假设您有一个包含如下频率信息的 pandas DataFrame:
data = [[1,1,2,3],
[1,2,3,5],
[2,1,6,1],
[2,2,2,4]]
df = pd.DataFrame(data, columns=['id', 'time', 'CountX1', 'CountX2'])
# id time CountX1 CountX2
# 0 1 1 2 3
# 1 1 2 3 5
# 2 2 1 6 1
# 3 2 2 2 4
我正在寻找一个简单命令(例如使用pd.pivot
或pd.melt()
)将这些频率恢复为tidy data应该是这样的:
id time variable
0 1 X1
0 1 X1
0 1 X2
0 1 X2
0 1 X2
1 1 X1
1 1 X1
1 1 X1
1 1 X2 ... # 5x repeated
2 1 X1 ... # 6x repeated
2 1 X2 ... # 1x repeated
2 2 X1 ... # 2x repeated
2 2 X2 ... # 4x repeated
最佳答案
你需要:
a = df.set_index(['id','time']).stack()
df = a.loc[a.index.repeat(a)].reset_index().rename(columns={'level_2':'a'}).drop(0, axis=1)
print(df)
id time a
0 1 1 CountX1
1 1 1 CountX1
2 1 1 CountX2
3 1 1 CountX2
4 1 1 CountX2
5 1 2 CountX1
6 1 2 CountX1
7 1 2 CountX1
8 1 2 CountX2
9 1 2 CountX2
10 1 2 CountX2
11 1 2 CountX2
12 1 2 CountX2
13 2 1 CountX1
14 2 1 CountX1
15 2 1 CountX1
16 2 1 CountX1
17 2 1 CountX1
18 2 1 CountX1
19 2 1 CountX2
20 2 2 CountX1
21 2 2 CountX1
22 2 2 CountX2
23 2 2 CountX2
24 2 2 CountX2
25 2 2 CountX2
第一个解决方案首先被删除,因为顺序不同:
a = df.melt(['id','time'])
df = (a.loc[a.index.repeat(a['value'])]
.drop('value', 1)
.sort_values(['id', 'time'])
.reset_index(drop=True))
关于python - 恢复频率表,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48459465/