Python converting nested dictionary with list containing float elements as individual elements

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))
]