Reputation: 159
Working with a dataframe that contain an specific binary column (all in numpy array), in example:
[1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0.]
[1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
[1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0.]
[1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]
[1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0.]
[1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0.]
That's possible to apply a loop for iterate all columns inside that column?
i.e
In my case i need to get the mean of values in each column, like:
# Mean of position [0] # Mean of position[3]
1. 1.
1. 0.
1. 0.
0. 1.
1. 1.
0. 1.
1. 0.
1. 1.
1. 1.
0. 0.
There is any way to do that?
Thanks!
Upvotes: 1
Views: 1173
Reputation: 3133
You can use the mean function from numpy. https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html
So in your case, I think you are looking for np.mean(a, axis=1)
Upvotes: 1
Reputation: 5459
Just use iloc
with mean
:
meanZero = df.iloc[0].mean()
meanThird= df.iloc[3].mean()
Upvotes: 2