I need to do some analysis on a large dataset from a hydrolgeology field work. I am using NumPy. I want to know how I can:
multiply e.g. the 2nd column of my array by a number (e.g. 5.2). And then
calculate the cumulative sum of the numbers in that column.
As I mentioned I only want to work on a specific column and not the whole array.
you can do this in two simple steps using NumPy:
>>> # multiply column 2 of the 2D array, A, by 5.2
>>> A[:,1] *= 5.2
>>> # assuming by 'cumulative sum' you meant the 'reduced' sum:
>>> A[:,1].sum()
>>> # if in fact you want the cumulative sum (ie, returns a new column)
>>> # then do this for the second step instead:
>>> NP.cumsum(A[:,1])
with some mocked data:
>>> A = NP.random.rand(8, 5)
>>> A
array([[ 0.893, 0.824, 0.438, 0.284, 0.892],
[ 0.534, 0.11 , 0.409, 0.555, 0.96 ],
[ 0.671, 0.817, 0.636, 0.522, 0.867],
[ 0.752, 0.688, 0.142, 0.793, 0.716],
[ 0.276, 0.818, 0.904, 0.767, 0.443],
[ 0.57 , 0.159, 0.144, 0.439, 0.747],
[ 0.705, 0.793, 0.575, 0.507, 0.956],
[ 0.322, 0.713, 0.963, 0.037, 0.509]])
>>> A[:,1] *= 5.2
>>> A
array([[ 0.893, 4.287, 0.438, 0.284, 0.892],
[ 0.534, 0.571, 0.409, 0.555, 0.96 ],
[ 0.671, 4.25 , 0.636, 0.522, 0.867],
[ 0.752, 3.576, 0.142, 0.793, 0.716],
[ 0.276, 4.255, 0.904, 0.767, 0.443],
[ 0.57 , 0.827, 0.144, 0.439, 0.747],
[ 0.705, 4.122, 0.575, 0.507, 0.956],
[ 0.322, 3.71 , 0.963, 0.037, 0.509]])
>>> A[:,1].sum()
25.596156138451427
just a few simple rules are required to grok element selection (indexing) in NumPy:
NumPy, like Python, is 0-based, so eg, the "1" below refers to the second column
commas separate the dimensions inside the brackets, so [rows, columns], eg, A[2,3] means the item ("cell") at row three, column four
a colon means all of the elements along that dimension, eg, A[:,1] creates a view of A's column 2; A[3,:] refers to the fourth row
Sure:
import numpy as np
# Let a be some 2d array; here we just use dummy data
# to illustrate the method
a = np.ones((10,5))
# Multiply just the 2nd column by 5.2 in-place
a[:,1] *= 5.2
# Now get the cumulative sum of just that column
csum = np.cumsum(a[:,1])
If you don't want to do this in-place you would need a slightly different strategy:
b = 5.2*a[:,1]
csum = np.cumsum(b)
To multiply a constant with a specific column or row:
import numpy as np;
X=np.ones(shape=(10,10),dtype=np.float64);
X;
### this is our default matrix
array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
## now say we want to multiple it with 10
X=X*10;
array([[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],
[10., 10., 10., 10., 10., 10., 10., 10., 10., 10.]])
### Now if, we want to mulitply 3,5, 7 column with 5
X[:,[3,5,7]]=X[:,[3,5,7]]*5
array([[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.],
[10., 10., 10., 50., 10., 50., 10., 50., 10., 10.]])
Similarly, we can do it for any columns. Hope it clarifies.
