Accessing SQLite Databases Using Python & Pandas

Last updated on 2024-02-13 | Edit this page

Estimated time: 45 minutes

Python and SQL

When you open a CSV in python, and assign it to a variable name, you are using your computers memory to save that variable. Accessing data from a database like SQL is not only more efficient, but also it allows you to subset and import only the parts of the data that you need.

In the following lesson, we’ll see some approaches that can be taken to do so.

The sqlite3 module

The sqlite3 module provides a straightforward interface for interacting with SQLite databases. A connection object is created using sqlite3.connect(); the connection must be closed at the end of the session with the .close() command. While the connection is open, any interactions with the database require you to make a cursor object with the .cursor() command. The cursor is then ready to perform all kinds of operations with .execute().

PYTHON 

import sqlite3

# Create a SQL connection to our SQLite database
con = sqlite3.connect("eebo.db")

cur = con.cursor()

# the result of a "cursor.execute" can be iterated over by row
for row in cur.execute('SELECT * FROM eebo;'):
    print(row)

#Be sure to close the connection.
con.close()

Queries

One of the most common ways to interact with a database is by querying: retrieving data based on some search parameters. Use a SELECT statement string. The query is returned as a single tuple or a tuple of tuples. Add a WHERE statement to filter your results based on some parameter.

PYTHON 

import sqlite3

# Create a SQL connection to our SQLite database
con = sqlite3.connect("eebo.db")

cur = con.cursor()

# Return all results of query
cur.execute('SELECT Title FROM eebo WHERE Status="Free"')
cur.fetchall()

# Return first result of query
cur.execute('SELECT Title FROM eebo WHERE Status="Free"')
cur.fetchone()

#Be sure to close the connection.
con.close()

Accessing data stored in SQLite using Python and Pandas

Using pandas, we can import results of a SQLite query into a dataframe. Note that you can use the same SQL commands / syntax that we used in the SQLite lesson. An example of using pandas together with sqlite is below:

PYTHON 

import pandas as pd
import sqlite3

# Read sqlite query results into a pandas DataFrame
con = sqlite3.connect("eebo.db")
df = pd.read_sql_query("SELECT * from eebo", con)

# verify that result of SQL query is stored in the dataframe
print(df.head())

con.close()

Storing data: CSV vs SQLite

Storing your data in an SQLite database can provide substantial performance improvements when reading/writing compared to CSV. The difference in performance becomes more noticable as the size of the dataset grows (see for example these benchmarks).

Challenge - SQL

  1. Create a query that contains title data published between 1550 - 1650 that includes book’s Title, Author, and TCP id. How many records are returned?

Storing data: Create new tables using Pandas

We can also us pandas to create new tables within an SQLite database. Here, we run we re-do an excercise we did before with CSV files using our SQLite database. We first read in our survey data, then select only those survey results for 2002, and then save it out to its own table so we can work with it on its own later.

PYTHON 

import pandas as pd
import sqlite3

con = sqlite3.connect("eebo.db")

# Load the data into a DataFrame
books_df = pd.read_sql_query("SELECT * from eebo", con)

# Select only data for 1640
titles1640 = books_df[books_df.Date == '1640']

# Write the new DataFrame to a new SQLite table
titles1640.to_sql("titles1640", con, if_exists="replace")

con.close()

Challenge - Saving your work

  1. For each of the challenges in the previous challenge block, modify your code to save the results to their own tables in the eebo database.

  2. What are some of the reasons you might want to save the results of your queries back into the database? What are some of the reasons you might avoid doing this.