numpy.argmax()也可以在这种数组中使用。 示例代码5:结构化数组 importnumpyasnp dtype=[('name','S10'),('height',float),('age',int)]values=[('Arthur',1.8,41),('Lancelot',1.9,38),('Galahad',1.85,35)]a=np.array(values,dtype=dtype)# 创
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 arr=np.array([1,2,3,4,5,3,7,8,9])first_index=np.where(arr==3)[0][0]print("numpyarray.com: First index of value 3:",first_index) Python Copy ...
[-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) >>> # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) >>> # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data....
np.maxnp.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.anyN/A Evaluate whetheranye...
np.argmaxnp.nanargmaxFind index of maximum value np.mediannp.nanmedianCompute median of elements np.percentilenp.nanpercentileCompute rank-based statistics of elements np.anyN/AEvaluate whether any elements are true np.allN/AEvaluate whether all elements are true ...
max(a,b) np.maximum(a, b) 逐元素比较a和b,并返回每对中的最大值 norm(v) np.sqrt(v @ v)或np.linalg.norm(v) 向量v的 L2 范数 a & b logical_and(a,b) 逐元素的与运算符(NumPy ufunc)查看逻辑运算符注意事项 a | b np.logical_or(a,b) 逐元素的或运算符(NumPy ufunc)查看逻辑...
>>> ind = data.argmax(axis=0) # index of the maxima for each series >>> ind array([2, 0, 3, 1]) >>> >>> time_max = time[ind] # times corresponding to the maxima >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]......
min(a) max(a) : 计算数组a的最小值和最大值 argmin(a) argmax(a) : 计算数组a的最小、最大值的下标(注:是一维的下标) unravel_index(index, shape) : 根据shape将一维下标index转成多维下标 ptp(a) : 计算数组a最大值和最小值的差 median(a) : 计算数组a中元素的中位数(中值) eg:a = [...
arr_2.argmax() #This shows the index of the highest value in the array arr_2.argmin() #This shows the index of the lowest value in the array 假设存在大量数组，而你需要弄清楚数组的形态，你想知道这个数组是一维数组还是二维数组，只需要使用 shape 函数即可：arr.shape 从 NumPy 数组中索引／...
[-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series ind = data.argmax(axis=0) ind array([2, 0, 3, 1]) # times corresponding to the maxima time_max = time[ind] >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0], 0...