python - 从 Numpy Ndarray 构建字典和列表

Question

我想使用 2D ndarray 为数百万个数据创建一个字典。

寻找一种pythonic和高性能的方式来实现这一目标

我的数组:

格式:[id、origin_lat、origin_lon、dest_lat、dest_lon、距离]

my_array = np.array([[245, 32.45,63.89,72.1,63.57,123.45],
[246, 61.73,42.71,75.54,-81.69,16.32]])

预期输出:

my_dict = {
        245: {
            'origin_lat_lon': {
                'lat': 32.45,
                'lon': 63.89
            },
            'dest_lat_lon': {
                'lat': 72.1,
                'lon': 63.57
            },
            'distance': 123.45
        },
        246: {
            'origin_lat_lon': {
                'lat': 61.73,
                'lon': 42.71
            },
            'dest_lat_lon': {
                'lat': 75.54,
                'lon': -81.69
            },
            'distance': 16.32
        }
    }

my_list = [{'lat': 32.45, 'lon': 63.89},
 {'lat': 72.1, 'lon': 63.57},
 {'lat': 61.73, 'lon': 42.71},
 {'lat': 75.54, 'lon': -81.69}]

我的代码:

my_dict = dict()
my_list = list()

for arr in my_array:
    origin_lat_lon = {'lat': arr[1],
                            'lon': arr[2]}
    dest_lat_lon  = {'lat': arr[3],
                  'lon': arr[4]}
    value = {'origin_lat_lon':origin_lat_lon,'dest_lat_lon':dest_lat_lon,'distance':arr[5]}
    my_dict[int(arr[0])]=value
    my_list.append(origin_lat_lon)
    my_list.append(dest_lat_lon)

Answer 1

这是使用 dict 的一种方法与 zip和 slicing .

例如:

import numpy as np

my_array = np.array([[245, 32.45,63.89,72.1,63.57,123.45],[246, 61.73,42.71,75.54,-81.69,16.32]])
keys = ['origin_lat', 'origin_lon', 'dest_lat','dest_lon', 'distance']
keys_2 = ['lat', 'lon']

my_dict = {}
my_list = []

for arr in my_array:
    key, vals = arr[0], arr[1:]
    my_dict[int(key)] = dict(zip(keys, vals))
    my_list.extend([[dict(zip(keys_2, vals[0:2]))],[dict(zip(keys_2, vals[2:4]))]])

print(my_dict)
print(my_list)

输出:

{245: {'dest_lat': 72.1,
       'dest_lon': 63.57,
       'distance': 123.45,
       'origin_lat': 32.45,
       'origin_lon': 63.89},
 246: {'dest_lat': 75.54,
       'dest_lon': -81.69,
       'distance': 16.32,
       'origin_lat': 61.73,
       'origin_lon': 42.71}}
[[{'lat': 32.45, 'lon': 63.89}],
 [{'lat': 72.1, 'lon': 63.57}],
 [{'lat': 61.73, 'lon': 42.71}],
 [{'lat': 75.54, 'lon': -81.69}]]