| If used either R or Python for data analysis/data mining in 2013, did you switch, for some tasks [562 voters total] | |
| Kept using R (286) |  50.7% |
| From R to Python (115) |  20.5% |
| From R to other tools (not Python) (33) |  5.9% |
| Kept using Python (143) | 25.4% |
| From Python to R (32) | 5.7% |
| From Python to other tools (not R) (7) | 1.2% |
| From other tools (not Python) to R (59) | 10.5% |
| From other tools (not R) to Python (30) | 5.3% |
| Did not use Python in 2013 (75) | 13.3% |
| Did not use R in 2013 (68) | 12.1% |
Here is full analysis and discussion of R / Python switching among Data Scientists.
Comments
rdavila, R vs Python
A programming language is best for what it was designed to do. Given the significant amount of statistics in data analytics, the fact that R is a major tool is to be expected. The fact that Python is gaining a foothold given that it was not designed for scientific/statistical computation but more as a scripting/general purpose hlanguage is telling. Except for pure academic settings or just research projects the choice of language really does not impact one in a significant manner. Now that data analytics results are expected to be integrated into formal decision processes and even operations the demands on the tools are different. Integration with current IT infrastructure, maintainability, documentation, extendability,reliability come to the fore - thus, Python.
Python is an exceptionally beautiful language that has been adapted in so many areas. For non-academic environments expect it to be come the tool of choice - for now.