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Excel Analysis Toolpak

Running Statistical Data Analysis (EDA) using Excel Functions and Formulas.

Back in August Microsoft made a momentous announcement of good ole’ Excel now supporting Python scripts (at least for those Office 365 subscribers who are part of the Beta Insider channel.) While this functionality brings an array of virtually unlimited possibilities to an already powerful tool, many analysts recognize that support for various statistical analysis techniques is one of the most promising areas to capitalize here. Having said that, some users do not realize that they don’t need to leverage sophisticated Python functions to conduct exploratory data analysis of their datasets – they can instead install Excel add-in called “Analysis Toolpak” to get them started on their statistical learning journey. In this post we will use this tool to analyze weight distribution of players appearing on the 2023/24 Chicago Bears Roster. These summary statistics will help us get a quick overview of this dataset, and in turn, make it easier for us to spot patterns and outliers. We will then recreate these capabilities via Excel functions and formulas.

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Array Functions in MS Excel

Working with MS Excel Array Functions (FILTER, RANDARRAY, SEQUENCE, UNIQUE, SORT, SORTBY)

It’s hard to keep up with seemingly non-stop additions to Excel’s impressive catalog of functions. If you happen to belong to Office Beta channel you might have started working with the newest release of text and array functions. As a regular Office 365 subscriber I’ve already covered newish XLOOKUP and LAMBDA functions on this blog. It was last year when the Excel team introduced dynamic array functions, also known as spilled array functions. These functions return arrays of of values of different sizes and spill them into adjacent cells. You can usually specify how long and how wide the output ranges should be. These functions include – UNIQUE (returns unique values from the range of values), SEQUENCE (retrieves a sequence of values), SORT, SORTBY, RANDARRAY(array of random numbers based on specified parameters), and FILTER. In this post we will explore examples on how to use these functions.

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LAMBDA Function - MS Excel - MPG

Having fun with Excel LAMBDA function – building a BMI calculator and MPG conversion formulas

Let’s face it, despite the ever-growing catalog of 500(!) or so Excel functions, we cannot expect Microsoft developers to create all functions that meet the needs of every Excel user. Even if this feat were possible, the program itself would become unusable due to the sheer number of functions made available blocking us from using a few that we actually need. Traditional solution is age-old – using VBA to code a User Defined Function (UDF.) In fact, we’ve already covered the process of creating UDFs here and here.

Fast forward to 2016, when Excel teased the idea of the LAMBDA function, initially limiting its release to Office 365 subscribers opted in into the Office Insider edition. Since then this “ultimate” Excel function went through a number of revisions and enhancements and while it didn’t make its wider debut in time for Excel 2021 version, it was eventually released to the general public (i.e. all current Office 365 subscribers) earlier this month. We can now create a custom Excel function without having to deal with the Visual Basic Editor window ever again.

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Table Joins

Understanding Table Joins in SQL

Working with data often involves the need to utilize multiple data sources, usually stored in different data tables (in case of database storage) or data frames (when it comes to programming languages or data visualization tools.)  In order to put power of this data to a good use we want to be able to join these tables based on a field or fields they have in common (foreign key[s]) or sometimes values in the field that are different. Not only basic principles of table joins – INNER, OUTER (FULL, LEFT, and RIGHT), CROSS (or Cartesian) or even UNION-ing tables are universal to most relational databases and flavors of SQL, they also apply to working with data frames. In this post we will explore examples of using these table joins in a PostgreSQL database, while adding SELF, and LEFT/RIGHT exclusive joins for a good measure.

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