熊猫GROUPBY与时间序列库计数

Question

对样本数据帧

data = pd.DataFrame(np.random.rand(6,2), columns = list('ab'))
dti = pd.date_range(start='2019-02-12', end='2019-02-12', periods=6)
data.set_index(dti, inplace=True)

收益率：

                            a         b
2019-02-12 00:00:00  0.909822  0.548713
2019-02-12 01:00:00  0.295730  0.452881
2019-02-12 02:00:00  0.889976  0.042893
2019-02-12 03:00:00  0.466465  0.971178
2019-02-12 04:00:00  0.532618  0.769210
2019-02-12 05:00:00  0.947362  0.021689

现在，我怎么能混上两列编组和分级的功能呢？说我有bins = [0, 0.2, 0.4, 0.6, 0.8, 1]，对列data我斌a以及如何能得到mean在山坳b（或最大值，最小值，总和等），每个箱的每一天，一周，一个月？

Answer 1

使用cut与DatetimeIndex.day，或DatetimeIndex.week，DatetimeIndex.month和聚集min或max，mean，sum：

bins = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])] 

s = pd.cut(data['a'], bins=bins, labels=labels)

df = data.groupby([data.index.day.rename('day'), s])['b'].min().reset_index()

#df = data.groupby([data.index.week.rename('week'), s])['b'].min().reset_index()
#df = data.groupby([data.index.month.rename('month'), s])['b'].min().reset_index()
print (df)
   day        a         b
0   12  1.4-0.6  0.267070
1   12  1.6-0.8  0.637877
2   12  1.8-1.0  0.299172

也是可能DataFrameGroupBy.agg通多种功能

df2 = (data.groupby([data.index.day.rename('day'), s])['b']
           .agg(['min','max','sum','mean'])
           .reset_index())
print (df2)
   day        a       min       max       sum      mean
0   12  1.4-0.6  0.267070  0.267070  0.267070  0.267070
1   12  1.6-0.8  0.637877  0.903206  1.541084  0.770542
2   12  1.8-1.0  0.299172  0.405750  1.098002  0.366001

或使用DataFrameGroupBy.describe：

df3 = (data.groupby([data.index.day.rename('day'), s])['b']
           .describe()
           .reset_index())
print (df3)
   day        a  count      mean       std       min       25%       50%  \
0   12  1.4-0.6    1.0  0.267070       NaN  0.267070  0.267070  0.267070   
1   12  1.6-0.8    2.0  0.770542  0.187616  0.637877  0.704210  0.770542   
2   12  1.8-1.0    3.0  0.366001  0.058221  0.299172  0.346126  0.393081   

        75%       max  
0  0.267070  0.267070  
1  0.836874  0.903206  
2  0.399415  0.405750