Delete values in a table but keep the cells

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I have a 1846x72 table that contains strings (I assume they are strings since the value is in '') and numbers. The first two columns are either NaN or a number. I want to go through each row and, if the second column in that row is not NaN, make the following columns all NaN (or empty) for only that row.
This is the code I a have:
for r = 1:nRows
if isnumeric(df{r,2})
for c = 3:nCols
df{r,c} = NaN;
This is the error I am receiving:
Conversion to cell from double is not possible.
I can not include the dataset as it pertains to my work and I am not allowed.
Daniel Garratt
Daniel Garratt on 2 Mar 2021
That returns the error:
"The value on the right-hand side of the assignment has the wrong width. The assignment requires a value whose width is 1."

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Accepted Answer

Jorg Woehl
Jorg Woehl on 2 Mar 2021
Edited: Jorg Woehl on 2 Mar 2021
df{r,c} = {''}
instead. You should be able to avoid the inner loop entirely by using
df{r,3:nCols} = {''}
Also, to check if the second column in row r is not NaN, use
if ~isnan(df{r,2})
isnumeric(NaN) will return logical true, as NaNs belong to the floating point class.
Jorg Woehl
Jorg Woehl on 2 Mar 2021
I'm glad you were able to make it work! Not sure why you've still received a type conversion error when working with the table... probably something specific to the imported table.
If you can provide a small table with (made-up) sample data, we might be able to figure out what is going on. But the main thing is that your code is running.

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