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

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

Connecting to Mouseflow Data

Connecting to Mouseflow 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 Mouseflow Profile on disk (e.g. C:\profiles\Mouseflow.apip). Next, set the ProfileSettings connection property to the connection string for Mouseflow (see below).

Mouseflow API Profile Settings

Retrieve your API key from API > API Key within your Mouseflow account settings. Your region (us or eu) can be determined from your account URL.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Mouseflow.apip&ProfileSettings='User=your_email&Password=your_api_key&Region=us'")

Execute SQL to Mouseflow

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

df = pandas.read_sql("SELECT WebsiteId, CampaignId FROM FeedbackCampaigns WHERE Enabled = 'true'", engine)

Visualize Mouseflow Data

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

df.plot(kind="bar", x="WebsiteId", y="CampaignId")
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 Mouseflow 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\Mouseflow.apip&ProfileSettings='User=your_email&Password=your_api_key&Region=us'")
df = pandas.read_sql("SELECT WebsiteId, CampaignId FROM FeedbackCampaigns WHERE Enabled = 'true'", engine)

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