How to get mean / averaged values among of duplicated data array?

4 Ansichten (letzte 30 Tage)
Hi, Community
I have a question about how to get mean value among of duplicated data array. So i have this data for example :
{TIME} {DATA}
2015-11-08 12:00:00 31.68
2015-11-08 12:01:00 37.67
2015-11-08 12:02:00 36.66
2015-11-08 12:03:00 39.66
2015-11-08 12:04:00 31.15 %% Duplicated Time Data
2015-11-08 12:04:00 33.75 %% Duplicated Time Data
2015-11-08 12:04:00 35.65 %% Duplicated Time Data
2015-11-08 12:04:00 39.75 %% Duplicated Time Data
2015-11-08 12:05:00 39.64
2015-11-08 12:06:00 31.64
2015-11-08 12:07:00 31.63
2015-11-08 12:08:00 31.62
2015-11-08 12:09:00 41.81
2015-11-08 12:10:00 31.61 %% Duplicated Time Data
2015-11-08 12:10:00 41.51 %% Duplicated Time Data
2015-11-08 12:10:00 38.61 %% Duplicated Time Data
2015-11-08 12:10:00 33.61 %% Duplicated Time Data
2015-11-08 12:11:00 30.60
The data above is a timetable array with variable data in each of timestamp data. I want to automatically change the duplicated timestamp data to become 1 timestamp data by averaging the data in each of those duplicated data's variables. In this case, i want to change all of four (4) duplicated 2015-11-08 12:04:00 timestamp data's variable to become 1 data (2015-11-08 12:04:00) instead by averaging :
31.15
33.75
35.65
39.75
---------- +
140.3 / 4 = 35.075
And implemented to the duplicated 2015-11-08 12:10:00 timestamp data's variable also automatically, so the data become :
{TIME} {DATA}
2015-11-08 12:00:00 31.68
2015-11-08 12:01:00 37.67
2015-11-08 12:02:00 36.66
2015-11-08 12:03:00 39.66
2015-11-08 12:04:00 35.075 %% Duplicated Time Data
2015-11-08 12:05:00 39.64
2015-11-08 12:06:00 31.64
2015-11-08 12:07:00 31.63
2015-11-08 12:08:00 31.62
2015-11-08 12:09:00 41.81
2015-11-08 12:10:00 36.335 %% Duplicated Time Data
2015-11-08 12:11:00 30.60
Could it possible to create a code or function to do that? Iam very grateful if anyone would lend me a hand to help me in solve my probleme here... Thank you so much, Everyone.... /.\ /.\ /.\

Akzeptierte Antwort

Simon Chan
Simon Chan am 26 Mär. 2022
Use function groupsummary
opts = detectImportOptions('demo.txt');
opts.VariableNames = {'Time','Data'};
T = readtable('demo.txt',opts);
groupsummary(T,'Time','mean')
ans = 12×3 table
Time GroupCount mean_Data ___________________ __________ _________ 2015-11-08 12:00:00 1 31.68 2015-11-08 12:01:00 1 37.67 2015-11-08 12:02:00 1 36.66 2015-11-08 12:03:00 1 39.66 2015-11-08 12:04:00 4 35.075 2015-11-08 12:05:00 1 39.64 2015-11-08 12:06:00 1 31.64 2015-11-08 12:07:00 1 31.63 2015-11-08 12:08:00 1 31.62 2015-11-08 12:09:00 1 41.81 2015-11-08 12:10:00 4 36.335 2015-11-08 12:11:00 1 30.6
  2 Kommentare
Tyann Hardyn
Tyann Hardyn am 26 Mär. 2022
thats SOOO great, Sir... I remember you, Mr Simon, you helped me a lot a long time ago... Thank you so much..... And one question, Sir. Is the function can ignore NaN value to be averaged?
Simon Chan
Simon Chan am 26 Mär. 2022
Hi Tyann, the function ignore NaN value as well.
See the example where I modified the .txt file a little bit.
opts = detectImportOptions('demo.txt');
opts.VariableNames = {'Time','Data'};
T = readtable('demo.txt',opts)
T = 18×2 table
Time Data ___________________ _____ 2015-11-08 12:00:00 31.68 2015-11-08 12:01:00 37.67 2015-11-08 12:02:00 36.66 2015-11-08 12:03:00 39.66 2015-11-08 12:04:00 31.15 2015-11-08 12:04:00 NaN 2015-11-08 12:04:00 35.65 2015-11-08 12:04:00 39.75 2015-11-08 12:05:00 39.64 2015-11-08 12:06:00 NaN 2015-11-08 12:07:00 31.63 2015-11-08 12:08:00 31.62 2015-11-08 12:09:00 41.81 2015-11-08 12:10:00 31.61 2015-11-08 12:10:00 41.51 2015-11-08 12:10:00 38.61
groupsummary(T,'Time','mean')
ans = 12×3 table
Time GroupCount mean_Data ___________________ __________ _________ 2015-11-08 12:00:00 1 31.68 2015-11-08 12:01:00 1 37.67 2015-11-08 12:02:00 1 36.66 2015-11-08 12:03:00 1 39.66 2015-11-08 12:04:00 4 35.517 2015-11-08 12:05:00 1 39.64 2015-11-08 12:06:00 1 NaN 2015-11-08 12:07:00 1 31.63 2015-11-08 12:08:00 1 31.62 2015-11-08 12:09:00 1 41.81 2015-11-08 12:10:00 4 36.335 2015-11-08 12:11:00 1 30.6

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