python - multivariate regression with discrete and continuous
Correlation is used only for numeric data, discrete / binary data need to be treated differently. Have a look at Phi coefficient for binary.
As for correlation coefficient (for numeric data), it depends on the relationship between the variables. If these are linear then Pearson is preferred, otherwise Spearman (or something else).
I have a dataset with 53 independent variables (X) and 1 dependent (Y).
The dependent variable is a boolean (either 1 or 0), while the independent set is made of both continuous and discrete variables. 
I was planning to use pandas.DataFrame.corr() to list the most influencing variables for the output Y. 
corr can be:

pearson regression
kendall regression
spearman regression

I get different results for 3 approaches.
Do you have suggestions on which one would be the most suitable given the shape (discrete+continuos) of the dataset?