importnumpyasnp arr=np.array([5,2,8,1,9,3,7])max_index=np.argmax(arr)print("numpyarray.com: Index of maximum value:",max_index) Python Copy Output: np.argmax()返回数组中最大值的索引。 6. 查找满足条件的第一个值的索引 有时我们只需要找到满足条件的第一个值的索引。 importnumpyasnp...
0]中找到非0元素的索引 nz = np.nonzero([...a 10x10 array with random values and find the minimum and maximum values (★☆☆) 创建一个10*10的随机值数组,并找到最大最小值...(★★☆) 如何在数组中找到最接近给定值的值 Z = np.arange(100) v = np.random.uniform(0,100) index = (...
NumPy 的结果是布尔值 False 和 True 的数组。 find(a > 0.5) np.nonzero(a > 0.5) 找出(a > 0.5) 的索引 a(:,find(v > 0.5)) a[:,np.nonzero(v > 0.5)[0]] 提取a 中向量 v > 0.5 的列 a(:,find(v>0.5)) a[:, v.T > 0.5] 提取a 中列向量 v > 0.5 的列 a(a<0.5)=0 ...
import numpy def mode(ndarray, axis=0): # Check inputs ndarray = numpy.asarray(ndarray) ndim = ndarray.ndim if ndarray.size == 1: return (ndarray[0], 1) elif ndarray.size == 0: raise Exception('Cannot compute mode on empty array') try: axis = range(ndarray.ndim)[axis] except: ...
np.min np.nanmin Find minimum value np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum value np.argmax np.nanargmax Find index of maximum value np.median np.nanmedian Compute median of elements np.percentile np.nanpercentile Compute rank-based statistics of ...
np.min np.nanmin Find minimum value np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum value np.argmax np.nanargmax Find index of maximum value np.median np.nanmedian Compute median of elements np.percentile np.nanpercentile Compute rank-based statistics of ...
np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum value np.argmax np.nanargmax Find index of maximum value np.median np.nanmedian Compute median of elements np.percentile np.nanpercentile Compute rank-based statistics of elements ...
np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum value np.argmax np.nanargmax Find index of maximum value np.median np.nanmedian Compute median of elements np.percentile np.nanpercentile Compute rank-based statistics of elements ...
线性索引在 MATLAB 程序中很常见,例如,对矩阵进行find()返回它们,而 NumPy 的find()行为有所不同。在转换 MATLAB 代码时,可能需要首先将矩阵重塑为线性序列,执行一些索引操作,然后再进行重塑。由于 reshape(通常)提供对相同存储的视图,因此应该可以相当高效地完成此操作。请注意,NumPy 中 reshape 的扫描顺序默认为...
(hint: minimum, maximum) Z = np.random.randint(0,10,(10,10)) shape = (5,5) fill = 0 position = (1,1) R = np.ones(shape, dtype=Z.dtype)*fill P = np.array(list(position)).astype(int) Rs = np.array(list(R.shape)).astype(int) Zs = np.array(list(Z.shape)).astype(in...