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    | Food insecurity        | Affected 2017   | In Need 2017   | Targeted 2017   | % targeted   |
|:-----------|:-----------------------|:----------------|:---------------|:----------------|:-------------|
| Nigeria    | severe food unsecure   | 5118752         | 5118752        | -               | -            |
| Senegal    | food insecure          | 3988146         | 881365         | 250000          | 28           |
| Mali       | food insecure          | 3529468         | 3529468        | 700000          | 20           |
| Mali       | moderate food insecure | 3034468         | 3034468        | 205000          | 7            |
| Mali       | severe food unsecure   | 495000          | 495000         | 495000          | 100          |
| Gambia     | severe food unsecure   |                 |                |                 | -            |
| Mauritania | moderate food insecure | 859471          | -              | -               | -            |
| Niger      | food insecure          | 5118867         | 1270739        | 650000          | 51           |
| Cameroon   | food insecure          | 2819000         | 2819000        | 839260          | 30           |
| Mauritania | severe food unsecure   | 443497          | -              | -               | -            |

TableB:
| country   | country_pcode   | region     | region_pcode   | cercle         | cercle_pcode   |   population_2021 | sector                         |   people_affected |
|:----------|:----------------|:-----------|:---------------|:---------------|:---------------|------------------:|:-------------------------------|------------------:|
| Mali      | ML              | Mopti      | ML05           | Tenenkou       | ML0508         |  221551           | Eau, assainissement et hygiène |         15308     |
| Mali      | ML              | Koulikoro  | ML02           | Kangaba        | ML0204         |  136965           | Abris d'urgence et NFI         |             0     |
| Mali      | ML              | Tombouctou | ML06           | Tombouctou     | ML0601         |  283476           | Education                      |         58852.8   |
| Mali      | ML              | Sikasso    | ML03           | Kadiolo        | ML0301         |  324477           | Nutrition                      |         31036.2   |
| Mali      | ML              | Kidal      | ML08           | Tin-Essako     | ML0802         |   11221.4         | Eau, assainissement et hygiène |           491.924 |
| Mali      | ML              | Bamako     | ML09           | Bamako         | ML0901         |       2.55704e+06 | Eau, assainissement et hygiène |         86928     |
| Mali      | ML              | Koulikoro  | ML02           | Kangaba        | ML0204         |  136965           | Eau, assainissement et hygiène |         15781     |
| Mali      | ML              | Kayes      | ML01           | Diema          | ML0103         |  294528           | Santé                          |        128325     |
| Mali      | ML              | Kayes      | ML01           | Diema          | ML0103         |  294528           | Eau, assainissement et hygiène |          9904.41  |
| Mali      | ML              | Tombouctou | ML06           | Gourma-Rharous | ML0604         |  286677           | Santé                          |         42582.8   |



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