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I have an numpy array with 4 columns and want to select columns 1, 3 and 4, where the value of the second column meets a certain condition (i.e. a fixed value). I tried to first select only the rows, but with all 4 columns via:
I = A[A[:,1] == i] which works. Then I further tried (similarly to matlab which I know very well):
I = A[A[:,1] == i, [0,2,3]] which doesn't work. How to do it?
EXAMPLE DATA:
>>> A = np.array([[1,2,3,4],[6,1,3,4],[3,2,5,6]]) >>> print A [[1 2 3 4] [6 1 3 4] [3 2 5 6]] >>> i = 2 # I want to get the columns 1, 3 and 4 # for every row which has the value i in the second column. # In this case, this would be row 1 and 3 with columns 1, 3 and 4: [[1 3 4] [3 5 6]] I am now currently using this:
I = A[A[:,1] == i] I = I[:, [0,2,3]] But I thought that there had to be a nicer way of doing it... (I am used to MATLAB)
847
To remove rows containing missing values, use any() method that returns True if there is at least one True in ndarray . With the argument axis=1 , any() tests whether there is at least one True for each row. Use the negation operator ~ to make rows with no missing values True .
If you want to extract elements that meet the condition, you can use ndarray[conditional expression] . Even if the original ndarray is a multidimensional array, a flattened one-dimensional array is returned. A new ndarray is returned, and the original ndarray is unchanged.
Slice Two-dimensional Numpy Arrays To slice elements from two-dimensional arrays, you need to specify both a row index and a column index as [row_index, column_index] . For example, you can use the index [1,2] to query the element at the second row, third column in precip_2002_2013 .
>>> a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) >>> a array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]]) >>> a[a[:,0] > 3] # select rows where first column is greater than 3 array([[ 5, 6, 7, 8], [ 9, 10, 11, 12]]) >>> a[a[:,0] > 3][:,np.array([True, True, False, True])] # select columns array([[ 5, 6, 8], [ 9, 10, 12]]) # fancier equivalent of the previous >>> a[np.ix_(a[:,0] > 3, np.array([True, True, False, True]))] array([[ 5, 6, 8], [ 9, 10, 12]]) For an explanation of the obscure np.ix_(), see https://stackoverflow.com/a/13599843/4323
Finally, we can simplify by giving the list of column numbers instead of the tedious boolean mask:
>>> a[np.ix_(a[:,0] > 3, (0,1,3))] array([[ 5, 6, 8], [ 9, 10, 12]]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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