分组并根据Pandas中的多种条件计算计数和均值

对于给定的数据帧，如下所示：

  id|address|sell_price|market_price|status|start_date|end_date
  1|7552 Atlantic Lane|1170787.3|1463484.12|finished|2019/8/2|2019/10/1
  1|7552 Atlantic Lane|1137782.02|1422227.52|finished|2019/8/2|2019/10/1
  2|888 Foster Street|1066708.28|1333385.35|finished|2019/8/2|2019/10/1
  2|888 Foster Street|1871757.05|1416757.05|finished|2019/10/14|2019/10/15
  2|888 Foster Street|NaN|763744.52|current|2019/10/12|2019/10/13
  3|5 Pawnee Avenue|NaN|928366.2|current|2019/10/10|2019/10/11
  3|5 Pawnee Avenue|NaN|2025924.16|current|2019/10/10|2019/10/11
  3|5 Pawnee Avenue|Nan|4000000|forward|2019/10/9|2019/10/10
  3|5 Pawnee Avenue|2236138.9|1788938.9|finished|2019/10/8|2019/10/9
  4|916 W. Mill Pond St.|2811026.73|1992026.73|finished|2019/9/30|2019/10/1
  4|916 W. Mill Pond St.|13664803.02|10914803.02|finished|2019/9/30|2019/10/1
  4|916 W. Mill Pond St.|3234636.64|1956636.64|finished|2019/9/30|2019/10/1
  5|68 Henry Drive|2699959.92|NaN|failed|2019/10/8|2019/10/9
  5|68 Henry Drive|5830725.66|NaN|failed|2019/10/8|2019/10/9
  5|68 Henry Drive|2668401.36|1903401.36|finished|2019/12/8|2019/12/9

#copy above data and run below code to reproduce dataframe
df = pd.read_clipboard(sep='|') 

我想根据以下条件对id和address进行分组，并计算mean_ratio和result_count：

1. ` mean_ratio ` : which is groupby ` id ` and ` address ` and calculate mean for the rows meet the following conditions: ` status ` is ` finished ` and ` start_date ` isin the range of ` 2019-09 ` and ` 2019-10 `
2. ` result_count ` : which is groupby ` id ` and ` address ` and count the rows meet the following conditions: ` status ` is either ` finished ` or ` failed ` , and ` start_date ` isin the range of ` 2019-09 ` and ` 2019-10 `

所需的输出将如下所示：

   id               address  mean_ratio  result_count
0   1    7552 Atlantic Lane         NaN             0
1   2     888 Foster Street        1.32             1
2   3       5 Pawnee Avenue        1.25             1
3   4  916 W. Mill Pond St.        1.44             3
4   5        68 Henry Drive         NaN             2

到目前为止，我已经尝试过：

# convert date
df[['start_date', 'end_date']] = df[['start_date', 'end_date']].apply(lambda x: pd.to_datetime(x, format = '%Y/%m/%d'))
# calculate ratio
df['ratio'] = round(df['sell_price']/df['market_price'], 2)

为了过滤start_date在2019-09和2019-10的范围内：

L = [pd.Period('2019-09'), pd.Period('2019-10')] 
c = ['start_date']
df = df[np.logical_or.reduce([df[x].dt.to_period('m').isin(L) for x in c])]

要过滤行状态为“完成”或“失败”，我使用：

mask = df['status'].str.contains('finished|failed')
df[mask]

但是我不知道如何使用这些来获得最终结果。预先感谢您的帮助。

问题来源：stackoverflow