如何从 numpy 数组创建 NxM 矩阵？

Question

我正在研究从 NumPy 数组中形成大约 60 行和 11 列的矩阵。 我研究了几种方法，但我无法让它发挥作用。 我尝试了以下代码并收到此错误，

stats_features_full = np.empty((0, 11))
for ls in range(60):
    current_list = ls
    print('Entering list {0} for feature extraction'.format(current_list))
    stats_features = get_selected_statistics_features(list_values=list[ls])
    stats_features_np_shape = np.array(stats_features).shape
    print('Statistical Features Extracted from list: ', stats_features)
    print('Statistical Features Shape Extracted from list: ', stats_features_np_shape)
    stats_features_full = np.concatenate([stats_features_full, np.array(stats_features)], axis=0)
    # stats_features_full = np.append(arr=stats_features_full, values=np.array(stats_features), axis=0)
    stats_features_full_np_shape = np.array(stats_features_full).shape
    print('Statistical Features Extracted from all lists: ', stats_features_full)
    print('Statistical Features Shape Extracted from all lists: ', stats_features_full_np_shape)

错误信息：

(1)

stats_features_full = np.concatenate([stats_features_full, np.array(stats_features)], axis=0)
  File "<__array_function__ internals>", line 6, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)

(2)

print('Entering list {0} for feature extraction'.format(current_list))
  File "<__array_function__ internals>", line 6, in append
return concatenate((arr, values), axis=axis)
  File "<__array_function__ internals>", line 6, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)

有没有办法创建一个 60x11 数组？

编辑1：

感谢@Krish，它似乎工作正常。 我还有一个问题，我想将stats_features_full变量转换为 pandas dataframe 以便将结果保存为文本文件。 我该如何解决这个问题？ 请参阅下面的方法：

    ########################################################################################################################
########################################################################################################################
############################################### Feature Datasets #######################################################
########################################################################################################################
########################################################################################################################
Stats_DataFrame_Feature = stats_features_full
Stats_DataFrame_Feature_Data_list = list(Stats_DataFrame_Feature)
# print('Statistical DataFrame Featureset list: ', Stats_DataFrame_Feature_Data_list)
Stats_DataFrame_Feature_Data_list_shape = np.array(Stats_DataFrame_Feature_Data_list).shape
Stats_DataFrame_Feature_Data_list_shape_1 = np.array(Stats_DataFrame_Feature_Data_list).shape
print('Statistical DataFrame Featureset list shape: ', Stats_DataFrame_Feature_Data_list_shape)
print('Statistical DataFrame Featureset list shape: ', Stats_DataFrame_Feature_Data_list_shape_1[0])


for Stat_row in range(60):
    StatsData.append(Stats_DataFrame_Feature[0:Stats_DataFrame_Feature_Data_list_shape[0]])
    StatsData_np = np.array(StatsData)
    with open('filepath\dataset.txt', 'w') as out_file:
        for i in range(60):
            print('Opened file number: {0}'.format(i))
            out_string = ""
            out_string += pd.DataFrame(data=StatsData_np).to_string()
            out_file.write(out_string)
            break
        # break
    # break

Stats_DataFrame_Feature_Matrix = StatsData
print('Final Saved Statistical Feature Dataset file: ', Stats_DataFrame_Feature_Matrix)
print('Shape Final Saved Statistical Feature Dataset file: ', np.array(Stats_DataFrame_Feature_Matrix).shape)

我的错误信息：

out_string += pd.DataFrame(data=StatsData_np).to_string()
mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
values = prep_ndarray(values, copy=copy)
raise ValueError("Must pass 2-d input")
ValueError: Must pass 2-d input

编辑2：

我设法通过更改以下几行使其工作，

StatsData.append(Stats_DataFrame_Feature[0:Stats_DataFrame_Feature_Data_list_shape[1]])
StatsData_np = np.array(StatsData[Stat_row])

但是，我让保存的文件遵循尺寸（60、11、11）。 这是为什么？

编辑3：

假设我创建了 6 个字典键，每个键有 10 个列表。 我想实现同样的事情，但我不断收到索引错误。

for key in range(0, 6, 1):
    list_key = np.array(dict_list[key])
    print('Key value: {0}'.format(key))
    arr_list = []
    for list_num in range(0, 10, 1):
        list_val_num = np.array(list(list_key[:, list_num][0]))
        # stats_features = get_statistics_features_final(list_values=list_val_num)
        stats_features = get_selected_statistics_features(list_values=list_val_num)
        stats_features_np_shape = np.array(stats_features).shape
        print('Statistical Features Extracted from list: ', stats_features)
        print('Statistical Features Shape Extracted from list: ', stats_features_np_shape)
        arr_list += [stats_features]
f_arr_list += arr_list
stats_features_full = np.vstack(f_arr_list)
stats_features_full_np_shape = np.array(stats_features_full).shape
print('Statistical Features Shape Extracted from all lists: ', stats_features_full_np_shape)

错误信息：

IndexError: index 1 is out of bounds for axis 1 with size 1

Answer 1

我使用np.random.rand(11)代替数据：

import numpy as np
arr_list = []
for ls in range(60):
    stats_features_np_shape = np.random.rand(11)
    arr_list += [stats_features_np_shape]

stats_features_full = np.vstack(arr_list)
print(stats_features_full)

不在这里的关键是stats_features_np_shape应该具有(11) （或任何整数）的形状，并且stats_features_full最好在循环之外生成。

Answer 2

感谢@Krish 和@hpaulj，我设法解决了这个问题。 在下面找到完整的代码：

########################################################################################################################
########################################################################################################################
############################################ Feature Extraction ########################################################
########################################################################################################################
########################################################################################################################
arr_list = []
for ls in range(60):
    current_list = ls
    print('Entering list {0} for feature extraction'.format(current_list))
    stats_features = get_selected_statistics_features(list_values=list[ls])
    arr_list += [stats_features]
    stats_features_np_shape = np.array(stats_features).shape
    print('Statistical Features Extracted from list: ', stats_features)
    print('Statistical Features Shape Extracted from list: ', stats_features_np_shape)
stats_features_full = np.vstack(arr_list)
stats_features_full_np_shape = np.array(stats_features_full).shape
print('Statistical Features Shape Extracted from all lists: ', stats_features_full_np_shape)

########################################################################################################################
########################################################################################################################
############################################### Feature Datasets #######################################################
########################################################################################################################
########################################################################################################################
Stats_DataFrame_Feature = stats_features_full
Stats_DataFrame_Feature_Data_list = list(Stats_DataFrame_Feature)
print('Statistical DataFrame Featureset list: ', Stats_DataFrame_Feature_Data_list)
Stats_DataFrame_Feature_Data_list_shape = np.array(Stats_DataFrame_Feature_Data_list).shape
print('Statistical DataFrame Featureset list shape: ', Stats_DataFrame_Feature_Data_list_shape)

for Stat_row in range(60):
    StatsData.append(Stats_DataFrame_Feature[0:Stats_DataFrame_Feature_Data_list_shape[0]])
    Stats_DataFrame_Feature_Data_list_list = Stats_DataFrame_Feature_Data_list[0:Stats_DataFrame_Feature_Data_list_shape[0]]
    StatsData_np = np.array(StatsData[Stat_row])
    with open('filepath\list dataset.txt', 'w') as out_file:
        for i in range(60):
            print('Opened file number: {0}'.format(i))
            out_string = ""
            out_string += pd.DataFrame(data=StatsData_np).to_string()
            out_file.write(out_string)
            break
Stats_DataFrame_Feature_Matrix = StatsData
print('Shape Final Saved Statistical Feature Dataset file: ', np.array(Stats_DataFrame_Feature_Matrix).shape)
StatsData_np = np.array(StatsData)

column_no = 0
StatsData = []
stats_features_full = np.array([])