问题

Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \n CSV Table A: valor,unidad,vigenciadesde,vigenciahasta\n3843.59,COP,2020-10-15T00:00:00.,2020-10-15T00:00:00.\n3997.09,COP,2021-12-24T00:00:00.,2021-12-24T00:00:00.\n3450.74,COP,2021-01-06T00:00:00.,2021-01-06T00:00:00.\n4003.95,COP,2022-01-20T00:00:00.,2022-01-20T00:00:00.\n3993.53,COP,2023-09-13T00:00:00.,2023-09-13T00:00:00.\n3639.12,COP,2021-04-22T00:00:00.,2021-04-22T00:00:00.\n3784.44,COP,2021-10-30T00:00:00.,2021-11-02T00:00:00.\n3927.25,COP,2022-02-19T00:00:00.,2022-02-22T00:00:00.\n4039.31,COP,2022-01-07T00:00:00.,2022-01-07T00:00:00.\n3905.95,COP,2023-09-19T00:00:00.,2023-09-19T00:00:00.\n4506.49,COP,2023-05-16T00:00:00.,2023-05-16T00:00:00.\n3827.27,COP,2020-08-22T00:00:00.,2020-08-24T00:00:00.\n3743.79,COP,2020-05-28T00:00:00.,2020-05-28T00:00:00.\n \n CSV Table B: e8EOCOtc2tE,92E9ya41vLI,Qiz4gNNSkjU\nCOP,2023-01-20T00:00:00.,0\nCOP,2022-12-23T00:00:00.,0\nCOP,2023-07-06T00:00:00.,0\nCOP,2023-05-15T00:00:00.,0\nCOP,2021-11-18T00:00:00.,0\nCOP,2021-08-25T00:00:00.,0\nCOP,2022-10-03T00:00:00.,0\nCOP,2022-01-27T00:00:00.,0\nCOP,2022-08-18T00:00:00.,0\nCOP,2022-03-24T00:00:00.,0\nCOP,2021-04-14T00:00:00.,0\nCOP,2023-06-05T00:00:00.,0\nCOP,2021-03-26T00:00:00.,0\nCOP,2023-08-14T00:00:00.,0\n \n Output: \n

答案

模型 模型答案 正确答案
金声玉亮2.0 ```python
{
"unidad": "e8EOCOtc2tE",
"vigenciadesde": "92E9ya41vLI",
"vigenciahasta": "92E9ya41vLI"
}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
deepseek-r1

{'unidad': 'e8EOCOtc2tE', 'vigenciadesde': '92E9ya41vLI'}		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
gpto1 {'unidad': 'e8EOCOtc2tE'} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
gpt-4o-2024-08-06 ```python
{
"valor": None,
"unidad": "e8EOCOtc2tE",
"vigenciadesde": None,
"vigenciahasta": None
}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
DeepSeek-Coder-V2-Lite-Instruct ```python
{
"valor": "e8EOCOtc2tE",
"unidad": "92E9ya41vLI",
"vigenciadesde": "Qiz4gNNSkjU"
}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
Meta-Llama-3.1-405B-Instruct-Turbo {'unidad': 'e8EOCOtc2tE'} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
Meta-Llama-3.1-70B-Instruct-Turbo {'unidad': 'e8EOCOtc2tE', 'vigenciadesde': '92E9ya41vLI'} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
Qwen2-72B-Instruct {'valor': None, 'unidad': 'COP', 'vigenciadesde': None, 'vigenciahasta': None} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
baichuan4 Based on the provided CSV tables, it seems that there is no direct relationship between the columns of Table A and Table B that would allow for a traditional join operation (such as an inner join, outer join, etc.). The columns in Table A include 'valor', 'unidad', 'vigenciadesde', and 'vigenciahasta', while Table B contains three unnamed columns with values that do not correspond directly to any of the columns in Table A.

However, if we are to create a mapping based on the assumption that the first column in Table B corresponds to the 'unidad' column in Table A, we could establish a simple one-to-one mapping between these two columns. Here is the Python dictionary representing this mapping:

```python
{
'unidad': 'e8EOCOtc2tE' # Assuming 'e8EOCOtc2tE' is the column name in Table B that corresponds to 'unidad' in Table A
}
```

Please note that this mapping is based on the assumption provided and does not reflect a typical join condition where columns from both tables can be matched based on common data. If there is additional context or a specific relationship between the tables that was not mentioned, please provide more details for a more accurate mapping.		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
doubao {"unidad": "e8EOCOtc2tE", "vigenciahasta": "92E9ya41vLI"} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
gpto1 {'unidad': 'e8EOCOtc2tE'} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
kimi {'unidad': 'e8EOCOtc2tE', 'vigenciadesde': '92E9ya41vLI', 'vigenciahasta': 'Qiz4gNNSkjU'} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
minimax ```python
{
'valor': 'e8EOCOtc2tE',
'unidad': '92E9ya41vLI',
'vigenciadesde': 'Qiz4gNNSkjU',
'vigenciahasta': None
}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
wenxinyiyan ```python
{'unidad': 'e8EOCOtc2tE', 'vigenciadesde': '92E9ya41vLI', 'vigenciahasta': 'Qiz4gNNSkjU'}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
zhipu ```python
{
"unidad": "e8EOCOtc2tE",
"vigenciadesde": "92E9ya41vLI",
"vigenciahasta": "Qiz4gNNSkjU"
}
```		 {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}
deepseek-v3 {"unidad": "e8EOCOtc2tE", "vigenciadesde": "92E9ya41vLI"} {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"}