There is a temptation for accounting PhD students to invest in learning Python. However, I would recommend accounting PhD students focus more on SAS + Stata than on Python in their first year for a few practical and technical reasons:
- Although Python looks trendy, still few accounting researchers master this language. Research is oftentimes a co-authorship. If the research code can only be understood by one person in a research team, it will be counter productive.
- Because not many researchers use Python yet, Python users cannot take advantage of many ready-to-use code written in SAS which has a larger user base.
- Debugging in Python is harder than in commercial software such as SAS and Stata. Freeware sounds appealing, but I always find I end up spending more time when using freeware which typically lacks detailed and excellent help documentation.
- Like Stata, at least for now, Python cannot manipulate large datasets (limited by the memory size of computer). If you are going to handle mega datasets such as intra-day bond/stock transaction data, Python will be a pain.
SAS + Stata is a solid solution I advocate. I use SAS for SQL and Stata for everything else. Python is suitable for certain research topics such as textual analysis. If you decide to invest in learning Python, you can count on the following learning resources:
- The Python Tutorial on the official Python website: https://docs.python.org/3.10/tutorial/index.html – easy for a fast read.
- There are tons of Python courses on Udemy: https://www.udemy.com – year-round discounted price is $13 per course.
- Anand et al. authored an excellent methodology article – Vic Anand, Khrystyna Bochkay, Roman Chychyla and Andrew Leone (2020 isbn), “Using Python for Text Analysis in Accounting Research (forthcoming)”, Foundations and Trends ® in Accounting: Vol. xx, No. xx, pp 1–18. DOI: 10.1561/XXXXXXXXX.