Got it, let's fix this step by step. The issue here is that np.loadtxt splits your dictionary string into separate elements in the array because it treats spaces as delimiters by default. Here's how you can turn that array into a proper Python dictionary:

Step 1: Load the data correctly #

First, load the file as an array of string fragments (using dtype=str instead of dtype=dict, since np.loadtxt can't directly parse dictionaries this way):

import numpy as np
# Load the file content as an array of string pieces
stock_array = np.loadtxt("wh_stock.dat", dtype=str)

Step 2: Reconstruct the JSON string and convert to dictionary #

Use Python's built-in json module to parse the reconstructed string into a dictionary:

import json
# Join all string fragments into one valid JSON-formatted string
full_dict_string = ''.join(stock_array)
# Convert the JSON string to a Python dictionary
stock_dict = json.loads(full_dict_string)

Verify the result #

If you print stock_dict, you'll get the standalone dictionary you need:

print(stock_dict)
# Output:
# {"01": 115, "02": 34, "03": 350, "04": 273, "05": 922, "06": 844, "07": 575, "08": 523, "09": 179, "10": 676, "11": 825, "12": 693, "13": 632, "14": 195, "15": 692, "16": 956, "17": 619, "18": 588, "19": 580, "20": 648}

Why this works #

Your .dat file contains a single JSON-formatted string, but np.loadtxt splits it into multiple elements whenever it encounters a space. By joining all those fragments back together, you create a valid JSON string that the json module can easily parse into a proper dictionary.

内容的提问来源于stack exchange，提问作者Freya Haslam