- ‘str_kind’ : sets ‘numpystrNumpy的 set_printoptions 函数可以用来设置numpy数组的打印参数，以控制打印格式。importnumpyasnp# Create a 2D numpy arrayx=np.array([[1,2,3],[4,5,6]])# Print array without setting parametersprint(x)# Output[[123][456]]# Set the print options to set the precision to 2np.set_print...
np.set_printoptions(precision=3, suppress=True)print('after set precision: \n',a) np.set_printoptions(formatter={'float':'{: 0.3f}'.format})print('after set formatter: \n',a) 结果: beforesetoptions: [0.058563480.54000390.70000603] aftersetprecision: [0.0590.540.7] aftersetformatter: [0.0590...
np.set_printoptions(precision=3, suppress=True)print('after set precision: \n',a) np.set_printoptions(formatter={'float':'{: 0.3f}'.format})print('after set formatter: \n',a) 结果: beforesetoptions: [0.058563480.54000390.70000603] aftersetprecision: [0.0590.540.7] aftersetformatter: [0.0590...
整数和 Void 标量现在不受 np.set_string_function 影响 0 维数组打印已更改,array2string 的style 参数已弃用 使用数组种子 RandomState 需要一个一维数组 MaskedArray 对象显示更有用的 repr np.polynomial 类的repr 更加明确 1.13.3 贡献者 已合并的拉取请求 1.13.2 贡献者 合并的拉取请求 1....
print(val1) # [123. 110. 164.] print(val2) # [1.4 0.5 2.1] 文本格式选项 numpy.set_printoptions(precision=None,threshold=None, edgeitems=None,linewidth=None, suppress=None, nanstr=None, infstr=None,formatter=None, sign=None, floatmode=None, **kwarg) ...
NumPy.set_printoptions() method Example-2: Long arrays can be summarised: >>> import numpy as np >>> np.set_printoptions(threshold=5) >>> print(np.arange(12)) Output: [ 0 1 2 ... 9 10 11] NumPy.set_printoptions() method Example-3: ...
np.set_printoptions(suppress=True) data.files ['z_d', 'arr_0', 'arr_1'] print(data['arr_0']) # [0. 0.78539816 1.57079633 2.35619449 3.14159265] print(data['arr_1']) # [0. 0.70710678 1. 0.70710678 0. ] print(data['z_d']) ...
np.set_printoptions(suppress=True) print(data.files) # ['z_d', 'arr_0', 'arr_1'] print(data['arr_0']) # [0. 0.78539816 1.57079633 2.35619449 3.14159265] print(data['arr_1']) # [0. 0.70710678 1. 0.70710678 0. ] print(data['z_d']) ...
Finally print() function prints the ‘nums’ array. Pictorial Presentation: For more Practice: Solve these Related Problems: Write a NumPy program to set the print options so that an array with thousands of elements is fully displayed.
print(a) # [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] print(np.arange(16).reshape(4,4)) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11] # [12 13 14 15]] 但是若以'F'为序的话会是这样的(这也是matlab或octave中的reshape): ...