# Baseline version (Inefficient way) # Calculating the power of numbers # Without using List Comprehension def test_01_v0(numbers): output = [] for n in numbers: output.append(n ** 2.5) return output
# Improved version # (Using List Comprehension) def test_01_v1(numbers): output = [n ** 2.5 for n in numbers] return output
結(jié)果如下：
# Summary Of Test Results Baseline: 32.158 ns per loop Improved: 16.040 ns per loop % Improvement: 50.1 % Speedup: 2.00x
可以看到使用列表推導式可以得到2倍速的提高
2、在外部計算長度
如果需要依靠列表的長度進行迭代，請在for循環(huán)之外進行計算。
# Baseline version (Inefficient way) # (Length calculation inside for loop) def test_02_v0(numbers): output_list = [] for i in range(len(numbers)): output_list.append(i * 2) return output_list
# Improved version # (Length calculation outside for loop) def test_02_v1(numbers): my_list_length = len(numbers) output_list = [] for i in range(my_list_length): output_list.append(i * 2) return output_list
通過將列表長度計算移出for循環(huán)，加速1.6倍，這個方法可能很少有人知道吧。
# Summary Of Test Results Baseline: 112.135 ns per loop Improved: 68.304 ns per loop % Improvement: 39.1 % Speedup: 1.64x
3、使用Set
在使用for循環(huán)進行比較的情況下使用set。
# Use for loops for nested lookups def test_03_v0(list_1, list_2): # Baseline version (Inefficient way) # (nested lookups using for loop) common_items = [] for item in list_1: if item in list_2: common_items.append(item) return common_items
# Summary Of Test Results Baseline: 9047.078 ns per loop Improved: 18.161 ns per loop % Improvement: 99.8 % Speedup: 498.17x
4、跳過不相關(guān)的迭代
避免冗余計算，即跳過不相關(guān)的迭代。
# Example of inefficient code used to find # the first even square in a list of numbers def function_do_something(numbers): for n in numbers: square = n * n if square % 2 == 0: return square
return None # No even square found
# Example of improved code that # finds result without redundant computations def function_do_something_v1(numbers): even_numbers = [i for n in numbers if n%2==0] for n in even_numbers: square = n * n return square
# Example of inefficient code # Loop that calls the is_prime function n times. def is_prime(n): if n <= 1: return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False
return True
def test_05_v0(n): # Baseline version (Inefficient way) # (calls the is_prime function n times) count = 0 for i in range(2, n + 1): if is_prime(i): count += 1 return count
def test_05_v1(n): # Improved version # (inlines the logic of the is_prime function) count = 0 for i in range(2, n + 1): if i <= 1: continue for j in range(2, int(i**0.5) + 1): if i % j == 0: break else: count += 1 return count
這樣也可以提高1.3倍
# Summary Of Test Results Baseline: 1271.188 ns per loop Improved: 939.603 ns per loop % Improvement: 26.1 % Speedup: 1.35x
def test_07_v0(n): # Example of inefficient code # Repetitive calculation within nested loop result = 0 for i in range(n): for j in range(n): result += i * j return result
def test_07_v1(n): # Example of improved code # Utilize precomputed values to help speedup pv = [[i * j for j in range(n)] for i in range(n)] result = 0 for i in range(n): result += sum(pv[i][:i+1]) return result
結(jié)果如下
# Summary Of Test Results Baseline: 139.146 ns per loop Improved: 92.325 ns per loop % Improvement: 33.6 % Speedup: 1.51x
def test_08_v0(n): # Baseline version (Inefficient way) # (Inefficiently calculates the nth Fibonacci # number using a list) if n <= 1: return n f_list = [0, 1] for i in range(2, n + 1): f_list.append(f_list[i - 1] + f_list[i - 2]) return f_list[n]
def test_08_v1(n): # Improved version # (Efficiently calculates the nth Fibonacci # number using a generator) a, b = 0, 1 for _ in range(n): yield a a, b = b, a + b
可以看到提升很明顯:
# Summary Of Test Results Baseline: 0.083 ns per loop Improved: 0.004 ns per loop % Improvement: 95.5 % Speedup: 22.06x
def some_function_X(x): # This would normally be a function containing application logic # which required it to be made into a separate function # (for the purpose of this test, just calculate and return the square) return x**2
def test_09_v0(numbers): # Baseline version (Inefficient way) output = [] for i in numbers: output.append(some_function_X(i))
def test_11_v0(n): # Baseline version # (Inefficient way of summing numbers in a range) output = 0 for i in range(0, n): output = output + i
return output
def test_11_v1(n): # Improved version # (# Efficient way of summing numbers in a range) output = np.sum(np.arange(n)) return output
向量化一般用于機器學習的數(shù)據(jù)處理庫numpy和pandas
# Summary Of Test Results Baseline: 32.936 ns per loop Improved: 1.171 ns per loop % Improvement: 96.4 % Speedup: 28.13x
11、避免創(chuàng)建中間列表
使用filterfalse可以避免創(chuàng)建中間列表。它有助于使用更少的內(nèi)存。
def test_12_v0(numbers): # Baseline version (Inefficient way) filtered_data = [] for i in numbers: filtered_data.extend(list( filter(lambda x: x % 5 == 0, range(1, i**2))))
def test_12_v1(numbers): # Improved version # (using filterfalse) filtered_data = [] for i in numbers: filtered_data.extend(list( filterfalse(lambda x: x % 5 != 0, range(1, i**2))))
# Summary Of Test Results Baseline: 333167.790 ns per loop Improved: 2541.850 ns per loop % Improvement: 99.2 % Speedup: 131.07x
12、高效連接字符串
任何使用+操作符的字符串連接操作都會很慢，并且會消耗更多內(nèi)存。使用join代替。
def test_13_v0(l_strings): # Baseline version (Inefficient way) # (concatenation using the += operator) output = "" for a_str in l_strings: output += a_str
return output
def test_13_v1(numbers): # Improved version # (using join) output_list = [] for a_str in l_strings: output_list.append(a_str)
def generate_fake_names(count : int=10000): # Helper function used to generate a # large-ish list of names fake = Faker() output_list = [] for _ in range(count): output_list.append(fake.name())
return output_list
l_strings = generate_fake_names(count=50000)
結(jié)果如下：
# Summary Of Test Results Baseline: 32.423 ns per loop Improved: 21.051 ns per loop % Improvement: 35.1 % Speedup: 1.54x
