Fill NAN values in a table

% Create data because the user forgot to post any!
% Make random values for Date, temp, humidity, pressure, precip, depth 
numRows = 50;
temperature = randi(100, [numRows, 1]);
humidity = randi(100, [numRows, 1]);
pressure = randi(100, [numRows, 1]);
precip = randi(100, [numRows, 1]);
depth = randi(100, [numRows, 1]);
% Make some of the temperature elements nan
temperature(10:20) = nan;
indexes = randperm(length(temperature), 8); % 8 other random locations.
temperature(indexes) = nan;
% Create a table, T, as a container for the workspace variables. 
T = table(temperature, humidity, pressure, precip, depth)
%------------------------------------------------------------------------
% % Now we have our table and we can begin.
% Now fix the nans.  First extract the temperature column.
temperature = T.temperature
% Find nan locations (rows)
nanRows = isnan(temperature)
% Sometimes, either the first or last element(s) are nan, and the interpolation will leave a nan there.
% So to prevent that we have to find the first non-nan element and tack it onto the beginning,
% and find the last non-nan element and tack it on to the end.
indexes = find(~nanRows)
repairedTemperature = [temperature(indexes(1)); temperature; temperature(indexes(end))]; % Make sure to use semicolon rather than comma.
% Find nan locations (rows) again with the "fixed" array.
nanRows = isnan(repairedTemperature)
% Interpolate non-nan locations
repairedTemperature = interp1(find(~nanRows), repairedTemperature(~nanRows), 1:length(repairedTemperature))
% Now truncate off the first and last elements we tacked on.  Also need to transpose since interp1() gives a row vector.
repairedTemperature = repairedTemperature(2:end-1)';
% Stick the results back into the table.
T.temperature = repairedTemperature;
fprintf('All done with demo by Image Analyst.\n');

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Sarah Yun 2019-12-15

编辑：Sarah Yun 2019-12-15

This is great, thanks Image Analyst.

Question:

If I want to ignore NANs instead of interpolate (and put ignored values back in table), will this work - or maybe it will cause problems for future analysis?

Thank you.

Image Analyst 2019-12-16

在 MATLAB Online 中打开

I don't know. If you want to extract all non-nan rows, you can do this:

temperature = T.temperature
% Find nan locations (rows)
nanRows = isnan(temperature)
temperature = temperature(~nanRows);

Whether that shortened list causes problems is something you would know, not me. Depends on how you're going to use them. There is no need to put the "ignored" values back in the table since they never left the table. If you want a whole table without the nan rows, I think you can do this

T = T(~nanRows, :); % Remove rows where temperature is nan from the table.

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