Python is So fast? 🔥💣 Lets try benchmarking with numpy
Originally Python was not designed for numeric computation. As people started using python for various tasks, the need for fast numeric computation arose. And the Numpy was created by a group of people in 2005 to address this challenge.
Many Numpy operations are implemented in C, avoiding the general cost of loops in Python, pointer indirection and per-element dynamic type checking. The speed boost depends on which operations you're performing, but a few orders of magnitude isn't uncommon in number crunching programs.
The following are the main reasons behind the fast speed of Numpy.
1. Numpy array is a collection of similar data-types that are densely packed in memory. A Python list can have different data-types, which puts lots of extra constraints while doing computation on it.
2. Numpy is able to divide a task into multiple subtasks and process them parallelly.
3. Numpy functions are implemented in C. Which again makes it faster compared to Python Lists.
If you found the video valuable, please leave a like and subscribe ❤️ It helps the channel grow and helps me pumping out more such content.
#shorts
#python3 #forloop #forelse #programming #algorithm #programmintips #pythontips #codingtips #coding #developmnet #dev programmer banda
Auf dieser Seite können Sie das Online-Video Python is So fast? 🔥💣 Lets try benchmarking with numpy mit der Dauer stunde minuten sekunde in guter Qualität ansehen, das der Benutzer Programmer Banda 16 Dezember 2021 hochgeladen hat, den Link mit Freunden und Bekannten teilen, dieses Video wurde auf Youtube bereits 682 Mal angesehen und es wurde von 26 den Zuschauern gefallen. Viel Spaß beim Betrachtenden Zuschauern gefallen!
