问题描述
我正在尝试在分组的DataFrame中进行升采样，但是不确定如何将其仅在组中进行升采样.我有一个看起来像这样的DataFrame:
I am trying to upsample within a grouped DataFrame but am unsure how to get it to only upsample within the groups. I have a DataFrame that looks like:
cat weekstart date
0.0 2016-07-04 00:00:00+00:00 2016-07-04 1
2016-07-06 1
2016-07-07 2
2016-08-15 00:00:00+00:00 2016-08-16 1
2016-08-19 1
2016-09-19 00:00:00+00:00 2016-09-20 1
2016-09-21 1
2016-12-19 00:00:00+00:00 2016-12-19 1
2016-12-21 1
1.0 2016-07-25 00:00:00+00:00 2016-07-26 2
2016-08-01 00:00:00+00:00 2016-08-03 1
2016-08-08 00:00:00+00:00 2016-08-12 1
如果我做类似df.unstack().fillna(0).stack()的操作，则会导致:
If I do something like df.unstack().fillna(0).stack() leads to:
cat weekstart date
0.0 2016-07-04 00:00:00+00:00 2016-1-1 0
.
.
.
2016-07-04 1
2016-07-06 1
2016-07-07 2
因为日期栏中的最小值是2016-1-1.不过，我只在每个猫"和工作日"内采样工作日，例如:
because the minimum in the date column is 2016-1-1. What i'm after though is only sampling business days within each 'cat' and 'weekstart', like:
cat weekstart date
0.0 2016-07-04 00:00:00+00:00 2016-07-04 1
2016-07-05 0
2016-07-06 1
2016-07-07 2
2016-07-8 0
2016-08-15 00:00:00+00:00 2016-08-15 0
2016-08-16 1
2016-08-17 0
2016-08-18 0
2016-08-19 1
我尝试使用:
level_values = df.index.get_level_values
df.groupby(
[level_values(i) for i in [0, 1]] + [pd.Grouper('B', level=-1)]
)
.sum()
但是它没有按预期工作.
but it isn't working as expected.
推荐答案
我认为您需要使用 reindex 由MultiIndex创建. bdate_range.html"rel =" nofollow noreferrer> bdate_range :
I think you need custom function with reindex by MultiIndex created by bdate_range:
def f(x):
lvl0 = x.index.get_level_values(0)[0]
lvl1 = x.index.get_level_values(1)[0]
lvl2 = pd.bdate_range(start=lvl1, periods=5)
mux = pd.MultiIndex.from_product([[lvl0], [lvl1], lvl2], names=x.index.names)
return (x.reindex(mux, fill_value=0))
s1 = s.groupby(['cat','weekstart'], group_keys=False).apply(f)
print (s1)
cat weekstart date
0.0 2016-07-04 2016-07-04 1
2016-07-05 0
2016-07-06 1
2016-07-07 2
2016-07-08 0
2016-08-15 2016-08-15 0
2016-08-16 1
2016-08-17 0
2016-08-18 0
2016-08-19 1
2016-09-19 2016-09-19 0
2016-09-20 1
2016-09-21 1
2016-09-22 0
2016-09-23 0
2016-12-19 2016-12-19 1
2016-12-20 0
2016-12-21 1
2016-12-22 0
2016-12-23 0
1.0 2016-07-25 2016-07-25 0
2016-07-26 2
2016-07-27 0
2016-07-28 0
2016-07-29 0
2016-08-01 2016-08-01 0
2016-08-02 0
2016-08-03 1
2016-08-04 0
2016-08-05 0
2016-08-08 2016-08-08 0
2016-08-09 0
2016-08-10 0
2016-08-11 0
2016-08-12 1
Name: a, dtype: int64
设置:
d = {(0.0, pd.Timestamp('2016-07-04 00:00:00'), pd.Timestamp('2016-07-07 00:00:00')): 2, (1.0, pd.Timestamp('2016-07-25 00:00:00'), pd.Timestamp('2016-07-26 00:00:00')): 2, (0.0, pd.Timestamp('2016-08-15 00:00:00'), pd.Timestamp('2016-08-16 00:00:00')): 1, (0.0, pd.Timestamp('2016-07-04 00:00:00'), pd.Timestamp('2016-07-04 00:00:00')): 1, (0.0, pd.Timestamp('2016-09-19 00:00:00'), pd.Timestamp('2016-09-20 00:00:00')): 1, (0.0, pd.Timestamp('2016-09-19 00:00:00'), pd.Timestamp('2016-09-21 00:00:00')): 1, (0.0, pd.Timestamp('2016-12-19 00:00:00'), pd.Timestamp('2016-12-19 00:00:00')): 1, (1.0, pd.Timestamp('2016-08-08 00:00:00'), pd.Timestamp('2016-08-12 00:00:00')): 1, (0.0, pd.Timestamp('2016-07-04 00:00:00'), pd.Timestamp('2016-07-06 00:00:00')): 1, (1.0, pd.Timestamp('2016-08-01 00:00:00'), pd.Timestamp('2016-08-03 00:00:00')): 1, (0.0, pd.Timestamp('2016-12-19 00:00:00'), pd.Timestamp('2016-12-21 00:00:00')): 1, (0.0, pd.Timestamp('2016-08-15 00:00:00'), pd.Timestamp('2016-08-19 00:00:00')): 1}
s = pd.Series(d).rename_axis(['cat','weekstart','date'])
print (s)
cat weekstart date
0.0 2016-07-04 2016-07-04 1
2016-07-06 1
2016-07-07 2
2016-08-15 2016-08-16 1
2016-08-19 1
2016-09-19 2016-09-20 1
2016-09-21 1
2016-12-19 2016-12-19 1
2016-12-21 1
1.0 2016-07-25 2016-07-26 2
2016-08-01 2016-08-03 1
2016-08-08 2016-08-12 1
dtype: int64
这篇关于 pandas 多元指数上采样的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！