Finding Patterns in Error Reports - How to Approach?
You need data for when the error occurred and for normal operation as well, so that you can gain insights from comparing these two groups of log data in analyses and visualizations, e.g. box plots.

You also should consider to regard a time span of log data from before the error happened as possibly related to the occurance of the error. So, you should group your log data into the following two groups by adding a boolean feature to each log entry or data set row: (1) a specific error happened within the next x seconds / minutes / hours / days and (0) a specific error did not happen within the next x seconds / minutes / hours / days.

It is always a good idea to visualize your data, because it helps you to understand your data, e.g. to see if your data is "dirty" or if your assumptions about a specific feature behavior are correct or not as well as related behavior of two or three features. In chapter 4 of the "Python Data Science Handbook" by Jake VanderPlas, the author shows how plot data using python and matplotlib.

In a next step, you can do preprocessing tasks like e.g. to clean your data so that you can later apply statistical methods or machine learning to it to find feature behavior related to error occurrances.

And here is a link to the free online course at edX "Analyzing Data with Python" which you could take.