11 tips for speeding up Python programs
By and large, people use Python because it’s convenient and programmer-friendly, not because it’s fast. The plethora of third-party libraries and the breadth of industry support for Python compensate heavily for its not having the raw performance of Java or C. Speed of development takes precedence over speed of execution.But in many cases, it doesn’t have to be an either/or proposition. Properly optimized, Python applications can run with surprising speed—perhaps not Java or C fast, but fast enough for Web applications, data analysis, management and automation tools, and most other purposes. You might actually forget that you were trading application performance for developer productivity.To read this article in full, please click here
By and large, people use Python because it’s convenient and programmer-friendly, not because it’s fast. The plethora of third-party libraries and the breadth of industry support for Python compensate heavily for its not having the raw performance of Java or C. Speed of development takes precedence over speed of execution.
But in many cases, it doesn’t have to be an either/or proposition. Properly optimized, Python applications can run with surprising speed—perhaps not Java or C fast, but fast enough for Web applications, data analysis, management and automation tools, and most other purposes. You might actually forget that you were trading application performance for developer productivity.
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