python Pandas : Merge Columns of Data Frame with column name into one column

Question

我的数据框中有以下格式的数据:

>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
>>> df
          A         B         C         D
0  0.578095 -1.985742 -0.269517 -0.180319
1 -0.618431 -0.937284  0.556290 -1.416877
2  1.695109  0.122219  0.182450  0.411448
3  0.228466  0.268943 -1.249488  3.227840
4  0.005990 -0.805618 -1.941092 -0.146649
5 -1.116451 -0.649854  1.272314  1.422760

我想通过附加行数据和列名创建以下输出来组合每一行的一些列:

          A         B        New Column
0  0.578095 -1.985742  {"C":"-0.269517","D":"-0.180319"}
1 -0.618431 -0.937284  {"C":"0.556290","D":"-1.416877"}
2  1.695109  0.122219  {"C":"0.182450","D":"0.411448"}
3  0.228466  0.268943  {"C":"-1.249488","D":"3.227840"}
4  0.005990 -0.805618  {"C":"-1.941092","D":"-0.146649"}
5 -1.116451 -0.649854  {"C":"1.272314","D":"1.422760"}

我怎样才能在 pandas 中实现这一点？

最终游戏是将数据采用 JSON 格式，其中 C-D 列作为维度 A-B 的度量，然后将它们存储到 Snowflake 中的表中。

Answer 1

删除列并使用 agg 创建一个新列:

df2 = df.drop(['C', 'D'], axis=1).assign(New_Column=
    df[['C', 'D']].agg(pd.Series.to_dict, axis=1))

df2
          A         B                                         New_Column
0 -0.645719 -0.757112  {'D': 0.8923148471642509, 'C': -0.685995130541...
1 -0.124200 -0.578526  {'D': -0.5457121278891495, 'C': -1.46006615752...
2  2.160417 -0.985475  {'D': -0.49915307027471345, 'C': 0.85388172610...
3  2.111050  1.384887  {'D': -0.4617380879640236, 'C': 0.907519279458...
4  0.781630 -0.366445  {'D': -0.3105127375402184, 'C': 0.295808587414...
5  0.460773  0.549545  {'D': -0.993162129461116, 'C': 0.8163378188816...