我想创建一个新的值列表，my_qty，其中每个项目等于 d[key]['qty'] 中所有值的平均值其中 d[key]['start date'] 匹配 my_dates 中的值。我想我已经很接近了，但是我被嵌套部分挂断了。

import datetime 
import numpy as np 
my_dates = [datetime.datetime(2014, 10, 12, 0, 0), datetime.datetime(2014, 10, 13, 0, 0), datetime.datetime(2014, 10, 14, 0, 0)] 
 
d = { 
    'ID1' : {'start date': datetime.datetime(2014, 10, 12, 0, 0) , 'qty': 12}, 
    'ID2' : {'start date': datetime.datetime(2014, 10, 13, 0, 0) , 'qty': 34}, 
    'ID3' : {'start date': datetime.datetime(2014, 10, 12, 0, 0) , 'qty': 35}, 
    'ID4' : {'start date': datetime.datetime(2014, 10, 11, 0, 0) , 'qty': 40}, 
} 
 
my_qty = [] 
for item in my_dates: 
  my_qty.append([np.mean(x for x in d[key]['qty']) if d[key]['start date'] == my_dates[item]]) 
 
print my_qty

期望的输出:

[23.5,34,0]

阐明每个请求的输出:

[average of d[key]['qty'] where d[key]['start date '] == my_dates[0], average of d[key]['qty'] where d[key]['start date '] == my_dates[1], average of d[key]['qty'] where d[key]['start date '] == my_dates[2],]

请您参考如下方法:

使用纯 python

简单的方法是将数量按日期分组到字典中:

import collections 
 
quantities = collections.defaultdict(lambda: []) 
 
for k,v in d.iteritems(): 
    quantities[v["start date"]].append(v["qty"])

然后运行该字典以计算均值:

means = {k: float(sum(q))/len(q) for k,q in quantities.iteritems()}

给予:

>>> means 
{datetime.datetime(2014, 10, 11, 0, 0): 40.0, 
 datetime.datetime(2014, 10, 12, 0, 0): 23.5, 
 datetime.datetime(2014, 10, 13, 0, 0): 34.0}

如果您想变得更聪明，可以通过保持当前平均值和您所看到的值的总数来一次计算平均值。您甚至可以在类中对此进行抽象:

class RunningMean(object): 
    def __init__(self, mean=None, n=0): 
        self.mean = mean 
        self.n = n 
 
    def insert(self, other): 
        if self.mean is None: 
            self.mean = 0.0 
        self.mean = (self.mean * self.n + other) / (self.n + 1) 
        self.n += 1 
 
    def __repr__(self): 
        args = (self.__class__.__name__, self.mean, self.n) 
        return "{}(mean={}, n={})".format(*args)

一次通过你的数据就会给你答案:

import collections 
means = collections.defaultdict(lambda: RunningMean()) 
for k,v in d.iteritems(): 
    means[v["start date"]].insert(v["qty"])

与 Pandas

真正简单的方法是使用 pandas图书馆，因为它是为这样的事情而制作的。这是一些代码:

import pandas as pd 
df = pd.DataFrame.from_dict(d, orient="index") 
means = df.groupby("start date").aggregate(np.mean)

给予:

>>> means 
             qty 
start date       
2014-10-11  40.0 
2014-10-12  23.5 
2014-10-13  34.0

python之嵌套字典中的平均值

使用纯 python

与 Pandas