我有一個包含兩個子列表的列表。這里看起來像這樣a = [['user1', 'referral'], ['user2', 'referral'], ['user1', 'referral'], ['user1', 'affiliate'], ['user7', 'affiliate'], ['user1', 'affiliate'], ['user9', 'affiliate'], ['user4', 'cpc'], ['user4', 'referral'], ['user2', 'referral'], ['user7', 'affiliate'], ['user14', 'cpc'], ['user3', 'orgainic'], ['user2', 'orgainic'], ['user4', 'cpc'], ['user2', 'cpc'], ['user8', 'cpc'], ['user2', 'orgainic']]我想根據類別計算用戶（唯一）。必需的：required = [['referral',3],['affiliate',3],['cpc',4],['orgainic',2]]我得到的輸出：{'referral': 3, 'affiliate': 2, 'cpc': 4, 'orgainic': 3}算錯了。這是我嘗試過的代碼：a = [['user1', 'referral'], ['user2', 'referral'], ['user1', 'referral'], ['user1', 'affiliate'], ['user7', 'affiliate'], ['user1', 'affiliate'], ['user9', 'affiliate'], ['user4', 'cpc'], ['user4', 'referral'], ['user2', 'referral'], ['user7', 'affiliate'], ['user14', 'cpc'], ['user3', 'orgainic'], ['user2', 'orgainic'], ['user4', 'cpc'], ['user2', 'cpc'], ['user8', 'cpc'], ['user2', 'orgainic']]required = [['referral',3],['affiliate',3],['cpc',4],['orgainic',2]]c = {}visits = []for i in a: # print(i) for j in i[1:]: if j not in c and i[0] not in visits: c[j] = 1 visits.append(i[0]) elif j in c and i[0] not in visits: c[j] = c[j]+1print(c)幫我解決一些問題...
4 回答
牧羊人nacy
TA貢獻1862條經驗 獲得超7個贊
這是一種使用collections.defaultdict.
前任：
from collections import defaultdict
a = [['user1', 'referral'], ['user2', 'referral'], ['user1', 'referral'], ['user1', 'affiliate'], ['user7', 'affiliate'], ['user1', 'affiliate'], ['user9', 'affiliate'], ['user4', 'cpc'], ['user4', 'referral'], ['user2', 'referral'], ['user7', 'affiliate'], ['user14', 'cpc'], ['user3', 'orgainic'], ['user2', 'orgainic'], ['user4', 'cpc'], ['user2', 'cpc'], ['user8', 'cpc'], ['user2', 'orgainic']]
result = defaultdict(int)
seen = set()
for k, v in a:
key = "{}_{}".format(k, v)
if key not in seen:
result[v] += 1
seen.add(key)
print(list(map(list, result.items())))
輸出：
[['referral', 3], ['affiliate', 3], ['cpc', 4], ['orgainic', 2]]
白衣非少年
TA貢獻1155條經驗 獲得超0個贊
首先讓我們使條目獨一無二：
c = {tuple(sublist) for sublist in a}
現在我們有了一對獨特的用戶和類型。
對于我們不需要用戶的計數，因此讓我們將其設為僅包含第二個參數的列表：
c = [elem[1] for elem in c]
現在我們可以很容易地計算它：
from collections import Counter
c = Counter(c)
結果：Counter({'cpc': 4, 'affiliate': 3, 'referral': 3, 'orgainic': 2})
現在把它們放在一起：
from collections import Counter
c = Counter(elem[1] for elem in {tuple(sublist) for sublist in a})
繁星coding
TA貢獻1797條經驗 獲得超4個贊
defaultdict和基于循環的解決方案
這可以使用defaultdict：
d = defaultdict(set)
for user, category in a:
d[category].add(user)
res = [[category, len(users)] for category, users in d.items()]
輸出：
# [['affiliate', 3], ['cpc', 4], ['orgainic', 2], ['referral', 3]]
groupby基于解決方案
或者，這可以使用groupbyfrom來完成itertools：
from itertools import groupby
from operator import itemgetter
a = [['user1', 'referral'], ['user2', 'referral'], ['user1', 'referral'], ...]
# Sort the items according to the category so groupby will collect the pairs accordingly
res = {category: len({user for user, _ in pairs}) for category, pairs in
groupby(sorted(a, key=itemgetter(1)), key=itemgetter(1))}
res = [list(pair) for pair in res.items()]
輸出：
# [['affiliate', 3], ['cpc', 4], ['orgainic', 2], ['referral', 3]]
撒科打諢
TA貢獻1934條經驗 獲得超2個贊
這聽起來像是熊貓的案例，您的列表已經是正確的形狀：
import pandas as pd
a = [['user1', 'referral'], ['user2', 'referral'], ['user1', 'referral'], ['user1', 'affiliate'], ['user7', 'affiliate'], ['user1', 'affiliate'], ['user9', 'affiliate'], ['user4', 'cpc'], ['user4', 'referral'], ['user2', 'referral'], ['user7', 'affiliate'], ['user14', 'cpc'], ['user3', 'orgainic'], ['user2', 'orgainic'], ['user4', 'cpc'], ['user2', 'cpc'], ['user8', 'cpc'], ['user2', 'orgainic']]
df = pd.DataFrame(a)
df.columns=["user", "type"]
unique_per_type = df.groupby("type")["user"].unique()
現在 unique_per_type 是：
type
affiliate [user1, user7, user9]
cpc [user4, user14, user2, user8]
orgainic [user3, user2]
referral [user1, user2, user4]
Name: user, dtype: object
您可以執行以下操作：
# access length by key
len(unique_per_type["affiliate"])
# or use it like a dict
for key, val in unique_per_type.items():
print(key, len(val)))
這個解決方案添加了 pandas，這是一個巨大的依賴。但是，一旦您將數據放入 DataFrame 中，您就可以用它做很多事情：
df["user"].unique() # shows all unique users
df.query("user=='user1'") # shows all observations involving user1
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