### 统计代写|python代考|Numbers and Operators

Python是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用大量的缩进。

Python是动态类型的，并且是垃圾收集的。它支持多种编程范式，包括结构化（特别是程序化）、面向对象和函数式编程。由于其全面的标准库，它经常被描述为一种 “包含电池 “的语言。

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|python代考|Different Kinds of Numbers

When you think of numbers, you can probably invoke pleasant memories like Sesame Street and its counting routine or more serious memories like math lessons. Either way, you are familiar with numbers. Indeed, numbers are such a familiar concept that you probably don’t notice the many different ways in which you use them depending on their context.
In this chapter, you will be re-introduced to numbers and some of the ways in which Python works with them, including basic arithmetic and special string format specifiers for its different types of numbers. When you have finished the chapter, you will be familiar with the different basic categories of numbers that Python uses and with the methods for using them, including displaying and mixing the various number types.

If you have ever used a spreadsheet, you’ve noticed that the spreadsheet doesn’t just look at numbers as numbers but as different kinds of numbers. Depending on how you’ve formatted a cell, the spreadsheet will have different ways of displaying the numbers. For instance, when you deal with money, your spreadsheet will show one dollar as $1.00$. However, if you’re keeping track of the miles you’ve traveled in your car, you’d probably only record the miles you’ve traveled in tenths of a mile, such as $10.2$. When you name a price you’re willing to pay for a new house, you probably only think to the nearest thousand dollars. At the large end of numbers, your electricity bills are sent to you with meter readings that come in at kilowatt hours, which are each one thousand watts per hour.

What this means in terms of Python is that, when you want to use numbers, you sometimes need to be aware that not all numbers relate to each other (as you’ll see with imaginary numbers in this chapter), and sometimes you’ll have to be careful about what kind of number you have and what you’re trying to do with it. However, in general, you will use numbers in two ways: The first way will be to tell Python to repeat a certain action, while the second way will be to represent things that exist in the real world (that is, in your program, which is trying to model something in the real world). You will rarely have to think of numbers as anything besides simple numbers when you are counting things inside of Python. However, when you move on to trying to solve problems that exist in the real world – things that deal with money, science, cars, electricity, or anything else, you’ll find yourself more aware about how you use numbers.

## 统计代写|python代考|Numbers in Python

Python offers four different kinds of numbers with which you can work: integers, long numbers (or longs), floating-point numbers (or floats), and imaginary numbers.
Integers and longs are very closely related and can be mixed freely. Each one is a whole number, positive or negative, but plain integers only run between $-2,147,483,648$ and $+2,147,483,647$. That’s pretty big big enough for a lot of tasks. However, if you find that you need more than that, Python will notice this and automatically promote your number from a plain integer to a long number.

To determine the type of a number, you can use a special function that is built into Python, called type. When you use type, Python will tell you what kind of data you’re looking at. Let’s try this with a few examples.

## 统计代写|python代考|How It Works

Although in everyday life $1.0$ is the same number as 1 , Python will automatically perceive $1.0$ as being a float; without the $.0$, the number 1 would be dealt with as the integer number one (which you probably learned as a whole number in grade school), which is a different kind of number.
In essence, the special distinction between a float and an integer or a long integer is that a float has a component that is a fraction of 1 . Numbers such as $1.01,2.34,0.02324$, and any other number that contains a fractional component is treated as a floating-point number (except for imaginary numbers, which have rules of their own). This is the type that you would want to use for dealing with money or with things dealt with in partial quantities, like gasoline or pairs of socks. (There’s always a stray single sock in the drawers, right?)
The last type of number that Python offers is oriented toward engineers and mathematicians. It’s the imaginary number, and you may remember it from school; it’s defined as the square root of $-1$. Despite being named imaginary, it does have a lot of practical uses in modeling real-world engineering situations, as well as in other disciplines like physics and pure math. The imaginary number is built into Python so that it’s easily usable by user communities who frequently need to solve their problems with computers. Having this built-in type enables Python to help them do that. If you happen to be one of those people, you will be happy to learn that you’re not alone, and Python is there for you.

## 统计代写|python代考|Numbers in Python

Python 提供了四种不同类型的数字供您使用：整数、长整数（或长整数）、浮点数（或浮点数）和虚数。

## 统计代写|python代考|How It Works

Python 提供的最后一种数字是面向工程师和数学家的。它是虚数，你可能在学校记得它；它被定义为的平方根−1. 尽管被命名为虚构的，但它在模拟现实世界的工程情况以及物理和纯数学等其他学科中确实有很多实际用途。虚数内置于 Python 中，因此经常需要解决计算机问题的用户社区可以轻松使用它。拥有这种内置类型使 Python 能够帮助他们做到这一点。如果你碰巧是这些人中的一员，你会很高兴得知你并不孤单，Python 就在你身边。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。