b.shape)\n",
"print(c.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ `ndim`:数组维数"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建不同形状的数组\n",
"a = np.array([[1, 2, 3],[4, 5, 6]])\n",
"b = np.array([1, 2, 3, 4])\n",
"c = np.array([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6.0]]])\n",
"\n",
"# 分别打印出维数\n",
"print(a.ndim)\n",
"print(b.ndim)\n",
"print(c.ndim)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ `size`：数组元素数量"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建不同形状的数组\n",
"a = np.array([[1, 2, 3],[4, 5, 6]])\n",
"b = np.array([1, 2, 3, 4])\n",
"c = np.array([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6.0]]])\n",
"\n",
"# 分别打印出数组元素数量\n",
"print(a.size)\n",
"print(b.size)\n",
"print(c.size)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ `dtype`：数组元素的类型"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建不同形状的数组\n",
"a = np.array([[1, 2, 3],[4, 5, 6]])\n",
"b = np.array([1, 2, 3, 4])\n",
"c = np.array([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6.0]]])\n",
"\n",
"# 分别打印出数组元素数量\n",
"print(a.dtype)\n",
"print(b.dtype)\n",
"print(c.dtype)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：把二维数组 [[1, 2, 3],[4, 5, 6],[7,8,9]] 转为 ndarray 格式。并查看其形状、维数和元素数量。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
"print(a)\n",
"print(a.shape)\n",
"print(a.ndim)\n",
"print(a.size)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `ndarray` 的类型\n",
"\n",
"|名称|描述|\n",
"|--|--|\n",
"|np.bool|用一个字节存储的布尔类型（True或False）|\n",
"|np.int8|一个字节大小，-128 至 127|\n",
"|np.int16|整数，-32768 至 32767|\n",
"|np.int32|整数，$-2^{31}$ 至 $2^{32} -1$|\n",
"|np.int64|整数，$-2^{63}$ 至 $2^{63} - 1$|\n",
"|np.uint8|无符号整数，0 至 255|\n",
"|np.uint16|无符号整数，0 至 65535|\n",
"|np.uint32|\t无符号整数，0 至 $2^{32} - 1$|\n",
"|np.uint64|\t无符号整数，0 至 $2^{64} - 1$ |\n",
"|np.float16\t|半精度浮点数：16位，正负号1位，指数5位，精度10位|\n",
"|np.float32\t|单精度浮点数：32位，正负号1位，指数8位，精度23位|\n",
"|np.float64\t|双精度浮点数：64位，正负号1位，指数11位，精度52位|\n",
"|np.complex64|复数，分别用两个32位浮点数表示实部和虚部|\n",
"|np.complex128|复数，分别用两个64位浮点数表示实部和虚部|\n",
"|np.object_|python对象|\n",
"|np.string_|字符串|\n",
"|np.unicode_|unicode类型| \n",
"\n",
"\n",
"**注意：创建数组的时候指定类型**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 创建数组时指定类型为 np.float32\n",
"a = np.array([[1, 2, 3],[4, 5, 6]], dtype=np.float32)\n",
"\n",
"# 创建数组时未指定类型\n",
"b = np.array([[1, 2, 3],[4, 5, 6]])\n",
"\n",
"# 打印结果\n",
"print(\"数组a：\\n%s,\\n数据类型：%s\"%(a,a.dtype))\n",
"print(\"数组b：\\n%s,\\n数据类型：%s\"%(b,b.dtype))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"### 基本操作"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 生成元素值为 `0 ` 和 `1` 的数组的方法 \n",
"\n",
"+ 生成全部元素值为 `0` 的数组"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"zero = np.zeros([3, 4])\n",
"zero\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ 生成全部元素值为 `1` 的数组"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"one = np.ones([3, 4])\n",
"one\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ 生成对角数组(对角线的地方是 `1`，其余地方是 `0`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eyes = np.eye(10, 5)\n",
"eyes\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ 创建方阵对角矩阵"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# np.eye(5, 5) 可简写为 (5)\n",
"eyes1 = np.eye(5)\n",
"eyes1\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 从现有数组生成"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = [[1, 2, 3], [4, 5, 6]]\n",
"\n",
"# 从现有的数组中创建\n",
"a1 = np.array(a)\n",
"a\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a1\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 生成固定范围的数组"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 生成等间隔的数组\n",
"a = np.linspace(0, 90, 10)\n",
"a\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 生成等间隔的数组\n",
"b = np.arange(0, 90, 10)\n",
"b\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 形状修改"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numpy import array\n",
"a = array([[ 0, 1, 2, 3, 4, 5],\n",
" [10,11,12,13,14,15],\n",
" [20,21,22,23,24,25],\n",
" [30,31,32,33,34,35]])\n",
"a.shape\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 在转换形状的时候，一定要注意数组的元素匹配\n",
"# 只是将形状进行了修改，但并没有将行列进行转换\n",
"b = a.reshape([3, 8])\n",
"b\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 数组的形状被修改为: (2, 12), -1: 表示同过自动计算得到此处的值\n",
"c = a.reshape([-1, 12])\n",
"c\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d = a.T\n",
"d.shape\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 类型修改"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"arr = np.array([[[1, 2, 3], [4, 5, 6]], [[12, 3, 34], [5, 6, 7]]])\n",
"arr.dtype\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"arr.astype(np.float32)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：创建一个 3x3 并且值从0到8的矩阵\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"Z = np.arange(9).reshape(3,3)\n",
"print(Z)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 数组去重"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"arr = np.array([[1, 2, 3, 4],[3, 4, 5, 6]])\n",
"np.unique(arr)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 数组运算\n",
"\n",
"数组的算术运算是元素级别的操作，新的数组被创建并且被结果填充。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"运算|函数\n",
"--- | --- \n",
"`a + b` | `add(a,b)`\n",
"`a - b` | `subtract(a,b)`\n",
"`a * b` | `multiply(a,b)`\n",
"`a / b` | `divide(a,b)`\n",
"`a ** b` | `power(a,b)`\n",
"`a % b` | `remainder(a,b)`\n",
"\n",
"以乘法为例，数组与标量相乘，相当于数组的每个元素乘以这个标量："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([1, 2, 3, 4])\n",
"a * 3\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"数组按元素相乘："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([1, 2])\n",
"b = np.array([3, 4])\n",
"a * b\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用函数"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.multiply(a, b)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"函数还可以接受第三个参数，表示将结果存入第三个参数中："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.multiply(a, b, a)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：把二维数组 [[1, 2, 3],[4, 5, 6],[7,8,9]] 转为 ndarray 格式。并对每个元素 +1。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
"print(a+1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 矩阵 \n",
"使用 `mat` 方法将 `2` 维数组转化为矩阵："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([[1, 2, 4],\n",
" [2, 5, 3],\n",
" [7, 8, 9]])\n",
"A = np.mat(a)\n",
"A\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 也可以使用 **Matlab** 的语法传入一个字符串来生成矩阵：\n",
"A = np.mat('1,2,4;2,5,3;7,8,9')\n",
"A\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"矩阵与向量的乘法："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([[1], [2], [3]])\n",
"x\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A*x\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b = np.array([[1, 2],\n",
" [3, 4],\n",
" [5, 6]])\n",
"B = np.mat(b)\n",
"A*B\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`A.I` 表示 `A` 矩阵的逆矩阵："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A.I\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"矩阵指数表示矩阵连乘："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A ** 4\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：分别把二维数组 [[1, 2] ,[3, 4]] 和 [5, 6] ,[7, 8]]转为 matrix 格式。并计算矩阵的乘积。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.mat([[1, 2],[4, 5]])\n",
"b = np.mat([[5, 6],[7, 8]])\n",
"print(a*b)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 统计函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|方法|作用|\n",
"|--|--|\n",
"|`a.sum(axis=None)`|求和|\n",
"|`a.prod(axis=None)`|求积|\n",
"|`a.min(axis=None)`|最小值|\n",
"|`a.max(axis=None)`|最大值|\n",
"|`a.argmin(axis=None)`|最小值索引|\n",
"|`a.argmax(axis=None)`|最大值索引|\n",
"|`a.ptp(axis=None)`|最大值减最小值|\n",
"|`a.mean(axis=None)`|平均值|\n",
"|`a.std(axis=None)`|标准差|\n",
"|`a.var(axis=None)`|方差|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"from numpy import array\n",
"a = array([[1, 2, 3],\n",
" [4, 5, 6]])\n",
"a\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"求所有元素的和："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sum(a)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.sum()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**指定求和的维度**：\n",
"沿着第一维求和"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sum(a, axis=0)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.sum(axis=0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"沿着第二维求和："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sum(a, axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.sum(axis=1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"沿着最后一维求和："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sum(a, axis=-1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.sum(axis=-1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：创建一个长度为30的随机向量并找到它的平均值\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"Z = np.random.random(30)\n",
"m = Z.mean()\n",
"print(m)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 比较和逻辑函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"运算符|函数|\n",
":---: | :---: \n",
"`==` | `equal`\n",
"`!=` | `not_equal`\n",
"`>` | `greater`\n",
"`>=` | `greater_equal`\n",
"`<` | `less`\n",
"`<=` | `less_equal`\n",
"\n",
"数组元素的比对，我们可以直接使用运算符进行比较，比如判断数组中元素是否大于某个数："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numpy import array\n",
"a = array([[ 0, 1, 2, 3, 4, 5],\n",
" [10,11,12,13,14,15],\n",
" [20,21,22,23,24,25],\n",
" [30,31,32,33,34,35]])\n",
"\n",
"a > 10\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 判断数组中元素大于10的元素赋值为 -10 \n",
"a[a > 10] = -10\n",
"a\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"但是当数组元素较多时，查看输出结果便变得很麻烦，这时我们可以使用`all()`方法，直接比对矩阵的所有对应的元素是否满足条件。假如判断某个区间的值是否全是大于 `20`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numpy import array\n",
"a = array([[ 0, 1, 2, 3, 4, 5],\n",
" [10,11,12,13,14,15],\n",
" [20,21,22,23,24,25],\n",
" [30,31,32,33,34,35]])\n",
"\n",
"a[1:3,1:3]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.all(a[1:3,1:3] > 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用 `any()` 来判断数组某个区间的元素是否存在大于 `20`的元素:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.any(a[1:3,1:3] > 20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://imgbed.momodel.cn/5cc1a0b8e3067ce9b6abf76f.jpg' width=16px height=16px> **编程练习**\n",
"\n",
"要求：把二维数组 [[1, 2, 3],[4, 5, 6],[7,8,9]] 转为 ndarray 格式。判断是否全部大于 0。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 请编写你的答案\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
"print(np.all(a > 0))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `IO` 操作"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`savetxt` 可以将数组写入文件，默认使用科学计数法的形式保存："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"data = np.array([[1, 2],\n",
" [3, 4]])\n",
"\n",
"# 保存文件\n",
"np.savetxt('out.txt', data)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 读取文件\n",
"with open('out.txt') as f:\n",
" for line in f:\n",
" print(line)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 读取文件\n",
"np.loadtxt('out.txt')\n"
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"file_extension": ".py",
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