我有团队名称和分数的数据:
Team A 9,
Team A 13,
Team B 24,
Team C 6,
Team A 15,
Team B 10,
Team C 19,
Team A 30,
Team B 5,
但信息存储在2个列表中:
List_team = ['Team A', 'Team A', 'Team B', 'Team C',
'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5,]
我需要A队得分和B队得分的总和。
也许在压缩2个列表之后对它们进行排序是第一步。
找出最好的方法是什么?
答案 0 :(得分:2)
这是一种方法,用字典:
teams = {}
teams_and_scores = zip(List_team,List_score) #this will pair each team with their score
for team, score in teams_and_scores:
if team in teams:
teams[team] += score #add score if team is already in dictionary
else:
teams[team] = score #add team to dictionary with their score
使用您的示例:
>>> List_team = ['Team A', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
>>> List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5,]
>>> teams = {}
>>> teams_and_scores = zip(List_team,List_score) #this will pair each team with their score
>>> for team, score in teams_and_scores:
if team in teams:
teams[team] += score #add score if team is already in dictionary
else:
teams[team] = score #add team to dictionary with their score
>>> teams
{'Team A': 67, 'Team B': 39, 'Team C': 25}
>>> teams['Team A']
67
答案 1 :(得分:2)
使用defaultdict
,它将使用给定的密钥填充字典条目,如果它尚不存在则使用默认值:
import collections
List_team = ['Team A', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5]
result = collections.defaultdict(list)
for k, s in zip(List_team, List_score):
result[k].append(s)
然后,您可以使用这些数据执行任何操作,例如将值传递给sum()
:
>>> sum(result['Team A'])
67
答案 2 :(得分:2)
In [9]: result = {}
In [10]: for x in enumerate(List_team):
...: result.setdefault(x[1], 0)
...: result[x[1]] += List_score[x[0]]
...:
In [11]: result
Out[11]: {'Team A': 67, 'Team B': 39, 'Team C': 25}
或
In [15]: result = {}
In [16]: for x, y in zip(List_team, List_score):
...: result.setdefault(x, 0)
...: result[x] += y
...:
In [17]: result
Out[17]: {'Team A': 67, 'Team B': 39, 'Team C': 25}
答案 3 :(得分:1)
你可以用熊猫来做:
import pandas as pd
List_team = ['Team A', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5]
df = pd.DataFrame({'Team': List_team, 'Score': List_score})
df1 = df.groupby('Team').sum()
In [47]: df1
Out[47]:
Score
Team
Team A 67
Team B 39
Team C 25
答案 4 :(得分:1)
使用理解将默认字典设置为零,然后逐个位置填充。其他解决方案可能更具惯用性,但这个解决方案似乎最具人性化。
List_team = ['Team A', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5]
scores = { x : 0 for x in List_team }
for idx,team in enumerate(List_team):
scores[team] += List_score[idx]
print scores # all scores
结果:
{'Team A': 67, 'Team C': 25, 'Team B': 39}
更新:一次只获得一个团队的分数:
print scores['Team A'] # Just Team A
结果:
67
答案 5 :(得分:1)
这是基于TigerhawkT3的答案,我发现最好使用int代替list作为defaultdict。
import collections
List_team = ['Team A', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B', 'Team C', 'Team A', 'Team B']
List_score = [9, 13, 24, 6, 15, 10, 19, 30, 5]
result = collections.defaultdict(int)
for k, s in zip(List_team, List_score):
result[k] += s
结果会退还{'Team A': 67, 'Team B': 39, 'Team C': 25}
无需在此处汇总列表。