Delete rows of excel if any empty cell found

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

[Adi Purwandana]

I think I have the solution by using:

data_new = rmmissing(datax);

My remaining problem for next process is... reordering the Station from the shortest to the longest. Anyone can help?

[Walter Roberson]

I do not see any height information in that table?

If you are trying to sort by depth see sortrows

[Adi Purwandana]

Thank you for your response @Walter Roberson. Here is what I want:

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.

Answer 1

[Star Strider]

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]}

.

[Adi Purwandana]

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

[Adi Purwandana]

I got it!

I use unique(Stations_Sorted.Station, 'stable')

Thank you!

[Star Strider]

As always, my pleasure!

I was away for a few minutes. That is exactly the approach I would have taken!

[Adi Purwandana]

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]

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]

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]

Solved, sorry!

[Su,~,s] = unique(Stations_Sorted.Station,'stable');

Stations_Sorted.Station = s;

[Star Strider]

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]}

.

[Adi Purwandana]

Thank you @Star Strider for completing the solution!

[Star Strider]

As always, my pleasure!