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# How to Set Axis for Rows and Columns in NumPy ?

• Difficulty Level : Expert
• Last Updated : 30 May, 2021

In this article, we are going to see how to set the axis for rows and columns in NumPy.

### Functions Used

• np.array(object): to create a NumPy array, the object is the parameter that contains the array
• np.reshape(rows, columns): to reshape the array into the specified number of rows and columns. Here in the below examples, we have given -1 in place of rows to let numpy figure it out if there are 3 columns in each row.
• np.sum(axis): to calculate the sum or addition of the elements. Here we have mentioned the axis to do the operation array-wise, row-wise, or column-wise as per requirement.

Example 1: Set axis for array-wise calculation

In this example, we will reshape the NumPy array into rows having 3 columns each i.e nparray.reshape(-1, 3) to make it two-dimensional. Then we will perform the sum operation of the array elements array-wise that is in normal order starting from the first to last element of the NumPy array. We specifically set the axis= None to trigger the normal array-wise operation.

Code:

## Python3

 `import` `numpy as np``nparray ``=` `np.array([[``1``, ``2``, ``3``], [``11``, ``22``, ``33``],``                    ``[``4``, ``5``, ``6``], [``8``, ``9``, ``10``], ``                    ``[``20``, ``30``, ``40``]])`` ` `nparray ``=` `nparray.reshape(``-``1``, ``3``)``print``(nparray)`` ` `# calculating sum along ``# axix=None i.e array-wise``output ``=` `nparray.``sum``(axis``=``None``)``print``(``"\n\nSum array-wise: "``, output)`

Output :

```[[ 1  2  3]
[11 22 33]
[ 4  5  6]
[ 8  9 10]
[20 30 40]]

Sum array-wise:  204```

Example 2: Set axis for column-wise calculation

In this example, we will reshape the numpy array into rows having 3 columns each. Then performe the sum operation of the array elements using the sum() function column-wise. We specifically set the axis= 0 to trigger the normal array-wise operation.

Code:

## Python3

 `import` `numpy as np`` ` ` ` `nparray ``=` `np.array([[``1``, ``2``, ``3``], [``11``, ``22``, ``33``],``                    ``[``4``, ``5``, ``6``], [``8``, ``9``, ``10``],``                    ``[``20``, ``30``, ``40``]])``nparray ``=` `nparray.reshape(``-``1``, ``3``)``print``(nparray)`` ` `# calculating sum along axix=0 ``# i.e column-wise``output ``=` `nparray.``sum``(axis ``=` `0``)``print``(``"\n\nSum column-wise: "``, output)`

Output :

```[[ 1  2  3]
[11 22 33]
[ 4  5  6]
[ 8  9 10]
[20 30 40]]

Sum column-wise:  [44 68 92]```

Example 3: Set axis for row-wise calculation

We will specifically set the axis = 1 to trigger the normal row-wise calculation.

Code:

## Python3

 `import` `numpy as np``nparray ``=` `np.array([[``1``, ``2``, ``3``], [``11``, ``22``, ``33``],``                    ``[``4``, ``5``, ``6``], [``8``, ``9``, ``10``], ``                    ``[``20``, ``30``, ``40``]])`` ` `nparray ``=` `nparray.reshape(``-``1``, ``3``)``print``(nparray)`` ` `# calculating sum along axix=1 ``# i.e row0wise``output ``=` `nparray.``sum``(axis ``=` `1``)``print``(``"\n\nSum row-wise: "``, output)`

Output :

```[[ 1  2  3]
[11 22 33]
[ 4  5  6]
[ 8  9 10]
[20 30 40]]

Sum row-wise:  [ 6 66 15 27 90]```

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