Pandas groupby 时间序列的 bin 计数

Question

在样本数据帧上

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)

产量：

                            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，如何在两列上混合使用分组和装箱功能？假设我有 bins = [0, 0.2, 0.4, 0.6, 0.8, 1]，我如何在列 a 上装箱 data 并在 col b 上获得 mean（或最大、最小、总和等）每天、每周、每月的每个箱子？

Answer 1

使用cut with DatetimeIndex.day, or DatetimeIndex.week, DatetimeIndex.month并汇总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

也可以通过 DataFrameGroupBy.agg

传递多个函数

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

Pandas groupby 时间序列的 bin 计数

Pandas groupby with bin counts for timeseries

python

binning

pandas

pandas-groupby