一个 df 的行在另一个 df 的一行中有多少次值更高

Question

I have two df frames

df = pd.DataFrame({'1': [0, 0, 0, 0, 0, 0],
                   '2': [0, 0, 0, 0, 0, 0],
                   '3': [0, 10, 20, 30, 40, 50]})

df2 = pd.DataFrame({'1': [53, 76, 77, 96, 58, 64],
                    '2': [42, 61, 65, 74, 45, 54],
                    '3': [36, 42, 24, 54, 10, 80],})  

What I am looking for is a new column in df which states how many times that row has values from df2 in a row which are >= to each number.

Hope I've explained it well enough thanks

df = pd.DataFrame({'1': {0: 62, 1: 35, 2: 80, 3: 78, 4: 80, 5: 60, 6: 67, 7: 65, 8: 55, 9: 62}, 
                   '2': {0: 52, 1: 28, 2: 43, 3: 57, 4: 60, 5: 37, 6: 32, 7: 23, 8: 33, 9: 38}, 
                   '3': {0: 32, 1: 8, 2: 12, 3: 30, 4: 38, 5: 18, 6: 10, 7: 10, 8: 13, 9: 25}, 
                   '4': {0: 43, 1: 23, 2: 50, 3: 47, 4: 50, 5: 37, 6: 35, 7: 42, 8: 28, 9: 32}, 
                   '5': {0: 62, 1: 45, 2: 55, 3: 75, 4: 62, 5: 63, 6: 43, 7: 58, 8: 55, 9: 72}, 
                   '6': {0: 80, 1: 50, 2: 83, 3: 63, 4: 73, 5: 52, 6: 62, 7: 75, 8: 72, 9: 72}, 
                   '7': {0: 2, 1: 1, 2: 2, 3: 3, 4: 0, 5: 0, 6: 0, 7: 1, 8: 0, 9: 0}})

df2 = pd.DataFrame({'1': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}, 
                    '2': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}, 
                    '3': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6:0, 7: 0, 8: 0, 9: 0}, 
                    '4': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}, 
                    '5': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}, 
                    '6': {0: 0, 1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60, 7: 70, 8: 80, 9: 90}, 
                    '7': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1}})

Answer 1

Try with apply :

df["count"] = df.apply(lambda row: row.ge(df2).all(1).sum(), axis=1)

>>> df
   1   2   3   4   5   6  7  count
0  62  52  32  43  62  80  2      9
1  35  28   8  23  45  50  1      6
2  80  43  12  50  55  83  2      9
3  78  57  30  47  75  63  3      7
4  80  60  38  50  62  73  0      0
5  60  37  18  37  63  52  0      0
6  67  32  10  35  43  62  0      0
7  65  23  10  42  58  75  1      8
8  55  33  13  28  55  72  0      0
9  62  38  25  32  72  72  0      0

Answer 2

Try with outer sub

df['count'] = (np.subtract.outer(df2.to_numpy(),df.to_numpy())>=0).all((1,-1)).sum(0)
df
Out[307]: 
   1  2   3  count
0  0  0   0      6
1  0  0  10      6
2  0  0  20      5
3  0  0  30      4
4  0  0  40      3
5  0  0  50      2