使用transpose()方法 在实际编程中,我们可以通过transpose()方法来处理嵌套列表。首先,我们需要创建一个嵌套列表,然后调用transpose()方法对其进行转置。下面是一个简单的示例: matrix = [[1, 2], [3, 4], [5, 6]] transposed_matrix = matrix.transpose() print(transposed_matrix) 运行上述代码,我们可以得到...
In the above example, thetranspose()function returns a new array with the axes switched. In the case of the 2D array like our list, the rows and columns have been swapped. You will notice that all three examples return the same results, but in slightly different structures. Therefore, sele...
以下是transpose函数的实现示例: deftranspose(input_sequence):# 检查输入是否为空ifnotinput_sequence:return[]# 检查是否为二维结构ifisinstance(input_sequence[0],(list,tuple)):returnlist(map(list,zip(*input_sequence)))raiseTypeError("Unsupported data type. Input should be a list or tuple of lists....
我们再来看一个例子-实现矩阵转置 def transpose_list(list_of_lists): return [list(row) for row in zip(*list_of_lists)] matrix = [[1, 4, 7], [2, 5, 8], [3, 6, 9]] transpose_list(matrix) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 运行结果 [[1, 2, 3], [4,...
# a polygon can be imbided into a circletheta=np.linspace(0,2*np.pi,6)# generates an arrayvertical=np.vstack((np.cos(theta),np.sin(theta))).transpose()# vertical stack clubs the two arrays.#print vertical,print and see how the array looksplt.gca().add_patch(plt.Circle((0,0),...
] The following list comprehension will transpose rows and columns: >>> [[row[i] for row in matrix] for i in range(4)] [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] 也可以 >>> zip(*matrix) [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)] ...
size)).transpose() fig, ax = plt.subplots(figsize=(12,10)) ylabel="微克/立方米" if colName!='优良天数' else '天' cmap='Wistia'if colName!='优良天数' else 'YlGn' im, cbar = heatmap(values, months, citys, ax=ax, cmap=cmap, cbarlabel=ylabel) texts = annotate_heatmap(im, ...
to_sql to_string to_timestamp to_xarray tolist 47. transform transpose truediv truncate tshift 48. tz_convert tz_localize unique unstack update 49. value_counts values var view where 50. xs 两者同名的方法有181个,另各有30个不同名的: 1. >>> A,B = [_ for _ in dir(pd.DataFrame) ...
descent using backpropagation to a single mini batch. The ``mini_batch`` is a list of tuples ``(x, y)``, ``eta`` is the learning rate, ``lmbda`` is the regularization parameter, and ``n`` is the total size of the training data set. ...
df2 = pd.DataFrame(doc_vec.toarray.transpose, index=vectorizer.get_feature_names) # Change column headers df2.columns = df1.columns print(df2) Output: Go Java Python and 2 2 2 application 0 1 0 are 1 0 1 bytecode 0 1 0 can 0 1 0 ...