How to statistically prove that a column in a dataframe is not needed I have a pandas dataframe consisting of dimensions and features of a different fabric materials. I have several rows per product material type causing the dataset to seem very huge. From basic logic and domain knowledge I can judge that the columns that show dimensions of the fabric is not necessary to analyse the data I am looking for. 

I tried to run a correlation and print a heatmap but it doesnt show any output for most of the columns. Only white color is printed out for most of the column correlation. This makes it difficult for me to judge.

Please advice if there is a better way to statistically prove the relationship between the columns and justify the reason to drop irrelevant columns
With respect to the other answers i observed your point about having features which are strings, you must first find a way to encode them as numerical features which can then help you with PCA and the problem with the heat-map will be resolved too. Also if your problem is of regression then you can use methods such as L1-Regularization which help you in feature selection and you don't have to remove the features while pre-processing.