python - Pandas :比较组内的行

Question

我有一个按“键”分组的数据框。我需要比较每个组中的行以确定我是要保留组中的每一行还是只想要组中的一行。

在保留一组所有行的情况下:如果有一行的颜色为“红色”，面积为“12”，形状为“圆形”，并且另一行(在同一组内)具有“绿色”的颜色和“13”的面积以及“正方形”的形状，那么我想保留该组中的所有行。否则，如果这种情况不存在，我想保留该组中具有最大“num”值的行。

df = pd.DataFrame({'KEY': ['100000009', '100000009', '100000009', '100000009', '100000009','100000034','100000034', '100000034'], 
              'Date1': [20120506, 20120506, 20120507,20120608,20120620,20120206,20120306,20120405],
              'shape': ['circle', 'square', 'circle','circle','circle','circle','circle','circle'],
              'num': [3,4,5,6,7,8,9,10],
              'area': [12, 13, 12,12,12,12,12,12],
              'color': ['red', 'green', 'red','red','red','red','red','red']})


    Date1       KEY        area color   num shape
0   2012-05-06  100000009   12  red     3   circle
1   2012-05-06  100000009   13  green   4   square
2   2012-05-07  100000009   12  red     5   circle
3   2012-06-08  100000009   12  red     6   circle
4   2012-06-20  100000009   12  red     7   circle
5   2012-02-06  100000034   12  red     8   circle
6   2012-03-06  100000034   12  red     9   circle
7   2012-04-05  100000034   12  red     10  circle

预期结果:

    Date1       KEY        area color   num shape
0   2012-05-06  100000009   12  red     3   circle
1   2012-05-06  100000009   13  green   4   square
2   2012-05-07  100000009   12  red     5   circle
3   2012-06-08  100000009   12  red     6   circle
4   2012-06-20  100000009   12  red     7   circle
7   2012-04-05  100000034   12  red     10  circle

我是 python 的新手，groupby 向我抛出一个曲线球。

maxnum = df.groupby('KEY')['num'].transform(max)
df = df.loc[df.num == maxnum]

cond1 = (df[df['area'] == 12]) & (df[df['color'] == 'red']) & (df[df['shape'] == 'circle'])
cond2 = (df[df['area'] == 13]) & (df[df['color'] == 'green']) & (df[df['shape'] == 'square'])

Answer 1

定义一个名为function的自定义函数:

def function(x):
    i = x.query(
        'area == 12 and color == "red" and shape == "circle"'
    )
    j = x.query(
        'area == 13 and color == "green" and shape == "square"'
    )
    return x if not (i.empty or j.empty) else x[x.num == x.num.max()].head(1)

此函数在指定条件下测试每个组并根据需要返回行。特别是，它使用 df.empty 查询条件和测试是否为空。

将此传递给 groupby + apply:

df.groupby('KEY', group_keys=False).apply(function)


      Date1        KEY  area  color  num   shape
0  20120506  100000009    12    red    3  circle
1  20120506  100000009    13  green    4  square
2  20120507  100000009    12    red    5  circle
3  20120608  100000009    12    red    6  circle
4  20120620  100000009    12    red    7  circle
7  20120405  100000034    12    red   10  circle