Python Pandas：将具有列名的数据框列合并为一列

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来组合每一行的一些列，以创建以下输出：

          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"}

我怎样才能在熊猫中实现这一目标？

最终的游戏是将数据采用 JSON 格式，其中 Column CD 作为 Dimensions AB 的度量，然后将它们存储到 Snowflake 中的表中。

Answer 1

将to_dict与'r'

df['New c']=df[['C','D']].to_dict('r')
df
Out[580]: 
          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   
                                        New c  
0            {'C': -0.269517, 'D': -0.180319}  
1              {'C': 0.55629, 'D': -1.416877}  
2               {'C': 0.18245, 'D': 0.411448}  
3              {'C': -1.249488, 'D': 3.22784}  
4  {'C': -1.9410919999999998, 'D': -0.146649}  
5               {'C': 1.272314, 'D': 1.42276}  

Answer 2

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...

Answer 3

与之前的答案类似，但直接为您提供 JSON：

df["New Column"] = df[["C", "D"]].agg(lambda x: x.to_json(), axis=1)
df.drop(columns=["C", "D"], inplace=True)

          A         B                           New Column
0  0.203402  0.963421  {"C":0.0006991508,"D":0.6259404479}
1  0.259584  0.992885   {"C":0.4362059517,"D":0.198117864}
2  0.470500  0.242945  {"C":0.6507973014,"D":0.8585516803}
3  0.337716  0.937279  {"C":0.7682917478,"D":0.4398740192}
4  0.449790  0.863678  {"C":0.9256099517,"D":0.4139063442}
5  0.837881  0.310204  {"C":0.2481016705,"D":0.8652550757}

如果您有 NaN 值，则可以扩展 lambda：

df["New Column"] = df[["C", "D"]].agg(lambda x: x.dropna().to_json(), axis=1)
df.drop(columns=["C", "D"], inplace=True)

          A         B         C         D
0  0.247098  0.318231  0.188487  0.604020
1  0.696833  0.554107  0.982078  0.047739
2  0.874721  0.557809       NaN  0.474376
3  0.185668  0.477824  0.900544       NaN
4  0.085932  0.808342  0.360703  0.331273
5  0.665791  0.011564  0.785515  0.177014

          A         B                           New Column
0  0.247098  0.318231  {"C":0.1884867142,"D":0.6040197923}
1  0.696833  0.554107  {"C":0.9820776439,"D":0.0477394369}
2  0.874721  0.557809                   {"D":0.4743764396}
3  0.185668  0.477824                   {"C":0.9005440032}
4  0.085932  0.808342  {"C":0.3607030306,"D":0.3312725694}
5  0.665791  0.011564  {"C":0.7855148493,"D":0.1770143921}