If you haven't done so already, make sure that SQLite3 is installed. NOTE : Depending on what distribution of Python you are on, you might have to specify python3 instead of python. You will be writing functions and learning about how Python deals with blob data in this file, but for now, you have a simple main() function that prints "Hello World!" when you enter the command python app.py in your terminal. If you are using a Mac or Unix computer enter these commands in your terminal: Some prior knowledge in SQL logic or a willingness to learn.Ĭreate a project directory in your terminal called “sqlite3_blob_data” to follow along.SQLite3 in order to open the SQLite shell and make executable scripts.If your operating system does not provide a Python interpreter, you can go to to download an installer. Then you will learn how to retrieve the blobs and convert them back into regular files for use by your application. In this tutorial, you will learn about blob (binary large object) data and why you need to convert your multimedia files to binary objects in order to store them properly in a database. You could store the files directly in the server’s file system, integrate with APIs from cloud storage platforms, or you can learn how to store the information in your database! Python’s great support for sqlite will make you love it in no time.If you're a developer who is building out a website that allows users to upload image files for reasons such as updating a profile picture, sharing images with other users, or utilizing other multimedia files, you will find yourself wondering, "what's the safest way to save their data?". It’s a great database when you’d like relational database query functionality without the overhead of Postgres. Sqlite databases are great for local experimentation and are used extensively on mobile phones. Python’s build in sqlite library coupled with Pandas DataFrames makes it easy to load CSV data into sqlite databases. pd.read_sql('''SELECT * FROM users u LEFT JOIN orders o ON u.user_id = o.user_id''', conn) Next steps You can also read the SQL query directly into a Pandas DataFrame. Here’s the array that’s returned: [(1, 'pokerkid', 1, 1, 'speaker'), Join the users and orders tables on the user_id value and print the results: c.execute('''SELECT * FROM users u LEFT JOIN orders o ON u.user_id = o.user_id''') Orders.to_sql('orders', conn, if_exists='append', index = False) # write to sqlite table Fetch results of database join Orders = pd.read_csv('orders.csv') # load to DataFrame c.execute('''CREATE TABLE orders (order_id int, user_id int, item_name text)''') Suppose you have the following orders.csv file: order_id,user_id,item_nameĬreate a table and then load the orders data into the database. Cursors can be thought of as iterators in the database world. The fetchall() method returns an array of tuples.Ĭ.execute() returns a sqlite3.Cursor object. Fetch values from sqlite tableįetch all the rows from the users table: c.execute('''SELECT * FROM users''').fetchall() # The to_sql method makes it easy to write DataFrames to databases. Users.to_sql('users', conn, if_exists='append', index = False) Pandas makes it easy to load this CSV data into a sqlite table: import pandas as pd Suppose you have the following users.csv file: user_id,username c.execute('''CREATE TABLE users (user_id int, username text)''') Load CSV file into sqlite table import sqlite3Įxecute a query that’ll create a users table with user_id and username columns. You can create the file with touch my_data.db or with this equivalent Python code: from pathlib import PathĪ zero byte text file is a great starting point for a lightweight database! Creating sqlite tableĬreate a database connection and cursor to execute queries. Sqlite is a lightweight database that can be started as an empty text file. Python is perfect language for this task because it has great libraries for sqlite and CSV DataFrames. This blog post demonstrates how to build a sqlite database from CSV files.
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