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Create Python applications that use pandas and Dash to build Harvest-connected web apps.
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 Harvest-connected web applications for Harvest data. This article shows how to connect to Harvest with the CData Connector and use pandas and Dash to build a simple web app for visualizing Harvest data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Harvest data in Python. When you issue complex SQL queries from Harvest, the driver pushes supported SQL operations, like filters and aggregations, directly to Harvest and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Harvest Data
Connecting to Harvest 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.
Start by setting the Profile connection property to the location of the Harvest Profile on disk (e.g. C:\profiles\Harvest.apip). Next, set the ProfileSettings connection property to the connection string for Harvest (see below).
Harvest API Profile Settings
To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.
Using Token Authentication
To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.
Using OAuth Authentication
First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.
After setting the following connection properties, you are ready to connect:
- ProfileSettings: Set your AccountId in ProfileSettings.
- AuthScheme: Set this to OAuth.
- OAuthClientId: Set this to the client ID that you specified in your app settings.
- OAuthClientSecret: Set this to the client secret that you specified in your app settings.
- CallbackURL: Set this to the Redirect URI that you specified in your app settings.
- InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.
After installing the CData Harvest Connector, follow the procedure below to install the other required modules and start accessing Harvest 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 Harvest 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 Harvest Connector to create a connection for working with Harvest data.
cnxn = mod.connect("Profile=C:\profiles\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';")
Execute SQL to Harvest
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, ClientName FROM Invoices WHERE State = 'open'", 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 Harvest data and configure the app layout.
trace = go.Bar(x=df.Id, y=df.ClientName, 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='Harvest 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 Harvest data.
python api-dash.py
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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\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';") df = pd.read_sql("SELECT Id, ClientName FROM Invoices WHERE State = 'open'", 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.Id, y=df.ClientName, 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='Harvest Invoices Data', barmode='stack') }) ], className="container") if __name__ == '__main__': app.run_server(debug=True)