How to find correlations among repeated measures and how to analyze them?

I have a dataset of repeated measures and I would like to find any correlation between those measures. Also, after finding the correlation I would like to feed the dataset to an appropriate model and predict some values or find some patterns inside the dataset. The example of the dataset is shown below,

A       B         C         D       E       F
20.2    885.970   43.352    135.000 26.4    54.874
20.2    885.850   43.352    136.000 26.4    54.971
20.2    885.850   43.435    136.000 26.4    43.971
20.2    885.790   44.051    47.000  26.4    56.765
20.2    885.830   44.663    63.000  26.4    57.363
20.2    885.840   44.018    73.000  26.4    56.667
20.2    885.840   43.664    81.000  26.3    56.270
22.1    992.800   40        297     25.8    46
.
.

and the measurements go on in a similar fashion. As it can be seen from the dataset, for the values of column A there are mostly small changes in corresponding values of other columns and they don’t always get the same values. I tried to find Pearson correlation among columns which I think is not a good approach in this case. Then I tried different methods, first I extracted unique values of the column A found a correlation, and in the second method, I categorized the dataset by column A and then found mean values for the other columns. I am not sure how to approach this dataset and with the last two methods, I lose too many values from my dataset. My question is how can I approach to these kind of datasets and what do I have to do to find corellation among the columns?