Code Optimization Part 1
Share some tips on code optimization that I have seen recently.
Short-circuit characteristics of if judgment
For and, the conditions that meet the fewest conditions should be placed first, so that when a large number of judgments are made, the conditions that satisfy the fewest conditions will directly cause other subsequent expressions not to be calculated, thus saving time (because False and True or False)
import timeit s1 = """ a = range(2000) [i for i in a if i % 2 ==0 and i > 1900] """ s2 = """ a = range(2000) [i for i in a if i > 1900 and i % 2 ==0] """ print timeit.timeit(stmt=s1, number=1000) print timeit.timeit(stmt=s2, number=1000)
The operation results are as follows:
➜ python test6.py 0.248532056808 0.195827960968 # 可以看到s2 表达式计算更快, 因为大部分情况都不满足 i>1900, 所以这些情况下, i % 2 == 0 也没有计算,从而节约了时间
Similarly for or, put the one that meets the most conditions first.
import timeit s1 = """ a = range(2000) [i for i in a if 10 < i <20 or 1000 < i < 2000] """ s2 = """ a = range(2000) [i for i in a if 1000 < i < 2000 or 10 < i <20] """ print timeit.timeit(stmt=s1, number=1000) print timeit.timeit(stmt=s2, number=1000)
Run results:
0.253124952316 0.202992200851
join merge strings
join merge strings faster than looping + to merge.
import timeit s1 = """ a = [str(x) for x in range(2000)] s = '' for i in a: s += i """ s2 = """ a = [str(x) for x in range(2000)] s = ''.join(a) """ print timeit.timeit(stmt=s1, number=1000) print timeit.timeit(stmt=s2, number=1000)
The running results are as follows:
python test6.py 0.558945894241 0.422435998917
while 1 and while True
In python2.x, True and False are not reserved keywords, but a global variable, which means you can do this
>>> True = 0 >>> True 0 >>> if not True: ... print '1' ... 1
So the following two In this case:
import timeit s1 = """ n = 1000000 while 1: n -= 1 if n <= 0: break """ s2 = """ n = 1000000 while True: n -= 1 if n <= 0: break """ print timeit.timeit(stmt=s1, number=100) print timeit.timeit(stmt=s2, number=100)
The operation result is as follows:
➜ python test6.py 5.18007302284 6.84624099731
Because every time when judging "while True", we must first find the value of True.
In python3.x, True becomes a keyword argument, so the above two situations are the same.
cProfile, cStringIO and cPickle
Extensions written using the C version are faster than the native ones. cPickle vs pickle is as follows:
import timeit s1 = """ import cPickle import pickle n = range(10000) cPickle.dumps(n) """ s2 = """ import cPickle import pickle n = range(10000) pickle.dumps(n) """ print timeit.timeit(stmt=s1, number=100) print timeit.timeit(stmt=s2, number=100)
The running results are as follows:
➜ python test6.py 0.182178974152 1.70917797089
Use the generator appropriately
Difference
Using () to get a generator object, the memory space required has nothing to do with the size of the list, so the efficiency will be higher .
import timeit s1 = """ [i for i in range (100000)] """ s2 = """ (i for i in range(100000)) """ print timeit.timeit(stmt=s1, number=1000) print timeit.timeit(stmt=s2, number=1000)
Result:
➜ python test6.py 5.44327497482 0.923446893692
But for situations where loop traversal is required: using iterators is not efficient, as follows:
import timeit s1 = """ ls = range(1000000) def yield_func(ls): for i in ls: yield i+1 for x in yield_func(ls): pass """ s2 = """ ls = range(1000000) def not_yield_func(ls): return [i+1 for i in ls] for x in not_yield_func(ls): pass """ print timeit.timeit(stmt=s1, number=10) print timeit.timeit(stmt=s2, number=10)
The result is as follows:
➜ python test6.py 1.03186702728 1.01472687721
So using a generator is a trade-off, for memory and speed. Consider the results.
xrange
在python2.x里xrange 是纯C实现的生成器,相对于range来说,它不会一次性计算出所有值在内存中。但它的限制是只能和整型一起工作:你不能使用long或者float。
import 语句的开销
import语句有时候为了限制它们的作用范围或者节省初始化时间,被卸载函数内部,虽然python的解释器不会重复import同一个模块不会出错,但重复导入会影响部分性能。有时候为了实现懒加载(即使用的时候再加载一个开销很大的模块),可以这么做:
email = None def parse_email(): global email if email is None: import email ... # 这样一来email模块仅会被引入一次,在parse_email()被第一次调用的时候。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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