Why does the axis argument in NumPy change?

user_15040738 0
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I am very confused when it comes to the logic of the NumPy axis argument. In some cases it affects the row when axis = 0 and in some cases it affects the columns when axis = 0. Example:

a = np.array([[1,3,6,7,4],[3,2,5,9,1]])
array([[1,3,6,7,4],
       [3,2,5,9,1]])

np.sort(a, axis = 0)   #This sorts the columns
array([[1, 2, 5, 7, 1],  
       [3, 3, 6, 9, 4]])

np.sort(a, axis=1)     #This sorts the rows           
array([[1, 3, 4, 6, 7],
       [1, 2, 3, 5, 9]])

#####################################################################
arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
arr
array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

np.delete(arr,obj = 1, axis = 0)        # This deletes the row
array([[ 1,  2,  3,  4],
       [ 9, 10, 11, 12]])

np.delete(arr,obj = 1, axis = 1)        #This deletes the column
array([[ 1,  3,  4],
       [ 5,  7,  8],
       [ 9, 11, 12]])

If there is some logic here that I am missing I would love to learn it.

numpy arrays python
2 Answers

It's perhaps simplest to remember it as 0=down and 1=across.

This means: enter image description here Use axis=0 to apply a method down each column, or to the row labels (the index). Use axis=1 to apply a method across each row, or to the column labels. Here's a picture to show the parts of a DataFrame that each axis refers to:

It's also useful to remember that Pandas follows NumPy's use of the word axis. The usage is explained in NumPy's glossary of terms:

Axes are defined for arrays with more than one dimension. A 2-dimensional array has two corresponding axes: the first running vertically downwards across rows (axis 0), and the second running horizontally across columns (axis 1). [my emphasis]

So, concerning the method in the question, np.sort(axis=1), seems to be correctly defined. It takes the mean of entries horizontally across columns, that is, along each individual row. On the other hand, np.sort(axis=0) would be an operation acting vertically downwards across rows.

Similarly, np.delete(name, axis=1) refers to an action on column labels, because they intuitively go across the horizontal axis. Specifying axis=0 would make the method act on rows instead.

user_35618650 
arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
arr
# array([[ 1,  2,  3,  4],
#        [ 5,  6,  7,  8],
#        [ 9, 10, 11, 12]])

arr has 2 dimensions, use the empty slice : to select the first and second axis arr[:,:]. From the documentation of np.delete regarding the second parameter obj:

obj : slice, int or array of ints Indicate indices of sub-arrays to remove along the specified axis.

If we want to delete obj=1 from axis=0 we are effectively removing arr[[1],:] from arr

arr[[1],:] # array([[5, 6, 7, 8]])

With the same intuition, we can remove obj=1 from axis=1

arr[:,[1]] # array([[ 2],
           #        [ 6],
           #        [10]])

When sorting the array arr above along axis=0 we are comparing the following elements:

# array([[1, 2, 5, 7, 1]])
# array([[5, 6, 7, 8]])
# array([[ 9, 10, 11, 12]])

The array is already sorted in this case but the comparison is done between two rows. For example array([[5, 6, 7, 8]]) is compared with array([[ 9, 10, 11, 12]]) by doing an element-wise comparison.

Sorting the array on axis=1 we are comparing the following elements

# array([[1],    array([[ 2],    array([[ 3],    array([[ 4],
#        [5],           [ 6],           [ 7],           [ 8],
#        [9]])          [10]])          [11]])          [12]])

Notice the difference of axis usage between np.delete and np.sort. np.delete will remove the complete row/column while np.sort will use the complete row/column for comparison.

user_149232270
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