groupby加权平均值和pandas数据帧中的总和

I have a dataframe ,

我有一个数据帧，

    Out[78]: 
   contract month year  buys  adjusted_lots    price
0         W     Z    5  Sell             -5   554.85
1         C     Z    5  Sell             -3   424.50
2         C     Z    5  Sell             -2   424.00
3         C     Z    5  Sell             -2   423.75
4         C     Z    5  Sell             -3   423.50
5         C     Z    5  Sell             -2   425.50
6         C     Z    5  Sell             -3   425.25
7         C     Z    5  Sell             -2   426.00
8         C     Z    5  Sell             -2   426.75
9        CC     U    5   Buy              5  3328.00
10       SB     V    5   Buy              5    11.65
11       SB     V    5   Buy              5    11.64
12       SB     V    5   Buy              2    11.60

I need a sum of adjusted_lots , price which is weighted average , of price and ajusted_lots , grouped by all the other columns , ie. grouped by (contract, month , year and buys)

我需要一个adjust_lots的总和，价格是加权平均值，价格和ajusted_lots，按所有其他列分组，即。按（合同，月份，年份和购买）分组

Similiar solution on R was achieved by following code, using dplyr, however unable to do the same in pandas.

使用dplyr通过以下代码实现对R的类似解决方案，但无法在pandas中执行相同操作。

> newdf = df %>%
  select ( contract , month , year , buys , adjusted_lots , price ) %>%
  group_by( contract , month , year ,  buys) %>%
  summarise(qty = sum( adjusted_lots) , avgpx = weighted.mean(x = price , w = adjusted_lots) , comdty = "Comdty" )

> newdf
Source: local data frame [4 x 6]

  contract month year comdty qty     avgpx
1        C     Z    5 Comdty -19  424.8289
2       CC     U    5 Comdty   5 3328.0000
3       SB     V    5 Comdty  12   11.6375
4        W     Z    5 Comdty  -5  554.8500

is the same possible by groupby or any other solution ?

groupby或任何其他解决方案是否可能相同？

2 个解决方案

#1

To pass multiple functions to a groupby object, you need to pass a dictionary with the aggregation functions corresponding to the columns:

要将多个函数传递给groupby对象，需要传递一个字典，其中包含与列对应的聚合函数：

# Define a lambda function to compute the weighted mean:
wm = lambda x: np.average(x, weights=df.loc[x.index, "adjusted_lots"])

# Define a dictionary with the functions to apply for a given column:
f = {'adjusted_lots': ['sum'], 'price': {'weighted_mean' : wm} }

# Groupby and aggregate with your dictionary:
df.groupby(["contract", "month", "year", "buys"]).agg(f)

                         adjusted_lots         price
                                   sum weighted_mean
contract month year buys                            
C        Z     5    Sell           -19    424.828947
CC       U     5    Buy              5   3328.000000
SB       V     5    Buy             12     11.637500
W        Z     5    Sell            -5    554.850000

You can see more here:

你可以在这里看到更多：

http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-multiple-functions-at-once
http://pandas.pydata.org/pandas-docs/stable/groupby.html#applying-multiple-functions-at-once

and in a similar question here:

在这里类似的问题：

Apply multiple functions to multiple groupby columns
将多个函数应用于多个groupby列

Hope this helps

希望这可以帮助

#2

Doing weighted average by groupby(...).apply(...) can be very slow (100x from the following). See my answer (and others) on this thread.

通过groupby（...）执行加权平均.applied（...）可能非常慢（从以下100倍）。在这个帖子上查看我的答案（和其他人）。

def weighted_average(df,data_col,weight_col,by_col):
    df['_data_times_weight'] = df[data_col]*df[weight_col]
    df['_weight_where_notnull'] = df[weight_col]*pd.notnull(df[data_col])
    g = df.groupby(by_col)
    result = g['_data_times_weight'].sum() / g['_weight_where_notnull'].sum()
    del df['_data_times_weight'], df['_weight_where_notnull']
    return result

#1