Move data to a Database with Python: Pandas

In Data Analysis, a common task is taking data from external sources, such as CSV files, and storing their data in a Database. Many tools exist for this, but a great option is to use Python.

In the final article of this series, we'll look at one more method for moving data to a Database using the popular data library, pandas. Pandas is the Python library for data manipulation and analysis. Think of pandas as the data swiss army knife for Python.

In the previous articles in this series, we looked at using pure Python and SQLAlchemy. These methods offer high levels of flexibility but are somewhat code-heavy when compared to pandas.

Prerequisites:

In this article, I'm going to assume a couple of things:

You're familiar with Python, installing and using packages, and running a Python script
Some familiarity with databases and SQL

You won't need to know anything about pandas to understand this tutorial. I'll explain everything along the way.

Setting up

For this tutorial, we'll be using the New York City Airbnb Open Data from Kaggle's excellent public dataset repository.

Each row of the CSV represents a listing on Airbnb and includes a range of data types. I've chosen this dataset because its typical of the kinds of datasets encountered in the real world.

You can find the CSV file and final code in the accompanying repository for this tutorial on my GitHub.

Let's get started.

Reading the data

To begin, we need to load the data from our CSV file into a pandas DataFrame. If you've never encountered a DataFrame before, these are a pandas data structure representing data in a tabular format with rows and columns.

We start with importing pandas. The standard convention with pandas is to alias the library to pd To read the CSV, we use the read_csv method.

pd.py

import pandas as pd


df = pd.read_csv(
    filepath_or_buffer="AB_NYC_2019.csv",
    header=0,
    index_col="id",
    quotechar='"',
    parse_dates=["last_review"],
)

You'll have noticed that we've passed in a few arguments to the read_csv method. Let's take a closer look at those:

filepath_or_buffer - The path to the CSV file we want to read
header - The row number of the header row. Including this instructs pandas to use the header row values as column names in the returned DataFrame.
index_col - The name of the column that contains the indexes (row labels) for the DataFrame. The index column is typically a unique id. While not required in this scenario in non-trivial data analysis, it is useful to include
quotechar - Character indicating the start and end of a quoted item. These will typically include the delimiter (in our file, a comma)
parse_dates - A list of column names containing dates. By default, pandas will not attempt to parse strings that look like dates, so we must provide a list of column names we want to convert to dates. For date parsing, pandas uses the popular dateutil library.

At this point, we should have a functioning DataFrame. While outside the scope of this article, this would be an excellent time to inspect the DataFrame to ensure data was loaded as expected.

Now that we have prepared a DataFrame let's look at how we can use it to move data into a Database.

Store the data in the Database

One of the (many) useful features of pandas is it works with SQLAlchemy. Pandas can use any Database supported by SQLAlchemy.

Because we'll use SQLAlchemy for Database support, we need to generate a SQLAlchemy Engine object. The Engine object gives pandas the database connectivity it needs to store the data. We do this through the create_engine method and pass in a database URL (similar to a connection string).

We'll connect to the Database using the engine.connect method inside a with statement (context manager). See the note below about closing Database connections.

To store the data, we call the to_sql method of our DataFrame.

pd.py

import pandas as pd
import sqlalchemy


df = pd.read_csv(
    filepath_or_buffer="AB_NYC_2019.csv",
    header=0,
    index_col="id",
    quotechar='"',
    parse_dates=["last_review"],
)

engine = sqlalchemy.create_engine("sqlite:///ab_nyc.sqlite3")

with engine.connect() as connection:
    df.to_sql(
        name="listings_pandas",
        con=connection,
        if_exists="replace",
        index=True,
        index_label="id",
        dtype={"last_review": sqlalchemy.Date},
    )

To fine-tune the load process, we included some additional arguments. Let's look further at what those do:

name - The destination table name.
con - The connection to use to connect to the Database.
if_exists - Sets what to do if the table already exists in the Database. In our case, we'll replace the table and its contents.
index - A True or False value indicating whether or not to include the index as a column in the destination table.
index_label - Column label if including the index in the destination table.
dtype - A dictionary mapping column names in the DataFrame to SQLAlchemy data types. We can use this option to force the last_review column to be stored as a Date Type.

A note about Database connections

You might have noticed the work of storing the data occurs inside a with statement. There's a reason for that.

It's important to note that pandas doesn't close a Database connection when it's finished. According to their documentation, "The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable."

Using to_sql inside a with statement ensures that the database connection will be closed appropriately and released when the code block finishes.

Not explicitly closing a Database connection can lead to other Database users getting locked out of a table because pandas hasn't released the lock on the table.

Finishing up

Here is a look at the final code.

pd.py

import pandas as pd
import sqlalchemy


df = pd.read_csv(
    filepath_or_buffer="AB_NYC_2019.csv",
    header=0,
    index_col="id",
    quotechar='"',
    parse_dates=["last_review"],
)

engine = sqlalchemy.create_engine("sqlite:///ab_nyc.sqlite3")

with engine.connect() as connection:
    df.to_sql(
        name="listings_pandas",
        con=connection,
        if_exists="replace",
        index=True,
        index_label="id",
        dtype={"last_review": sqlalchemy.Date},
    )

All that's left now is to run the script and inspect the Database. You should see the newly created table populated with data from the CSV file.

Conclusion

And we're done! You've successfully moved data from a CSV file into a Database.

As you've seen, this common Data Analysis task was easy with pandas. For a relatively simple import of data such as this, pandas is an excellent choice. But for more complicated scenarios, I'd recommend using SQLAlchemy or pure Python for more flexibility and control.

I hope you've found this series on moving data to a Database with Python useful.

Move data to a Database with Python: Pandas

Prerequisites:

Setting up

Reading the data

Store the data in the Database

A note about Database connections

Finishing up

Conclusion

Further reading