Python 使用包含浮动元素作为单个元素的列表转换嵌套字典

Question

I'm collecting values from different arrays and nested dictionary containing list values, like below. The lists contains millions of rows, I tried pandas dataframe concatenation But getting out of memory, so I resorted to a for loop.

array1_str = ['user_1', 'user_2', 'user_3','user_4' , 'user_5']
array2_int = [3,3,1,2,4]
nested_dict_w_list = {'outer_dict' : { 'inner_dict' : [[1.0001],[2.0033],[1.3434],[2.3434], [0.44224]}}
    
final_out = [array1_str[i], array2_int[i], nested_dict_w_list['outer_dict']['inner_dict'][array2_int[i]]] for i in range(len(array2_int))]

I'm getting the output as

user_1, 3, [2.3434]
user_2, 3, [2.3434]
user_3, 1, [1.0001]
user_4, 2, [1.3434]
user_5, 4, [0.44224]

But I want the output as

user_1, 3, 2.3434
user_2, 3, 2.3434
user_3, 1, 1.0001
user_4, 2, 1.3434
user_5, 4, 0.44224

I need to eventually convert this to parquet file, I'm using spark dataframe to convert this to parquet, but the schema is appearing as array(double)). But I need it as just double. Any input is appreciated.

The below for loop is working, but any other efficient and elegant solution.

final_output = []
for i in range(len(array2_int)-1)):
  index = nested_dict_w_list['outer_dict']['inner_dict'][array2_int[i]]
  final_output.append(array1_str[i], array2_int[i], index[0])

Answer 1

You can modify your original list comprehension, by indexing to item zero:

final_out = [
    (array1_str[i], array2_int[i], nested_dict_w_list['outer_dict']['inner_dict'][array2_int[i]][0])
    for i in range(len(array2_int))
]