![]() ![]() 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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