Python 具有timeseries的bin计数的Pandas groupby
在示例数据帧上Python 具有timeseries的bin计数的Pandas groupby,python,pandas,pandas-groupby,binning,Python,Pandas,Pandas Groupby,Binning,在示例数据帧上 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) 收益率: a b 2019-02-12 00:00:00 0.909822
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,如何在两列上混合使用分组和装箱功能?
假设我有bin=[0,0.2,0.4,0.6,0.8,1]
,我如何才能在a
列上获取data
数据,并在b列上获得平均值(或max,min,sum等)b
每天、每周、每月的每个bin与或一起使用,并聚合min
或max
,平均值
,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
也可以通过传递多个函数
或使用:
与或一起使用,并聚合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
也可以通过传递多个函数
或使用:
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