I suggest you read this thread, which sheds an interesting light on differences between both coefficients in rather simple datasets. I guess, that afterwards you will probably decide not to mix them:
What is the explanation for having a Pearson's correlation coefficient significantly larger than the Spearman's rank correlation coefficient?
Edit: Greenparker is right in his comment, that I should explain here. In the thread I linked, I give an example of a dataset in which Pearson and Spearman differ a lot, as much as one being positive an the other being negative. A shorter example for the purpose of this thread would be the following in R:
> x=c(1.0, 1.01, 1.02, 1.03, 1.04, 100)
> y=c(0.04, 0.03, 0.02, 0.01, 0, 100)
> cor(x,y, method="spearman")
> cor(x,y, method="pearson")
As you can see, for the same six simple data points, Spearman is almost zero and negative whilst Pearson is almost 1. I think this should be illustrative enough, not to mix them as "almost the same".