How to map a specific index in my array to an html table using jQuery

I need to map specific data from this array that’s returned from my API and list it onto a table that’s created by jQuery. However, I only need specific data from this array to be displayed and not the whole thing. The array is listed below and the amount of data that is returned is fairly large. I only need to map through the data: “raw_value”: [] and then from there I only need the last 10 data points, so in this case I would need to start at the data point 2850. I know how to map through an entire array, but I’m not sure how to start at a specific value. Any advice is greatly appreciated!

This is the ajax call I use to access the API. The data gets stored in response and then parsed. Then I try to map through parsed obj at the end.

const modelApi = function () {
       

        $.ajax({
            url: `api_url`,
            method: 'GET',
            error: function (xhr) {
                if (xhr.status === 504) {
                    modelApi()
                    $('.overlayMessage').text('Server not responding, trying your search again. Please do not refresh the page')
                }
                if (xhr.status === 502) {
                    $('#overlay').fadeOut()
                    alert('No data was returned for your current search criteria. Please try selecting a different meter or a different date range')
                }
            }
        }).then(response => {
            const obj = JSON.parse(response.body)
            console.log(obj)
           
        obj.map(data, () => {
            
        })
{
"building": {
"building_abbreviation": "ECJ",
"building_number": "0227",
"use_type": "Laboratory",
"aliases": null,
"is_dorm": 0,
"cooling_commodity": "CHW",
"heating_commodity": "STM"
},
"meter": "2032",
"commodity": {
"tag": "S"
},
"model": {
"x": "hdd",
"train_start": "2019-05-01",
"train_end": "2020-04-29",
"analysis_start": "2020-05-01",
"analysis_end": "2020-05-31",
"iqr_errors": [
{
"lower_bound": -6684.776494485483,
"upper_bound": 7416.929568407427
}
],
"auto_ignored_percentage": 20.454545454545453,
"search": {
"base_temperatures": [
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
],
"r2_scores": [
0,
0,
0,
0,
0,
0,
0,
0,
]
},
"base_temperature": 84,
"max_train_r2": 0.4810672409755815,
"slope": 474.72313695289665,
"intercept": 3058.7897948225855,
"std": {
"analysis": 2126.1299232079455,
"train": 6244.513670227164
},
"data": {
"timestamp": [
"2019-05-01",
"2019-05-02",
"2019-05-03",
"2019-05-04",
"2019-05-05",
"2019-05-06",
"2019-05-07",
"2019-05-08",
"2019-05-09",
"2019-05-10",
],
"average_dry_bulb_temperature": [
77.9,
69.4,
68.7,
71.1,
73.8,
71.8,
74.7,
71.8,
75.9,
60.8,
60.6,
68.9,
70.5,
73.2,
75.2,
76.5,
79.3,
79.2,
82.9,
81.3,
78.3,
82.2,
],
"degree_day": [
6.099999999999994,
14.599999999999994,
15.299999999999997,
12.900000000000006,
10.200000000000003,
12.200000000000003,
9.299999999999997,
12.200000000000003,
8.099999999999994,
23.200000000000003,
23.4,
15.099999999999994,
13.5,
10.799999999999997,
8.799999999999997,
7.5,
4.700000000000003,
4.799999999999997,
1.0999999999999943,
2.700000000000003,
5.700000000000003,
1.7999999999999972,
1.4000000000000057,
2.0999999999999943,
3.200000000000003,
3,
4.099999999999994,
4.099999999999994,
1.5999999999999943,
6.599999999999994,
3,
1.2000000000000028,
],
"raw_value": [
1420,
3920,
8170,
8270,
7170,
3230,
2900,
5140,
6060,
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],
"preprocessed_value": [
1420,
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3700,
3810,
3820,
],
"null_reason": [
null,
null,
null,
null,
null,
null,
null,
null,
null,
"IQR boundary error",
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
],
"predicted_value": [
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],
"predicted_value_lower_bound": [
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]
}
}
}

Answer

Array slice can help you.

let obj = {
"building": {
"building_abbreviation": "ECJ",
"building_number": "0227",
"use_type": "Laboratory",
"aliases": null,
"is_dorm": 0,
"cooling_commodity": "CHW",
"heating_commodity": "STM"
},
"meter": "2032",
"commodity": {
"tag": "S"
},
"model": {
"x": "hdd",
"train_start": "2019-05-01",
"train_end": "2020-04-29",
"analysis_start": "2020-05-01",
"analysis_end": "2020-05-31",
"iqr_errors": [
{
"lower_bound": -6684.776494485483,
"upper_bound": 7416.929568407427
}
],
"auto_ignored_percentage": 20.454545454545453,
"search": {
"base_temperatures": [
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
],
"r2_scores": [
0,
0,
0,
0,
0,
0,
0,
0,
]
},
"base_temperature": 84,
"max_train_r2": 0.4810672409755815,
"slope": 474.72313695289665,
"intercept": 3058.7897948225855,
"std": {
"analysis": 2126.1299232079455,
"train": 6244.513670227164
},
"data": {
"timestamp": [
"2019-05-01",
"2019-05-02",
"2019-05-03",
"2019-05-04",
"2019-05-05",
"2019-05-06",
"2019-05-07",
"2019-05-08",
"2019-05-09",
"2019-05-10",
],
"average_dry_bulb_temperature": [
77.9,
69.4,
68.7,
71.1,
73.8,
71.8,
74.7,
71.8,
75.9,
60.8,
60.6,
68.9,
70.5,
73.2,
75.2,
76.5,
79.3,
79.2,
82.9,
81.3,
78.3,
82.2,
],
"degree_day": [
6.099999999999994,
14.599999999999994,
15.299999999999997,
12.900000000000006,
10.200000000000003,
12.200000000000003,
9.299999999999997,
12.200000000000003,
8.099999999999994,
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23.4,
15.099999999999994,
13.5,
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8.799999999999997,
7.5,
4.700000000000003,
4.799999999999997,
1.0999999999999943,
2.700000000000003,
5.700000000000003,
1.7999999999999972,
1.4000000000000057,
2.0999999999999943,
3.200000000000003,
3,
4.099999999999994,
4.099999999999994,
1.5999999999999943,
6.599999999999994,
3,
1.2000000000000028,
],
"raw_value": [
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8270,
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