python - How do I convert list of correlations to covariance matrix? -

i have list of correlations generated text file form:

(first 2 values indicate between points correlation)

2     1  -0.798399811877855e-01 3     1   0.357718108972297e+00 3     2  -0.406142457763738e+00 4     1   0.288467030571132e+00 4     2  -0.129115034405361e+00 4     3   0.156739504479856e+00 5     1  -0.756332254716083e-01 5     2   0.479036971438800e+00 5     3  -0.377545460300584e+00 5     4  -0.265467953118191e+00 6     1   0.909003414436468e-01 6     2  -0.363568902645620e+00 6     3   0.482042347959232e+00 6     4   0.292931692897587e+00 6     5  -0.739868576924150e+00 

i have list standard deviations associated of points. how combine these 2 in numpy/scipy create covariance matrix?

it needs efficient method since there 300 points, ~ 50 000 correlations.

assuming table named df , first column labeled a , second b correlation value labeled correlation:

df2 = df.pivot(index='a', columns='b', values='correlation') >>> df2 b       1      2      3      4     5                                    2 -0.0798    nan    nan    nan   nan 3  0.3580 -0.406    nan    nan   nan 4  0.2880 -0.129  0.157    nan   nan 5 -0.0756  0.479 -0.378 -0.265   nan 6  0.0909 -0.364  0.482  0.293 -0.74 

to convert symmetrical square matrix ones in diagonal:

# unique list of items in rows , columns. items = list(df2) items.extend(list(df2.index)) items = list(set(items))  # create square symmetric correlation matrix corr = df2.values.tolist() corr.insert(0, [np.nan] * len(corr)) corr = pd.dataframe(corr) corr[len(corr) - 1] = [np.nan] * len(corr) in range(len(corr)):     corr.iat[i, i] = 1.  # set diagonal 1.00     corr.iloc[i, i:] = corr.iloc[i:, i].values  # flip matrix.  # rename rows , columns. corr.index = items corr.columns = items  >>> corr         1       2      3      4       5       6 1  1.0000 -0.0798  0.358  0.288 -0.0756  0.0909 2 -0.0798  1.0000 -0.406 -0.129  0.4790 -0.3640 3  0.3580 -0.4060  1.000  0.157 -0.3780  0.4820 4  0.2880 -0.1290  0.157  1.000 -0.2650  0.2930 5 -0.0756  0.4790 -0.378 -0.265  1.0000 -0.7400 6  0.0909 -0.3640  0.482  0.293 -0.7400  1.0000 

do same steps std dev data if not in matrix form.

assuming matrix named df_std, can covariance matrix follows:

df_cov = corr.multiply(df_std.multiply(df_std.t.values))