我想制作带字母到字母表的映射表。我们无法使用相同的字母值和密钥。
例如，我们可以使用密钥A - 值C，密钥B - 密钥D.但是我们不能使用密钥A - 值A，密钥C - 值C.在这种情况下，数量是多少我们可以制作映射表吗？
答案 0 :(得分：0)
148,362,637,348,470,135,821,287,825。
在组合数学中，derangement是一组元素的排列，因此没有元素出现在其原始位置。换句话说，紊乱是一种没有固定点的排列。 -Wikipedia
假设你只使用了26次替换（例如大写字母），问题可以归结为“一组26个项目的紊乱次数是多少？”;这是import json
import pandas as pd
# Source Data string
json_raw = '''{"Loc":"TDM","Topic":"location","LocMac":"location/fe:7a:xx:xx:xx:xx","seq":"296083773","timestamp":1488986751,"op":"OP_UPDATE","topicSeq":"46478211","sourceId":"AFBWmHSe","location":{"staEthMac":{"addr":"/xxxxx"},"staLocationX":1643.8915,"staLocationY":571.04205,"errorLevel":1076,"associated":0,"campusId":"n5THo6IINuOSVZ/cTidNVA==","buildingId":"7hY/xx==","floorId":"xxxxxxxxxx+BYoo0A==","hashedStaEthMac":"xxxx/pMVyK4Gu9qG6w=","locAlgorithm":"ALGORITHM_ESTIMATION","unit":"FEET"},"EventProcessedUtcTime":"2017-03-08T15:35:02.3847947Z","PartitionId":3,"EventEnqueuedUtcTime":"2017-03-08T15:35:03.7510000Z","IoTHub":{"MessageId":null,"CorrelationId":null,"ConnectionDeviceId":"xxxxx","ConnectionDeviceGenerationId":"636243184116591838","EnqueuedTime":"0001-01-01T00:00:00.0000000","StreamId":null}}'''
# Load source data string to a Python dict
json_data = json.loads(json_raw)
# The key method `flattern` for converting `dict` to `2D-list`
def flattern(data, key):
keys = []
values = []
if key is None:
for key in data:
if type(data[key]) is dict:
keys.extend(flattern(data[key], key)[0])
values.extend(flattern(data[key], key)[1])
else:
keys.append(key)
values.append(data[key])
else:
for subkey in data:
if type(data[subkey]) is dict:
keys.extend(flattern(data[subkey], key+"."+subkey)[0])
values.extend(flattern(data[subkey], subkey)[1])
else:
keys.append(key+"."+subkey)
values.append(data[subkey])
return [keys, values]
list2D = flattern(json_data, None)
df = pd.DataFrame([list2D[1],], columns=list2D[0])
# If you want to extract the items `Loc` & `Topic` & others like `location.staEthMac.addr`, you just need to create a list for them.
selected = ["Loc", "Topic"]
# Use `selected` list to select the columns you want.
result = df.ix[:,selected]
# Transform DataFrame to csv string
csv_raw = "\n".join([",".join(lst) for lst in pd.np.array(result)])
，总密钥空间减少了约63％（26！== 1.48e26）。
对于较大的值，它大约等于4.03e26（e是大约等于2.71828的数字）。