c/c++一维数组简单介绍
定义:同一种类型数据的集合
通俗的讲就是,将多个同一种类型的数据按一定的内存顺序写在一起。
注意我的几个关键字“多个”,“同一种”,“一定的内存顺序”。如果理解了这几个关键词,说明你的数组已经掌握了。
我们分开了解这几个关键词:
多个:首先数组是为了存储多个数据而产生的,如果你只有一个数据那就没必要用数组了,当然你非要定义数组存储单个数据也是不会报错的。
//eg
#include<iostream>
using namespace std;
void main()
{
int a; //等效于 int a[1];
int num[10]; //一般用于定于多个,此处就表示,定义10个int类型的数据
//注意数组是从零开始计数的
}
同一种:数组最重要的特点就是将相同类型的数据放在了一起,便于以后的各种迭代处理,直接看代码更容易理解
//eg
#include<iostream>
using namespace std;
void main()
{
int a[10]; //假如你现在需要十个正整型数据 先赋值再求和
int sun = 0; //定义sum的初始值为0
for(int i = 0; i < 10; ++i)
{
a[i] = i;
}
for(int j = 0; j < 10; ++j)
{
sum += a[j];
}
cout << sum << endl;
}
一定的内存顺序:这块是很重要的,即数组在内存中的相邻数据之间的间隔一定的(数据类型的长度),数组和指针可以相互使用,现在很好的理解数组的内存结构,在后面指针那里就很容易学懂了。
#include<iostream>
using namespace std;
void main()
{
int a[10] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }; //我们可以先去打印a[0] 与 a[9]之间的内存差看看效果
cout << &a[0] <<" "<< &a[9] << endl; //& 在这里是取地址符
cin.get();
}
用两个地址作差除去,size(int),看看是个什么结果。下面我将用图来解释:
aaarticlea/png;base64,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" alt="" />
数组的初始化
数组的初始化有很多的种方法,这里我将写出最长见的几种:
#include<iostream>
using namespace std;
int main()
{
int a[10] = {0}; //这种方式将默认是个元素全部为零
int b[10] = {0,1,2,3,4,5,6,7,8,9};//一一对应的方式。
//也可以在后续的过程中给出自己的操作
return 0;
}
这里还有二维数组未说明,后期继续,写得有问题的地方请指出,我改正,谢谢!
c/c++一维数组简单介绍的更多相关文章
- JavaScript数组的简单介绍
㈠对象分类 ⑴内建对象 ⑵宿主对象 ⑶自定义对象 ㈡数组(Array) ⑴简单介绍 ①数组也是一个对象 ②它和我们普通对象功能类似,也是用来存储一些值的 ③不同的是普通对象是使用字符串作为属性名的 ...
- html标签内部简单加js 一维数组求最大值 最小值两个值位置和数字金字塔图形
html标签内部,简单加js <a href=""></a><!DOCTYPE html PUBLIC "-//W3C//DTD XHTM ...
- 利用Python进行数据分析(7) pandas基础: Series和DataFrame的简单介绍
一.pandas 是什么 pandas 是基于 NumPy 的一个 Python 数据分析包,主要目的是为了数据分析.它提供了大量高级的数据结构和对数据处理的方法. pandas 有两个主要的数据结构 ...
- python numpy 模块简单介绍
用python自带的list去处理数组效率很低, numpy就诞生了, 它提供了ndarry对象,N-dimensional object, 是存储单一数据类型的多维数组,即所有的元素都是同一种类型. ...
- 【浅墨著作】《OpenCV3编程入门》内容简单介绍&勘误&配套源码下载
经过近一年的沉淀和总结,<OpenCV3编程入门>一书最终和大家见面了. 近期有为数不少的小伙伴们发邮件给浅墨建议最好在博客里面贴出这本书的文件夹,方便大家更好的了解这本书的内容.事实上近 ...
- 二维数组转化为一维数组 contact 与apply 的结合
将多维数组(尤其是二维数组)转化为一维数组是业务开发中的常用逻辑,除了使用朴素的循环转换以外,我们还可以利用Javascript的语言特性实现更为简洁优雅的转换.本文将从朴素的循环转换开始,逐一介绍三 ...
- python之pandas简单介绍及使用(一)
python之pandas简单介绍及使用(一) 一. Pandas简介1.Python Data Analysis Library 或 pandas 是基于NumPy 的一种工具,该工具是为了解决数据 ...
- Smali语法简单介绍
Smali语言其实就是Davlik的寄存器语言: Smali语言就是android的应用程序.apk通过apktool反编译出来的都有一个smali文件夹,里面都是以.smali结尾的文件,文件的展示 ...
- professional cuda c programming--CUDA库简单介绍
CUDA Libraries简单介绍 上图是CUDA 库的位置.本文简要介绍cuSPARSE.cuBLAS.cuFFT和cuRAND.之后会介绍OpenACC. cuSPARSE线性代数库,主要针 ...
随机推荐
- Python【每日一问】12
问:请解释线程.进程.协程 答: [定义] 进程 进程:一个运行的程序(代码)就是一个进程,进程是系统资源分配的最小单位.进程拥有自己独立的内存空间,多个进程间资源不共享 线程 线程:调度执行的最小单 ...
- localStorage溢出问题
项目使用的store.js库 store.js库不能管理localStorage中的过期项到时清除,只能在再次调用get的时候才做处理,如果一直不调用get,过期了也还是占用着空间.溢出后,再储存项目 ...
- 适用于nodercms的打包构建脚本
背景 最近自己用nodercms搭建了一个简单的博客系统,用户发布一些自己谁便谢谢的文章.感谢nodercms团队,这个cms轻量易用,用于做个人博客太方便了.开发了博客系统,肯定设计到部署到AWS或 ...
- JUnit报告美化——ExtentReports
美化后效果 美化后的报告,页面清晰简洁.重要信息都可以体现出来,用例通过率,失败的用例和失败原因 主要技术点 ExtentReports JUnit的@Rule 重写TestWatcher的succe ...
- IDEA开发环境配置
1.JDK 2.Maven 3.Tomcat 当找不到 Artifacts , 可以查看一下: 4.配置 terminal 为 git 终端 5.MySQL 6.文件服务器 7.配置 mybatis
- react-native shadow失效
做边框阴影,但是有时候会失效,内容产生阴影,而边框无效,今天发现了原因,没错,就是没有设置背景颜色导致的.如图
- [编码实践]SpringBoot实战:利用Spring AOP实现操作日志审计管理
设计原则和思路: 元注解方式结合AOP,灵活记录操作日志 能够记录详细错误日志为运营以及审计提供支持 日志记录尽可能减少性能影响 操作描述参数支持动态获取,其他参数自动记录. 1.定义日志记录元注解, ...
- 强大的oracle分析函数
转载:https://www.cnblogs.com/benio/archive/2011/06/01/2066106.html 学习步骤:1. 拥有Oracle EBS demo 环境 或者 PRO ...
- Exp1 PC平台逆向破解 20164311
实验目标: 本次实践的对象是一个名为pwn1的linux可执行文件. 该程序正常执行流程是:main调用foo函数,foo函数会简单回显任何用户输入的字符串. 该程序同时包含另一个代码片段,getSh ...
- AE10.0及AE10.0以上的版本调用ESRI.ArcGIS.esriSystem出现的问题
如果本地安装的是AE10.0以上,那么添加ESRI.ArcGIS.esriSystem引用时,会出现esriLicenseProductCode并不包含esriLicenseProductCodeAr ...