# np.nan is replaced with 0.0 and np.inf # is replaced with 999999 print("After replacement the array is : ",np.nan_to_num(array,posinf=999999)) 输出: [nan+infj] Shapeofthe arrayis:(1,) Thedimensionofthe arrayis:1 DatatypeofourArrayis:complex128 Afterreplacement the arrayis:[0.+...
输出结果为1.0 1.0。 2.2.3numpy.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])print(np.n...
#X = X.astype(str).replace('nan', 0).astype(float) #np.frompyfunc(lambda x: x.replace(',',''),1,1)(X).astype(float) np.array([v.replace(',', '') for v in X], dtype=np.float32) print('replaced values') #X=X.replace([np.inf,-np.inf],np.nan) #X=X.replace(np....
Python program to replace -inf with zero value in NumPy array# Import numpy import numpy as np from numpy import inf # Creating a numpy array arr = np.array([-inf, -inf, 3,7,4,9,6,6,8,5,7,9]) # Display original array print("Original array:\n",arr,"\n") # replacing -inf...
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)...
TD-0 Q-learning agent Dyna-Q / Dyna-Q+ with prioritized sweeping Nonparameteric models Nadaraya-Watson kernel regression k-Nearest neighbors classification and regression Gaussian process regression Matrix factorization Regularized alternating least-squares Non-negative matrix factorization Preprocessing Di...
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 == np.nan)print(np.inf > np.nan)print(np.nan - np.nan)print(np.nan in set([np.nan]))print(0.3 == 3 * 0.1) ...
nanstr：浮点非数字的字符串表示形式，默认nan。infstr：浮点无穷大的字符串表示形式，默认inf。These options determine the way floating point numbers, arrays and other NumPy objects are displayed.【例】import numpy as npnp.set_printoptions(precision=4)x = np.array([1.123456789])print(x) # [1....
Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0) print(Z) 17. 下面表达式运行的结果是什么?(★☆☆) (提示: NaN = not a number, inf = infinity) (提示:NaN : 不是一个数,inf : 无穷) 表达式 # 结果 0 * np.nan nan ...
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[:...