In [40]: a = np.array([[2,2], [2,3]]) In [41]: a.flatten() Out[41]: array([2, 2, 2, 3]) In [43]: a.reshape(-1) Out[43]: array([2, 2, 2, 3]) 但是像这种不规则维度的多维数组就不能转换成功了,还是本身 a = np.array([[[2,3]], [2,3]]) 转换成二维表示的...
close):flag=Truewhileflag is not False:array=a.sort_values().unique()l=len(array)flag=Falsefo...
a = np.array(...): Create a NumPy array 'a' containing the given integer values. np.unique(a, return_counts=True): Find the unique elements in the array 'a' and their counts using the np.unique function. The return_counts parameter is set to True, so the function returns two arra...
Find the unique elements of an array. Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values the indices of the unique array that reconstruct the input array the number of...
numpy.unique Find the unique elements of an array. Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values, the indices of the unique array that reconstruct the input array...
arr = np.array([1, 2, 2, 3, 3, 3, 4]) unique_elements = np.unique(arr) print('The unique values of the input array is:\n', unique_elements) Output:Here, simply thenp.unique()function in Python will return all the unique values from the input array. ...
[index] # Sort array sort = numpy.sort(ndarray, axis=axis) # Create array to transpose along the axis and get padding shape transpose = numpy.roll(numpy.arange(ndim)[::-1], axis) shape = list(sort.shape) shape[axis] = 1 # Create a boolean array along strides of unique values ...
numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False) Find the intersection of two arrays. x = np.array([1, 1, 2, 3, 4]) y = np.array([2, 1, 4, 6]) xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True) print(x_ind) print(y_ind) print(xy...
import numpy as np the_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) filter_arr = np.logical_and(np.greater(the_array, 3), np.less(the_array, 8)) print(the_array[filter_arr]) Output: [4 5 6 7] Example 2 import numpy as np the_array = np.array([1, 2, 3...
numpy.ravel(a, order='C')Return a contiguous flattened array. 【例】ravel()返回的是视图。 importnumpyasnp x = np.array([[11,12,13,14,15], [16,17,18,19,20], [21,22,23,24,25], [26,27,28,29,30], [31,32,33,34,35]]) ...