Python 把两层列表展开平铺成一层(5种实现方式)

# 期望输入
input = [[('A', 1), ('B', 2)], [('C', 3), ('D', 4)]]
 
# 期望输出
output = [('A', 1), ('B', 2), ('C', 3), ('D', 4)]

>>> new = []; map(new.extend, input); new
[None, None]
[('A', 1), ('B', 2), ('C', 3), ('D', 4)]

>>> sum(input, [])
[('A', 1), ('B', 2), ('C', 3), ('D', 4)]

>>> reduce(list.__add__, input)
[('A', 1), ('B', 2), ('C', 3), ('D', 4)]

>>> [item for sublist in input for item in sublist]
[('A', 1), ('B', 2), ('C', 3), ('D', 4)]

>>> list(itertools.chain(*input))
[('A', 1), ('B', 2), ('C', 3), ('D', 4)]

Python -mtimeit -s'l=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'reduce(list.__add__,l)'
1000 loops, best of 3: 547 usec per loop
python -mtimeit -s'l=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'sum(l, [])'
1000 loops, best of 3: 509 usec per loop
python -mtimeit -s'l=[[1,2,3],[4,5,6], [7], [8,9]]*99' '[item for sublist in l for item in sublist]'
10000 loops, best of 3: 52.8 usec per loop
python -mtimeit -s'l=[[1,2,3],[4,5,6], [7], [8,9]]*99; import itertools;' 'list(itertools.chain(*l))'
10000 loops, best of 3: 35.9 usec per loop
python -mtimeit -s'l=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'new = []; map(new.extend, l); new'
10000 loops, best of 3: 34.1 usec per loop

def prefix_dict(di_, prefix_s=''):
  """
  把字典的每个key都带上前缀prefix_s
  :param di_:
  :param prefix_s:
  :return:
  """
  return {prefix_s + k: v for k, v in di_.items()} 
 
def spear_dict(di_, con_s='.'):
  """
  展开dict(如果下层还是dict)，需要递归，展开到下层的数据类型不是字典为止
  可能实用的地方：将文档类的数据格式化成更加关系化的样子可能有用
  :param di_: 输入字典
  :param con_s: 层级间的连接符号
  :return: 深度不大于1的字典，嵌套的其他数据类型照旧
  """
  ret_di = {}
  for k, v in di_.items():
    if type(v) is dict:
      v = spear_dict(v)
      # 这里或许有不写到这一层的更好写法
      # for k_, v_ in v.items():
      #   ret_di.update({con_s.join([k, k_]): v_})
      ret_di.update(prefix_dict(v, prefix_s=k + con_s))
    else:
      ret_di.update({k: v})
  return ret_di

>>> di_
{'title': '新田商业街', 'reliability': 7, 'addressComponents': {'streetNumber': '', 'city': '深圳市', 'street': '', 'province': '广东省', 'district': '龙华区'}, 'location': {'lng': 114.09127044677734, 'lat': 22.700519561767578}, 'adInfo': {'adcode': '440309'}, 'level': 11, 'more_deep': {'loca': {'lng': 114.09127044677734, 'lat': 22.700519561767578}}}
>>> spear_dict(di_)
{'title': '新田商业街', 'reliability': 7, 'addressComponents.streetNumber': '', 'addressComponents.city': '深圳市', 'addressComponents.street': '', 'addressComponents.province': '广东省', 'addressComponents.district': '龙华区', 'location.lng': 114.09127044677734, 'location.lat': 22.700519561767578, 'adInfo.adcode': '440309', 'level': 11, 'more_deep.loca.lng': 114.09127044677734, 'more_deep.loca.lat': 22.700519561767578}
spear_dict(di_, '_')
{'title': '新田商业街', 'reliability': 7, 'addressComponents_streetNumber': '', 'addressComponents_city': '深圳市', 'addressComponents_street': '', 'addressComponents_province': '广东省', 'addressComponents_district': '龙华区', 'location_lng': 114.09127044677734, 'location_lat': 22.700519561767578, 'adInfo_adcode': '440309', 'level': 11, 'more_deep_loca.lng': 114.09127044677734, 'more_deep_loca.lat': 22.700519561767578}