I’m trying to generate simulated student grades in 4 subjects, where a student record is a single row of data. The code shown here will generate normally distributed random numbers with a mean of 60 and a standard deviation of 15.

df = pd.DataFrame(15 * np.random.randn(5, 4) + 60, columns=['Math', 'Science', 'History', 'Art'])

What I can’t figure out is how to make it so that a student’s Science mark is highly correlated to their Math mark, and that their History and Art marks are less so, but still somewhat correlated to the Math mark.

I’m neither a statistician or an expert programmer, so a less sophisticated but more easily understood solution is what I’m hoping for.

Let's put what has been suggested by @Daniel into code.

Step 1

Let's import multivariate_normal:

import numpy as np
from scipy.stats import multivariate_normal as mvn

Step 2

Let's construct covariance data and generate data:

cov = np.array([[1, 0.8,.7, .6],[.8,1.,.5,.5],[0.7,.5,1.,.5],[0.6,.5,.5,1]])
covarray([[ 1. ,  0.8,  0.7,  0.6],[ 0.8,  1. ,  0.5,  0.5],[ 0.7,  0.5,  1. ,  0.5],[ 0.6,  0.5,  0.5,  1. ]])

This is the key step. Note, that covariance matrix has 1's in diagonal, and the covariances decrease as you step from left to right.

Now we are ready to generate data, let's sat 1'000 points:

scores = mvn.rvs(mean = [60.,60.,60.,60.], cov=cov, size = 1000)

Sanity check (from covariance matrix to simple correlations):

np.corrcoef(scores.T):array([[ 1.        ,  0.78886583,  0.70198586,  0.56810058],[ 0.78886583,  1.        ,  0.49187904,  0.45994833],[ 0.70198586,  0.49187904,  1.        ,  0.4755558 ],[ 0.56810058,  0.45994833,  0.4755558 ,  1.        ]])

Note, that np.corrcoef expects your data in rows.

Finally, let's put your data into Pandas' DataFrame:

df = pd.DataFrame(data = scores, columns = ["Math", "Science","History", "Art"])
df.head()Math        Science     History     Art
0   60.629673   61.238697   61.805788   61.848049
1   59.728172   60.095608   61.139197   61.610891
2   61.205913   60.812307   60.822623   59.497453
3   60.581532   62.163044   59.277956   60.992206
4   61.408262   59.894078   61.154003   61.730079

Step 3

Let's visualize some data that we've just generated:

ax = df.plot(x = "Math",y="Art", kind="scatter", color = "r", alpha = .5, label = "Art, $corr_{Math}$ = .6")
df.plot(x = "Math",y="Science", kind="scatter", ax = ax, color = "b", alpha = .2, label = "Science, $corr_{Math}$ = .8")
ax.set_ylabel("Art and Science");