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 ...
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.any N/A Evaluate whether ...
np.maxnp.nanmaxFind maximum value np.argminnp.nanargminFind index of minimum value 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 ...
np.prod np.nanprod Compute product of elements np.mean np.nanmean Compute mean of elements np.std np.nanstd Compute standard deviation np.var np.nanvar Compute variance np.min np.nanmin Find minimum value np.max np.nanmax Find maximum value np.argmin np.nanargmin Find index of minimum v...
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.any N/A Evaluate whether any elements are true np.all N/A Evaluate whether all elements are true ...
将满足条件的项目替换为 Numpy 数组中的另一个值 将所有大于 30 的元素替换为 0 将大于 30 小于 50 的所有元素替换为 0 给所有大于 40 的元素加 5 用Nan 替换数组中大于 25 的所有元素 将数组中大于 25 的所有元素替换为 1,否则为 0 对NumPy 数组中的所有元素求和 创建3D NumPy 零数组 计算NumPy 数组...
导入numpy库:在代码中导入numpy库,以便使用其中的函数和方法。 代码语言:txt 复制 import numpy as np 创建数组:使用numpy库的array函数创建一个数组。 代码语言:txt 复制 arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) 定义部分:确定要查找平均值的数组部分的起始索引和结束索引。
print(np.union1d(array1, array2)) # Find the intersection of two arrays. print(np.intersect1d(array1, array2)) # Find the set difference of two arrays. print(np.setdiff1d(array1, array2)) Output: [10 14 20 24 30 34 36 40 46 50] [20 24] [10 14 30 36]...
Return a with each element rounded to the given number of decimals. searchsorted(v[, side, sorter]) Find indices where elements of v should be inserted in a to maintain order. setfield(val, dtype[, offset]) Put a value into a specified place in a field defined by a data-type. set...
# There is no direct function to do this as of 1.13.3 # Create an all True array out = np.full(a.shape[0], True) # Find the index positions of unique elements unique_positions = np.unique(a, return_index=True)[1] # Mark those positions as False ...