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: :@computed_region_dqjc_k29y,:@computed_region_jdnu_jmst,:@computed_region_5d9v_6bui,permitnum,worktype,applicationtype,location,:@computed_region_mfuy_bee2,:@computed_region_2fpw_swv9,:@computed_region_9p4x_9cjt\n16.0,78.0,26.0,BLD2023-08018,Residential,Building,{'latitude': '40.785,19.0,19.0,350.0\n12.0,78.0,26.0,BLD2023-08311,Residential,Building,{'latitude': '40.777,19.0,19.0,582.0\n12.0,70.0,26.0,BLD2023-07867,Residential,Building,{'latitude': '40.759,19.0,24.0,567.0\n12.0,71.0,26.0,BLD2023-02507,Residential,Building,{'latitude': '40.762,19.0,21.0,567.0\n1.0,77.0,26.0,BLD2023-07072,Commercial,Building,{'latitude': '40.782,19.0,18.0,367.0\n1.0,72.0,26.0,BLD2023-08689,Commercial,Building,{'latitude': '40.735,19.0,21.0,364.0\n24.0,97.0,26.0,BLD2023-06295,Residential,Building,{'latitude': '40.708,19.0,27.0,245.0\n12.0,72.0,26.0,BLD2023-05359,Residential,Building,{'latitude': '40.738,19.0,21.0,472.0\n16.0,80.0,26.0,BLD2023-06139,Commercial,Building,{'latitude': '40.808,19.0,18.0,278.0\n12.0,78.0,26.0,BLD2023-07750,Commercial,Building,{'latitude': '40.770,19.0,19.0,240.0\n \n CSV Table B: v02+v1698aE,ZswU2nie504,q6rFvdGN4F0,sXpNMhZkCLA,R1VkE8XKb0E,+nTxjQhBWmY,a8tgQid0Dvs,AJ7cmCm31yg\nNo,Building,{'latitude': '40.739,26.0,472.0,19.0,BLD2023-08495,21.0\nNo,Building,{'latitude': '40.738,26.0,358.0,19.0,BLD2023-04923,26.0\nNo,Building,{'latitude': '40.715,26.0,384.0,19.0,BLD2023-07730,27.0\nNo,Building,{'latitude': '40.733,26.0,360.0,19.0,BLD2023-07089,24.0\nNo,Building,{'latitude': '40.786,26.0,352.0,19.0,BLD2023-04229,18.0\nSi,Building,{'latitude': '40.749,26.0,361.0,19.0,BLD2023-08476,20.0\nSi,Building,{'latitude': '40.739,26.0,474.0,19.0,BLD2023-05808,20.0\nSi,Building,{'latitude': '40.785,26.0,350.0,19.0,BLD2023-08019,19.0\nNo,Building,{'latitude': '40.725,26.0,277.0,19.0,BLD2023-03316,27.0\nNo,Building,{'latitude': '40.784,26.0,495.0,19.0,BLD2023-04556,18.0\nSi,Building,{'latitude': '40.714,26.0,573.0,19.0,BLD2023-07673,27.0\n \n Output: \n

答案

模型 模型答案 正确答案
金声玉亮2.0 ```python
{
"permitnum": "sXpNMhZkCLA",
"worktype": "R1VkE8XKb0E",
"applicationtype": "ZswU2nie504",
"location": "q6rFvdGN4F0"
}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
deepseek-r1

{"applicationtype": "ZswU2nie504", "location": "q6rFvdGN4F0"}		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
gpto1 ```python
{'permitnum': 'R1VkE8XKb0E', 'applicationtype': 'ZswU2nie504', 'location': 'q6rFvdGN4F0', ':@computed_region_mfuy_bee2': '+nTxjQhBWmY', ':@computed_region_5d9v_6bui': 'a8tgQid0Dvs'}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
gpt-4o-2024-08-06 ```python
{
"permitnum": "R1VkE8XKb0E",
"worktype": "ZswU2nie504",
"applicationtype": "ZswU2nie504"
}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
DeepSeek-Coder-V2-Lite-Instruct ```python
{
"permitnum": "v02+v1698aE",
"worktype": "ZswU2nie504",
"applicationtype": "q6rFvdGN4F0",
"location": "sXpNMhZkCLA",
"R1VkE8XKb0E": "+nTxjQhBWmY",
"AJ7cmCm31yg": "a8tgQid0Dvs"
}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
Meta-Llama-3.1-405B-Instruct-Turbo {'permitnum': 'R1VkE8XKb0E', 'worktype': 'q6rFvdGN4F0', 'location': 'sXpNMhZkCLA'} {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
Meta-Llama-3.1-70B-Instruct-Turbo {'permitnum': 'sXpNMhZkCLA', 'worktype': 'R1VkE8XKb0E', 'applicationtype': '+nTxjQhBWmY', 'location': 'a8tgQid0Dvs'} {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
Qwen2-72B-Instruct {'permitnum': 'BLD2023-08018', 'worktype': 'Building', 'applicationtype': 'Residential', 'location': "{'latitude': '40.785,19.0,19.0,350.0'}"}

However, this is a one-to-one mapping for just one row. For a general mapping that adheres to the instruction, it would be:

{'permitnum': None, 'worktype': None, 'applicationtype': None, 'location': None}

This needs to be dynamically filled based on the actual data comparison of both tables. The provided data does not have identical columns in both tables to create a direct mapping, so a direct column-to-column mapping cannot be created. If 'permitnum' from Table A is to be mapped with permit numbers from Table B, a code that iterates through both datasets would be required.
For an exact match mapping, the dictionary would look something like this if we assume 'permitnum' from Table A matches with the permit number in the last column of Table B:

{'permitnum': 'BLD2023-08018', 'worktype': None, 'applicationtype': None, 'location': None}
Again, this is a simplified example assuming a match was found for 'permitnum'. For other columns, similar logic would apply if matching criteria can be established.		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
baichuan4 Based on the provided CSV tables, it appears that the common columns between Table A and Table B are related to the permit number (`permitnum` in Table A) and some of the location information (`latitude` in both tables). However, the format of the `latitude` data in both tables is not consistent, and there are no direct matches for other columns such as `worktype`, `applicationtype`, etc.

Given the limited information and the lack of clear one-to-one mappings for most columns, I will create a basic join mapping based on the permit number and latitude. The latitude values need to be extracted from the dictionaries in Table A and compared with the latitude values in Table B.

Here's a Python dictionary representing the join mapping based on the permit number and latitude:

```python
join_mapping = {
'permitnum': 'permitnum', # Assuming 'permitnum' is the correct column name in Table B
'location': 'latitude' # Extracting latitude from the 'location' dictionary in Table A
}
```

Please note that this mapping assumes that the `permitnum` column in Table B is correctly named and that the latitude can be directly compared after extraction from the dictionary in Table A. If the actual column names in Table B differ or if additional processing is required for the latitude values, the mapping would need to be adjusted accordingly.		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
doubao {"latitude": "latitude"} {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
gpto1 ```python
{'permitnum': 'R1VkE8XKb0E', 'applicationtype': 'ZswU2nie504', 'location': 'q6rFvdGN4F0', ':@computed_region_mfuy_bee2': '+nTxjQhBWmY', ':@computed_region_5d9v_6bui': 'a8tgQid0Dvs'}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
kimi {'permitnum': 'BLD2023-08019'} {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
minimax Based on the provided CSV data, we can see that the join mapping between Table A and Table B should be based on the 'permitnum' column in Table A and the 'permitnum' column in Table B, as they both contain unique identifiers for building permits. However, the 'permitnum' in Table B seems to be the last element in the 'location' column, which is a dictionary-like structure. We will need to extract the 'permitnum' from the 'location' column in Table A and match it with the 'permitnum' in Table B.

Here is the join mapping dictionary:

```python
{
'permitnum': 'permitnum'
}
```

Please note that the 'location' column in Table A is not a direct match to any column in Table B, and the 'latitude' information is also not directly comparable since it's part of a dictionary-like structure in both tables. If you need to join based on 'latitude', you would need to extract the latitude value from the dictionaries in both tables and then perform the join on that value. However, since the question specifies a join mapping based on columns, and the 'permitnum' is the only directly comparable column, the above mapping is the one that can be directly inferred		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
wenxinyiyan ```python
{
'permitnum': 'R1VkE8XKb0E',
'worktype': 'ZswU2nie504',
'applicationtype': 'q6rFvdGN4F0',
'location': 'sXpNMhZkCLA'
}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
zhipu ```python
{
"permitnum": "BLD2023-08495",
"worktype": "Building",
"location": "{'latitude': '40.739",
":@computed_region_5d9v_6bui": "26.0",
":@computed_region_mfuy_bee2": "472.0",
":@computed_region_2fpw_swv9": "19.0"
}
```		 {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}
deepseek-v3 {"permitnum": "sXpNMhZkCLA", "worktype": "R1VkE8XKb0E", "applicationtype": "+nTxjQhBWmY", "location": "a8tgQid0Dvs"} {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"}