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Exporting pandas DataFrames into SQLite with SQLAlchemy

Posted by Matt Makai on March 30, 2020.

It is common when performing exploratory data analysis, for example when examining COVID-19 data with pandas, to load from files like a CSV, XML, or JSON into a pandas DataFrame. You may then do some work with the data in the DataFrame and want to store it in a more durable location like a relational database.

This tutorial walks through how to load a pandas DataFrame from a CSV file, pull out some data from the full data set, then save the subset of data to a SQLite database using SQLAlchemy.

Configuring our development environment

Make sure you have Python 3 installed. As of right now, Python 3.8.2 is the latest version of Python.

During this tutorial we're also going to use:

  • pandas (project homepage and source code), version 1.0.3 in this tutorial
  • SQLAlchemy (project homepage and source code), version 1.3.15 for this tutorial
  • SQLite (project homepage and source code), which Python includes a connector for as part of the Python standard library

Install the above code libraries into a new Python virtual environment using the following commands:

python -m venv pandasexport
source pandasexport/bin/activate

pip install pandas==1.0.3 sqlalchemy==1.3.15

Our development environment is now ready to download an example COVID-19 data set, load it into a pandas DataFrame, perform some analysis on it then save into a SQLite database.

Obtaining COVID-19 data

Go to the download today’s data on the geographic distribution of COVID-19 cases worldwide page in your web browser. It should look something like the following screenshot.

Download the CSV version of the COVID-19 March 29, 2020 data.

There should be a link to download the data in CSV format, but the organization has changed the page layout several times in the past few weeks, which makes it difficult to find formats other than Excel (XLSX). If you have trouble obtaining the CSV version, just download this one from GitHub which is pegged to a copy downloaded on March 28th, 2020.

Importing the CSV into pandas

The raw data is in a CSV file and we need to load it into memory via a pandas DataFrame.

Start by running the Python Read-Evaluate-Print Loop (REPL) on the command line:

python

>>>

The REPL is ready to execute code, but we first need to import the pandas library so we can use it.

from pandas import read_csv

df = read_csv("covid-19-cases-march-28-2020.csv", encoding="ISO-8859-1")

The data is now loaded into the df variable which is an instance of the pandas DataFrame class.

When we run the count function on this DataFrame, we get back that it has 7320 rows.

df.count()

Next, we'll take this set of 7320 rows of data and slice out only the rows that pertain to the United States.

Creating a new DataFrame from the original DataFrame

We can pick out all of the rows of data for a single country using a pandas function to match the countriesAndTerritories column to the country of our choice.

save_df = df[df['countriesAndTerritories']=="United_States_of_America"]

The save_df variable contains the smaller subset of data. You can find out what's in it by having it print itself:

save_df

You should see something like the following output:

         dateRep  day  month  year  cases  deaths   countriesAndTerritories geoId countryterritoryCode  popData2018
7082  28/03/2020   28      3  2020  18695     411  United_States_of_America    US                  USA  327167434.0
7083  27/03/2020   27      3  2020  16797     246  United_States_of_America    US                  USA  327167434.0
7084  26/03/2020   26      3  2020  13963     249  United_States_of_America    US                  USA  327167434.0
7085  25/03/2020   25      3  2020   8789     211  United_States_of_America    US                  USA  327167434.0
7086  24/03/2020   24      3  2020  11236     119  United_States_of_America    US                  USA  327167434.0
...          ...  ...    ...   ...    ...     ...                       ...   ...                  ...          ...
7166  04/01/2020    4      1  2020      0       0  United_States_of_America    US                  USA  327167434.0
7167  03/01/2020    3      1  2020      0       0  United_States_of_America    US                  USA  327167434.0
7168  02/01/2020    2      1  2020      0       0  United_States_of_America    US                  USA  327167434.0
7169  01/01/2020    1      1  2020      0       0  United_States_of_America    US                  USA  327167434.0
7170  31/12/2019   31     12  2019      0       0  United_States_of_America    US                  USA  327167434.0

[89 rows x 10 columns]

89 rows of data out of the original 7320 rows. Let's proceed with saving this subset to a SQLite relational database.

Saving the DataFrame to SQLite

We are going to use SQLAlchemy to create a connection to a new SQLite database, which in this example will be stored in file named save_pandas.db. You can of course save the file with whatever name you want and in any location, not just the directory where you are executing the Python REPL.

Start by importing the create_engine function from the sqlalchemy library.

from sqlalchemy import create_engine

Create the connection using the imported create_engine function and then invoking the connect method on it.

engine = create_engine('sqlite:///save_pandas.db', echo=True)
sqlite_connection = engine.connect()

We set echo=True to see all of the output that comes from our database connection. When the connection is successful you will see output similar to the following:

2020-03-29 20:44:08,198 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2020-03-29 20:44:08,198 INFO sqlalchemy.engine.base.Engine ()
2020-03-29 20:44:08,199 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2020-03-29 20:44:08,199 INFO sqlalchemy.engine.base.Engine ()
<sqlalchemy.engine.base.Connection object at 0x7fd4d932ec88>

Set a variable name with the string of a table name you would like to create. Then use that variable when invoking the to_sql method on the save_df object, which is our pandas DataFrame that is a subset of the original data set with 89 rows filtered from the original 7320.

Note that in this case we are going to fail if the table already exists in the database. You can change if_exists to to replace or append and add your own exception handling in a more robust version of this program. Check the pandas.DataFrame.to_sql documentation for the extensive details on your options.

sqlite_table = "Covid19"
save_df.to_sql(sqlite_table, sqlite_connection, if_exists='fail')

The echo output should spin up with a bunch of output. 

2020-03-29 20:45:09,066 INFO sqlalchemy.engine.base.Engine PRAGMA main.table_info("Covid19")
2020-03-29 20:45:09,066 INFO sqlalchemy.engine.base.Engine ()
2020-03-29 20:45:09,067 INFO sqlalchemy.engine.base.Engine PRAGMA temp.table_info("Covid19")
2020-03-29 20:45:09,067 INFO sqlalchemy.engine.base.Engine ()
2020-03-29 20:45:09,069 INFO sqlalchemy.engine.base.Engine 
CREATE TABLE "Covid19" (
    "index" BIGINT, 
    "dateRep" TEXT, 
    day BIGINT, 
    month BIGINT, 
    year BIGINT, 
    cases BIGINT, 
    deaths BIGINT, 
    "countriesAndTerritories" TEXT, 
    "geoId" TEXT, 
    "countryterritoryCode" TEXT, 
    "popData2018" FLOAT
)


2020-03-29 20:45:09,069 INFO sqlalchemy.engine.base.Engine ()
2020-03-29 20:45:09,070 INFO sqlalchemy.engine.base.Engine COMMIT
2020-03-29 20:45:09,070 INFO sqlalchemy.engine.base.Engine CREATE INDEX "ix_Covid19_index" ON "Covid19" ("index")
2020-03-29 20:45:09,070 INFO sqlalchemy.engine.base.Engine ()
2020-03-29 20:45:09,071 INFO sqlalchemy.engine.base.Engine COMMIT
2020-03-29 20:45:09,072 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)
2020-03-29 20:45:09,074 INFO sqlalchemy.engine.base.Engine INSERT INTO "Covid19" ("index", "dateRep", day, month, year, cases, deaths, "countriesAndTerritories", "geoId", "countryterritoryCode", "popData2018") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
2020-03-29 20:45:09,074 INFO sqlalchemy.engine.base.Engine ((7082, '28/03/2020', 28, 3, 2020, 18695, 411, 'United_States_of_America', 'US', 'USA', 327167434.0), (7083, '27/03/2020', 27, 3, 2020, 16797, 246, 'United_States_of_America', 'US', 'USA', 327167434.0), (7084, '26/03/2020', 26, 3, 2020, 13963, 249, 'United_States_of_America', 'US', 'USA', 327167434.0), (7085, '25/03/2020', 25, 3, 2020, 8789, 211, 'United_States_of_America', 'US', 'USA', 327167434.0), (7086, '24/03/2020', 24, 3, 2020, 11236, 119, 'United_States_of_America', 'US', 'USA', 327167434.0), (7087, '23/03/2020', 23, 3, 2020, 8459, 131, 'United_States_of_America', 'US', 'USA', 327167434.0), (7088, '22/03/2020', 22, 3, 2020, 7123, 80, 'United_States_of_America', 'US', 'USA', 327167434.0), (7089, '21/03/2020', 21, 3, 2020, 5374, 110, 'United_States_of_America', 'US', 'USA', 327167434.0)  ... displaying 10 of 89 total bound parameter sets ...  (7169, '01/01/2020', 1, 1, 2020, 0, 0, 'United_States_of_America', 'US', 'USA', 327167434.0), (7170, '31/12/2019', 31, 12, 2019, 0, 0, 'United_States_of_America', 'US', 'USA', 327167434.0))
2020-03-29 20:45:09,074 INFO sqlalchemy.engine.base.Engine COMMIT
2020-03-29 20:45:09,075 INFO sqlalchemy.engine.base.Engine SELECT name FROM sqlite_master WHERE type='table' ORDER BY name
2020-03-29 20:45:09,075 INFO sqlalchemy.engine.base.Engine ()

Our table with all of its data should now be all set. Close the database connection.

sqlite_connection.close()

We can take a look at the data through the sqlite3 command line viewer to make sure it was properly saved to the SQLite file.

On the command line (not in the Python REPL), type:

sqlite3

This will open up the command line prompt to interact with SQLite databases. However, we are not yet connected to our save_pandas.db file.

SQLite version 3.28.0 2019-04-15 14:49:49
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
sqlite>

Use the .open command with our save_pandas.db file name to access the database. Then use a standard SQL query to obtain all of the records from the Covid19 table.

sqlite> .open save_pandas.db
sqlite> select * from Covid19;

The SQLite explorer should produce output like you see below:

7082|28/03/2020|28|3|2020|18695|411|United_States_of_America|US|USA|327167434.0
7083|27/03/2020|27|3|2020|16797|246|United_States_of_America|US|USA|327167434.0
7084|26/03/2020|26|3|2020|13963|249|United_States_of_America|US|USA|327167434.0
7085|25/03/2020|25|3|2020|8789|211|United_States_of_America|US|USA|327167434.0
7086|24/03/2020|24|3|2020|11236|119|United_States_of_America|US|USA|327167434.0
7087|23/03/2020|23|3|2020|8459|131|United_States_of_America|US|USA|327167434.0
7088|22/03/2020|22|3|2020|7123|80|United_States_of_America|US|USA|327167434.0
7089|21/03/2020|21|3|2020|5374|110|United_States_of_America|US|USA|327167434.0
7090|20/03/2020|20|3|2020|4835|0|United_States_of_America|US|USA|327167434.0
7091|19/03/2020|19|3|2020|2988|42|United_States_of_America|US|USA|327167434.0
7092|18/03/2020|18|3|2020|1766|23|United_States_of_America|US|USA|327167434.0
7093|17/03/2020|17|3|2020|887|16|United_States_of_America|US|USA|327167434.0
7094|16/03/2020|16|3|2020|823|12|United_States_of_America|US|USA|327167434.0
7095|15/03/2020|15|3|2020|777|10|United_States_of_America|US|USA|327167434.0
7096|14/03/2020|14|3|2020|511|7|United_States_of_America|US|USA|327167434.0
7097|13/03/2020|13|3|2020|351|10|United_States_of_America|US|USA|327167434.0
7098|12/03/2020|12|3|2020|287|2|United_States_of_America|US|USA|327167434.0
7099|11/03/2020|11|3|2020|271|2|United_States_of_America|US|USA|327167434.0
7100|10/03/2020|10|3|2020|200|5|United_States_of_America|US|USA|327167434.0
7101|09/03/2020|9|3|2020|121|4|United_States_of_America|US|USA|327167434.0
7102|08/03/2020|8|3|2020|95|3|United_States_of_America|US|USA|327167434.0
7103|07/03/2020|7|3|2020|105|2|United_States_of_America|US|USA|327167434.0
7104|06/03/2020|6|3|2020|74|1|United_States_of_America|US|USA|327167434.0
7105|05/03/2020|5|3|2020|34|2|United_States_of_America|US|USA|327167434.0
7106|04/03/2020|4|3|2020|22|3|United_States_of_America|US|USA|327167434.0
7107|03/03/2020|3|3|2020|14|4|United_States_of_America|US|USA|327167434.0
7108|02/03/2020|2|3|2020|20|1|United_States_of_America|US|USA|327167434.0
7109|01/03/2020|1|3|2020|3|1|United_States_of_America|US|USA|327167434.0
7110|29/02/2020|29|2|2020|6|0|United_States_of_America|US|USA|327167434.0
7111|28/02/2020|28|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7112|27/02/2020|27|2|2020|6|0|United_States_of_America|US|USA|327167434.0
7113|26/02/2020|26|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7114|25/02/2020|25|2|2020|18|0|United_States_of_America|US|USA|327167434.0
7115|24/02/2020|24|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7116|23/02/2020|23|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7117|22/02/2020|22|2|2020|19|0|United_States_of_America|US|USA|327167434.0
7118|21/02/2020|21|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7119|20/02/2020|20|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7120|19/02/2020|19|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7121|18/02/2020|18|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7122|17/02/2020|17|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7123|16/02/2020|16|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7124|15/02/2020|15|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7125|14/02/2020|14|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7126|13/02/2020|13|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7127|12/02/2020|12|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7128|11/02/2020|11|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7129|10/02/2020|10|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7130|09/02/2020|9|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7131|08/02/2020|8|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7132|07/02/2020|7|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7133|06/02/2020|6|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7134|05/02/2020|5|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7135|04/02/2020|4|2|2020|0|0|United_States_of_America|US|USA|327167434.0
7136|03/02/2020|3|2|2020|3|0|United_States_of_America|US|USA|327167434.0
7137|02/02/2020|2|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7138|01/02/2020|1|2|2020|1|0|United_States_of_America|US|USA|327167434.0
7139|31/01/2020|31|1|2020|1|0|United_States_of_America|US|USA|327167434.0
7140|30/01/2020|30|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7141|29/01/2020|29|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7142|28/01/2020|28|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7143|27/01/2020|27|1|2020|3|0|United_States_of_America|US|USA|327167434.0
7144|26/01/2020|26|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7145|25/01/2020|25|1|2020|1|0|United_States_of_America|US|USA|327167434.0
7146|24/01/2020|24|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7147|23/01/2020|23|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7148|22/01/2020|22|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7149|21/01/2020|21|1|2020|1|0|United_States_of_America|US|USA|327167434.0
7150|20/01/2020|20|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7151|19/01/2020|19|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7152|18/01/2020|18|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7153|17/01/2020|17|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7154|16/01/2020|16|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7155|15/01/2020|15|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7156|14/01/2020|14|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7157|13/01/2020|13|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7158|12/01/2020|12|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7159|11/01/2020|11|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7160|10/01/2020|10|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7161|09/01/2020|9|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7162|08/01/2020|8|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7163|07/01/2020|7|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7164|06/01/2020|6|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7165|05/01/2020|5|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7166|04/01/2020|4|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7167|03/01/2020|3|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7168|02/01/2020|2|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7169|01/01/2020|1|1|2020|0|0|United_States_of_America|US|USA|327167434.0
7170|31/12/2019|31|12|2019|0|0|United_States_of_America|US|USA|327167434.0
sqlite>

All of the data with the countriesAndTerritories column matching United_States_of_America is there! We successfully exported the data from the DataFrame into the SQLite database file.

What's Next?

We just imported data from a CSV into a pandas DataFrame, selected a subset of that data then saved it to a relational database.

You should take a look at the Learning pandas by Exploring COVID-19 Data tutorial to learn more about how to select subsets of data from a larger DataFrame, or head to the pandas page for more tutorials by the rest of the Python community.

You can also get an idea of what to code next in your Python project by reading the Full Stack Python table of contents page.

Questions? Contact me via Twitter @fullstackpython or @mattmakai. I'm also on GitHub with the username mattmakai.

Something wrong with this post? Fork this page's source on GitHub and submit a pull request.

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