How to select variables based on the mean correlation in a correlation matrix?

I have a set of independent variables and I am calculating the correlation matrix between them using the Pearson Correlation Coefficient in Python. A part of the matrix looks like this: From this matrix, suppose I want to find out the strongly correlated components between the variable NoOfDoors and the rest(Symboling...Compression Ratio). The process I have adopted is that I have taken the mean of that column(which is calculated as 0.039604) and based on that, I have only considered those values greater than 0.039604.

Based on that, the following variables have been selected as strongly correlated:

(Make, Aspiration, Wheel Base, Length, Width, Height, Curb Weight, Engine Type, Bore, Compression Ratio)

I want to ask, is this selection correct? If yes then is there an efficient way to do this? And if no, what is the correct way? Since I am new to this field, a well explained article would be appreciated. Thanks!

• I would suggest to calculate the mean of the absolute value of that column, because a "very negative" value also means strong correlation, just in the opposite direction. You can also consider rank the entire correlation matrix by absolute value. Jan 31 '19 at 7:08