I have a numpy array where each cell of a specific row represents a value for a feature. I store all of them in an 100*4 matrix.
A B C
1000 10 0.5
765 5 0.35
800 7 0.09
Any idea how I can normalize rows of this numpy.array where each value is between 0 and 1?
My desired output is:
A B C
1 1 1
0.765 0.5 0.7
0.8 0.7 0.18(which is 0.09/0.5)
If I understand correctly, what you want to do is divide by the maximum value in each column. You can do this easily using broadcasting.
Starting with your example array:
import numpy as np
x = np.array([[1000, 10, 0.5],
[ 765, 5, 0.35],
[ 800, 7, 0.09]])
x_normed = x / x.max(axis=0)
print(x_normed)
# [[ 1. 1. 1. ]
# [ 0.765 0.5 0.7 ]
# [ 0.8 0.7 0.18 ]]
x.max(0) takes the maximum over the 0th dimension (i.e. rows). This gives you a vector of size (ncols,) containing the maximum value in each column. You can then divide x by this vector in order to normalize your values such that the maximum value in each column will be scaled to 1.
If x contains negative values you would need to subtract the minimum first:
x_normed = (x - x.min(0)) / x.ptp(0)
Here, x.ptp(0) returns the "peak-to-peak" (i.e. the range, max - min) along axis 0. This normalization also guarantees that the minimum value in each column will be 0.
.max(), .min(), .sum(), .mean() etc.), you just need to remember that axis specifies the dimension that you want to "collapse" during the reduction. If you want the maximum for each column, then you need to collapse the the row dimension.
keepdims=True to the reduction ufunc. This arg prevents the reduction axis from getting "squeezed out" so that broadcasting will still work correctly, e.g. x / x.max(axis=1, keepdims=True).You can use sklearn.preprocessing:
from sklearn.preprocessing import normalize
data = np.array([
[1000, 10, 0.5],
[765, 5, 0.35],
[800, 7, 0.09], ])
data = normalize(data, axis=0, norm='max')
print(data)
>>[[ 1. 1. 1. ]
[ 0.765 0.5 0.7 ]
[ 0.8 0.7 0.18 ]]
1 and ``2`? Using MinMaxScaler?
setis a particular object in Python, and you can't have a set of numpy arrays. Python doesn't have a matrix, but numpy does, and thatmatrixtype isn't the same as a numpyarray/ndarray(which is itself different from Python'sarraytype, which is not the same as alist). And none of these are pandasDataFrames..