dict

Question

有人可以建议一些优化以下代码的方法吗？

第一种方法

board =[
{"Pakistan": 123},
{"Pakistan": 27},
{"India": 3},
{"India": 71},
{"Australia": 31},
{"India": 22},
{"Pakistan": 81},
{"India": 171},
{"India": 171},
]

country = {} 
for item in board:
    for team,value in item.items():
        try: country[team].append(value)
        except KeyError: country[team] = [value]
print(country)

avg={}
for key, value in country.iteritems():
    avg[key]= sum(value)/len(value)

print(avg)

def maximum(team):
    d3={v:k for k,v in team.items()}
    return d3[max(d3)]

print(maximum(avg))

第二种方法

avgDict = {}
for k,v in [(key,d[key]) for d in board for key in d]:
    if k not in avgDict: avgDict[k]=[v]
    else: avgDict[k].append(v)
print(avgDict)

只需获取名称和分数列表。

先谢谢。

Answer 1

这是清洁国家和平均水平的部分解决方案：

import numpy as np

keys = [k for d in board for k in d.keys()]
values = [v for d in board for k,v in d.items()]
avg = int(np.mean(values))

Answer 2

您可以将var a = "23 years 08 months 12 days"; var b = "5 years 8 months 1 days"; var res = a.substring(0, a.indexOf('months')) + 'months'; console.log(res); var res = b.substring(0, b.indexOf('months')) + 'months'; console.log(res);与字典/列表推导结合使用。通常，这些方法应该更有效。

collections.defaultdict

<强>结果

from collections import defaultdict

d = defaultdict(list)

for i in board:
    for k, v in i.items():
        d[k].append(v)

d_avg = {k: sum(v)/len(v) for k, v in d.items()}

max_country = [k for k in d_avg if d_avg[k] == max(d_avg.values())]

Answer 3

使用pandas可能会更有效率

import pandas as pd

items = ((k,v) for d in board for k,v in d.items())
df = pd.DataFrame(items)
df.groupby(0).mean()

Answer 4

使用pandas：

import pandas as pd
boardf = pd.DataFrame(board)
finaldict = {}
for country, values in boardf.items():
    finaldict[country] = values.dropna().values.tolist()
print(finaldict)
print(boardf.mean().to_dict())
print(boardf.max().sort(inplace=False, ascending=False).index[0])

输出结果为：

{'Australia': [31.0], 'Pakistan': [123.0, 27.0, 81.0], 'India': [3.0, 71.0, 22.0, 171.0, 171.0]}
{'Australia': 31.0, 'Pakistan': 77.0, 'India': 87.599999999999994}
India