获取具有相应索引值的每日数据帧的每月最大值
it1352
帮助1人
问题说明
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
正确答案
#1
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
这篇好文章是转载于：学新通技术网
