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Delete rows of excel if any empty cell found
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Dear everyone,
I have an excel file containing some Stations defined in the first column (attached). Here, as shown below, I have 4 stations. Some stations contain no value (empty cell) like Station 1; and some stations not all cells have values.
Does anyone know how to delete the row if there's any cell with no value detected? In this case, there will be 3 stations left and the rows with empty cells in those 3 stations will be omited :
best regards
3 Comments
Walter Roberson
on 13 Oct 2023
I do not see any height information in that table?
If you are trying to sort by depth see sortrows
Adi Purwandana
on 13 Oct 2023
Edited: Adi Purwandana
on 13 Oct 2023
Attached file is the excel file. So, it must be ordered from the shorter (shallower) station to the longer one and so on... Any suggestions? I tried using sortrows but it doesn't solve.
Accepted Answer
Star Strider
on 13 Oct 2023
I am not certain how you want to sort the ‘Station’ by ‘shortest to the longest’.
Try this —
T1 = readtable('datax.xlsx', 'VariableNamingRule','preserve')
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 2 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 4 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 6 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 8 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {'2023-10-08T00:00:00'}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {'2023-10-08T00:00:00'}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {'2023-10-08T00:00:00'}
T1{:,end} = cellfun(@(x)datetime(x, 'InputFormat','yyyy-MM-dd''T''HH:mm:ss', 'Format','yyyy-MM-dd HH:mm:ss'), T1{:,end}, 'Unif',0)
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 2 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 4 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 6 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 8 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
T1 = rmmissing(T1)
T1 = 15×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
4 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
4 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
Stations = accumarray(T1{:,1}, (1:size(T1,1)).', [], @(x){T1(x,:)})
Stations = 4×1 cell array
{0×0 double}
{5×9 table }
{4×9 table }
{6×9 table }
[sz,ix] = sort(cellfun(@(x)size(x,1), Stations));
valid = [sz(sz~=0) ix(sz~=0)]
valid = 3×2
4 3
5 2
6 4
Stations_Sorted = cat(1,Stations{valid(:,2)}) % Reordered
Stations_Sorted = 15×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
4 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
4 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
.
10 Comments
Adi Purwandana
on 13 Oct 2023
Edited: Adi Purwandana
on 13 Oct 2023
Aha! @Star Strider exactly what I want. Anyway, is there any line I should add to re-numbering the column of Station number?
Station 3 --> Station 1
Station 2 --> Station 2
Station 4 --> Station 3
Thanks
Star Strider
on 13 Oct 2023
As always, my pleasure!
I was away for a few minutes. That is exactly the approach I would have taken!
Adi Purwandana
on 13 Oct 2023
Edited: Adi Purwandana
on 13 Oct 2023
Maybe my last question following this thread @Star Strider, do you know the way to omit the stations with the Depth layer less than a certain depth?
For example in this datasets:
Station 1 --> has 4 layers (0 m, 2 m, 4, 6);
Station 2 --> has 5 layers (0, 2, 4, 6, 8,)
Station 3 --> has 6 layers
For example, my purpose is... excluding the Stations with depth of less than 8 m depth; so that the Station 2 and Station 1 will be omited from the Stations_Sorted. So, there will be Stations with depth layer > 8 m (in this case is only Station 3) inside Stations_Sorted.
Thank you!
Star Strider
on 14 Oct 2023
This will delete ‘Station’ table arrays with no ‘Depth’ value greater than 8 and will retain ‘Station’ table arrays with at least one ‘Depth’ value greater than 8, regardless of what the other ‘Depth’ values are. It also re-numbers the stations after using rmmissing so they begin at 1.
The revised version —
T1 = readtable('datax.xlsx', 'VariableNamingRule','preserve')
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 2 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 4 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 6 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 8 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {'2023-10-08T00:00:00'}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {'2023-10-08T00:00:00'}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {'2023-10-08T00:00:00'}
T1{:,end} = cellfun(@(x)datetime(x, 'InputFormat','yyyy-MM-dd''T''HH:mm:ss', 'Format','yyyy-MM-dd HH:mm:ss'), T1{:,end}, 'Unif',0)
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 2 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 4 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 6 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 8 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
T1 = rmmissing(T1)
T1 = 15×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
4 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
4 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
[Su,~,s] = unique(T1.Station,'stable');
T1.Station = s % Renumber 'Station'
T1 = 15×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
1 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
2 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
2 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
2 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
3 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
3 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
Stations = accumarray(T1{:,1}, (1:size(T1,1)).', [], @(x){T1(x,:)}); % Segment Table By Station
% Stations = Stations(cellfun(@(x)~isempty(x), Stations)) % Delete Empty Cells
Lv = cellfun(@(x)any(x.Depth > 8), Stations); % Logical Vector To Select Cells With At Least One 'Depth' > 8
Stations = Stations(Lv); % Select Cells With At Least One 'Depth' > 8
[sz,ix] = sort(cellfun(@(x)size(x,1), Stations)); % Sort By Row Size
Stations_Sorted = cat(1,Stations{ix}) % Reordered
Stations_Sorted = 6×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ _____ _______________________
3 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
3 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
3 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
.
Adi Purwandana
on 14 Oct 2023
Great as always!
My curious question... If I want to re-numbering the stations again (here for example I only consider the station with depth >6m), it gives:
Do you know the line I should modify if I want to re-numbering the stations again, so that:
Station 1 --> Station 1
Station 3 --> Station 2
Thank you!
Adi Purwandana
on 14 Oct 2023
Solved, sorry!
[Su,~,s] = unique(Stations_Sorted.Station,'stable');
Stations_Sorted.Station = s;
Star Strider
on 14 Oct 2023
Thank you!
As always, my pleasure!
The only thing that comes to mind is to mpve these assignments:
[Su,~,s] = unique(T1.Station,'stable');
T1.Station = s % Renumber 'Station'
to later in the code.
This re-numbers them and then re-sorts them —
T1 = readtable('datax.xlsx', 'VariableNamingRule','preserve')
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 2 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 4 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 6 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 8 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
1 114.8 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {'2023-10-08T00:00:00'}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {'2023-10-08T00:00:00'}
2 114.88 -8.16 10 NaN NaN NaN NaN {'2023-10-08T00:00:00'}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {'2023-10-08T00:00:00'}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {'2023-10-08T00:00:00'}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {'2023-10-08T00:00:00'}
T1{:,end} = cellfun(@(x)datetime(x, 'InputFormat','yyyy-MM-dd''T''HH:mm:ss', 'Format','yyyy-MM-dd HH:mm:ss'), T1{:,end}, 'Unif',0)
T1 = 24×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.8 -8.16 0 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 2 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 4 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 6 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 8 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
1 114.8 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 10 NaN NaN NaN NaN {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
T1 = rmmissing(T1)
T1 = 15×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
3 114.96 -8.16 0 33.995 29.171 0.109 -0.037 {[2023-10-08 00:00:00]}
3 114.96 -8.16 2 33.992 29.108 0.104 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 4 33.992 29.11 0.102 -0.03 {[2023-10-08 00:00:00]}
3 114.96 -8.16 6 33.992 29.114 0.102 -0.03 {[2023-10-08 00:00:00]}
4 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
4 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
% [Su,~,s] = unique(T1.Station,'stable');
% T1.Station = s % Renumber 'Station'
Stations = accumarray(T1{:,1}, (1:size(T1,1)).', [], @(x){T1(x,:)}) % Segment Table By Station
Stations = 4×1 cell array
{0×0 double}
{5×9 table }
{4×9 table }
{6×9 table }
Stations = Stations(cellfun(@(x)~isempty(x), Stations)) % Delete Empty Cells
Stations = 3×1 cell array
{5×9 table}
{4×9 table}
{6×9 table}
Lv = cellfun(@(x)any(x.Depth > 6), Stations) % Logical Vector To Select Cells With At Least One 'Depth' > 8
Lv = 3×1 logical array
1
0
1
Stations = Stations(Lv); % Select Cells With At Least One 'Depth' > 8
Stations
Stations = 2×1 cell array
{5×9 table}
{6×9 table}
[sz,ix] = sort(cellfun(@(x)size(x,1), Stations)); % Sort By Row Size
Stations_Sorted = cat(1,Stations{ix}) % Reordered
Stations_Sorted = 11×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
2 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
2 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
4 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
4 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
4 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
[Su,~,s] = unique(Stations_Sorted.Station,'stable');
Stations_Sorted.Station = s % Renumber 'Station'
Stations_Sorted = 11×9 table
Station Lon Lat Depth Salinity Temperature u v yyyy-mm-ddThh:mm:ss.sss
_______ ______ _____ _____ ________ ___________ _____ ______ _______________________
1 114.88 -8.16 0 33.981 29.324 0.11 -0.017 {[2023-10-08 00:00:00]}
1 114.88 -8.16 2 33.979 29.248 0.106 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 4 33.979 29.25 0.106 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 6 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
1 114.88 -8.16 8 33.978 29.251 0.105 -0.011 {[2023-10-08 00:00:00]}
2 115.04 -8.16 0 33.992 29.273 0.048 0.005 {[2023-10-08 00:00:00]}
2 115.04 -8.16 2 33.988 29.264 0.05 0.009 {[2023-10-08 00:00:00]}
2 115.04 -8.16 4 33.988 29.266 0.052 0.009 {[2023-10-08 00:00:00]}
2 115.04 -8.16 6 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
2 115.04 -8.16 8 33.988 29.267 0.052 0.009 {[2023-10-08 00:00:00]}
2 115.04 -8.16 10 33.987 29.259 0.048 0.01 {[2023-10-08 00:00:00]}
.
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