How to Build an ETL App for Mouseflow Data in Python with CData
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 and the petl framework, you can build Mouseflow-connected applications and pipelines for extracting, transforming, and loading Mouseflow data. This article shows how to connect to Mouseflow with the CData Python Connector and use petl and pandas to extract, transform, and load Mouseflow data.
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.
After installing the CData Mouseflow Connector, follow the procedure below to install the other required modules and start accessing Mouseflow through Python objects.
Install Required Modules
Use the pip utility to install the required modules and frameworks:
pip install petl pip install pandas
Build an ETL App for Mouseflow Data in Python
Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.
First, be sure to import the modules (including the CData Connector) with the following:
import petl as etl import pandas as pd import cdata.api as mod
You can now connect with a connection string. Use the connect function for the CData Mouseflow Connector to create a connection for working with Mouseflow data.
cnxn = mod.connect("Profile=C:\profiles\Mouseflow.apip;ProfileSettings='User=your_email;Password=your_api_key;Region=us';")
Create a SQL Statement to Query Mouseflow
Use SQL to create a statement for querying Mouseflow. In this article, we read data from the FeedbackCampaigns entity.
sql = "SELECT WebsiteId, CampaignId FROM FeedbackCampaigns WHERE Enabled = 'true'"
Extract, Transform, and Load the Mouseflow Data
With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Mouseflow data. In this example, we extract Mouseflow data, sort the data by the CampaignId column, and load the data into a CSV file.
Loading Mouseflow Data into a CSV File
table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'CampaignId') etl.tocsv(table2,'feedbackcampaigns_data.csv')
With the CData API Driver for Python, you can work with Mouseflow data just like you would with any database, including direct access to data in ETL packages like petl.
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 petl as etl
import pandas as pd
import cdata.api as mod
cnxn = mod.connect("Profile=C:\profiles\Mouseflow.apip;ProfileSettings='User=your_email;Password=your_api_key;Region=us';")
sql = "SELECT WebsiteId, CampaignId FROM FeedbackCampaigns WHERE Enabled = 'true'"
table1 = etl.fromdb(cnxn,sql)
table2 = etl.sort(table1,'CampaignId')
etl.tocsv(table2,'feedbackcampaigns_data.csv')