### 统计代写|python代考|Making Decisions

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。

statistics-lab™ 为您的留学生涯保驾护航 在代写python方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写python代写方面经验极为丰富，各种代写python相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Making Decisions

So far, you have only seen how to manipulate data directly or through names to which the data is bound. Now that you have the basic understanding of how those data types can be manipulated manually, you can begin to exercise your knowledge of data types and use your data to make decisions.
In this chapter, you’ll learn about how Python makes decisions using True and False and how to make more complex decisions based on whether a condition is True or False.
You will learn how to create situations in which you can repeat the same actions using loops that give you the capability to automate stepping through lists, tuples, and dictionaries. You’ll also learn how to use lists or tuples with dictionaries cooperatively to explore the contents of a dictionary.

You will also be introduced to exception handling, which enables you to write your programs to cope with problematic situations that you can handle within the program.

## 统计代写|python代考|Comparing Values — Are They the Same

When you use the equality comparison, Python will compare the values on both sides. If the numbers are different, False will be the result. If the numbers are the same, then True will be the result.
If you have different types of numbers, Python will still be able to compare them and give you the correct answer:
$x>1.23==1$
False
$>>>1.0==1$
True
You can also use the double equals to test whether strings have the same contents, and you can even restrict this test to ranges within the strings (remember from the last chapter that slices create copies of the part of the strings they reference, so you’re really comparing two strings that represent just the range that a slice covers):
$>>>a=$ “Mackintosh apples”
$\rightarrow>>b=$ “Black Berries”
$>>c=$ “Golden Delicious apples”
$>>>a==b$
False
$x>b=a c$
False
$>>>$ a $[-1$ en $($ “apples “):-1] == c[-len $($ “apples ” $):-1]$
True
Sequences can be compared in Python with the double equals as well. Python considers two sequences to be equal when every element in the same position is the same in each list. Therefore, if you have three items each in two sequences and they contain the same data but in a different order, they are not equal:
$>>>$ apples $=[$ “Mackintosh”, “Golden Delicious”, “Fuji “, “Mitsu”]

apple_trees = [“Golden Delicious”, “Fuji”, “Mitsu”, “Mackintosh”]
$>>>$ apples = apple_trees
False
$>>>$ apple_trees = [“Mackintosh”, “Golden Delicious”, “Fuji ” “Mitsu*]
$>>$ apples = = apple_trees
True
In addition, dictionaries can be compared. Like lists, every key and value (paired, together) in one dictionary has to have a key and value in the other dictionary in which the key in the first is equal to the key in the second, and the value in the first is equal to the value in the second:
$>>>$ tuesday_breakfast_sold $=$ “pancakes” :10, ” french toast”: 4 , “bagels”:32,
“omelets”:12, “eggs and sausages” :131
$>>>$ wednesday_breakfast_sold $=$ “pancakes” $: 8$, “french toast” : 5 , “bagels” $: 22$,
“omelets”:16, “eggs and sausages” :22}
$>>$ tuesday_breakfast_sold $==$ wednesday_breakfast_sold
False
$>>>$ thursday_breakfast_sold = {“pancakes” :10, “french toast” $: 4$, “bagels” $: 32$,
“omelets” $: 12$, “eggs and sausages” :13)
tuesday_breakfast_sold == thursday_breakfast_sold
True

## 统计代写|python代考|Comparing Values — Which One Is More

The number on the left is compared to the number on the right. You can compare letters, too. There are a few conditions where this might not do what you expect, such as trying to compare letters to numbers. (The question just doesn’t come up in many cases, so what you expect and what Python expects is

probably not the same.) The values of the letters in the alphabet run roughly this way: A capital “A” is the lowest letter. “B” is the next, followed by ” $\mathrm{C}^{\prime \prime}$, and so on until ” $\mathrm{Z}$.” This is followed by the lowercase letters, with “a” being the lowest lowercase letter and ” $\mathrm{z}^{\prime \prime}$ the highest. However, “a” is higher than “Z”:
$>>>” a “>” b$ “
False
$>>$ “A” $^{\prime \prime} \mathrm{b}$ “
False
$>>>^{\prime A} |^{\prime \prime} a$ “
False
$>>>” b “>\mathrm{A}^{n}$
True
$>>$ ” $^{\prime \prime}>” a$ “
False
If you wanted to compare two strings that were longer than a single character, Python will look at each letter until it finds one that’s different. When that happens, the outcome will depend on that one difference. If the strings are completely different, the first letter will decide the outcome:
$>>>$ “Zebra” > “aardvark”
False
$>>>$ “Zebra” > “Zebrb”
False
$x>>$ “Zebra” $<$ “Zebrb* True You can avoid the problem of trying to compare two words that are similar but have differences in capitalization by using a special method of strings called lower, which acts on its string and return a new string with all lowercase letters. There is also a corresponding upper method. These are available for every string in Python: $\Rightarrow>>$ “Pumpkin” $==$ “pumpkin”
False
$>>>$ “Pumpkin”. lower( ()$==$ “pumpkin”. lower()
True
s $>$ “Pumpkin”. lower()
‘pumpkin’
$>>>$ “Pumpkin”. upper() == “pumpkin”. upper()
True
$>>>$ “pumpkin”. upper()
$>>>$ “Pumpkin” == “pumpkin”
False
$>>>$ “Pumpkin”. 1ower() == “pumpkin”. lower()
True
$>>>$ “Pumpkin”. 1ower()
‘pumpkin’
$>>>$ “Pumpkin” , upper() == “pumpkin” upper()
True
$>>>$ “pumpkin”. upper()
‘ PUMPKIN’
‘PUMPKIN’ ‘
When you have a string referenced by a name, you can still access all of the methods that strings normally have:
$>>>$ gourd $=$ “Calabash”
$>>>$ gourd
‘Calabash’
$>>>$ gourd = “Calabash”
$>>>$ gourd
‘Calabash’
$>>>$ gourd. lower()
‘calabash’
$>>$ gourd. upper()
‘CALABASH’
$>>>$ gourd. Lower()
‘calabash’
$>>>$ gourd. upper()
‘CALABASH’

## 统计代写|python代考|Comparing Values — Are They the Same

X>1.23==1

>>>1.0==1
True

>>>一种=“麦金托什苹果”
→>>b=“黑莓”
>>C=“金冠苹果”
>>>一种==b

X>b=一种C

>>>一种[−1在(“苹果”):-1] == c[-len(“苹果 ”):−1]

>>>苹果=[“麦金托什”、“金冠”、“富士”、“美津”]

apple_trees = [“金冠”、“富士”、“美津”、“麦金托什”]
>>>苹果 = apple_trees

>>>apple_trees = [“麦金托什”、“金冠”、“富士”“美津*]
>>apples = = apple_trees
True

>>>tuesday_breakfast_sold=“煎饼”：10，“法式吐司”：4，“百吉饼”：32，
“煎蛋卷”：12，“鸡蛋和香肠”：131
>>>星期三_早餐_已售出=“薄煎饼”:8, “法式吐司” : 5 , “百吉饼”:22，
“煎蛋卷”：16，“鸡蛋和香肠”：22}
>>tuesday_breakfast_sold==wednesday_breakfast_sold

>>>thursday_breakfast_sold = {“煎饼”：10，“法式吐司”:4， “贝果”:32,
“煎蛋卷”:12, “鸡蛋和香肠” :13)
tuesday_breakfast_sold == thursday_breakfast_sold
True

## 统计代写|python代考|Comparing Values — Which One Is More

>>>”一种“>”b“

>>“一种”′′b“

>>>′一种|′′一种“

>>>”b“>一种n

>> ” ′′>”一种“
False

>>>“斑马”>“土豚”

>>>“斑马”>“斑马”

X>>“斑马”<“Zebrb* True 您可以避免尝试比较两个相似但大小写不同的单词的问题，方法是使用一种称为 lower 的特殊字符串方法，该方法作用于其字符串并返回一个全小写字母的新字符串。还有对应的upper方法。这些可用于 Python 中的每个字符串：⇒>>“南瓜”==“南瓜”

>>>“南瓜”。降低（ （）==“南瓜”。lower( )

>“南瓜”。lower()
‘南瓜’
>>>“南瓜”。上（）==“南瓜”。上（）

>>>“南瓜”。上（）
>>>“南瓜” == “南瓜”

>>>“南瓜”。1ower() == “南瓜”。较低（）

>>>“南瓜”。1ower()
‘南瓜’
>>>“南瓜”，上（）==“南瓜”上（）

>>>“南瓜”。upper()
‘ PUMPKIN’
‘PUMPKIN’ ‘

>>>葫芦=“蠡”
>>>葫芦
‘葫芦’
>>>葫芦=“葫芦”
>>>葫芦
‘葫芦’
>>>葫芦。lower()
‘葫芦’
>>葫芦。上（）
‘葫芦’
>>>葫芦。Lower()
‘葫芦’
>>>葫芦。上（）
‘葫芦’

## 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。