11.1. Exploring Bike Rental Data with SQL¶
A lot of the data that we interact with today is stored in databases. For example:
Student records, including grades, at a school
Posts and friends in your favorite social network
News stories on a newspaper’s website
Your contacts list on your mobile phone
All images that make up Google Maps
All these bits of information are stored in various kinds of databases. Some of these are stored in relational databases that are available as open source tools like Postgresql, MySQL and SQLite, as well as commercial databases such as Google BigQuery, Oracle, Microsoft SQL Server, or Amazon Aurora
11.1.1. Query Language¶
Whatever the database might, there needs to be a way to extract data from it and a lot of these systems have agreed on a shared language for accessing data. For relational database, this language is called SQL (Structured Query Language, pronounced like “sequel”).
Before you stress out about learning a new language, let’s take a minute and review the things you have already learned how to do with Pandas.
You can change the shape of a DataFrame by selecting the columns you want or computing new columns.
You can filter a DataFrame by using conditions to select just the rows you want.
You can reorder a DataFrame by sorting on one or more columns.
You can group by one or more columns and compute aggregate summaries of other columns in the group.
You can join two dataframes together using the merge function.
The operations just described comprise a basic set of tools that any data manipulation language should support, and SQL supports these operations very well, in a very natural way. You are not going to have to learn any new concepts in this chapter you are just learning some new query syntax that will open up whole new worlds of data access for you. Most businesses run on a relational database of some kind, so it follows that a lot of real world data analysis requires you to get data from one. In this section we will teach you how to get started.
11.2. Getting Started with the Bike Data¶
In this Lesson, we will be hands on and try out SQL with the Capital bike sharing dataset, hosted on a SQLLite database. You don’t have to do anything as we have a full version of the SQLLite database management system running the browser.
11.2.1. Verify access to the dataset¶
Let’s verify that you have access to the dataset by running a simple SQL query.
The code snippet contains a few lines: * The first line of that code block is just a magic invocation that lets Jupyter know that this cell contains SQL and not Python. * The second line introduces SQL syntax for the first time. To help you understand the SQL commands we are using, the SQL syntax words are listed in CAPITAL letters, the lowercase words are the names of tables or columns. The SQL statement translates to: grab (SELECT) all the values (*) in the table called trip_data but only show me the first ten (LIMIT 10).
The trip_data table is composed of several columns:
index BIGINT, duration BIGINT, start_date DATETIME, end_date DATETIME, start_station BIGINT, end_station BIGINT, bike_number TEXT, member_type TEXT
We don’t always want to read all the columns in a table. For example, if we just want the subscriber type, start time, and duration in minutes columns we could select:
Tips: SQL doesn’t care about line breaks so we can spread a SQL query over multiple lines just to make it easier to read.
It’s also really easy to forget the exact names of all of the columns in a table, especially when you are just getting started with a new database. Here’s a handy one-liner that will remind you of the names of your tables and all of their columns and types:
Note, this works fine for SQLITE but will not work for Postgresql or MySQL or other databases, each database has their own query for things like this, and once you get more experience you’ll be able to easily find them on the internet.