Python: creating a covariance matrix from lists

Question

Is there a quickest way to go from the following three lists to a covariance matrix in Python (numpy array)? ``` Fac2 Fac1 VarCovar a a 1.4 a b 0.7 a c 0.3 b a 0.7 b b 1.8 b c 6.3 c a 0.3 c b 6.3 c c 2.4 ```

Accepted Answer

You can create the 3x3 matrix easily using Pandas. Create a DataFrame `df` from the above array and pivot on the third column using `pivot_table`.

For example if you have the following dictionary `d` of lists:

```
{'Fac1': ['a', 'b', 'c', 'a', 'b', 'c', 'a', 'b', 'c'],
 'Fac2': ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c'],
 'VarCovar': [1.4, 0.7, 0.3, 0.7, 1.8, 6.3, 0.3, 6.3, 2.4]}
```

Create the DataFrame like this:

```
df = pd.DataFrame(d)
```

And then:

```
>>> df.pivot_table(rows='Fac1', cols='Fac2', values='VarCovar')
Fac2    a    b    c
Fac1               
a     1.4  0.7  0.3
b     0.7  1.8  6.3
c     0.3  6.3  2.4
```

Using the `values` attribute on the end returns a NumPy array from the table:

```
>>> df.pivot_table(rows='Fac1', cols='Fac2', values='VarCovar').values
array([[ 1.4,  0.7,  0.3],
       [ 0.7,  1.8,  6.3],
       [ 0.3,  6.3,  2.4]])
```

If you don't have all pairs, you can proceed in the same way and fill in the missing values with the transposed index pair:

```
>>> d = {'Fac1': ['a', 'b', 'c' , 'b', 'c', 'c'], 
         'Fac2': ['a', 'a', 'a' , 'b', 'b', 'c'], 
         'VarCovar': [1.4, 0.7, 0.3, 1.8, 6.3, 2.4]}
>>> df = pd.DataFrame(d)
>>> table = df.pivot_table(rows='Fac1', cols='Fac2', values='VarCovar')
>>> table.combine_first(table.T)
Fac2    a    b    c
Fac1               
a     1.4  0.7  0.3
b     0.7  1.8  6.3
c     0.3  6.3  2.4
```

(I took the idea of using `combine_first` from DSM's answer here)

Python: creating a covariance matrix from lists

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