How to Visualize Alchemy Data in Python with pandas

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

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

Connecting to Alchemy Data

Connecting to Alchemy 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.

Start by setting the Profile connection property to the location of the Alchemy Profile on disk (e.g. C:\profiles\Alchemy.apip). Next, set the ProfileSettings connection property to the connection string for Alchemy (see below).

Alchemy API Profile Settings

Alchemy uses API key authentication. The API key is supplied in the request URL path. To obtain an API key:

1. Sign in to your Alchemy account at https://dashboard.alchemy.com.
2. Open an existing app or create a new one.
3. Copy the app's API key from the API Key dialog.

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• Network: Optional. The blockchain network slug for NFT API requests (for example, eth-mainnet, base-mainnet, polygon-mainnet). Defaults to eth-mainnet.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Alchemy app API key.

Follow the procedure below to install the required modules and start accessing Alchemy 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 Alchemy Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\Alchemy.apip&AuthScheme=APIKey&Network=eth-mainnet&ProfileSettings='APIKey=your_api_key'")

Execute SQL to Alchemy

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

df = pandas.read_sql("SELECT Address, Name FROM ContractsForOwner WHERE Owner = '0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045'", engine)

Visualize Alchemy Data

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

df.plot(kind="bar", x="Address", y="Name")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Alchemy 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("api:///?Profile=C:\profiles\Alchemy.apip&AuthScheme=APIKey&Network=eth-mainnet&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Address, Name FROM ContractsForOwner WHERE Owner = '0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045'", engine)

df.plot(kind="bar", x="Address", y="Name")
plt.show()