Python 熊猫数据帧从多索引DateIndex每年获取第一天

我有这个数据框：

dft2 = pd.DataFrame(np.random.randn(20, 1),
                        columns=['A'],
                        index=pd.MultiIndex.from_product([pd.date_range('20130101',
                                                            periods=10,
                                                            freq='4M'),
                                                            ['a', 'b']]))

我打印出来的时候是这样的

输出：

                A
2013-01-31  a   0.275921
            b   1.336497
2013-05-31  a   1.040245
            b   0.716865
2013-09-30  a   -2.697420
            b   -1.570267
2014-01-31  a   1.326194
            b   -0.209718
2014-05-31  a   -1.030777
            b   0.401654
2014-09-30  a   1.138958
            b   -1.162370
2015-01-31  a   1.770279
            b   0.606219
2015-05-31  a   -0.819126
            b   -0.967827
2015-09-30  a   -1.423667
            b   0.894103
2016-01-31  a   1.765187
            b   -0.334844

如何选择“按当年最小值的行筛选”？比如2013-01-31，

2014-01-31

---

谢谢。

或者您可以尝试使用

isin

# Create dataframe from the dates in the first level of the index.
df = pd.DataFrame(dft2.index.get_level_values(0), columns=['date'], index=dft2.index)

# Add a `year` column that gets the year of each date.
df = df.assign(year=[d.year for d in df['date']])

# Find the minimum date of each year by grouping.
min_annual_dates = df.groupby('year')['date'].min().tolist()

# Filter the original dataframe based on these minimum dates by year.
>>> dft2.loc[(min_annual_dates, slice(None)), :]
                     A
2013-01-31 a  1.087274
           b  1.488553
2014-01-31 a  0.119801
           b  0.922468
2015-01-31 a -0.262440
           b  0.642201
2016-01-31 a  1.144664
           b  0.410701

dft1=dft2.reset_index()
dft1['Year']=dft1.level_0.dt.year
dft1=dft1.groupby('Year')['level_0'].min()
dft2[dft2.index.get_level_values(0).isin(dft1.values)]

Out[2250]: 
                     A
2013-01-31 a -1.072400
           b  0.660115
2014-01-31 a -0.134245
           b  1.344941
2015-01-31 a  0.176067
           b -1.792567
2016-01-31 a  0.033230
           b -0.960175