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SQL
SQL was a go-to tool when you needed to get a quick-and-dirty look at some data, and draw preliminary conclusions that might, eventually, lead to a report or an application being written. This is called exploratory analysis.
These days, data comes in many shapes and forms, and it’s not synonymous with “relational database” anymore. You may end up with CSV files, plain text, Parquet, HDF5, and who knows what else. This is where Pandas library shines.
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What is Pandas?
Python Data Analysis Library, called Pandas, is a Python library built for data analysis and manipulation. It’s open-source and supported by Anaconda. It is particularly well suited for structured (tabular) data.
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Where do I start?
- QL is a declarative programming language.
- With SQL, you declare what you want in a sentence that almost reads like English.
- Pandas’ syntax is quite different from SQL. In Pandas, you apply operations on the dataset, and chain them, in order to transform and reshape the data the way you want it.
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The anatomy of a SQL query
A SQL query consists of a few important keywords. Between those keywords, you add the specifics of what data, exactly, you want to see. Here is a skeleton query without the specifics:
SELECT… FROM… WHERE…
GROUP BY… HAVING…
ORDER BY…
LIMIT… OFFSET…
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