Replace nan with zero and inf with finite numbers,把np.nan(非常小的数字,接近0)用0取代。 np.inf,或者-np.inf(正负无穷大的数字)用有限数替代; np.set_printoptions(precision=8) x = np.array([np.inf, -np.inf, np.nan, -128,128])print(np.nan_to_num(x)) 输出结果为[ 1.79769313e+308 ...
array([1,2,3]) # 数值型数组 array(['w','s','q'],dtype = '<U1') # 字符型数组...
returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition,# second will replace the values that does notnp.where(y>5, "Hit", "Miss")array(['Miss', 'Miss', 'Hit...
numpy.isnan(element) Note: 不能使用array[0] == np.NaN,总是返回False! numpy数组元素替换numpy.nan_to_num(x) 判断某元素是否是nan,inf,neginf,如果是,nan换为0,inf换为一个非常大的数,neginf换为非常小的数 numpy.nan_to_num(x)Replace nan with zero and inf with finite numbers.Returns an ...
(3).np.nan_to_num:Replace nan with zero and inf with finite numbers. 把np.nan(非常小的数字,接近0)用0取代 np.inf,或者-np.inf(正负无穷大的数字)用有限数替代 np.set_printoptions(precision=8) x = np.array([np.inf, -np.inf, np.nan, -128, 128]) ...
array_nums2[np.isnan(array_nums2)]: This part selects all NaN values in array_nums2. array_nums2[np.isnan(array_nums2)] = np.nanmean(array_nums1) replaces the selected NaN values in array_nums2 with the computed mean from array_nums1. ...
Z = np.ones((5,5))Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)print(Z) 1. 17.以下表达式的结果是什么?(★☆☆) 0 * np.nannp.nan == np.nannp.inf > np.nannp.nan - np.nannp.nan in set([np.nan])0.3 == 3 * 0.1print(0 * np.nan)print(np.nan =...
numpy.array(object, dtype =None, copy =True, order =None, subok =False, ndmin =0) importnumpyasnp# 用np代替numpy,让代码更简洁a = [1,2,3,4]# 创建列表ab = np.array([1,2,3,4])# 从列表a创建数组b,array就是数组的意思print(a)print(type(a))# 打印a的类型print(b)print(type(b)...
0 * np.nannp.nan == np.nan np.inf > np.nannp.nan - np.nan0.3 == 3 * 0.1 16、创建一个5 × 5矩阵，对角线值为1,2,3,4 Z = np.diag(1+np.arange(4),k=-1)print(Z)17、创建一个8x8的矩阵，并使用0，1间隔填充 Z = np.zeros((8,8),dtype=int)Z[1::2,::2] = 1Z[:...
array([2, 3, 5, 7, 8], dtype=int64) # First will replace the values that match the condition, # second will replace the values that does not >>> np.where(y>5, "Hit", "Miss") array(['Miss', 'Miss', 'Hit', 'Hit', 'Miss', ...