print(x.real): Prints the real part of the square root of the complex number (1+0j) stored in ‘x’. print(y.real): Prints the real part of the square root of the complex number (0+1j) stored in ‘y’. print(x.imag): Prints the imaginary part of the square root of the com...
In: arange(7, dtype='f') Out: array([ 0., 1., 2., 3., 4., 5., 6.], dtype=float32) Likewise this creates an array of complex numbers In: arange(7, dtype='D') Out: array([ 0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 5.+0.j, 6.+0.j]) dtype构造器 ...
Out: array([0.,1.,2.,3.,4.,5.,6.], dtype=float32) Likewise this creates an array ofcomplexnumbers In: arange(7, dtype='D') Out: array([0.+0.j,1.+0.j,2.+0.j,3.+0.j,4.+0.j,5.+0.j,6.+0.j]) dtype构造器 我们有多种创建数据类型的方式。 以浮点数据为例(请参见...
3) >>> b.dtype.name 'float64' >>> c = a + b >>> c array([1\. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c * 1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' ...
large_arr=np.random.randint(0,5,1000000)non_zero_indices=np.flatnonzero(large_arr)non_zero=large_arr[non_zero_indices]print("Number of non-zero elements from numpyarray.com:",len(non_zero)) Python Copy Output: np.flatnonzero()直接返回非零元素的一维索引,对于大型数组来说,这可能比np.non...
Write a NumPy program to find the real and imaginary parts of an array of complex numbers.Expected Output:Original array [ 1.00000000+0.j 0.70710678+0.70710678j] Real part of the array: [ 1. 0.70710678] Imaginary part of the array: [ 0. 0.70710678] ...
array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') 1. 2. 3. 4. 5. 6. 7. 8. 9. 多维数组: >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b ...
Thenumpy.correlate()function can also be used to find the correlation between complex data types. importnumpyasnp# create two arrays of complex numbersarray1 = np.array([1+1j,2,3-1j]) array2 = np.array([2,1-1j,3+1j]) # calculate the correlation of the complex arrayscorr = np.corr...
Take a look at the following code to produce an array of single precision floats: In: arange(7, dtype='f') Out: array([ 0., 1., 2., 3., 4., 5., 6.], dtype=float32) Likewise, the following code creates an array of complex numbers: In: arange(7, dtype='D') Out: array...
array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) 数组的类型也可以在创建时显式指定: >>> c = np.array([[1, 2], [3, 4]], dtype=complex) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) 通常,数组的元素最初是未知的,但其尺寸大小是已知的。因此,NumPy提供了...