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numpy.apply_over_axes () in Python

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Parameters :

`  1d_func:  the required function to perform over 1D array. It can only be applied in 1D slices of input array and that too along a particular axis.  axis:  required axis along which we want input array to be sliced ​​ array:  Input array to work on  * args:  Additional arguments to 1D_function  ** kwargs:  Additional arguments to 1D_function `

Return:

` The output array. Shape of the output array can be different depending on whether  func  changes the shape of its output with respect to its input. `

Code 1:

 ` # Python program illustrating ` ` # apply_over_axis () in NumPy `   ` import ` ` numpy as geek `   ` # Using a 3D array ` ` geek_array ` ` = ` ` geek.arange (` ` 16 ` `). Reshape (` ` 2 ` `, ` ` 2 ` `, ` ` 4 ` `) ` ` print ` ` (` ` "geek array :" ` `, geek_array) `   ` # Applying a predefined sum function along the axis of a 3D array ` ` print ` ` (` ` "func sum:" ` `, geek.apply_over_axes (geek. ` ` sum ` `, geek_array, [` ` 1 ` `, ` ` 1 ` `, ` ` 0 ` `])) `   ` # Applying the predefined min function along the 3D array axis ` ` print ` ` (` `" func min: "` `, geek.apply_over_axes (geek. ` ` min ` `, geek_array, [` ` 1 ` `, ` ` 1 ` `, ` ` 0 ` `])) `

Output:

` geek array: [[[0 1 2 3] [4 5 6 7]] [[8 9 10 11 ] [12 13 14 15]]] func sum: [[[24 28 32 36]]] func min: [[[0 1 2 3]]] `

Code 2:

 ` # Python program illustrating ` ` # apply_over_axis () in NumPy `   ` import ` ` numpy as geek `   ` # Using 2D -array ` ` geek_array ` ` = ` ` geek.arange (` ` 16 ` `). reshape (` ` 4 ` `, ` ` 4 ` `) ` ` print ` ` (` ` "geek array :" ` `, geek_array) `   ` "" "` ` -" [[0 1 2 3] min: 0 max: 3 total = 0 + 1 + 2 + 3 ` ` -" [4 5 6 7] min: 4 max: 7 sum = 4 + 5 + 6 + 7 ` ` -" [8 9 10 11] min: 8 max: 11 sum = 8 + 9 + 10 + 11 ` ` -" [12 13 14 15]] min: 12 max: 15 sum = 12 + 13 + 14 + 15 `   ` "" "` ` `  ` # Applying a predefined min functions along the 2D array axis ` ` print ` ` (` `" Applying func max: "` `, geek.apply_over_axes (geek. ` ` max ` `, geek_array, [` ` 1 ` `, ` ` - ` ` 1 ` `])) `   ` # Applying the predefined min function along the 2D array axis ` ` print ` ` (` ` "Applying func min:" ` `, geek.apply_over_axes (geek. ` ` min ` `, geek_array, [` ` 1 ` `, ` ` - ` ` 1 ` `])) `   ` # Applies a predefined sum function along the axis of a two-dimensional array ` ` print ` ` (` ` "Applying func sum:" ` `, geek.apply_over_axes (geek. ` ` sum ` `, geek_array, [` ` 1 ` `, ` ` - ` ` 1 ` `])) `

Exit :

` geek array: [[0 1 2 3] [4 5 6 7] [8 9 10 11] [12 13 14 15]] Applying func max: [[3] [ 7] [11] [15]] Applying func min: [[ 0] [4] [8] [12]] Applying func sum: [[6] [22] [38] [54]] `

Code 3: Equivalent to Code 2 without using numpy.apply_over_axis ()

` `

` # Python program illustrating # equivalent to apply_over_axis ()   import numpy as geek    # Using a 3D array geek_array = geek.arange ( 16 ). Reshape ( 2 , 2 , 4 ) print ( " geek array : " , geek_array)   # return the sum of all elements along the axis print ( "func:" , geek. sum (geek_array, axis = ( 1 , 0 , 2 ), keepdims = True )) `

` `

Output:

` geek array: [[[0 1 2 3] [4 5 6 7]] [[8 9 10 11] [12 13 14 15]]] func: [[[120]]] `

Links:
https://docs.scipy.org/doc/numpy-dev/ reference / generated / numpy.apply_over_axes.html
Notes:
These codes will not work for online IDs. Please run them on your systems to see how they work.
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This article is provided by Mohit Gupta_OMG

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