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

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

Connecting to ScrapingBee Data

Connecting to ScrapingBee 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

ScrapingBee uses API key authentication. To obtain an API key:

1. Sign in to your ScrapingBee account at https://app.scrapingbee.com
2. Navigate to the Dashboard and locate your API key in the top section.
3. Copy the API key for use in the connection string.

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

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your ScrapingBee API key.

Example Connection String

Profile=C:\profiles\ScrapingBee.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";

Connecting to ScrapingBee

Once the authentication is configured, you can connect to ScrapingBee and query data from any of the available tables. All tables require at least one input parameter (such as a search query or product ID) to retrieve data.

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

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

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

Execute SQL to ScrapingBee

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 GoogleSearchResults WHERE SearchQuery = 'cdata drivers'", engine)

Visualize ScrapingBee Data

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

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

👁 ScrapingBee 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 ScrapingBee 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\ScrapingBee.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")
df = pandas.read_sql("SELECT , FROM GoogleSearchResults WHERE SearchQuery = 'cdata drivers'", engine)

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