Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 | Col_2             |            Col_3 |            Col_4 |   Col_5 |    Col_6 |    Col_7 |    Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |
|--------:|--------:|:------------------|-----------------:|-----------------:|--------:|---------:|---------:|---------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|      37 |   43011 | CITY OF MILPITAS  |      5.12141e+06 |      1.19052e+06 |       0 | 360965   | 428493   | 401063   |       0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |
|      50 |   10005 | CITY OF KINGSBURG | 595002           | 132338           |       0 |  54195.1 |  37539.9 |  40602.8 |       0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |
|      21 |   36002 | CITY OF COLTON    | 285414           |  81782.1         |       0 |  19942.2 |  39071.7 |  22768.2 |       0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |
|      39 |   19084 | CITY OF CARSON    |      6.21198e+06 |      1.59217e+06 |       0 | 509612   | 555147   | 527407   |       0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |
|      16 |   19004 | CITY OF AZUSA     |      1.54356e+06 | 381668           |       0 | 107234   | 168746   | 105688   |       0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |

Candidate column headers:
DISTRIBUTED September 2018
DISTRIBUTED January 2019
DISTRIBUTED August 2018
Prior Year
DISTRIBUTED  April 2019
Year to Year % change
DISTRIBUTED October 2018
Year to Date
DISTRIBUTED  May 2019
DISTRIBUTED March 2019
Agency Name
DISTRIBUTED February 2019
DISTRIBUTED  June 2019
DISTRIBUTED December 2018
DISTRIBUTED November 2018
City Code
DISTRIBUTED  July 2018
Agency Code



headers: DISTRIBUTED September 2018
DISTRIBUTED January 2019
DISTRIBUTED August 2018
Prior Year
DISTRIBUTED  April 2019
Year to Year % change
DISTRIBUTED October 2018
Year to Date
DISTRIBUTED  May 2019
DISTRIBUTED March 2019
Agency Name
DISTRIBUTED February 2019
DISTRIBUTED  June 2019
DISTRIBUTED December 2018
DISTRIBUTED November 2018
City Code
DISTRIBUTED  July 2018
Agency Code
label: Agency Code
City Code
Agency Name
Prior Year
Year to Date
Year to Year % change
DISTRIBUTED  July 2018
DISTRIBUTED August 2018
DISTRIBUTED September 2018
DISTRIBUTED October 2018
DISTRIBUTED November 2018
DISTRIBUTED December 2018
DISTRIBUTED January 2019
DISTRIBUTED February 2019
DISTRIBUTED March 2019
DISTRIBUTED  April 2019
DISTRIBUTED  May 2019
DISTRIBUTED  June 2019