如果我有一个像这样的桌子
ID Date Disease
1 03.07 A
1 03.07 B
1 03.09 A
1 03.09 C
1 03.10 D我写了这样的代码:
def combination(listData):
comListData = [];
for datum in listData :
start = listData.index(datum) + 1
while start < len(listData) :
if datum!=listData[start] :
comStr = datum+':'+listData[start]
if not comStr in comListData :
comListData.append(comStr)
start+=1;
return comListData
def insertToDic(dic,comSick):
for datum in comSick :
if dic.has_key(datum) :
dic[datum]+=1
else :
dic[datum] = 1
try:
con = mdb.connect('blahblah','blah','blah','blah')
cur = con.cursor()
sql ="select * from table"
cur.execute(sql);
data = cur.fetchall();
start = 0
end = 1
sick = []
dic = {}
for datum in data :
end = datum[0]
if end!=start:
start = end
comSick = combination(sick)
insertToDic(dic,comSick)
sick = []
sick.append(datum[2])
start = end
comSick = combination(sick)
insertToDic(dic,comSick)
for k,v in dic.items():
a,b = k.split(':')
print >>f, a.ljust(0), b.ljust(0), v
f.close()然后我得到了:
From To Count
A B 1
A A 1
A C 1
A D 1
B A 1
B C 1
B D 1
A C 1
A D 1
C D 1我得到的最终版本表是(在相同的ID,相同的方向,如A --> C计数为1而不是2。相同的疾病,如A --> A不算数。A --> B与B --> A不同)
From To Count
A B 1
A C 1
A D 1
B A 1
B C 1
B D 1
C D 1但我想要的是(不包括相同的日期案例版本):
From To Count
A A 1
A C 1
A D 1
B A 1
B C 1
B D 1
A D 1
C D 1最后
From To Count
A C 1
A D 1
B A 1
B C 1
B D 1
C D 1我应该编辑代码的哪一部分?
发布于 2016-03-11 22:05:14
让我试着改变一下你的问题。对于每个日期(为了简化问题,不包括ID ),您希望在Disease列中包含所有可能的值对,以及它们出现的频率、值对的顺序。现在,在Python中有一个内置函数可以实现这一点:
from itertools import permutations
all_pairs = permutations(diseases, 2)根据您的数据,我猜它在csv文件中。如果不是,请自己调整我的代码(这是一种琐碎的谷歌搜索)。我们将在数据科学堆栈中使用名为Pandas的著名库。下面是它的运行方式:
from itertools import permutations
import pandas as pd
df = pd.read_csv('data.csv', header=0)
pairs_by_did = df.groupby('ID').apply(lambda grp: pd.Series(list(permutations(grp['Disease'], 2))))
all_pairs = pd.concat([v for i, v in pairs_by_did.iterrows()])
pair_counts = all_pairs.value_counts()
print pair_counts对于您的示例,它将打印
>>> print pair_counts
(A, B) 2
(D, A) 2
(A, D) 2
(C, A) 2
(B, A) 2
(A, C) 2
(A, A) 2
(C, B) 1
(D, C) 1
(C, D) 1
(D, B) 1
(B, D) 1
(B, C) 1
Name: 1, dtype: int64现在同时对ID和date进行分组,看看会得到什么结果。
https://stackoverflow.com/questions/35941218
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