If you’re playing in PostgreSQL you may find its window functions of great use to perform calculations across sets within your data.įor a deep dive on data analysis in PostgreSQL, R, and Python check out this blog post from Zev Ross. The School of Data has a neat little tutorial on Using SQL for Lightweight Data Analysis that’ll get you started.
When datasets get larger, or the analysis requirements get more complex, you’ll probably find yourself reaching for a database to do the heavy lifting. And finally, check out this rundown of Excel plugins for analysing and visualising data. The Sunlight Foundation has a set of good videos as an intro to Data Visualisation in Google Docs which also covers analysis. If you really need to get back to first principles their Data Fundamentals series would be well worth a look. The School of Data has an excellent tutorial Using Excel to do precision data journalism. Sometimes the simplest tools are the best and a spreadsheet is all you need – Excel is, afterall, the world’s most widely used IDE! So you’ve got a nice clean dataset and now you want to do some analysis on it to understand if reality matches your hypothesis! Spreadsheets If you need to upskill your regex foo Debuggex and Regexpr should set you on the right path.
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If out-of-the-box tools aren’t cutting it and you need to dive into code take a look back at some of the Python libraries, like pandas, that we recommended in Converting between data formats. If you’re feeling brave take a look at dedupe, which leverages machine-learning to perform de-duplication and cleansing of data.Īnd if all else fails you can always fall back to reliable command-line tools like grep, awk, and sed combined with regular expressions. Their Cleaning Data with Spreadsheets walkthrough may also fit the bill. Check out the School of Data’s simple tutorial on using OpenRefine to see it in action.ĭepending on how badly munged your data is a simple old spreadsheet application may get you most of the way to having clean data – as per the excellent A Gentle Introduction to Data Cleaning series from the School of Data. OpenRefine (formerly Google Refine) is a powerful desktop tool for cleaning messy data, transforming it between different tabular data formats, and even integrates with web services via some simple connectors so you can, for example, geocode a bunch of addresses using Google directly in OpenRefine. Fortunately, there are a couple of great tools.
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If your data has had humans involved in entering it then it’s probably full of all sorts of small variations in how the data have been entered that you’ll need to clean up before it becomes machine-readable. If you need even more control consider the Python libraries pandas (which provides a whole data analysis and modelling framework as well), tablib, or any of the Science and Data Analysis libraries listed on Awesome Python. Data Converter is a simple web-based tool for converting from Excel, CSV, and TSV to JSON, HTML, MySQL, PHP, Python, Ruby, and more.įor batching up the conversion process to run across many datasets consider the dataconvert command-line tool developed by OKFN Labs for converting from CSV, XLS(X), and JSON to CSV. There’s a good chance that you’re going to want to convert your data from the format you’ve found into something a little more modern and useful (like JSON).
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CSV/TSV, JSON, XLS(X), and XML are some of the more common formats you’ll find tabular data in, though unfortunately it does still often appear in non machine-readable formats like PDF and DOC and most first be extracted and cleaned before being used. in tables), either in a flat file or a database, and usually consists of simple alphanumeric values. At its simplest, tabular data is data that is stored in rows and columns (hence the name “tabular” i.e.
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