Use Dash to Build to Web Apps on Wave Financial Data

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Wave Financial Python Connector

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



The CData Python Connector for Wave Financial enables you to create Python applications that use pandas and Dash to build Wave Financial-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 Wave Financial, the pandas module, and the Dash framework, you can build Wave Financial-connected web applications for Wave Financial data. This article shows how to connect to Wave Financial with the CData Connector and use pandas and Dash to build a simple web app for visualizing Wave Financial data.

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

Connecting to Wave Financial Data

Connecting to Wave Financial 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.

Connect using the API Token

You can connect to Wave Financial by specifying the APIToken You can obtain an API Token using the following steps:

  1. Log in to your Wave account and navigate to "Manage Applications" in the left pane.
  2. Select the application that you would like to create a token for. You may need to create an application first.
  3. Click the "Create token" button to generate an APIToken.

Connect using OAuth

If you wish, you can connect using the embedded OAuth credentials. See the Help documentation for more information.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Wave Financial

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 Id, DueDate FROM Invoices WHERE Status = 'SENT'", 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-wavefinancialedataplot'

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 Wave Financial data and configure the app layout.

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

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='Wave Financial Invoices 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 Wave Financial data.

python wavefinancial-dash.py

Free Trial & More Information

Download a free, 30-day trial of the Wave Financial Python Connector to start building Python apps with connectivity to Wave Financial 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.wavefinancial as mod
import plotly.graph_objs as go

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, DueDate FROM Invoices WHERE Status = 'SENT'", cnxn)
app_name = 'dash-wavefinancialdataplot'

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.Id, y=df.DueDate, name='Id')

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='Wave Financial Invoices Data', barmode='stack')
		})
], className="container")

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