计算机代写|数据库作业代写SQL代考|Detecting Duplicates

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结构化查询语言(SQL)是一种标准化的编程语言,用于管理关系型数据库并对其中的数据进行各种操作。

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计算机代写|数据库作业代写SQL代考|Detecting Duplicates

计算机代写|数据库作业代写SQL代考|Detecting Duplicates

A duplicate is when you have two (or more) rows with the same information. Duplicates can exist for any number of reasons. A mistake might have been made during data entry, if there is some manual step. A tracking call might have fired twice. A processing step might have run multiple times. You might have created it accidentally with a hidden many-to-many JOIN. However they come to be, duplicates can really throw a wrench in your analysis. I can recall times early in my career when I thought I had a great finding, only to have a product manager point out that my sales figure was twice the actual sales. It’s embarrassing, it erodes trust, and it requires rework and sometimes painstaking reviews of the code to find the problem. I’ve learned to check for duplicates as I go.

Fortunately, it’s relatively easy to find duplicates in our data. One way is to inspect a sample, with all columns ordered:
SELECT column_a, column_b, column_c…
FROM table
SELECT column_a, column_b, column_c.
FROM table
ORDER BY $1,2,3 \ldots$
;
ORDER BY $1,2,3 \ldots$
;

This will reveal whether the data is full of duplicates, for example, when looking at a brand-new data set, when you suspect that a process is generating duplicates, or after a possible Cartesian JOIN. If there are only a few duplicates, they might not show up in the sample. And scrolling through data to try to spot duplicates is taxing on your eyes and brain. A more systematic way to find duplicates is to SELECT the columns and then count the rows (this might look familiar from the discussion of histograms!):
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count() as records
FROM….
GROUP BY $1,2,3 \ldots$
) a
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count $^{}$ ) as records FROM… GROUP BY $1,2,3 \ldots$ ) a WHERE records > 1 ; WHERE records > 1 ; This will tell you whether there are any cases of duplicates. If the query returns 0 , you’re good to go. For more detail, you can list out the number of records $(2,3,4$, etc.): SELECT records, count $()$
FROM
(
SELECT column_a, column_b, column_c…, count(*) as records
FROM….
GROUP BY $1,2,3 \ldots$
) a
WHERE records > 1
GROUP BY 1
;

计算机代写|数据库作业代写SQL代考|Deduplication with GROUP BY and DISTINCT

Duplicates happen, and they’re not always a result of bad data. For example, imagine we want to find a list of all the customers who have successfully completed a transaction so we can send them a coupon for their next order. We might JOIN the custom ers table to the transactions table, which would restrict the records returned to only those customers that appear in the transactions table:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
This will return a row for each customer for each transaction, however, and there are hopefully at least a few customers who have transacted more than once. We have accidentally created duplicates, not because there is any underlying data quality problem but because we haven’t taken care to avoid duplication in the results. Fortunately, there are several ways to avoid this with SQL. One way to remove duplicates is to use the keyword DISTINCT:
SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers a
JoIN transactions b on a.customer_id = b.customer_id
SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
;
Another option is to use a GROUP BY, which, although typically seen in connection with an aggregation, will also deduplicate in the same way as DISTINCT. I remember the first time I saw a colleague use GROUP BY without an aggregation dedupe-I

didn’t even realize it was possible. I find it somewhat less intuitive than DISTINCT, but the result is the samc:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
GROUP BY $1,2,3$
;
Another useful technique is to perform an aggregation that returns one row per entity. Although technically not deduping, it has a similar effect. For example, if we have a number of transactions by the same customer and need to return one record per customer, we could find the min (first) and/or the max (most recent) transac tion_date:
SELECT customer_id
,min(transaction_date) as first_transaction_date
, max(transaction_date) as last_transaction_date
, count $()$ as total_orders FROM table GROUP BY customer_id SELECT customer_id ,min(transaction_date) as first_transaction_date ,max(transaction_date) as last_transaction_date , count $\left(^{}\right.$ ) as total_orders
FROM table
GROUP BY customer_id
;
uplicate data, or data that contains multiple records per entity even if they techni-
;
Duplicate data, or data that contains multiple records per entity even if they technically are not duplicates, is one of the most common reasons for incorrect query results. You can suspect duplicates as the cause if all of a sudden the number of customers or total sales returned by a query is many times greater than what you were expecting. Fortunately, there are several techniques that can be applied to prevent this from occurring.
Another common problem is missing data, which we’ll turn to next.

计算机代写|数据库作业代写SQL代考|Cleaning Data with CASE Transformations

CASE statements can be used to perform a variety of cleaning, enrichment, and summarization tasks. Sometimes the data exists and is accurate, but it would be more useful for analysis if values were standardized or grouped into categories. The structure of CASE statements was presented earlier in this chapter, in the section on binning.
Nonstandard values occur for a variety of reasons. Values might come from different systems with slightly different lists of choices, system code might have changed,

options might have been presented to the customer in different languages, or the customer might have been able to fill out the value rather than pick from a list.

Imagine a field containing information about the gender of a person. Values indicating a female person exist as “F” “female”, and “femme.” We can standardize the values like this:
CASE when gender $=$ ‘ $F$ ‘ then ‘Female’
when gender = ‘female’ then ‘Female’
when qender = ‘femme’ then ‘Female’
else gender
end as gender_cleaned
CASE statements can also be used to add categorization or enrichment that does not exist in the original data. As an example, many organizations use a Net Promoter Score, or NPS, to monitor customer sentiment. NPS surveys ask respondents to rate, on a scale of 0 to 10 , how likely they are to recommend a company or product to a friend or colleague. Scores of 0 to 6 are considered detractors, 7 and 8 are passive, and 9 and 10 are promoters. The final score is calculated by subtracting the percentage of detractors from the percentage of promoters. Survey result data sets usually include optional free text comments and are sometimes enriched with information the organization knows about the person surveyed. Given a data set of NPS survey responses, the first step is to group the responses into the categories of detractor, passive, and promoter:
SELECT response_id
, likelihood
, case when llkelthood $<=6$ then ‘Detractor’
when likelihood $<=8$ then ‘Passive’
else ‘Promoter’
SELECT response_id
, Likelihood
,case when Llkelthood $<=6$ then ‘Detractor’
when likelihood $<=8$ then ‘Passive’
else ‘Promoter’
end as response_type
FRoM nps_responses
;
end as response_type
FROM nps_responses
;

计算机代写|数据库作业代写SQL代考|Detecting Duplicates

SQL代考

计算机代写|数据库作业代写SQL代考|Detecting Duplicates

重复是当您有两个(或更多)行具有相同的信息时。由于多种原因,可能存在重复项。如果有一些手动步骤,则可能在数据输入过程中出现错误。跟踪呼叫可能已触发两次。一个处理步骤可能已运行多次。您可能使用隐藏的多对多 JOIN 意外创建了它。无论它们如何出现,重复项确实会给您的分析带来麻烦。我记得在我职业生涯的早期,当我认为我有一个很好的发现时,却有一个产品经理指出我的销售额是实际销售额的两倍。这很尴尬,会削弱信任,并且需要返工,有时还需要对代码进行艰苦的审查才能发现问题。我学会了边走边检查重复项。

幸运的是,在我们的数据中找到重复项相对容易。一种方法是检查样本,所有列都已排序:
SELECT column_a、column_b、column_c…
FROM table
SELECT column_a、column_b、column_c。
FROM 表
ORDER BY1,2,3…
;
订购方式1,2,3…
;

这将揭示数据是否充满重复,例如,在查看全新的数据集时,当您怀疑某个进程正在生成重复时,或者在可能的笛卡尔连接之后。如果只有几个重复项,它们可能不会出现在示例中。滚动数据以尝试发现重复项对您的眼睛和大脑造成负担。一种更系统的查找重复项的方法是选择列然后计算行数(从直方图的讨论中这可能看起来很熟悉!):
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count() as records
FROM ….
分组1,2,3…
) 一个
SELECT count() FROM ( SELECT column_a, column_b, column_c… , count) 作为记录来自… GROUP BY1,2,3…) a WHERE 记录 > 1 ;WHERE 记录 > 1 ; 这将告诉您是否存在重复的情况。如果查询返回 0 ,您就可以开始了。有关更多详细信息,您可以列出记录数(2,3,4等):SELECT 记录、计数()
FROM
(
SELECT column_a, column_b, column_c…, count(*) 作为记录
FROM….
GROUP BY1,2,3…
) a
WHERE 记录 > 1
GROUP BY 1

计算机代写|数据库作业代写SQL代考|Deduplication with GROUP BY and DISTINCT

重复发生,它们并不总是错误数据的结果。例如,假设我们想要找到所有成功完成交易的客户的列表,这样我们就可以为他们的下一个订单发送优惠券。我们可以将客户表连接到交易表,这将限制返回的记录只返回给交易表中出现的客户:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a。 customer_id = b.customer_id
;
但是,这将为每个客户的每笔交易返回一行,并且希望至少有几个客户进行了多次交易。我们不小心创建了重复,不是因为存在任何潜在的数据质量问题,而是因为我们没有注意避免结果中的重复。幸运的是,使用 SQL 有几种方法可以避免这种情况。删除重复项的一种方法是使用关键字 DISTINCT:
SELECT distinct a.customer_id, a.customer_name, a.customer_email
FROM customers
a 在 a.customer_id = b.customer_id 上加入交易 b
SELECT distinct a.customer_id, a.customer_name, a .customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
;
;
另一种选择是使用 GROUP BY,尽管它通常与聚合相关联,但也会以与 DISTINCT 相同的方式进行重复数据删除。记得第一次看到同事用 GROUP BY 没有聚合去重-我

甚至没有意识到这是可能的。我发现它不如 DISTINCT 直观,但结果是 samc:
SELECT a.customer_id, a.customer_name, a.customer_email
FROM customers a
JOIN transactions b on a.customer_id = b.customer_id
GROUP BY1,2,3
;
另一种有用的技术是执行聚合,每个实体返回一行。尽管从技术上讲不是重复数据删除,但它具有类似的效果。例如,如果我们有同一个客户的多笔交易,并且需要为每个客户返回一条记录,我们可以找到最小(第一次)和/或最大(最近)交易日期:
SELECT customer_id
,min(transaction_date)作为 first_transaction_date
, max(transaction_date) 作为 last_transaction_date
, count()as total_orders FROM table GROUP BY customer_id SELECT customer_id ,min(transaction_date) as first_transaction_date ,max(transaction_date) as last_transaction_date , count() 作为 total_orders
FROM table
GROUP BY customer_id

复制数据,或者每个实体包含多个记录的数据,即使它们具有技术性

重复数据,或每个实体包含多个记录的数据,即使它们在技术上不重复,也是查询结果不正确的最常见原因之一。如果查询返回的客户数量或总销售额突然比您预期的多很多倍,您可能会怀疑重复是原因。幸运的是,有几种技术可以用来防止这种情况发生。
另一个常见的问题是缺少数据,我们将在接下来讨论这个问题。

计算机代写|数据库作业代写SQL代考|Cleaning Data with CASE Transformations

CASE 语句可用于执行各种清理、扩充和汇总任务。有时数据存在并且是准确的,但如果将值标准化或分组到类别中,它将对分析更有用。CASE 语句的结构在本章前面的分箱一节中介绍过。
出现非标准值的原因有很多。值可能来自不同的系统,选择列表略有不同,系统代码可能已更改,

选项可能已经以不同的语言呈现给客户,或者客户可能已经能够填写值而不是从列表中选择。

想象一个包含一个人的性别信息的字段。表示女性的值以“F”“female”和“femme”存在。我们可以像这样标准化这些值:
CASE when gender= ‘ F’ 然后 ‘Female’
当性别 = ‘female’ 然后 ‘Female’
当 qender = ‘femme’ 然后 ‘Female’
否则性别
以 gender_cleaned 结尾
CASE 语句还可用于添加原始数据中不存在的分类或丰富。例如,许多组织使用净推荐值或 NPS 来监控客户情绪。NPS 调查要求受访者以 0 到 10 的等级对他们向朋友或同事推荐公司或产品的可能性进行评分。0 到 6 分被认为是批评者,7 和 8 分是被动的,9 和 10 是推动者。最终得分是通过从推荐者的百分比中减去批评者的百分比来计算的。调查结果数据集通常包括可选的自由文本评论,有时还包含组织了解的有关被调查人的信息。给定一组 NPS 调查响应的数据集,第一步是将响应分为批评者、被动者和促进者类别:
SELECT response_id
, 可能性
, case when llkelthood<=6然后是“贬低者”的
可能性<=8然后是“被动”,
否则是“发起人”
SELECT response_id
,可能性
,Llkelthood 时的情况<=6然后是“贬低者”的
可能性<=8然后“被动”,
否则“发起人”
以 response_type
FRoM nps_responses 结尾

以 response_type
FROM nps_responses 结尾

计算机代写|数据库作业代写SQL代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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