SAS vs Python in Pharma: What Actually Gets You Hired

If you are trying to break into clinical programming, you have probably asked it: "Should I learn SAS or Python?"

Career forums are full of this exact debate. Students and career-switchers ask whether SAS is still relevant, whether Python has replaced it, and whether a SAS + Python combination is a realistic path into the industry.

It is a fair question. It is also the wrong place to start.

The same question, two languages

Take a routine safety task: count the subjects with at least one treatment-emergent adverse event, by treatment arm, from an ADaM ADAE dataset.

In SAS:

proc sql;
    select trt01a,
           count(distinct usubjid) as n_subj
    from adae
    where trtemfl = 'Y'
    group by trt01a;
quit;

In Python with pandas:

import pandas as pd

adae = pd.read_sas("adae.xpt", format="xport")

(adae[adae.trtemfl == "Y"]
    .groupby("trt01a")["usubjid"]
    .nunique())

Same dataset. Same answer. Neither is hard.

The syntax was never the hard part.

What the code does not tell you

Look at the example again. To write either version correctly, you needed to know things that neither language teaches you:

1. What ADAE is. It is an ADaM analysis dataset derived from the SDTM AE domain, with one record per adverse event.
2. Why it is count(distinct usubjid) and not a row count. A subject with three events must be counted once in a "subjects with an AE" table.
3. What TRTEMFL means, and where its derivation rule lives (the SAP and the ADaM specification).
4. Which treatment variable the table uses. TRT01A (actual) and TRT01P (planned) can give different tables.
5. How you prove it is right. Regulated work expects independent validation and a traceable path from CRF data to the final table.

That list is the job. Python or SAS is the tool you apply it with.

Where each language fits today

• SAS remains deeply embedded in clinical reporting workflows. Many sponsors and CROs have established SAS programs, validated macro libraries and SDTM/ADaM standards built around it. SAS Transport (XPT) is also the file format used for submission datasets.
• Python is widely used for exploration, automation, data-quality checks and increasingly for analysis work. It reads XPT and sas7bdat files directly (pandas, pyreadstat), and saspy lets you drive SAS from Python.
• Both depend on the same standards. A Python programmer who does not understand ADaM structure will struggle just as a SAS programmer would.

A practical learning order

1. Learn the DATA step and PROC SQL well enough to read and write production-style code.
2. Learn CDISC: SDTM domains first, then ADaM (ADSL, ADAE, ADLB) and how they trace back.
3. Learn to read a SAP and a mock shell, and practice turning them into derivations.
4. Learn validation habits: independent double programming, clean logs, reproducible runs.
5. Then add Python for data checks and automation, including reading XPT files and comparing outputs.

Choosing a language is a five-minute decision. Learning to think in CDISC and validation is the part that takes months, and it transfers to whichever tool your next team uses.

Which one did you learn first, and would you do it the same way again?