Python长格式: subtract selection of rows

Question

全部，
我有以下长格式数据框:

df = pd.DataFrame({'date': ["2020-01-01","2020-01-01","2020-01-02","2020-01-02","2020-01-01","2020-01-01","2020-01-02","2020-01-02"], 'asset': ["x", "x","x", "x","y","y","y","y"], 'type': ["price", "spread","price","spread","price", "spread","price","spread"], 'value': ["1.5", "0.01","1.6", "0.01","1","0.08","1.2","0.09"]})

看起来像这样:

         date asset    type value
0  2020-01-01     x   price   1.5
1  2020-01-01     x  spread  0.01
2  2020-01-02     x   price   1.6
3  2020-01-02     x  spread  0.01
4  2020-01-01     y   price     1
5  2020-01-01     y  spread  0.08
6  2020-01-02     y   price   1.2
7  2020-01-02     y  spread  0.09

我想减去 y 的价格起价 x并保持相同的数据结构，结果应如下所示:

         date    asset       type value
0  2020-01-01        x      price   1.5
1  2020-01-01        x     spread  0.01
2  2020-01-02        x      price   1.6
3  2020-01-02        x     spread  0.01
4  2020-01-01        y      price     1
5  2020-01-01        y     spread  0.08
6  2020-01-02        y      price   1.2
7  2020-01-02        y     spread  0.09
8  2020-01-01  x_min_y  pricediff   0.5
9  2020-01-02  x_min_y  pricediff   0.4

我想使用 assign() Pandas 的功能来创建这个，但我不知道如何做到这一点。
提前致谢!

Answer 1

使用:

m = df['type'].eq('price') & df['asset'].isin(['x', 'y'])
d = df[m].pivot('date', 'asset', 'value').astype(float)

d = pd.concat(
    [df, d['x'].sub(d['y']).reset_index(name='value').assign(
        asset='x_min_y', type='pricediff')],
    ignore_index=True)

详情:
创建 bool 掩码 m过滤 type 所在的行是 price和 asset在 x, y并使用 DataFrame.pivot reshape 数据框:

print(d) # pivoted dataframe
asset         x    y
date                
2020-01-01  1.5  1.0
2020-01-02  1.6  1.2

使用 Series.sub 减去列 x来自 y在旋转数据框中并分配列 asset和 type ，然后使用 pd.concat 将这个旋转的数据帧与原始数据帧 df 连接起来.

print(d)
         date    asset       type value
0  2020-01-01        x      price   1.5
1  2020-01-01        x     spread  0.01
2  2020-01-02        x      price   1.6
3  2020-01-02        x     spread  0.01
4  2020-01-01        y      price     1
5  2020-01-01        y     spread  0.08
6  2020-01-02        y      price   1.2
7  2020-01-02        y     spread  0.09
8  2020-01-01  x_min_y  pricediff   0.5
9  2020-01-02  x_min_y  pricediff   0.4