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

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

Connecting to Scrapfly Data

Connecting to Scrapfly 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 Scrapfly API uses API Key authentication. The API key is passed as the key query parameter on every request.

Using API Key Authentication

Your Scrapfly API key is required to create a connection. To obtain your API key:

1. Log into your Scrapfly account at scrapfly.io.
2. Navigate to Dashboard and select API Keys.
3. Copy your API key (begins with scp-live- for production or scp-test- for the test environment).

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Scrapfly API key.

Example connection string:

Profile=C:\profiles\Scrapfly.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';

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

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

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

Execute SQL to Scrapfly

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 Account WHERE  = ''", engine)

Visualize Scrapfly Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Scrapfly 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 Scrapfly 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\Scrapfly.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT ,  FROM Account WHERE  = ''", engine)

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