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更新第三章：数据科学。
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‎03_data_science/01_Numpy数组.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"# Numpy数组\n",
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"\n",
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"\n",
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"## 数组：array\n",
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"\n",
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"\n",
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"很多其他科学计算的第三方库都是以Numpy为基础建立的。\n",
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"\n",
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"Numpy的一个重要特性是它的数组计算。\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"使用前一定要先导入 Numpy 包，导入的方法有以下几种：\n",
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"```\n",
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"import numpy\n",
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"import numpy as np\n",
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"from numpy import *\n",
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"from numpy import array, sin\n",
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"```\n",
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"\n",
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"导入numpy，最常用为这种:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"假如我们想将列表中的每个元素增加1，但列表不支持这样的操作（报错）："
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = [1, 2]\n",
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"\n",
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"# a + 1 # 报错"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"使用numpy.array："
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a = np.array(a)\n",
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"a # [1 2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2, 3])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"b = a + 1\n",
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"b # array([2,3])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"与另一个 array 相加，得到对应元素相加的结果："
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[3 5]\n",
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"[2 6]\n",
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"[1 8]\n"
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]
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}
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],
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"source": [
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"c = a + b\n",
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"print(c) # array([3,5])\n",
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"\n",
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"# 对应元素相乘：\n",
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"print(a * b) # [2 6]\n",
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"\n",
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"# 对应元素乘方：\n",
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"print(a ** b) # [1 8]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 提取数组中的元素"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n",
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"[1 2]\n",
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"[3 4]\n",
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"[4 6]\n"
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]
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}
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],
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"source": [
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"# 提取第一个\n",
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"a = np.array([1, 2, 3, 4])\n",
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"print(a[0]) # 1\n",
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"\n",
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"# 提取前两个元素：\n",
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"print(a[:2]) # [1 2]\n",
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"\n",
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"# 最后两个元素\n",
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"print(a[-2:]) # [3 4]\n",
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"\n",
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"# 相加：\n",
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"print(a[:2] + a[-2:]) # [4 6]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 修改数组形状\n",
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"\n",
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"查看array的形状："
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(4,)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"b = a.shape\n",
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"b # (4,)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 2],\n",
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" [3, 4]])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 修改 array 的形状：\n",
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"a.shape = 2, 2\n",
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"a\n",
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"# [[1 2]\n",
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"# [3 4]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[2, 4],\n",
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" [6, 8]])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 多维数组\n",
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"# a 现在变成了一个二维的数组，可以进行加法：\n",
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"a + a\n",
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"# [[2 4]\n",
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"# [6 8]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 1, 4],\n",
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" [ 9, 16]])"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 乘法仍然是对应元素的乘积，并不是按照矩阵乘法来计算：\n",
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"a * a\n",
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"# [[ 1 4]\n",
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"# [ 9 16]]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"本节完。"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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