我有如下数据集(这是一个示例,它实际上有66k行):
Type Food Loc Num
0 Fruit Banana House-1 15
1 Fruit Banana House-2 4
2 Fruit Apple House-2 6
3 Fruit Apple House-3 8
4 Vegetable Broccoli House-3 8
5 Vegetable Lettuce House-4 12
6 Vegetable Peppers House-5 3
7 Vegetable Corn House-4 4
8 Seasoning Olive Oil House-6 2
9 Seasoning Vinegar House-7 2
我想填满所有缺失的组合(房子3-7有多少香蕉?)例如,除了House-5之外,还有多少个辣椒?)输入0,得到如下结果:
Type Food Loc Num
0 Fruit Banana House-1 15
1 Fruit Banana House-2 4
2 Fruit Banana House-3 0
... fill remaining houses with zeros
6 Fruit Banana House-7 0
7 Fruit Apple House-1 0
8 Fruit Apple House-2 6
9 Fruit Apple House-3 8
... fill remaining houses with zeros
14 Vegetable Broccoli House-1 0
15 Vegetable Broccoli House-2 0
16 Vegetable Broccoli House-3 8
... etc
n Seasoning Vinegar House-7 2
我知道R有complete
函数的积分
现在我正在处理从原始DataFrame中提取的列表,我将其转换为字典。
for key,grp in fruit.groupby(level=0):
dir[key] = test.ix[key].values.tolist()
fruit = {'Banana': [[1.0,15.0], [2.0,4.0],
'Apple': [[2.0,6.0], [3.0,8.0]
#Type = {fruit1:[[Loc1,Count1],...,[Locn],[Countn],
#... fruitn:[...]}
我设计了这个函数来应用于字典的赋值规则:
def fill_zeros(list):
final = [0] * 127
for i in list:
final[int(i[0])] = i[1]
return final
适用于个别的"水果":
print fill_zeros(test.ix['QLLSEEEKK'].values.tolist())
print fill_zeros(test.ix['GAVPLEMLEIALR'].values.tolist())
print fill_zeros(test.ix['VPVNLLNSPDCDVK'].values.tolist())
但是不在字典上:
for key,grp in test.groupby(level=0):
dir[key] = fill_zeros(test.ix[key].values.tolist())
Traceback (most recent call last):
File "peptidecount.py", line 59, in <module>
print fill_zeros(test.ix[str(key)].values.tolist())
File "peptidecount.py", line 43, in fill_zeros
final[int(i[0])] = i[1]
TypeError: 'float' object has no attribute '__getitem__'
显然我没有正确地在字典上迭代。有办法纠正吗?或者是否有更合适的函数直接应用于DataFrame?
您可以使用reindex
首先你需要一个有效的(type, food)
对的列表。我将从数据本身得到它,而不是把它们写出来。
In [88]: kinds = list(df[['Type', 'Food']].drop_duplicates().itertuples(index=False))
In [89]: kinds
Out[89]:
[('Fruit', 'Banana'),
('Fruit', 'Apple'),
('Vegetable', 'Broccoli'),
('Vegetable', 'Lettuce'),
('Vegetable', 'Peppers'),
('Vegetable', 'Corn'),
('Seasoning', 'Olive Oil'),
('Seasoning', 'Vinegar')]
现在,我们将生成kinds
与使用itertools.product
的房屋的所有配对。
In [93]: from itertools import product
In [94]: houses = ['House-%s' % x for x in range(1, 8)]
In [95]: idx = [(x.Type, x.Food, house) for x, house in product(kinds, houses)]
In [96]: idx[:2]
Out[96]: [('Fruit', 'Banana', 'House-1'), ('Fruit', 'Banana', 'House-2')]
现在你可以使用set_index
和reindex
来获得缺失的观测值。
In [98]: df.set_index(['Type', 'Food', 'Loc']).reindex(idx, fill_value=0)
Out[98]:
Num
Type Food Loc
Fruit Banana House-1 15
House-2 4
House-3 0
House-4 0
House-5 0
... ...
Seasoning Vinegar House-3 0
House-4 0
House-5 0
House-6 0
House-7 2
[56 rows x 1 columns]
应该可以:
cond0 = df.Num.isnull()
cond1 = df.Food == 'Banana'
cond2 = df.Loc.str.match(r'House-[34567]')
cond3 = df.Food == 'Peppers'
cond4 = df.Loc != 'House-5'
missing_bananas = cond0 & cond1 & cond2
missing_peppers = cond0 & cond3 & cond4
missing_food = missing_bananas | missing_peppers
df.loc[missing_food] = df.loc[missing_food].fillna(0)