Python数据帧:在同一列上进行透视

Question

我有两列"ID"和"division",如下所示.

df = pd.DataFrame(np.array([['111', 'AAA'],['222','AAA'],['333','BBB'],['444','CCC'],['444','AAA'],['222','BBB'],['111','BBB']]),columns=['ID','division'])

    ID  division
0   111 AAA
1   222 AAA
2   333 BBB
3   444 CCC
4   444 AAA
5   222 BBB
6   111 BBB

预期的输出如下所示,我需要在同一列上进行旋转,但计数取决于"除法".这应该在热图中显示.

    df = pd.DataFrame(np.array([['0','2','1','1'],['2','0','1','1'],['1','1','0','0'],['1','1','0','0']]),columns=['111','222','333','444'],index=['111','222','333','444'])

    111 222 333 444
111 0   2   1   1
222 2   0   1   1
333 1   1   0   0
444 1   1   0   0

所以,从技术上讲,我在ID之间就分裂做了重叠.

示例:突出显示的框为红色,其中111和222 ID之间的重叠为2(AAA和BBB).其中111和444之间的重叠是1(黑框中突出显示AAA).

我可以通过两个步骤在excel中做到这一点.不确定是否有一个帮助.Step1:= SUM(COUNTIFS($B$2:$B$8,$B2,$A$2:$A$8,$G2),COUNTIFS($B$2:$B$8,$B2,$A$2:$A$8,H$1))-1 Step2:=IF($G12=H$1,0,SUMIFS(H$2:H$8,$G$2:$G$8,$G12))

但有没有办法我们可以使用数据框在Python中完成它.感谢您的帮助

案例2

if df = pd.DataFrame(np.array([['111', 'AAA','4'],['222','AAA','5'],['333','BBB','6'],
                            ['444','CCC','3'],['444','AAA','2'], ['222','BBB','2'],
                            ['111','BBB','7']]),columns=['ID','division','count'])

   ID   division count
0   111  AAA      4
1   222  AAA      5
2   333  BBB      6
3   444  CCC      3
4   444  AAA      2
5   222  BBB      2
6   111  BBB      7

预期的产出将是

df_result = pd.DataFrame(np.array([['0','18','13','6'],['18','0','8','7'],['13','8','0','0'],['6','7','0','0']]),columns=['111','222','333','444'],index=['111','222','333','444'])

   111 222  333 444
111 0   18  13  6
222 18  0   8   7
333 13  8   0   0
444 6   7   0   0

计算:这里相对于划分AAA和BBB在111和222之间存在重叠,因此总和将是4 + 5 + 2 + 7 = 18

Answer 1

另一种方式做,这是使用具有自连接merge和pd.crosstab:

df_out = df.merge(df, on='division')

results = pd.crosstab(df_out.ID_x, df_out.ID_y) 
np.fill_diagonal(results.values, 0)

输出:

ID_y  111  222  333  444
ID_x                    
111   0.0  2.0  1.0  1.0
222   2.0  0.0  1.0  1.0
333   1.0  1.0  0.0  0.0
444   1.0  1.0  0.0  0.0

案例2

df = pd.DataFrame(np.array([['111', 'AAA','4'],['222','AAA','5'],['333','BBB','6'],
                            ['444','CCC','3'],['444','AAA','2'], ['222','BBB','2'],
                            ['111','BBB','7']]),columns=['ID','division','count'])

df['count'] = df['count'].astype(int)
df_out = df.merge(df, on='division')

df_out = df_out.assign(count = df_out.count_x + df_out.count_y)

results = pd.crosstab(df_out.ID_x, df_out.ID_y, df_out['count'], aggfunc='sum').fillna(0) 
np.fill_diagonal(results.values, 0)

输出:

ID_y   111   222   333  444
ID_x                       
111    0.0  18.0  13.0  6.0
222   18.0   0.0   8.0  7.0
333   13.0   8.0   0.0  0.0
444    6.0   7.0   0.0  0.0