Development Tools & Resources

The example code demonstrating the use case is based on the following development tools and resources:

• Refinitiv's DataStream Web Services (DSWS): Access to DataStream data. A DataStream or Refinitiv Workspace IDentification (ID) will be needed to run the code below.

Get to Coding

We need to gather our data. Since Refinitiv's DataStream Web Services (DSWS) allows for access to ESG data covering nearly 70% of global market cap and over 400 metrics, naturally it is more than appropriate. We can access DSWS via the Python library "DatastreamDSWS" that can be installed simply by using pip install.

The library pandas imported is version 1.1.4
The library numpy imported is version 1.20.1
The library tqdm imported is version 4.48.2
The library ipywidgets imported is version 7.5.1

Step by Step Example

Collecting Datastream Data 

Users ought to be able to select their Country/Region of interest without knowing the Datastream Mnemonics for them. In line with this, the bellow creates tables of reference for Countries/Regions and such Mnemonics and Python functions to allow users easy data retrieval.

Data-Frame of Countries, Regions and their Complimentary DSWS Mnemonic and IBES Code

Python function Region_to_DSWS_region_mnemonic_and_IBES_code

As per its written definition bellow, this function returns a string of the Datastream Web Service (DSWS) region's mnemonic and IBES code. For certain requests, DSWS needs a ticker specified with a nomenclature that includes DSWS region's mnemonic.

For example, we could get the Mnemonic and Code for the United Kingdom:

('UK', '@:UKMSCIP')

Example with Region "EAFE + Canada"

We 1st need to setup baseline variables

Now we can go ahead and collect our data from Datastream. Note that we split requests in batches using ds_get_twice_data to keep well within request limits.

Now let's see our data-frame:

90 rows × 36 columns

We can select any section of our data-frame for focused analysis

90 rows × 4 columns

Create the Python function Get_IBES_GA with ipywidgets Dropdowns

Let's setup and use an interactive widget:

From there one can interact with widgets:

You may hover over concatinated text to read it all

And you may select from dropdowns

More interestingly, you may select the area of interest

Then, data is collected as the loading bar fills up before rendering our results

Now let's see our data-frame in the list:

Conclusion

One can easily use interactive widgets to intuitively get dsws IBES GA data using our Python Get_IBES_GA function created above. We can even generate excel sheets replicating the 'Datastream IBES Global Aggregate MSCI.xlsm' file. However, this is only Part 1; I will attempt to see the best ways in which the above can be used to extract insights, possibly in graphical ways. If you have a workflow in mind for which the above would be useful, I would be happy to hear about it. Please do not hesitate to submit your propositions to jonathan.legrand@refinitiv.com

References

• Reference Data Excel File
• Pivottable.js
• Dashboarding with Jupyter Notebooks, Voila and Widgets | SciPy 2019 | M. Breddels and M. Renou