Example of how to loop over a column in a numpy array (or 2D matrix) in python :

### Create an a numpy array

Let's first create a random numpy array:

`import numpy as np`

`data = np.random.randint(10, size=(10,8))`

`print(data)`

returns for example

`[[9 6 7 8 6 4 4 9]`

`[1 1 4 0 4 6 0 1]`

`[6 9 2 2 8 6 8 0]`

`[9 8 9 1 4 2 2 3]`

`[3 3 4 8 9 9 5 4]`

`[5 4 2 8 7 3 4 7]`

`[0 1 0 0 0 3 0 2]`

`[7 2 6 5 4 4 5 2]`

`[5 2 6 5 6 2 2 2]`

`[3 1 0 5 9 2 2 2]]`

### Array visualization with seaborn

Note: If you want to quickly visualize a not too large numpy array, a solution is to use seaborn with heatmap, example

`import seaborn as sns; sns.set()`

`import matplotlib.pyplot as plt`

`ax = sns.heatmap(data, annot=True, fmt="d")`

`plt.savefig("iterate_over_a_numpy_array_column.png", bbox_inches='tight', dpi=100)`

`plt.show()`

returns

### Select a given column

Note: in python column indices start at 0 (Zero-based numbering).

To select an entire column, for instance column associated with index 4:

`data[:,4]`

returns here

`array([6, 4, 8, 4, 9, 7, 0, 4, 6, 9])`

### Iterate over a given column

Now to Iterate over a column:

`for e in data[:,4]:`

`print(e)`

returns

`6`

`4`

`8`

`4`

`9`

`7`

`0`

`4`

`6`

`9`