Use pandas to Visualize Wave Financial Data in Python

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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 use pandas and other modules to analyze and visualize live Wave Financial data in Python.

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build Wave Financial-connected Python applications and scripts for visualizing Wave Financial data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Wave Financial data, execute queries, and visualize the results.

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.

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

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

engine = create_engine("wavefinancial:///?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 resultset in a DataFrame.

df = pandas.read_sql("SELECT Id, DueDate FROM Invoices WHERE Status = 'SENT'", engine)

Visualize Wave Financial Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Wave Financial Python Connector to start building Python apps and scripts with connectivity to Wave Financial 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("wavefinancial:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, DueDate FROM Invoices WHERE Status = 'SENT'", engine)

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