How to Visualize Mistral AI 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 Mistral AI-connected Python applications and scripts for visualizing Mistral AI data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Mistral AI data, execute queries, and visualize the results.

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

Connecting to Mistral AI Data

Connecting to Mistral AI 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.

The MistralAI API uses API key authentication.

Using API Key Authentication

Your MistralAI API Key is required to create a connection to MistralAI. API Keys can be obtained from your MistralAI account at console.mistral.ai by navigating to the API Keys section. Once you have obtained the API key, set it in the ProfileSettings connection property.

Example Connection string

Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;

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

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

engine = create_engine("api:///?Profile=C:\profiles\MistralAI.apip&ProfileSettings='APIKey=my_api_key&'&AuthScheme=APIKey")

Execute SQL to Mistral AI

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

df = pandas.read_sql("SELECT ,  FROM AudioTranscriptions WHERE Model = 'voxtral-mini-latest'", engine)

Visualize Mistral AI Data

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

df.plot(kind="bar", x="", y="")
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 Mistral AI 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\MistralAI.apip&ProfileSettings='APIKey=my_api_key&'&AuthScheme=APIKey")
df = pandas.read_sql("SELECT ,  FROM AudioTranscriptions WHERE Model = 'voxtral-mini-latest'", engine)

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