{ "cells": [ { "cell_type": "markdown", "id": "f11bbe2b-7325-4bf0-abfb-4b4e64292145", "metadata": {}, "source": [ "## NumPy入门\n", "\n", "NumPy是Python数据科学三方库中最为重要的基石，提供了数据存储和运算的能力，其他很多跟数据科学相关的库底层都依赖了NumPy。NumPy的核心是名为`ndarray`的数据类型，用来表示任意维度的数组，相较于Python的`list`，它具有以下优势：\n", "\n", "1. 有更好的性能，可以利用硬件的并行计算能力和缓存优化，相较于`list`在处理数据的性能上有着数量级的差异。\n", "2. 功能更加强大，`ndarray`提供了丰富的运算和方法来处理数据，NumPy中还针对数组操作封装了大量的函数。\n", "3. 向量化操作，NumPy中的函数以及`ndarray`的方法都是对作用于整个数组，无需使用显示的循环，代码更加简单优雅。" ] }, { "cell_type": "code", "execution_count": 1, "id": "15630f70-be3c-4690-96a6-0b134a685efb", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n", "plt.rcParams['axes.unicode_minus'] = False" ] }, { "cell_type": "code", "execution_count": 2, "id": "8a115f09-0477-4e62-a910-d9284f32fbd1", "metadata": {}, "outputs": [], "source": [ "# %save hello.py" ] }, { "cell_type": "markdown", "id": "a8c3b5a1-9ad1-4511-b7f8-beacce89cf69", "metadata": {}, "source": [ "### 创建数组对象\n", "\n", "1. 通过`array`/`asarray`函数将列表处理成数组对象\n", "2. 通过`arange`函数指定起始值、终止值和跨度创建数组对象\n", "3. 通过`linspace`函数指定起始值、终止值和元素个数创建等差数列\n", "4. 通过`logspace`函数指定起始值（指数）、终止值（指数）、元素个数、底数（默认10）创建等比数列\n", "5. 通过`fromstring`/`fromfile`函数从字符串或文件中读取数据创建数组对象\n", "6. 通过`fromiter`函数通过迭代器获取数据创建数组对象\n", "7. 通过生成随机元素的方式创建数组对象\n", "8. 通过`zeros`/`zeros_like`函数创建全0元素的数组对象\n", "9. 通过`ones`/`ones_like`函数创建全1元素的数组对象\n", "10. 通过`full`函数指定元素值创建数组对象\n", "11. 通过`eye`函数创建单位矩阵\n", "12. 通过`tile`/`repeat`函数重复元素创建数组对象" ] }, { "cell_type": "code", "execution_count": 3, "id": "a035b671-2f91-473c-ac2b-e25291cf664b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5], dtype=int32)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 方法一：通过array函数将列表处理成数组对象\n", "array1 = np.array([1, 2, 3, 4, 5], dtype='i4')\n", "array1" ] }, { "cell_type": "code", "execution_count": 4, "id": "28a8c2f7-c197-4d5e-9006-d99242edefee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(array1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "95eae152-6bfc-4707-bd04-50c7363e9315", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array2 = np.array([[1, 2, 3], [4, 5, 6]])\n", "array2" ] }, { "cell_type": "code", "execution_count": 6, "id": "3c662a6d-d017-4a85-b93c-8538f334db22", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 方法二：通过arange函数指定范围创建数组对象\n", "array3 = np.arange(1, 10)\n", "array3" ] }, { "cell_type": "code", "execution_count": 7, "id": "954605a1-1004-4595-ac90-523feac7f4e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49,\n", " 52, 55, 58, 61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array4 = np.arange(1, 100, 3)\n", "array4" ] }, { "cell_type": "code", "execution_count": 8, "id": "e479f343-7033-4b73-a0ce-e7f04444e915", "metadata": {}, "outputs": [], "source": [ "# 方法三：通过linspace函数创建等差数列\n", "array5 = np.linspace(-2 * np.pi, 2 * np.pi, 120)\n", "array6 = np.sin(array5)\n", "array7 = np.cos(array5)" ] }, { "cell_type": "code", "execution_count": 9, "id": "57b0e126-3276-4f10-b1d1-288597842d35", "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'svg'\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 10, "id": "62a6b0ed-acaf-45a1-90b0-7caf73184d09", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

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