数组求和：计算的平均 [英] Array summation: Calculating mean of
本文介绍了数组求和：计算的平均的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个排序的数组（如下），我可以用什么方法来总结等价的反应，因此在每个值计算意味着什么？例如综上所述对应于目标= 15.26（不能在其他试验中相同的值）的所有值。
我认为，通过循环来寻找类似的数字在第3列，但肯定有一个简单的解决方案。
非常感谢，
新手
响应目标
103 4.556049 15.260
55 10.549498 15.260
31 18.497221 15.260
130 13.275155 15.260
93 6.621331 15.260
35 7.653972 15.260
149 15.808224 15.260
84 4.556049 15.260
113 8.996922 15.260
152 18.282948 15.260
162 14.606229 15.260
60 5.588690 15.260
57 7.653972 15.260
159 20.902759 15.260
23 11.645647 15.260
40 21.385003 25.367
76 19.298551 25.367
71 17.724806 25.367
70 11.639967 25.367
178 36.359849 25.367
65 16.947743 25.367
32 25.811419 25.367
52 27.309781 25.367
26 27.160049 25.367
179 34.706590 25.367
54 22.157935 25.367
119 13.888544 25.367
123 39.826426 25.367
147 36.674288 25.367
151 33.022869 25.367
175 46.078035 39.536
4 34.567184 39.536
45 34.130893 39.536
8 51.218523 39.536
42 35.367997 39.536
78 26.210535 39.536
157 43.627062 39.536
116 22.879751 39.536
102 25.996262 39.536
135 45.632451 39.536
126 34.580608 39.536
125 40.390764 39.536
30 35.767629 39.536
86 33.775664 39.536
94 30.904922 39.536
88 68.897857 59.655
177 64.219992 59.655
171 63.937565 59.655
74 54.867877 59.655
163 39.521796 59.655
75 68.286534 59.655
148 70.700332 59.655
115 47.631128 59.655
167 55.704317 59.655
80 51.786992 59.655
0 54.931901 59.655
12 46.967656 59.655
10 62.339037 59.655
3 64.174040 59.655
43 66.948747 59.655
44 75.237758 89.387
101 82.690846 89.387
27 74.046606 89.387
105 94.395834 89.387
108 63.940663 89.387
112 111.211880 89.387
161 70.394671 89.387
29 116.047222 89.387
164 86.483221 89.387
96 83.612994 89.387
51 90.062871 89.387
49 99.448547 89.387
120 64.238064 89.387
121 99.623064 89.387
136 87.784865 89.387
53 99.587954 119.710
90 99.497598 119.710
155 117.134593 119.710
2 118.382540 119.710
87 123.984619 119.710
173 126.473800 119.710
124 128.213801 119.710
62 104.233807 119.710
142 151.525160 119.710
77 84.349268 119.710
46 128.496744 119.710
137 135.726266 119.710
6 106.980116 119.710
109 135.305464 119.710
56 146.565384 119.710
21 149.950898 155.140
144 138.947073 155.140
132 157.788645 155.140
25 138.816444 155.140
98 159.238989 155.140
97 136.179079 155.140
18 160.264919 155.140
92 106.939843 155.140
50 133.825173 155.140
156 160.650610 155.140
169 164.086207 155.140
7 120.081751 155.140
82 144.995253 155.140
73 148.619307 155.140
160 155.345932 155.140
154 286.343698 241.970
20 238.666653 241.970
17 243.265521 241.970
61 233.941803 241.970
67 225.647113 241.970
134 238.871632 241.970
141 257.964136 241.970
39 237.710944 241.970
106 267.179426 241.970
158 288.864375 241.970
104 219.470369 241.970
38 221.280073 241.970
36 216.673977 241.970
128 255.494058 241.970
91 222.512530 241.970
9 248.174697 281.250
143 339.346073 281.250
165 319.828122 281.250
166 339.152453 281.250
172 311.936161 281.250
14 229.413155 281.250
153 362.308915 281.250
117 334.014030 281.250
99 266.162791 281.250
85 307.998184 281.250
118 322.768051 281.250
22 247.992436 281.250
100 282.320528 281.250
24 308.043620 281.250
48 277.614265 281.250
89 324.674307 312.340
34 319.110436 312.340
83 294.367320 312.340
107 256.297453 312.340
5 330.217008 312.340
127 394.634200 312.340
66 335.137544 312.340
63 303.852646 312.340
16 336.398915 312.340
133 401.600397 312.340
176 336.454678 312.340
122 367.271789 312.340
1 330.173121 312.340
140 389.322293 312.340
33 306.170925 312.340
170 463.588300 365.130
68 354.929661 365.130
174 511.082051 365.130
41 407.971277 365.130
81 352.324308 365.130
19 455.697372 365.130
95 347.397060 365.130
13 374.191002 365.130
15 471.887121 365.130
146 420.940734 365.130
114 365.869462 365.130
138 466.096069 365.130
11 421.345013 365.130
139 451.122771 365.130
111 358.154084 365.130
129 513.113772 415.210
131 465.486811 415.210
58 498.471436 415.210
59 480.860257 415.210
64 435.301676 415.210
37 401.883341 415.210
28 520.876652 415.210
69 402.135305 415.210
145 514.131956 415.210
47 506.972655 415.210
72 402.655756 415.210
79 402.615483 415.210
150 519.844011 415.210
168 504.783972 415.210
110 435.998192 415.210
mean_plain = []
在范围（0,11）我：
mean_plain.append（[一个由[i] [1] .response.mean（），一个由[i] [0]）
困在这里，如此接近，但不完全正确的格式（理想我想原始列的目标和响应
解决方案最简单的事情是，如果你有一个数据库中的数据。如果数据已经是来自一个数据库或者你可以把它放在分贝这将是方便做这样的事情：
选择目标，COUNT（*），AVG（COL1），AVG（响应）
从your_table
按目标群体如果您没有访问DB例如，你可以尝试 https://www.sqlite.org/ 如果你是热衷于学习DB-的基本用法。
I have a sorted array (as below), what methods can I use to sum equivalent target responses and therefore calculate a mean at each value? e.g. sum all values corresponding to target = 15.26 (not the same values in other trials).
I considered looping through to look for similar numbers in the 3rd column but surely there is a simpler solution.
Many thanks, Newbie
response target 103 4.556049 15.260 55 10.549498 15.260 31 18.497221 15.260 130 13.275155 15.260 93 6.621331 15.260 35 7.653972 15.260 149 15.808224 15.260 84 4.556049 15.260 113 8.996922 15.260 152 18.282948 15.260 162 14.606229 15.260 60 5.588690 15.260 57 7.653972 15.260 159 20.902759 15.260 23 11.645647 15.260 40 21.385003 25.367 76 19.298551 25.367 71 17.724806 25.367 70 11.639967 25.367 178 36.359849 25.367 65 16.947743 25.367 32 25.811419 25.367 52 27.309781 25.367 26 27.160049 25.367 179 34.706590 25.367 54 22.157935 25.367 119 13.888544 25.367 123 39.826426 25.367 147 36.674288 25.367 151 33.022869 25.367 175 46.078035 39.536 4 34.567184 39.536 45 34.130893 39.536 8 51.218523 39.536 42 35.367997 39.536 78 26.210535 39.536 157 43.627062 39.536 116 22.879751 39.536 102 25.996262 39.536 135 45.632451 39.536 126 34.580608 39.536 125 40.390764 39.536 30 35.767629 39.536 86 33.775664 39.536 94 30.904922 39.536 88 68.897857 59.655 177 64.219992 59.655 171 63.937565 59.655 74 54.867877 59.655 163 39.521796 59.655 75 68.286534 59.655 148 70.700332 59.655 115 47.631128 59.655 167 55.704317 59.655 80 51.786992 59.655 0 54.931901 59.655 12 46.967656 59.655 10 62.339037 59.655 3 64.174040 59.655 43 66.948747 59.655 44 75.237758 89.387 101 82.690846 89.387 27 74.046606 89.387 105 94.395834 89.387 108 63.940663 89.387 112 111.211880 89.387 161 70.394671 89.387 29 116.047222 89.387 164 86.483221 89.387 96 83.612994 89.387 51 90.062871 89.387 49 99.448547 89.387 120 64.238064 89.387 121 99.623064 89.387 136 87.784865 89.387 53 99.587954 119.710 90 99.497598 119.710 155 117.134593 119.710 2 118.382540 119.710 87 123.984619 119.710 173 126.473800 119.710 124 128.213801 119.710 62 104.233807 119.710 142 151.525160 119.710 77 84.349268 119.710 46 128.496744 119.710 137 135.726266 119.710 6 106.980116 119.710 109 135.305464 119.710 56 146.565384 119.710 21 149.950898 155.140 144 138.947073 155.140 132 157.788645 155.140 25 138.816444 155.140 98 159.238989 155.140 97 136.179079 155.140 18 160.264919 155.140 92 106.939843 155.140 50 133.825173 155.140 156 160.650610 155.140 169 164.086207 155.140 7 120.081751 155.140 82 144.995253 155.140 73 148.619307 155.140 160 155.345932 155.140 154 286.343698 241.970 20 238.666653 241.970 17 243.265521 241.970 61 233.941803 241.970 67 225.647113 241.970 134 238.871632 241.970 141 257.964136 241.970 39 237.710944 241.970 106 267.179426 241.970 158 288.864375 241.970 104 219.470369 241.970 38 221.280073 241.970 36 216.673977 241.970 128 255.494058 241.970 91 222.512530 241.970 9 248.174697 281.250 143 339.346073 281.250 165 319.828122 281.250 166 339.152453 281.250 172 311.936161 281.250 14 229.413155 281.250 153 362.308915 281.250 117 334.014030 281.250 99 266.162791 281.250 85 307.998184 281.250 118 322.768051 281.250 22 247.992436 281.250 100 282.320528 281.250 24 308.043620 281.250 48 277.614265 281.250 89 324.674307 312.340 34 319.110436 312.340 83 294.367320 312.340 107 256.297453 312.340 5 330.217008 312.340 127 394.634200 312.340 66 335.137544 312.340 63 303.852646 312.340 16 336.398915 312.340 133 401.600397 312.340 176 336.454678 312.340 122 367.271789 312.340 1 330.173121 312.340 140 389.322293 312.340 33 306.170925 312.340 170 463.588300 365.130 68 354.929661 365.130 174 511.082051 365.130 41 407.971277 365.130 81 352.324308 365.130 19 455.697372 365.130 95 347.397060 365.130 13 374.191002 365.130 15 471.887121 365.130 146 420.940734 365.130 114 365.869462 365.130 138 466.096069 365.130 11 421.345013 365.130 139 451.122771 365.130 111 358.154084 365.130 129 513.113772 415.210 131 465.486811 415.210 58 498.471436 415.210 59 480.860257 415.210 64 435.301676 415.210 37 401.883341 415.210 28 520.876652 415.210 69 402.135305 415.210 145 514.131956 415.210 47 506.972655 415.210 72 402.655756 415.210 79 402.615483 415.210 150 519.844011 415.210 168 504.783972 415.210 110 435.998192 415.210 mean_plain = [] for i in range(0,11): mean_plain.append([a[i][1].response.mean(),a[i][0]])
Stuck here, so close but not quite in the right format (Ideally I want original columns target and response
解决方案The easiest thing would be if you had the data in a database. If the data already comes from a db or you could put it in db it would be convenient to do something like:
select target, count(*), avg(col1), avg(response) from your_table group by targetIf you don't have access to db you could for example try https://www.sqlite.org/ if you are eager to learn the basics of db-usage.
这篇关于数组求和：计算的平均的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
