How to Visualize SAP SuccessFactors Data in Python with pandas via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas and Matplotlib modules, you can build SAP SuccessFactors-connected Python applications and scripts for visualizing SAP SuccessFactors data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query SAP SuccessFactors data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Connect to SAP SuccessFactors in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SAP SuccessFactors" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAP SuccessFactors.

   You can authenticate to SAP Success Factors using Basic authentication or OAuth with SAML assertion.

   Basic Authentication

   You must provide values for the following properties to successfully authenticate to SAP Success Factors. Note that the provider will reuse the session opened by SAP Success Factors using cookies. Which means that your credentials will be used only on the first request to open the session. After that, cookies returned from SAP Success Factors will be used for authentication.

   • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
   • User: set this to the username of your account.
   • Password: set this to the password of your account.
   • CompanyId: set this to the unique identifier of your company.

   OAuth Authentication

   You must provide values for the following properties, which will be used to get the access token.

   • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
   • User: set this to the username of your account.
   • CompanyId: set this to the unique identifier of your company.
   • OAuthClientId: set this to the API Key that was generated in API Center.
   • OAuthClientSecret: the X.509 private key used to sign SAML assertion. The private key can be found in the certificate you downloaded in Registering your OAuth Client Application.
   • InitiateOAuth: set this to GETANDREFRESH.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize SAP SuccessFactors Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SAPSuccessFactors1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query SAP SuccessFactors with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT address1, zipCode "
    "FROM [SAPSuccessFactors1].[SAPSuccessFactors].[ExtAddressInfo] "
    "WHERE city = 'Springfield'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize SAP SuccessFactors Data

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

df.plot(kind="bar", x="address1", y="zipCode")
plt.show()

conn.close()

More Information and Free Trial

Now you can read live SAP SuccessFactors data into pandas through the CData Connect AI Python SDK. For more information on connecting to SAP SuccessFactors (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live SAP SuccessFactors data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT address1, zipCode "
    "FROM [SAPSuccessFactors1].[SAPSuccessFactors].[ExtAddressInfo] "
    "WHERE city = 'Springfield'",
    conn,
)

df.plot(kind="bar", x="address1", y="zipCode")
plt.show()

conn.close()