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

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

Connecting to Landbot Data

Connecting to Landbot 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.

Using API Key Authentication

Landbot uses token-based authentication. Obtain your agent token from Settings > Account in your Landbot account.

Set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Landbot agent token.

Sample Connection String

Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;

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

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

engine = create_engine("api:///?Profile=C:\profiles\Landbot.apip&AuthScheme=APIKey&APIKey=your_agent_token_here")

Execute SQL to Landbot

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 Agents WHERE = ''", engine)

Visualize Landbot Data

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

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

👁 Landbot data in a Python plot (Salesforce is shown).

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 Landbot data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

engine = create_engine("api:///?Profile=C:\profiles\Landbot.apip&AuthScheme=APIKey&APIKey=your_agent_token_here")
df = pandas.read_sql("SELECT , FROM Agents WHERE = ''", engine)

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