This is almost always harder than it should be: the CSV file will have inconsistent string/numeric types and null values, and the JSON documents will pose additional problems around missing fields and nesting that prevents their loading into a relational database. Most datasets are small, and many analyses start locally, so I try loading the data into a SQLite or DuckDB embedded database. When presented with a new CSV file or collection of JSON blobs, my first reaction is to load the data into some structured data store.
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You can’t summarize or analyze your data in its raw form: you have to turn it into a data frame or SQL-queriable database. In my experience, even enterprise pipelines start with one data practitioner tinkering in an ad-hoc way before more deeply reporting and modeling, and autodata projects will likely narrow the gap between tinkering and production.
#AUTODATA FRAME DIMENSIONS HOW TO#
One deliberate element of this survey is that I largely focus on tools that facilitate data tinkering rather than on how to create enterprise data pipelines. This area deserves a deeper survey: I’d love to collaborate with anyone that’s compiling one. The survey reflects the bias in my own default data stack, which combines the command line, Python, and SQL. I’m sure I’ve missed many projects, as well as entire categories in the space. Here are a few trailblazing open source projects in the world of autodata, categorized by stage in the data analysis pipeline. There’s no reason to trust the second part, but it might be fun to read nonetheless. In this post, I’ll first list some open source projects in the space of autodata, and then take a stab at what the future of autodata could look like.
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#AUTODATA FRAME DIMENSIONS CODE#
Fully realized, an autodata workflow will break a high-level goal like “I want to predict X” or “I want to know why Y is so high” into a set of declarative steps (e.g., “Summarize the data,” “Build the model”) that require little or no custom code to run, but still allow for introspection and iteration. Autodata tools, when used responsibly, can standardize data workflows, improve the quality of models and reports, and save practitioners time.Īutodata doesn’t replace critical thinking: it just means that in fewer lines of code, a data practitioner can follow a best practices. This new world of autodata tools takes some agency away from practitioners in exchange for repeatability and a reduction in repetitive error-prone work. The community has been automating common procedures including data loading, exploratory data analysis, feature engineering, and model-building. Luckily, the data community has been making a lot of common operations less arcane and more repeatable. It’s no surprise that even after checking off every item of a good data practices checklist, the data practitioner doesn’t fully trust their own work. Worse yet, because of the many dependent steps involved in a data workflow, errors compound. The process for each step involves modifications to hundreds/thousands of lines of copy/pasted code, making it easy to forget to tweak a parameter or default. Data practitioners have to perform tens of steps in order to believe their own analyses and models. Much of the work a data scientist or engineer performs today is rote and error-prone.