Mysql like on select9/1/2023 ![]() ![]() Here’s a simple example that returns all products that don’t contain the word white in their product name. ![]() To obtain items that do not contain a given value you can use NOT LIKE. Using NOT LIKE to return values that don’t match a string ![]() By putting a percentage symbol either side of the value we’ll return any products that contain the word white. To do this we can use LIKE along with the % symbol. For example, we might want to look at all the products in the order_items table that contain the word white in their product name held in the description column. Sometimes, you might want to perform a fuzzy match and return only items that match a partial string. Using LIKE to return values containing a string You can select the data from these tables simply by changing the database table parameter after FROM. The ecommerce database contains four database tables called customers, orders, order_items, and products. Since that’s just a regular Pandas dataframe, we can manipulate it in the same way we would an other dataframe, for example, by using head() to print the first five rows. Pandas will use SQLAlchemy and PyMSQL to connect to the database, run the query, and return the results in a Pandas dataframe that we’re assigning to the variable df. To execute the SQL statement or query, we’ll pass it to the Pandas read_sql_query() function along with the engine connection we created above. The statement SELECT * FROM customers uses the asterisk wildcard to to MySQL to return all columns in the customers table. Now you hopefully have a working database connection, we’ll write the most simple of all SQL queries - the SELECT statement. We’ll be using sqlalchemy and pymysql, so import the packages into your Jupyter environment using the commands below.Įngine = create_engine ( ) SELECT * FROM a SQL table To query a database with Pandas you’ll need an SQL driver - a bit of extra code that connects to the database and passes your SQL queries. To query the MySQL database on your Docker container we’ll need to install a couple of Python packages. Although it sounds scary, the whole process should only take a few minutes and I’ve provided the code you need for every step. To get started, you’ll first need to create a MySQL Docker container and then import the SQL database dump to create a new database. To make this easier to follow along, I’ve created a MySQL database dump file of the popular Online Retail Dataset that you can import into a Docker container. Fire up your database serverīefore you can start, you’ll need to have access to a MySQL database that you can query. In this simple tutorial I’ll explain how you can use SELECT with the FROM, WHERE and AND clauses to fetch a wide range of specific column values from the database tables in a MySQL database. The SELECT statement is the most simple of all SQL queries and allows you to retrieve the precise data you want from one or more tables, or even databases. ![]()
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