Incorrect correlation results
Pearson Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables, a number between -1 and 1. [reference]


This following scatter plots from SPSS tutorial explains different pearson correlations visually:



From your scatter plots, however, it looks like there is no linrar correlation between between sensor 6 vs. class label. I do not know why you expect a correlation??!! That is why using pandas.DataFrame.corr, which is by default based on Pearson correlation, should in principle should lead to a near-zero correlation. What strikes me though is that how this gives you high correlations for some sensory data namely sensor0, sensor4 and sensor8!! Maybe post part of your actual dataset rather just the statistics. I am curious why we see those correlations.

Two questions: 


What are the data types of the class_label and sensory data columns? Having a wrong data types could lead to wrong correlation values in pandas. See this post
Shouldn't you be looking at the correlation between a continuous value vs. categorical varaibles? See this post as a good reference.