{ "cells": [ { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 全0、1数组的生成" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0],\n", " [0, 0, 0]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 生成全0数组\n", "np.zeros((6,6,6))\n", "# 生成全1数组\n", "np.ones((6,6,6))\n", "\n", "a = np.array([[1,2,3],[4,5,6]])\n", "# 生成和a形状一样的全0、1数组\n", "np.ones_like(a)\n", "np.zeros_like(a)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 从现有数组生成" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "b = np.array(a) # 复制的形式生成新数组\n", "c = np.asarray(a) # 引用的形式生成新的数组\n", "\n", "a[0] = 10 # 修改a的值\n", "temp = np.zeros((2,3), dtype=np.int64)\n", "\n", "temp.dtype\n", "\n", "temp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 生成固定范围的数组" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(1, 10, 10) # 从指定范围内等间隔取指定数量的元素" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 3, 5, 7, 9])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(1, 11, 2) # 从指定范围内从开始值按照指定步长取数，一直到结束值为止（不包括结束值）" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2., 4., 8., 16., 32., 64., 128., 256., 512.,\n", " 1024.])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.logspace(1, 10, 10, base=2) # 生成等比数列 base：等比数列的比例" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 生成随机数组" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[0.73559078, 0.30589123, 0.32568083, 0.14014809],\n", " [0.76796653, 0.99066091, 0.20332346, 0.62377113],\n", " [0.73640678, 0.92920281, 0.53587389, 0.43354196]],\n", "\n", " [[0.82598753, 0.90088117, 0.20160621, 0.33523865],\n", " [0.6718942 , 0.1686006 , 0.58619419, 0.86310583],\n", " [0.61579575, 0.84663156, 0.14980755, 0.16868134]]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 均匀抽样生成随机数组\n", "np.random.rand(2,3,4) \n", "\n", "np.random.uniform(1, 10, (2,3))\n", "\n", "np.random.randint(1, 10, (2,3))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.15284906, 0.01163405, 1.49868393],\n", " [-0.94222366, -0.1884333 , 0.71855256]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 正态分布抽样生成随机数组\n", "np.random.randn(2,3) # 标准正态分布抽样\n", "\n", "np.random.normal(loc=1, scale=1, size=(2,3)) # loc 平均值，scale方差\n", "\n", "np.random.standard_normal((2,3)) # 标准正态分布抽样" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘制直方图" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# 绘制均匀分布抽样生成的数据的直方图\n", "x = np.random.uniform(-10, 10, 10000000)\n", "\n", "# 创建画布\n", "plt.figure(figsize=(20,8))\n", "\n", "# 绘制图像\n", "plt.hist(x, bins=1000)\n", "\n", "# 显示图像\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 绘制正态分布抽样生成的数据的直方图\n", "x_n = np.random.standard_normal((10000000,))\n", "\n", "# 创建画布\n", "plt.figure(figsize=(20,8))\n", "\n", "# 绘制图像\n", "plt.hist(x_n, bins=1000)\n", "\n", "# 显示图像\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 案例：随机生成8只股票1周的交易日涨幅数据" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# 随机生成一个8行5列的数组\n", "stock_change = np.random.standard_normal((8,5))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.18034193, 0.64992488, -2.27651322, -0.88154253, 1.57039639],\n", " [ 0.27126575, -0.31724391, 0.73567801, 1.78337018, 0.45531185],\n", " [-0.27526832, 0.86168297, 1.27118251, -0.42667843, 1.19810533],\n", " [-0.73357899, 0.69226277, 0.72346543, -1.41870678, 0.6872338 ],\n", " [-0.74016654, -0.08659411, 0.97317518, -0.5460637 , -0.78570216],\n", " [-1.59761172, -0.47689587, 2.69494497, -1.25192604, 0.27408855],\n", " [ 0.62655477, -1.55080165, -0.12133895, -1.26714644, -0.55410163],\n", " [-1.06529991, -1.5404814 , 1.34970882, -0.88888001, -0.29144208]])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stock_change" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[10. , 0.64992488, -2.27651322, -0.88154253, 1.57039639],\n", " [-0.27526832, 0.86168297, 1.27118251, -0.42667843, 1.19810533]])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 切片的方法，每个维度用冒号索引进行切片，有多少个维度，就传多少个冒号索引\n", "stock_change[:4:2,:]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "# 形状修改\n", "temp = np.linspace(1,12, 12)\n", "\n", "res = temp.reshape((3,4)) # 生成新的数组，不修改原数组\n", "\n", "temp.resize((3,4)) # 修改原数组\n", "\n", "# 转置 行列互换\n", "temp.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 类型修改" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# 修改dtype\n", "res = temp.astype(np.int64)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "# 把ndarray对象序列化\n", "s = temp.tostring()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3., 4.],\n", " [ 5., 6., 7., 8.],\n", " [ 9., 10., 11., 12.]])" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 把序列化数据转为ndarray对象\n", "np.frombuffer(s, dtype=np.float64).reshape((3,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数组去重\n" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "temp = np.array([[1, 2, 3, 4],[3, 4, 5, 6]])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3, 4],\n", " [3, 4, 5, 6]])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6])" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 剔除数组中重复的元素，以一维数组的形式返回结果\n", "np.unique(temp)" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }