Task: Schema-Matching (source dataset: HXD)

Task: 
Please identify the matching columns between Table A and Table B. For each column in Table A, specify the corresponding column in Table B. If a column in A has no corresponding column in Table B, you can map it to null. Represent each column mapping using a pair of column headers in a list, i.e., [Table A Column, Table B column or null]. Provide the mapping for each column in Table A and return all mappings in a list.

No explanation and return the matching columns in a structured JSON format: {"column_mappings": <a list of column pairs>}.

TableA:
| Country      | nutrition   | Affected 2017   | In Need 2017   | Targeted 2017   | % targeted   |
|:-------------|:------------|:----------------|:---------------|:----------------|:-------------|
| Chad         | MAM         | 330,211         | 330,211        | 330,211         | 100          |
| Nigeria      | PLW         | 208,012         | 208,012        | 104,006         | 50           |
| Mali         | PLW         | 58,565          | 58,500         | 49,000          | 84           |
| Gambia       | GAM         | -               | -              | -               |              |
| Burkina Faso | PLW         | 239,000         | 239,000        | 119,799         | 50           |
| Gambia       | PLW         |                 | -              |                 |              |
| Niger        | PLW         | 292,513         | 292,513        | 175,526         | 60           |
| Burkina Faso | MAM         | 433,412         | 433,412        | _               | 0            |
| Cameroon     | PLW         | 39,320          | 39,320         | 35,388          | 90           |
| Niger        | MAM         | 888,499         | 888,499        | 636,210         | 72           |

TableB:
| country   | country_pcode   | region     | region_pcode   | cercle     | cercle_pcode   |   population_2018 |   people_affected |   people_in_need |   people_targeted |
|:----------|:----------------|:-----------|:---------------|:-----------|:---------------|------------------:|------------------:|-----------------:|------------------:|
| Mali      | ML              | Kayes      | ML01           | Kita       | ML0105         |            577601 |             69383 |            19637 |             19637 |
| Mali      | ML              | Segou      | ML04           | Macina     | ML0403         |            315263 |            134899 |            56789 |             15943 |
| Mali      | ML              | Mopti      | ML05           | Douentza   | ML0504         |            330117 |            330117 |           128745 |            122308 |
| Mali      | ML              | Sikasso    | ML03           | Yorosso    | ML0307         |            282712 |             50912 |             7657 |              7657 |
| Mali      | ML              | Segou      | ML04           | Baroueli   | ML0401         |            271029 |            115595 |            43378 |             13700 |
| Mali      | ML              | Gao        | ML07           | Gao        | ML0703         |            320400 |            320400 |           151821 |            151821 |
| Mali      | ML              | Kidal      | ML08           | Abeibara   | ML0801         |             13841 |             13841 |             8534 |              8534 |
| Mali      | ML              | Tombouctou | ML06           | Tombouctou | ML0605         |            170606 |            170606 |            66536 |             63209 |
| Mali      | ML              | Mopti      | ML05           | Bankass    | ML0502         |            354060 |            152989 |            53073 |             32314 |
| Mali      | ML              | Koulikouro | ML02           | Dioila     | ML0202         |            653015 |             52275 |            25452 |             25452 |



source_headers: Country
Affected 2017
In Need 2017
Targeted 2017
target_headers: country
people_affected
people_in_need
people_targeted
column_mappings: Country
country
Affected 2017
people_affected
In Need 2017
people_in_need
Targeted 2017
people_targeted
output: {"column_mappings": [["Country", "country"], ["nutrition", null], ["Affected 2017", "people_affected"], ["In Need 2017", "people_in_need"], ["Targeted 2017", "people_targeted"], ["% targeted", null]]}