Accessing SQLite Databases Using Python and Pandas
Last updated on 2023-05-18 | Edit this page
Estimated time: 45 minutes
Overview
Questions
- What if my data are stored in an SQL database? Can I manage them with Python?
- How can I write data from Python to be used with SQL?
Objectives
- Use the sqlite3 module to interact with a SQL database.
- Access data stored in SQLite using Python.
- Describe the difference in interacting with data stored as a CSV file versus in SQLite.
- Describe the benefits of accessing data using a database compared to a CSV file.
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("data/portal_mammals.sqlite")
cur = con.cursor()
# The result of a "cursor.execute" can be iterated over by row
for row in cur.execute('SELECT * FROM species;'):
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("data/portal_mammals.sqlite")
cur = con.cursor()
# Return all results of query
cur.execute('SELECT plot_id FROM plots WHERE plot_type="Control"')
cur.fetchall()
# Return first result of query
cur.execute('SELECT species FROM species WHERE taxa="Bird"')
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:
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 noticeable as the size of the dataset grows (see for example these benchmarks).
Challenge - SQL
- Create a query that contains survey data collected between 1998 - 2001 for observations of sex “male” or “female” that includes observation’s genus and species and site type for the sample. How many records are returned?
- Create a dataframe that contains the total number of observations (count) made for all years, and sum of observation weights for each site, ordered by site ID.
-
PYTHON
#Connect to the database con = sqlite3.connect("data/portal_mammals.sqlite") cur = con.cursor() # Return all results of query: year, plot type (site type), genus, species and sex # from the join of the tables surveys, plots and species, for the years 1998-2001 where sex is 'M' or 'F'. cur.execute('SELECT surveys.year,plots.plot_type,species.genus,species.species,surveys.sex \ FROM surveys INNER JOIN plots ON surveys.plot = plots.plot_id INNER JOIN species ON \ surveys.species = species.species_id WHERE surveys.year>=1998 AND surveys.year<=2001 \ AND ( surveys.sex = "M" OR surveys.sex = "F")') print('The query returned ' + str(len(cur.fetchall())) + ' records.') # Close the connection con.close()OUTPUT
The query returned 5546 records. -
PYTHON
# Create two sqlite queries results, read as pandas DataFrame # Include 'year' in both queries so we have something to merge (join) on. con = sqlite3.connect("data/portal_mammals.sqlite") df1 = pd.read_sql_query("SELECT year,COUNT(*) FROM surveys GROUP BY year", con) df2 = pd.read_sql_query("SELECT year,plot,SUM(wgt) FROM surveys GROUP BY \ year,plot ORDER BY plot ASC",con) # Turn the plot_id column values into column names by pivoting df2 = df2.pivot(index='year',columns='plot')['SUM(wgt)'] df = pd.merge(df1, df2, on='year') # Verify that result of the SQL queries is stored in the combined dataframe print(df.head()) con.close()OUTPUT
year COUNT(*) 1 2 3 4 5 6 7 \ 0 1977 503 567.0 784.0 237.0 849.0 943.0 578.0 202.0 1 1978 1048 4628.0 4789.0 1131.0 4291.0 4051.0 2371.0 43.0 2 1979 719 1909.0 2501.0 430.0 2438.0 1798.0 988.0 141.0 3 1980 1415 5374.0 4643.0 1817.0 7466.0 2743.0 3219.0 362.0 4 1981 1472 6229.0 6282.0 1343.0 4553.0 3596.0 5430.0 24.0 8 ... 15 16 17 18 19 20 21 22 \ 0 595.0 ... 48.0 132.0 1102.0 646.0 336.0 640.0 40.0 316.0 1 3669.0 ... 734.0 548.0 4971.0 4393.0 124.0 2623.0 239.0 2833.0 2 1954.0 ... 472.0 308.0 3736.0 3099.0 379.0 2617.0 157.0 2250.0 3 3596.0 ... 1071.0 529.0 5877.0 5075.0 691.0 5523.0 321.0 3763.0 4 4946.0 ... 1083.0 176.0 5050.0 4773.0 410.0 5379.0 600.0 5268.0 23 24 0 169.0 NaN 1 NaN NaN 2 137.0 901.0 3 742.0 4392.0 4 57.0 3987.0 [5 rows x 26 columns]
Storing data: Create new tables using Pandas
We can also use pandas to create new tables within an SQLite database. Here, we re-do an exercise 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("data/portal_mammals.sqlite")
# Load the data into a DataFrame
surveys_df = pd.read_sql_query("SELECT * from surveys", con)
# Select only data for 2002
surveys2002 = surveys_df[surveys_df.year == 2002]
# Write the new DataFrame to a new SQLite table
surveys2002.to_sql("surveys2002", con, if_exists="replace")
con.close()Challenge - Saving your work
For each of the challenges in the previous challenge block, modify your code to save the results to their own tables in the portal database.
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.
PYTHON
#Connect to the database con = sqlite3.connect("data/portal_mammals.sqlite") # Read the results into a DataFrame df1 = pd.read_sql_query('SELECT surveys.year,plots.plot_type,species.genus,species.species,surveys.sex \ FROM surveys INNER JOIN plots ON surveys.plot = plots.plot_id INNER JOIN species ON \ surveys.species = species.species_id WHERE surveys.year>=1998 AND surveys.year<=2001 \ AND ( surveys.sex = "M" OR surveys.sex = "F")') df1.to_sql("New Table 1", con, if_exists="replace") # We already have the 'df' DataFrame created in the earlier exercise df.to_sql("New Table 2", con, if_exists="replace") # Close the connection con.close()-
If the database is shared with others and common queries (and potentially data corrections) are likely to be required by many it may be efficient for one person to perform the work and save it back to the database as a new table so others can access the results directly instead of performing the query themselves, particularly if it is complex.
However, we might avoid doing this if the database is an authoritative source (potentially version controlled) which should not be modified by users. Instead, we might save the qeury results to a new database that is more appropriate for downstream work.
Key Points
- sqlite3 provides a SQL-like interface to read, query, and write SQL databases from Python.
- sqlite3 can be used with Pandas to read SQL data to the familiar Pandas DataFrame.
- Pandas and sqlite3 can also be used to transfer between the CSV and SQL formats.