NumPy(Numerical Python)是一个开源的 Python 库,几乎在每个科学和工程领域中都被使用。它是 Python 中处理数值数据的通用标准,在科学 Python 和 PyData 生态系统的核心地位不可撼动。NumPy 的用户包括从初学者程序员到经验丰富的从事最前沿的科学和工业研究与开发的研究人员。NumPy API 在 Pandas、SciPy、Matplotlib、...
=len(index_pairs):raiseValueErrorifnisNone: n=max(itertools.chain.from_iterable(index_pairs))+1X=np.zeros(shape=(n,n))forv,(i,j)inzip(values,index_pairs): X[i,j]=v X[j,i]=vreturnX triangular matrix no builtin function for creating from values with indices extracting the triangular...
pop( 0 ) # discard overflow, XXX could check for letter,n in zip( "ABCDF", N) : print letter, n The produced report is: Command: python course_report.py Output: Course mark A Student : 72.4 B Student : 62.3 C Student : 80.0 D Student : 58.9 Statistics Assignments minimum ...
We can use conditional logic expressions when constructing arrays: xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5]) yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5]) cond = np.array([True, False, True, True, False]) result = [(x if c else y) for x, y, c in zip(xarr, yarr, cond...
这个例子中array方法的优势是很明显的,但是一开始不用这种方式来做也是可行的。使用NumPy和类似库的一个重要技能是,识别哪些地方可以用array代码来提到朴素的Python代码。 假设我们要计算两个向量的和。如果用非array的写法,大致是下面这样: importnumpyasnpdefsum_arrays...
如果你想生成这些非零元素的完整索引,可以用zip把它们打包起来: >>> list_of_coordinates= list(zip(b[0], b[1])) >>> list_of_coordinates [(0, 0), (0, 1), (0, 2), (0, 3)] np.nonzero()也可以用来选中元素: >>> a[b]
用数组表达式代替循环的做法,通常被称为矢量化。...' 关于zip函数的一点解释,zip可以接受任意多参数,然后重新组合成1个tuple列表。...用于布尔型数组的方法 sum对True值计数 any和all测试布尔型数组,对于非布尔型数组,所有非0元素将会被当做True import numpy as np import numpy.random...(0.05 * len(larg...
(Z_start,0)).tolist()R_stop=np.maximum(R_start,(R_stop-np.maximum(Z_stop-Zs,0))).tolist()Z_stop=(np.minimum(Z_stop,Zs)).tolist()r=[slice(start,stop)forstart,stopinzip(R_start,R_stop)]z=[slice(start,stop)forstart,stopinzip(Z_start,Z_stop)]R[r]=Z[z]print(Z)print...
You save multiple arrays in a zip archive using np.savez and passing the arrays as keyword arguments: In [186]: np.savez('array_archive.npz', a=arr, b=arr) When loading an .npz file, you get back a dict-like object which loads the individual arrays lazily: In [187]: arch = np...
If the arrays match in size along an axis, then elements will be operated on element-by-element, similar to how the built-in Python function zip() works. If one of the arrays has a size of 1 in an axis, then that value will be broadcast along that axis, or duplicated as many time...