Using SQLite in Python

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SQLite is a C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. SQLite is the most used database engine in the world.

In this tutorial, we will learn how to convert CSV files to database tables and run simple SQL queries in Python. We are using “surveys.csv” and “species.csv” files from here to practice our learning.

Usage

SQLite can be used in GUI software such as DB Browser for SQLite. But there are a lot of advantages to get queries directly in Python. In this document we are going to use module sqlite3 in Python to create a databases, import data and run queries.

import sqlite3

To create a database (db) or connect to an existing one and to add a cursor use:

con = sqlite3.connect('./mydb.db')
cur = con.cursor()

After setting “cur” pragma, we can start to execute SQL commands. For instance to create a table, called “survey”, with some fields we can run:

cur.execute("""
CREATE TABLE IF NOT EXISTS survey (
    record_id integer PRIMARY KEY,
    month text,
    day text,
    year text,
    plot_id text,
    species_id text,
    sex text,
    hindfoot_length real,
    weight real
    );""")

As you noticed we simply ran the SQL command to create the table with cur.execute. Later we will use this method to run SQL queries.

Importing data from CSV

Since we have a table, we can insert the data from “surveys.csv” into “survey” table by:

with open('./surveys.csv', 'r') as fl:
    hd = fl.readline()[:-1].split(',')
    ro = fl.readlines()
    db = [tuple(ro[i][:-1].split(',')) for i in range(len(ro))]

cur.executemany("INSERT INTO survey (record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight) VALUES (?,?,?,?,?,?,?,?,?);", db)

We need to commit the changes and close the connection to store the data into “survey” table:

con.commit()
con.close()

The following function can be used to insert CSV data to a database table. It adds fields’ name based on the CSV header and types from inputs:

#!/usr/bin/python3

import sqlite3

def csv_sql(file_dir,table_name,database_name,fields_type):
    con = sqlite3.connect(database_name)
    cur = con.cursor()
    # Drop the current table before re-create:
    cur.execute("DROP TABLE IF EXISTS %s;" % table_name)

    with open(file_dir, 'r') as fl:
        hd = fl.readline()[:-1].split(',')
        ro = fl.readlines()
        db = [tuple(ro[i][:-1].split(',')) for i in range(len(ro))]

    zip_ = list(zip(hd,list(fields_type)))
    hd_fl = ['%s %s' % (x,y) for x,y in zip_]
    header_field = ','.join(hd_fl)
    header = ','.join(hd)
    cur.execute("CREATE TABLE IF NOT EXISTS %s (%s);" % (table_name,header_field))
    cur.executemany("INSERT INTO %s (%s) VALUES (%s);" % (table_name,header,('?,'*len(hd))[:-1]), db)
    con.commit()
    con.close()
    
if __name__ == '__main__':
    # Example
    fields_type_1 = ['integer PRIMARY KEY'] + ['text']*6 + ['real']*2
    fields_type_2 = ['text PRIMARY KEY'] + ['text']*3
    csv_sql('./surveys.csv','survey','eco.db',fields_type_1)
    csv_sql('./species.csv','species','eco.db',fields_type_2)

Queries

Let’s use the above function to convert “survey.csv” and “species.csv” to tables in “eco.db”. Now, we can use execute command for running SQL commands in Python. For example:

import sqlite3

# Connect and read the db
con = sqlite3.connect('./eco.db')
cur = con.cursor()

# SQL query
cur.execute("SELECT DISTINCT year FROM survey;") # select unique survey years

# Store output
out = cur.fetchall()
print(out)

## [('1977',), ('1978',), ('1979',), ('1980',), ('1981',), ('1982',), ('1983',), ('1984',), ('1985',), ('1986',), ('1987',), ('1988',), ('1989',), ('1990',), ('1991',), ('1992',), ('1993',), ('1994',), ('1995',), ('1996',), ('1997',), ('1998',), ('1999',), ('2000',), ('2001',), ('2002',)]

# Close the connection
con.close()

# Output as a list
lis = [x[0] for x in out]
print(lis)

## ['1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002']

In the above example we stored the output as a Python list. We can also store the queries in Python dictionaries:

import sqlite3

# Connect and read the db
con = sqlite3.connect('./eco.db')
cur = con.cursor()

# SQL query
cur.execute("SELECT name FROM PRAGMA_TABLE_INFO('survey');") # select fields' name

# Store output
out = cur.fetchall()
name = [x[0] for x in out]
print(name)

## ['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex', 'hindfoot_length', 'weight']

# Exe query, store output and close the connection
cur.execute('SELECT * FROM survey LIMIT 2;') # select first two rows
out = cur.fetchall()
con.close()
print(out)

## [(1, '7', '16', '1977', '2', 'NL', 'M', 32.0, ''), (2, '7', '16', '1977', '3', 'NL', 'M', 33.0, '')]

# Output as a list of dictionaries
d = []
for i in out:
    d.append(dict(zip(name,i)))
print(d)

## [{'record_id': 1, 'month': '7', 'day': '16', 'year': '1977', 'plot_id': '2', 'species_id': 'NL', 'sex': 'M', 'hindfoot_length': 32.0, 'weight': ''}, {'record_id': 2, 'month': '7', 'day': '16', 'year': '1977', 'plot_id': '3', 'species_id': 'NL', 'sex': 'M', 'hindfoot_length': 33.0, 'weight': ''}]

The following are more examples of different SQL queries in Python:

import sqlite3

# Connect and read the db
con = sqlite3.connect('./eco.db')
cur = con.cursor()

# Queries
cur.execute("SELECT ROUND(weight/1000,2) FROM survey LIMIT 2;") # change weight to kg
print(cur.fetchall())
## [(0.0,), (0.0,)]

cur.execute("""
SELECT ROUND(weight/1000,2) 
FROM survey 
WHERE (year > 2000) AND (species_id IN ('DM','DS','DO')) AND (weight > 1)
LIMIT 2;""")
print(cur.fetchall())
## [(0.04,), (0.05,)]

cur.execute("""
SELECT species_id, sex, count(*) FROM survey
WHERE species_id IN ('DS','DO')
GROUP BY sex, species_id
ORDER BY count(*) DESC;""")
print(cur.fetchall())
## [('DO', 'M', 1707), ('DO', 'F', 1308), ('DS', 'M', 1270), ('DS', 'F', 1188), ('DS', '', 46), ('DO', '', 12)]

cur.execute("""
SELECT species_id, sex, count(*) FROM survey
GROUP BY sex, species_id
HAVING species_id IN ('DS','DO')
ORDER BY count(*) DESC;""")
print(cur.fetchall())
## [('DO', 'M', 1707), ('DO', 'F', 1308), ('DS', 'M', 1270), ('DS', 'F', 1188), ('DS', '', 46), ('DO', '', 12)]

cur.execute("""
SELECT species_id, sex, count(*) FROM survey
WHERE species_id LIKE 'D%'
GROUP BY sex, species_id
ORDER BY count(*) DESC;""")
print(cur.fetchall())
## [('DM', 'M', 5969), ('DM', 'F', 4554), ('DO', 'M', 1707), ('DO', 'F', 1308), ('DS', 'M', 1270), ('DS', 'F', 1188), ('DM', '', 73), ('DS', '', 46), ('DX', '', 40), ('DO', '', 12)]

cur.execute("""
SELECT * FROM survey
JOIN species
ON species.species_id = survey.species_id
WHERE weight < 5 AND sex = 'F'
LIMIT 2;""")
print(cur.fetchall())
## [(4052, '4', '5', '1981', '3', 'PF', 'F', 15.0, 4.0, 'PF', 'Perognathus', 'flavus', 'Rodent'), (5346, '2', '22', '1982', '21', 'PF', 'F', 14.0, 4.0, 'PF', 'Perognathus', 'flavus', 'Rodent')]

cur.execute("""
SELECT species_id ||'-'|| species FROM species;""")
print(cur.fetchone())
## ('AB-bilineata',)

Note that:

We can use fetchall() to fetch all outputs and fetchone() to get the first query
In LIKE operator, the wildcard % matches zero or more characters, so that %able% matches fixable and tablets
And we can combine fields by ||'<separator>'||

Also, we can use SQLite3 directly from command line for getting queries. For example, change the directory to your database location and enter sqlite3 eco.db to open the database and run:

.schema -- to see the schema of the databse
.tables -- to list database tables
.mode column -- to see in column mode
.header on -- to show headers
PRAGMA TABLE_INFO(species); -- to see tables info
SELECT * FROM survey ORDER BY year ASC LIMIT 10;