The Data Library are available in the following programming languages:

• Python
• .NET
• TypeScript

For more deep detail regarding the Data Library for Python, please refer to the following articles and tutorials:

• Quickstart.
• Documentation.
• Tutorials.
• GitHub.

Disclaimer

This article is based on Data Library Python versions 2.0.1 using the Desktop Session only.

Prerequisite

This article example application requires the following dependencies softwares and libraries.

1. LSEG Workspace desktop application with access to Data Library for Python.
2. Python (Ananconda or MiniConda distribution/package manager also compatible -- see Conda - Managing environments document).
3. Jupyter Lab application.

Note:

• If you are not familiar with Jupyter Lab application, the following tutorial created by DataCamp may help you.

Code Walkthrough

Now we come to coding part. There are three main steps to get and display company's geographic sale data.

1. Get the Company Geographic Sale Data.
2. Restructure Company Geographic Sale Data Dataframe object that returned from the Data Library.
3. Plotting the graph.

Please note that the Workspace desktop application integrates a Data API proxy that acts as an interface between the Python library and the Workspace Platform. For this reason, the Workspace application must be running when you use the Data library.

Data Library Configuration File Set Up

The Data library automatic loads configuration file name lseg-data.config.json for developers. You need to input the Workspace App-Key in this file before initial the session.

That’s all I have to say about the configuration and account setting.

Import Data Library

Let start with the first step, importing lseg.data and matplotlib libraries to the Notebook application.

Initiate and Getting Data from Data Library

The Data Library lets an application consume data from the following platforms:

• DesktopSession (Workspace desktop application)
• PlatformSession (RDP, Real-Time Optimized)
• DeployedPlatformSession (deployed Real-Time/ADS)

This article only focuses on the DesktopSession.

Next, use the Data Library open_session() method to load configuration file and initial Session.

Now our Jupyter Notebook is ready to get data from the Workspace platform.

Get Company Geographic Sale Data

Firstly, the Notebook application uses Data Library get_data method to request the company fundamentals via following fields:

• TR.BGS.GeoTotalRevenue.segmentName: Segment (Geographic) data
• TR.BGS.GeoTotalRevenue: Each segment revenue value
• TR.CompanyName

I am demonstrating with Arm Holdings (RIC Code: ARM.O) as an example data set.

This get_data function returns data as Pandas Dataframe object by default.

Plotting A Graph, First Try

So, now let’s look at what if I plot this data. The first step is changing the Segment Name column to be an index.

Then, I plot a bar graph with this DataFrame object.

You see that the graph above is hard to read and analysis the sale revenue values at all.

Restructure Company Geographic Sale Data Dataframe object

I need to restructure the Dataframe and make data easier to read before plotting a graph. The first step is to reindex from Segment Name to be a data column.

Moving on to the next step, renaming Segment Name and Geographic Total Revenues (Calculated) columns to be more readable names like Geographic and Revenue.

Then we get the Company name and consolidate total revenue information from Dataframe object.

And the last thing on this phase is to remove the Total Sale Revenue rows from the Dataframe, we will display the consolidated revenue information as a graph footer instead.

That covers a data transformation phase.

Plotting A Graph, Second Try

That brings us to plot a graph with the data that already transformed to match this purpose.

Firstly, I create a Python method format_revenues_number to reformat large revenue numbers into a readable numbers in trillions, billions or millions unit. This method source code is based on Dan Friedman's How to Format Large Tick Values tutorial source code via GitHub.

Then I use Python matplotlib.pyplot library to plot a bar graph that represent each region revenue from restructured Dataframe object in Jupyter Notebook.

The result is as follows:

That’s all I have to say about the example application implementation detail.

Conclusion

Workspace platform provides a wide range of Fundamentals data for your investment decisions including company geographic sale information. This information helps you analysis the revenue from each geographic region of your interested company in both panel data and graph formats.

That covers all I wanted to say today.

References

You can find more detail regarding the Data Library and related technologies for this Notebook from the following resources:

• LSEG Data Library for Python on the LSEG Developer Community
• Data Library for Python - Reference Guide
• The Data Library for Python - Quick Reference Guide (Access layer) article.
• Essential Guide to the Data Libraries - Generations of Python library (EDAPI, RDP, RD, LD) article.
• Upgrade from using Eikon Data API to the Data library article.
• Data Library for Python Examples on GitHub repository.
• Dan Friedman's Python programming, data analysis, data visualizations tutorials.
• Pandas API Reference.
• Pyplot Graph API Reference.

For any question related to this example or Data Library, please use the Developers Community Q&A Forum.