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Use pandas to Visualize Airtable Data in Python

The CData Python Connector for Airtable enables you use pandas and other modules to analyze and visualize live Airtable data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Airtable, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Airtable-connected Python applications and scripts for visualizing Airtable data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Airtable data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Airtable data in Python. When you issue complex SQL queries from Airtable, the driver pushes supported SQL operations, like filters and aggregations, directly to Airtable and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Airtable Data

Connecting to Airtable data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

APIKey, BaseId and TableNames parameters are required to connect to Airtable. ViewNames is an optional parameter where views of the tables may be specified.

  • APIKey : API Key of your account. To obtain this value, after logging in go to Account. In API section click Generate API key.
  • BaseId : Id of your base. To obtain this value, it is in the same section as the APIKey. Click on Airtable API, or navigate to https://airtable.com/api and select a base. In the introduction section you can find "The ID of this base is appxxN2ftedc0nEG7."
  • TableNames : A comma separated list of table names for the selected base. These are the same names of tables as found in the UI.
  • ViewNames : A comma separated list of views in the format of (table.view) names. These are the same names of the views as found in the UI.

Follow the procedure below to install the required modules and start accessing Airtable through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Airtable Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Airtable data.

engine = create_engine("airtable:///?APIKey=keymz3adb53RqsU&BaseId=appxxN2fe34r3rjdG7&TableNames=TableA,...&ViewNames=TableA.ViewA,...")

Execute SQL to Airtable

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column1 = 'Value1'", engine)

Visualize Airtable Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Airtable data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="Column1")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Airtable Python Connector to start building Python apps and scripts with connectivity to Airtable data. Reach out to our Support Team if you have any questions.



Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("airtable:///?APIKey=keymz3adb53RqsU&BaseId=appxxN2fe34r3rjdG7&TableNames=TableA,...&ViewNames=TableA.ViewA,...")
df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column1 = 'Value1'", engine)

df.plot(kind="bar", x="Id", y="Column1")
plt.show()