[Numpy-discussion] how to delete a particular column or row from numpy array based on some rule or value
Derek Homeier
derek@astro.physik.uni-goettingen...
Thu Mar 24 08:33:55 CDT 2011
> In a numpy array of m x n size, I would like to delete few rows when
> a given element in that row is ‘nan’ or say any other value.
>
>
> For e.g. as in example given below, if I wish to delete a row when
> the 3rd element of row is zero, or if 3rd, 4th, 5th element are zero
> or either of 3rd, 4th and 5th element is zero.
>
> array([[ 1900. , nan, nan, nan, nan, nan],
>
> [ 1900.5, nan, nan, nan, nan, nan],
>
> [ 1901. , nan, nan, nan, nan, nan],
>
> ...,
>
> [ 6724. , nan, nan, nan, nan, nan],
>
> [ 6724.5, nan, nan, nan, nan, nan],
>
> [ 6725. , nan, nan, nan, nan, nan]])
>
> Cheers
>
>
> Sachin
>
>
>
> I think u can use numpy.loadtext command
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
>
>
> --
> DILEEPKUMAR. R
> J R F, IIT DELHI
Wouldn't that require to transform the array to a text representation
first?
If you have an existing array, you can use the numpy test and logical
functions to index it. E.g.
myarr[:,2] == 0 selects the rows with 3rd column zero,
np.isnan(myarr[:,3]) those with 4th column NaN
and to select only rows where neither is the case:
clean = myarr[np.logical_not( (myarr[:,2]==0) | (np.isnan(darr[:,3])) )]
- combine as required with the '&' or '|' operators (there does not seem
to be a shorthand for logical_not...), and check np.isnan, np.isinf,
np.isfinite -
those should allow you to select what you need.
HTH,
Derek
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