![]() ![]() I would recommend that you get these problems sorted out while testing with a few hundred lines of that file, perhaps including line 13718893. I think these are not accepted because they are invalid CSV. import, produce an error to stderr such as: "adron_reducido_ruc.txt:13718893: unescaped " character". The import is very slow and noisy because of all the yapping about ignoring an extra field per record.Īnother issue, perhaps explaining your disappointment, is that a great many lines, during the. But the match is poor because 15 separators are needed for 16 fields. It is indeed using '|' as field separators, so it is very strange to be treating it as CSV.Īnother issue is that the lines have 15 separators, which almost matches the 15 columns you define for table SUNAT. With a few minutes to spare, I downloaded your large text file. Prior to import I perform an ANSI->UTF8 conversion from the text file.Ĭ:sqlite3.exe -csv -init config.cfg CONSULT.db ""ĬREATE TABLE IF NOT EXISTS SUNAT("RUC" INTEGER PRIMARY KEY, "NOMBRE O RAZÓN SOCIAL" TEXT, "ESTADO DEL CONTRIBUYENTE" TEXT, "CONDICIÓN DE DOMICILIO" TEXT, "UBIGEO" TEXT, "TIPO DE VÍA" TEXT, "NOMBRE DE VÍA" TEXT, "CÓDIGO DE ZONA" TEXT, "TIPO DE ZONA" TEXT, "NÚMERO" TEXT, "INTERIOR" TEXT, "LOTE" TEXT, "DEPARTAMENTO" TEXT, "MANZANA" TEXT, "KILÓMETRO" TEXT) Python’s great support for sqlite will make you love it in no time.Dear, I have a "small" problem doing the import of a txt file with csv mode, the file is just over 13 million records, but the import does not bring all of them to the db, from record 11million approx onwards one that another record is left out, thus until completing almost 300,000 records, the text file is in this format: 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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