axios我正在寻找一种方法，根据let variable = "value of a variable";
const axios = {
get: () => new Promise(
res => setTimeout(() => res('axios response'), 1000)
)
}
Promise.all([
axios.get(),
Promise.resolve(variable)
]).then(
resolved => console.log(resolved)
);中的组对df = pd.DataFrame({
'x':[1,1,1,1,0,0,0,0,2,2,2,2],
'y':[1.2,3.4,5.2,4.8,5.4,5.9,4.3,2.1,1.2,6.7,2.9,7.3]
})
进行二值化并拆分x
这是我要实现的输出：
y为实现上述结果，我实质上创建了新列
x，依此类推，但我希望有更好的方法
答案 0 :(得分：3)
get_dummies d = pd.get_dummies(df.x)
pd.concat(
{'x': d, 'y': d.mul(df.y, axis=0)},
axis=1
).swaplevel(0, 1, 1).sort_index(1)
0 1 2
x y x y x y
0 0 0.0 1 1.2 0 0.0
1 0 0.0 1 3.4 0 0.0
2 0 0.0 1 5.2 0 0.0
3 0 0.0 1 4.8 0 0.0
4 1 5.4 0 0.0 0 0.0
5 1 5.9 0 0.0 0 0.0
6 1 4.3 0 0.0 0 0.0
7 1 2.1 0 0.0 0 0.0
8 0 0.0 0 0.0 1 1.2
9 0 0.0 0 0.0 1 6.7
10 0 0.0 0 0.0 1 2.9
11 0 0.0 0 0.0 1 7.3
interleave 不同的组合概念
from more_itertools import interleave
x = pd.get_dummies(df.x)
y = x.mul(df.y, 0)
x = x.add_prefix('x_')
y = y.add_prefix('y_')
x.join(y)[[*interleave(x, y)]]
x_0 y_0 x_1 y_1 x_2 y_2
0 0 0.0 1 1.2 0 0.0
1 0 0.0 1 3.4 0 0.0
2 0 0.0 1 5.2 0 0.0
3 0 0.0 1 4.8 0 0.0
4 1 5.4 0 0.0 0 0.0
5 1 5.9 0 0.0 0 0.0
6 1 4.3 0 0.0 0 0.0
7 1 2.1 0 0.0 0 0.0
8 0 0.0 0 0.0 1 1.2
9 0 0.0 0 0.0 1 6.7
10 0 0.0 0 0.0 1 2.9
11 0 0.0 0 0.0 1 7.3
i, u = pd.factorize(df.x)
r = np.arange(len(df))
out = np.zeros((len(df), len(u) * 2))
out[r, i * 2] = 1
out[r, i * 2 + 1] = df.y
pd.DataFrame(out, df.index)
0 1 2 3 4 5
0 1.0 1.2 0.0 0.0 0.0 0.0
1 1.0 3.4 0.0 0.0 0.0 0.0
2 1.0 5.2 0.0 0.0 0.0 0.0
3 1.0 4.8 0.0 0.0 0.0 0.0
4 0.0 0.0 1.0 5.4 0.0 0.0
5 0.0 0.0 1.0 5.9 0.0 0.0
6 0.0 0.0 1.0 4.3 0.0 0.0
7 0.0 0.0 1.0 2.1 0.0 0.0
8 0.0 0.0 0.0 0.0 1.0 1.2
9 0.0 0.0 0.0 0.0 1.0 6.7
10 0.0 0.0 0.0 0.0 1.0 2.9
11 0.0 0.0 0.0 0.0 1.0 7.3
或
i, u = pd.factorize(df.x)
r = np.arange(len(df))
out = np.zeros((len(df), len(u), 2))
out[r, i, 0] = 1
out[r, i, 1] = df.y
pd.DataFrame(out.reshape(len(df), -1), df.index)
答案 1 :(得分：3)
替代
pd.concat({x:y.assign(x=1) for x , y in df.groupby('x')},1)
Out[431]:
0 1 2
x y x y x y
0 NaN NaN 1.0 1.2 NaN NaN
1 NaN NaN 1.0 3.4 NaN NaN
2 NaN NaN 1.0 5.2 NaN NaN
3 NaN NaN 1.0 4.8 NaN NaN
4 1.0 5.4 NaN NaN NaN NaN
5 1.0 5.9 NaN NaN NaN NaN
6 1.0 4.3 NaN NaN NaN NaN
7 1.0 2.1 NaN NaN NaN NaN
8 NaN NaN NaN NaN 1.0 1.2
9 NaN NaN NaN NaN 1.0 6.7
10 NaN NaN NaN NaN 1.0 2.9
11 NaN NaN NaN NaN 1.0 7.3
crosstab
s=pd.crosstab([df.x,df.y],df.x)
s1=s.copy()
s1[:]=s1.values*(s1.index.get_level_values(1).values[:,None])
pd.concat([s,s1],axis=1,keys=['x','y'])
Out[479]:
x y
x 0 1 2 0 1 2
x y
0 2.1 1 0 0 2.1 0.0 0.0
4.3 1 0 0 4.3 0.0 0.0
5.4 1 0 0 5.4 0.0 0.0
5.9 1 0 0 5.9 0.0 0.0
1 1.2 0 1 0 0.0 1.2 0.0
3.4 0 1 0 0.0 3.4 0.0
4.8 0 1 0 0.0 4.8 0.0
5.2 0 1 0 0.0 5.2 0.0
2 1.2 0 0 1 0.0 0.0 1.2
2.9 0 0 1 0.0 0.0 2.9
6.7 0 0 1 0.0 0.0 6.7
7.3 0 0 1 0.0 0.0 7.3