prosto VS SheetJS js-xlsx

> Joins are what makes relational modeling interesting!

It is the central part of RM which is difficult to model using other methods and which requires high expertise in non-trivial use cases. One alternative to how multiple tables can be analyzed without joins is proposed in the concept-oriented model [1] which relies on two equal modeling constructs: sets (like RM) and functions. In particular, it is implemented in the Prosto data processing toolkit [2] and its Column-SQL language. The idea is that links between tables are used instead of joins. A link is formally a function from one set to another set.

[1] Joins vs. Links or Relational Join Considered Harmful https://www.researchgate.net/publication/301764816_Joins_vs_...

[2] https://github.com/asavinov/prosto data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby

One idea is to use columns instead of cells. Each column has a definition in terms of other columns which might also be defined in terms of other columns. If you change value(s) in some source column then these changes will propagate through the graph of these column definitions. Some fragments of this general idea were implemented in different systems, for example, Power BI or Airtable.

This approach was formalized in the concept-oriented model of data which relies on two basic elements: mathematical functions and mathematical sets. In contrast, most traditional data models rely on only sets. Functions are implemented as columns. The main difficulty in any formalization is how to deal with columns in multiple tables.

This approach was implemented in the Prosto data processing toolkit: https://github.com/asavinov/prosto

Having Python expressions within a declarative language is a really good idea because we can combine low level logic of computations of values with high level logic of set processing.

A similar approach is implemented in the Prosto data processing toolkit:

https://github.com/asavinov/prosto

Although Prosto is viewed as an alternative to Map-Reduce by relying on functions, it also supports Python User-Defined Functions in its Column-SQL:

Most of the self-service or no-code BI, ETL, data wrangling tools are am aware of (like airtable, fieldbook, rowshare, Power BI etc.) were thought of as a replacement for Excel: working with tables should be as easily as working with spreadsheets. This problem can be solved when defining columns within one table: ``ColumnA=ColumnB+ColumnC, ColumnD=ColumnAColumnE`` we get a graph of column computations* similar to the graph of cell dependencies in spreadsheets.

Yet, the main problem is in working multiple tables: how can we define a column in one table in terms of columns in other tables? For example: ``Table1::ColumnA=FUNCTION(Table2::ColumnB, Table3::ColumnC)`` Different systems provided different answers to this question but all of them are highly specific and rather limited.

Why it is difficult to define new columns in terms of other columns in other tables? Short answer is that working with columns is not the relational approach. The relational model is working with sets (rows of tables) and not with columns.

One generic approach to working with columns in multiple tables is provided in the concept-oriented model of data which treats mathematical functions as first-class elements of the model. Previously it was implemented in a data wrangling tool called Data Commander. But them I decided to implement this model in the *Prosto* data processing toolkit which is an alternative to map-reduce and SQL:

https://github.com/asavinov/prosto

It defines data transformations as operations with columns in multiple tables. Since we use mathematical functions, no joins and no groupby operations are needed and this significantly simplifies and makes more natural the task of data transformations.

Moreover, now it provides *Column-SQL* which makes it even easier to define new columns in terms of other columns:

https://github.com/asavinov/prosto/blob/master/notebooks/col...

Hamilton is more similar to the Prosto data processing toolkit which also relies on column operations defined via Python functions:

https://github.com/asavinov/prosto

However, Prosto allows for data processing via column operations in many tables (implemented as pandas data frames) by providing a column-oriented equivalents for joins and groupby (hence it has no joins and no groupbys which are known to be quite difficult and require high expertise).

Prosto also provides Column-SQL which might be simpler and more natural in many use cases.

The whole approach is based on the concept-oriented model of data which makes functions first-class elements of the model as opposed to having only sets in the relational model.

One alternative to SQL (type of thinking) is Column-SQL [1] which is based on a new data model. This model is relies on two equal constructs: sets (tables) and functions (columns). It is opposed to the relational algebra which is based on only sets and set operations. One benefit of Column-SQL is that it does not use joins and group-by for connectivity and aggregation, respectively, which are known to be quite difficult to understand and error prone in use. Instead, many typical data processing patterns are implemented by defining new columns: link columns instead of join, and aggregate columns instead of group-by.

More details about "Why functions and column-orientation" (as opposed to sets) can be found in [2]. Shortly, problems with set-orientation and SQL are because producing sets is not what we frequently need - we need new columns and not new table. And hence applying set operations is a kind of workaround due the absence of column operations.

This approach is implemented in the Prosto data processing toolkit [0] and Column-SQL[1] is a syntactic way to define its operations.

[0] https://github.com/asavinov/prosto Prosto is a data processing toolkit - an alternative to map-reduce and join-groupby

[1] https://prosto.readthedocs.io/en/latest/text/column-sql.html Column-SQL (work in progress)

[2] https://prosto.readthedocs.io/en/latest/text/why.html Why functions and column-orientation?

> This is closer to the way that humans perceive the world — mapping between whatever aspect of external reality you are interested in and the data model is an order of magnitude easier than with relational databases.

One approach to modeling data based on mappings (mathematical functions) is the concept-oriented model [1] implemented in [2]. Its main feature is that it gets rid of joins, groupby and map-reduce by manipulating data using operations with functions (mappings).

> Everything is pre-joined — you don’t have to disassemble objects into normalised tables and reassemble them with joins.

One old related general idea is to assume the existence of universal relation. Such an approach is referred to as the universal relation model (URM) [3, 4].

[1] A. Savinov, Concept-oriented model: Modeling and processing data using functions, Eprint: arXiv:1911.07225 [cs.DB], 2019 https://www.researchgate.net/publication/337336089_Concept-o...

[2] https://github.com/asavinov/prosto Prosto Data Processing Toolkit: No join-groupby, No map-reduce

[3] https://en.wikipedia.org/wiki/Universal_relation_assumption

[4] R. Fagin, A.O. Mendelzon and J.D. Ullman, A Simplified Universal Relation Assumption and Its Properties. ACM Trans. Database Syst., 7(3), 343-360 (1982).

(Currently, it is not actively developed and the focus is moved to a similar project - https://github.com/asavinov/prosto - also focused on data preprocessing and feature engineering)

ExcelJS and XLSX (SheetJS) are great libraries to work with XLSX files. The former I've found a bit easier to work with but less efficient in general.

Sure. I manipulate excel files programatically in the browser all the time. I don't really understand your exact workflow, but I use Javascript with xlsx and React.

Looking at the xlsx documentation, to parse files in the browser, rather than readFile, you use read, which is designed to parse binary data directly, rather than read from disk. There are a bunch of different formats if you go to the XLSX NPM page and scroll down to "Acquiring and Extracting Data". Importantly, it seems the data must already be serialized, so a Blob won't work, but we can work with that.

Last year, Bing and Edge erroneously flagged our website https://sheetjs.com/ as "dangerous": https://i.imgur.com/BvA3zrk.png

At the time, there was no "Safety Report" to indicate why Bing thought it was dangerous. The report page linked to https://www.bing.com/toolbox/bing-site-safety?url=https%3a%2... and it said "That web page doesn't exist"

To fix it, we had to register with "Bing Webmaster Tools" (https://www.bing.com/webmasters/about) and raise a support ticket.

Within a few days, the issue "resolved itself". It's possible that raising a ticket forced some automatic refresh of the indexed data for the domain.

xlsx.

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For more, you can visit xlsx documentation Link.

There are libraries like https://github.com/SheetJS/sheetjs to parse excel and https://www.chartjs.org/ for all kinds of charts/graphs. Not really much HTML involved here.. the markup gets generated by the chart library.