我在Pandas中有一个收集数据的数据框;
import pandas as pd
df = pd.DataFrame({'Group': ['A','A','A','A','A','A','A','B','B','B','B','B','B','B'], 'Subgroup': ['Blue', 'Blue','Blue','Red','Red','Red','Red','Blue','Blue','Blue','Blue','Red','Red','Red'],'Obs':[1,2,4,1,2,3,4,1,2,3,6,1,2,3]})
+-------+----------+-----+
| Group | Subgroup | Obs |
+-------+----------+-----+
| A | Blue | 1 |
| A | Blue | 2 |
| A | Blue | 4 |
| A | Red | 1 |
| A | Red | 2 |
| A | Red | 3 |
| A | Red | 4 |
| B | Blue | 1 |
| B | Blue | 2 |
| B | Blue | 3 |
| B | Blue | 6 |
| B | Red | 1 |
| B | Red | 2 |
| B | Red | 3 |
+-------+----------+-----+
观察(' Obs')应该没有间隙编号,但你可以看到我们已经错过了#39; A组中的蓝色3和B组中的蓝色4和5.期望的结果是所有“错过”的百分比。每组的观察结果(' Obs'),例如:
+-------+--------------------+--------+--------+
| Group | Total Observations | Missed | % |
+-------+--------------------+--------+--------+
| A | 8 | 1 | 12.5% |
| B | 9 | 2 | 22.22% |
+-------+--------------------+--------+--------+
我尝试使用for循环和使用组(例如:
df.groupby(['Group','Subgroup']).sum()
print(groups.head)
)但我似乎无法以我尝试的任何方式工作。我是以错误的方式解决这个问题吗?
从another answer(大吼大叫到@Lie Ryan)我发现了一个寻找缺失元素的功能,但是我还不太了解如何实现这个功能;
def window(seq, n=2):
"Returns a sliding window (of width n) over data from the iterable"
" s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ... "
it = iter(seq)
result = tuple(islice(it, n))
if len(result) == n:
yield result
for elem in it:
result = result[1:] + (elem,)
yield result
def missing_elements(L):
missing = chain.from_iterable(range(x + 1, y) for x, y in window(L) if (y - x) > 1)
return list(missing)
任何人都可以给我指针是正确的方向吗?
答案 0 :(得分:4)
很简单,你在这里需要groupby
:
groupby
+ diff
,找出每Group
和SubGroup
df
上的Group
组,并计算上一步计算的列的size
和sum
f = [ # declare an aggfunc list in advance, we'll need it later
('Total Observations', 'size'),
('Missed', 'sum')
]
g = df.groupby(['Group', 'Subgroup'])\
.Obs.diff()\
.sub(1)\
.groupby(df.Group)\
.agg(f)
g['Total Observations'] += g['Missed']
g['%'] = g['Missed'] / g['Total Observations'] * 100
g
Total Observations Missed %
Group
A 8.0 1.0 12.500000
B 9.0 2.0 22.222222
答案 1 :(得分:2)
使用groupby,apply和assign的类似方法:
(
df.groupby(['Group','Subgroup']).Obs
.apply(lambda x: [x.max()-x.min()+1, x.max()-x.min()+1-len(x)])
.apply(pd.Series)
.groupby(level=0).sum()
.assign(pct=lambda x: x[1]/x[0]*100)
.set_axis(['Total Observations', 'Missed', '%'], axis=1, inplace=False)
)
Out[75]:
Total Observations Missed %
Group
A 8 1 12.500000
B 9 2 22.222222
答案 2 :(得分:2)
from collections import Counter
gs = ['Group', 'Subgroup']
old_tups = set(zip(*df.values.T))
missed = pd.Series(Counter(
g for (g, s), d in df.groupby(gs)
for o in range(d.Obs.min(), d.Obs.max() + 1)
if (g, s, o) not in old_tups
), name='Missed')
hit = df.set_index(gs).Obs.count(level=0)
total = hit.add(missed).rename('Total')
ratio = missed.div(total).rename('%')
pd.concat([total, missed, ratio], axis=1).reset_index()
Group Total Missed %
0 A 8 1 0.125000
1 B 9 2 0.222222