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