问题描述

我已经从雅虎财经下载了每日数据

I have dowloaded daily data from yahoo finance

                    Open          High           Low         Close     Volume  
Date                                                                            
2016-01-04  10485.809570  10485.910156  10248.580078  10283.440430  116249000   
2016-01-05  10373.269531  10384.259766  10173.519531  10310.099609   82348000   
2016-01-06  10288.679688  10288.679688  10094.179688  10214.019531   87751700   
2016-01-07  10144.169922  10145.469727   9810.469727   9979.849609  124188100   
2016-01-08  10010.469727  10122.459961   9849.339844   9849.339844   95672200   
...
2016-02-23   9503.120117   9535.120117   9405.219727   9416.769531   87240700   
2016-02-24   9396.480469   9415.330078   9125.190430   9167.799805   99216000   
2016-02-25   9277.019531   9391.309570   9199.089844   9331.480469          0   
2016-02-26   9454.519531   9576.879883   9436.330078   9513.299805   95662100   
2016-02-29   9424.929688   9498.570312   9332.419922   9495.400391   90978700   

我想找出每个月的最高收盘价以及这个收盘价的日期.

I would like to find the maximum closing price each month and also the date of this closing price.

使用 groupby dfM = df['Close'].groupby(df.index.month).max() 它会返回每月最大值，但我会失去每日索引位置.

With a groupby dfM = df['Close'].groupby(df.index.month).max() it returns me the monthly maximums but I am losing the daily index position.

   grouped by month 
1      10310.099609
2       9757.879883

有没有保存索引的好方法?

Is there a good way to to keep the index?

我会寻找这样的结果:

            grouped by month 
2016-01-05      10310.099609
2016-02-01       9757.879883

推荐答案

你可以使用 TimeGrouper 和 groupby 获得每月的最大值:

You can get the max value per month using TimeGrouper together with groupby:

from pandas.io.data import DataReader

aapl = DataReader('AAPL', data_source='yahoo', start='2015-6-1')
>>> aapl.groupby(pd.TimeGrouper('M')).Close.max()
Date
2015-06-30    130.539993
2015-07-31    132.070007
2015-08-31    119.720001
2015-09-30    116.410004
2015-10-31    120.529999
2015-11-30    122.570000
2015-12-31    119.029999
2016-01-31    105.349998
2016-02-29     98.120003
2016-03-31    100.529999
Freq: M, Name: Close, dtype: float64

使用idxmax会得到对应日期的最高价格.

Using idxmax will get the corresponding dates of the max price.

>>> aapl.groupby(pd.TimeGrouper('M')).Close.idxmax()
Date
2015-06-30   2015-06-01
2015-07-31   2015-07-20
2015-08-31   2015-08-10
2015-09-30   2015-09-16
2015-10-31   2015-10-29
2015-11-30   2015-11-03
2015-12-31   2015-12-04
2016-01-31   2016-01-04
2016-02-29   2016-02-17
2016-03-31   2016-03-01
Name: Close, dtype: datetime64[ns]

并排获取结果:

>>> aapl.groupby(pd.TimeGrouper('M')).Close.agg({'max date': 'idxmax', 'max price': np.max})
             max price   max date
Date                             
2015-06-30  130.539993 2015-06-01
2015-07-31  132.070007 2015-07-20
2015-08-31  119.720001 2015-08-10
2015-09-30  116.410004 2015-09-16
2015-10-31  120.529999 2015-10-29
2015-11-30  122.570000 2015-11-03
2015-12-31  119.029999 2015-12-04
2016-01-31  105.349998 2016-01-04
2016-02-29   98.120003 2016-02-17
2016-03-31  100.529999 2016-03-01

这篇关于获取具有相应索引值的每日数据帧的每月最大值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！