Use Dash to Build to Web Apps on ScrapingBee Data

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 module, and the Dash framework, you can build ScrapingBee-connected web applications for ScrapingBee data. This article shows how to connect to ScrapingBee with the CData Connector and use pandas and Dash to build a simple web app for visualizing ScrapingBee data.

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.

After installing the CData ScrapingBee Connector, follow the procedure below to install the other required modules and start accessing ScrapingBee through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize ScrapingBee Data in Python

Once the required modules and frameworks are installed, we are ready to build our web 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 os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData ScrapingBee Connector to create a connection for working with ScrapingBee data.

cnxn = mod.connect("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 result set in a DataFrame.

df = pd.read_sql("SELECT ,  FROM GoogleSearchResults WHERE SearchQuery = 'cdata drivers'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our ScrapingBee data and configure the app layout.

trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='ScrapingBee GoogleSearchResults Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the ScrapingBee data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to ScrapingBee data. Reach out to our Support Team if you have any questions.

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Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\ScrapingBee.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";")

df = pd.read_sql("SELECT ,  FROM GoogleSearchResults WHERE SearchQuery = 'cdata drivers'", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='ScrapingBee GoogleSearchResults Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)