Use Dash to Build to Web Apps on PayPal Data

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PayPal Python Connector

Python Connector Libraries for PayPal Data Connectivity. Integrate PayPal with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for PayPal enables you to create Python applications that use pandas and Dash to build PayPal-connected web apps.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for PayPal, the pandas module, and the Dash framework, you can build PayPal-connected web applications for PayPal data. This article shows how to connect to PayPal with the CData Connector and use pandas and Dash to build a simple web app for visualizing PayPal data.

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

Connecting to PayPal Data

Connecting to PayPal 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 provider surfaces tables from two PayPal APIs. The APIs use different authentication methods.

  • The REST API uses the OAuth standard. To authenticate to the REST API, you will need to set the OAuthClientId, OAuthClientSecret, and CallbackURL properties.
  • The Classic API requires Signature API credentials. To authenticate to the Classic API, you will need to obtain an API username, password, and signature.

See the "Getting Started" chapter of the help documentation for a guide to obtaining the necessary API credentials.

To select the API you want to work with, you can set the Schema property to REST or SOAP. By default the SOAP schema will be used.

For testing purposes you can set UseSandbox to true and use sandbox credentials.

After installing the CData PayPal Connector, follow the procedure below to install the other required modules and start accessing PayPal 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 PayPal 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.paypal as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to PayPal

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 Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'", 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-paypaledataplot'

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 PayPal data and configure the app layout.

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

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='PayPal Transactions 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 PayPal data.

python paypal-dash.py

Free Trial & More Information

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



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.paypal as mod
import plotly.graph_objs as go

cnxn = mod.connect("Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'", cnxn)
app_name = 'dash-paypaldataplot'

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.Date, y=df.GrossAmount, name='Date')

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='PayPal Transactions Data', barmode='stack')
		})
], className="container")

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