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Use pandas to Visualize TSheets Data in Python

The CData Python Connector for TSheets enables you use pandas and other modules to analyze and visualize live TSheets 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 TSheets, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build TSheets-connected Python applications and scripts for visualizing TSheets data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to TSheets data, execute queries, and visualize the results.

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

Connecting to TSheets Data

Connecting to TSheets 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.

TSheets uses the OAuth2 standard for authentication and authorization. To construct your own OAuth app and connect to data, refer to OAuth section in the Help.

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

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

engine = create_engine("tsheets:///?OAuthClientId=myclientid&OAuthClientSecret=myclientsecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to TSheets

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, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'", engine)

Visualize TSheets Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the TSheets Python Connector to start building Python apps and scripts with connectivity to TSheets 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("tsheets:///?OAuthClientId=myclientid&OAuthClientSecret=myclientsecret&CallbackUrl=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, JobcodeId FROM Timesheets WHERE JobCodeType = 'regular'", engine)

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