如何从python数据框获取过去几年的上周数据？

Question

我有从2010/12至2017/12的以下股票每日价格数据。我怎么能选择每一年的上周数据？我打算检查每年最后一周的表现。

2017-01-05 52.99 13018070.0 52.370 53.0600 51.4000 
2017-01-04 52.86 12556860.0 50.770 53.3400 50.7300 
2017-01-03 50.29 15794400.0 48.800 50.3000 48.4700 
2016-12-30 46.75 13593420.0 48.365 48.4000 46.3600 
2016-12-29 47.77 11728250.0 48.440 48.8600 47.1800 
2016-12-28 48.51 14636340.0 50.580 50.7300 48.4700 
2016-12-27 50.43 5594876.0 49.690 50.5500 49.6500 
2016-12-23 49.59 6966559.0 49.250 49.7200 48.9900 
2016-12-22 49.44 10918300.0 50.320 50.5500 49.1711 
2016-12-21 50.34 9279635.0 49.820 50.4400 49.6700 
2016-12-20 49.53 9533020.0 48.990 49.7900 48.9100 
2016-12-19 48.55 10323930.0 47.450 48.6700 47.4300 
... 
2010-12-20 ... 

Answer 1

您可以使用groupby传递日期时间年。但首先我们需要删除（过滤掉）不符合标准的数据。还要确保你的日期是日期时间。

此代码将检查该月是否等于12月（12），并且该日是大于或等于25（即每年的最后7天）。如果你想要一年的最后一周，你可以看看Wen's lambda函数。

data = '''\ 
2017-12-25 52.99 13018070.0 52.370 53.0600 51.4000 
2017-01-04 52.86 12556860.0 50.770 53.3400 50.7300 
2017-01-03 50.29 15794400.0 48.800 50.3000 48.4700 
2016-12-30 46.75 13593420.0 48.365 48.4000 46.3600 
2016-12-29 47.77 11728250.0 48.440 48.8600 47.1800 
2016-12-28 48.51 14636340.0 50.580 50.7300 48.4700 
2016-12-27 50.43 5594876.0 49.690 50.5500 49.6500 
2016-12-23 49.59 6966559.0 49.250 49.7200 48.9900 
2016-12-22 49.44 10918300.0 50.320 50.5500 49.1711 
2016-12-21 50.34 9279635.0 49.820 50.4400 49.6700 
2016-12-20 49.53 9533020.0 48.990 49.7900 48.9100 
2016-12-19 48.55 10323930.0 47.450 48.6700 47.4300''' 

import io 
import pandas as pd 

df = pd.read_csv(io.StringIO(data), sep='\s+', header=None, parse_dates=[0]) 
df = df[df[0].dt.month.eq(12) & df[0].dt.day.le(25)] # remove data 

# Groupby year according to: https://stackoverflow.com/a/11397052/7386332 
for idx, dfx in df.groupby(df[0].map(lambda x: x.year)): 
    print('Dataframe containing {}\'s last week:'.format(idx)) 
    print(dfx) 
    print() 

打印

Dataframe containing 2016's last week: 
      0  1   2  3  4  5 
7 2016-12-23 49.59 6966559.0 49.25 49.72 48.9900 
8 2016-12-22 49.44 10918300.0 50.32 50.55 49.1711 
9 2016-12-21 50.34 9279635.0 49.82 50.44 49.6700 
10 2016-12-20 49.53 9533020.0 48.99 49.79 48.9100 
11 2016-12-19 48.55 10323930.0 47.45 48.67 47.4300 

Dataframe containing 2017's last week: 
      0  1   2  3  4  5 
0 2017-12-25 52.99 13018070.0 52.37 53.06 51.4 

Answer 2

从安东:-)

df[df.groupby(df[0].dt.year)[0].apply(lambda x : x.dt.week==x.dt.week.max())] 
Out[1471]: 
      0  1   2  3  4  5 
0 2017-12-25 52.99 13018070.0 52.370 53.06 51.40 
3 2016-12-30 46.75 13593420.0 48.365 48.40 46.36 
4 2016-12-29 47.77 11728250.0 48.440 48.86 47.18 
5 2016-12-28 48.51 14636340.0 50.580 50.73 48.47 
6 2016-12-27 50.43 5594876.0 49.690 50.55 49.65