Re- standardise data after excluding outliers?
As part of the Exploratory Data Analysis(EDA) Process, you'll want to visualize your data with all data points, identify outliers and then further investigate those outliers to figure out what to do with them. Are these outliers inaccurate values that need to be corrected? Perhaps erroneous entries in the raw data? Or are they valid data points that might point to something interesting? You can also assess the distribution of your data with df.describe()
If they are errors, correct them in your dataset and don't delete them. If they are accurate, valid outliers, just exclude them from the visualization to have a better picture of the rest of your data. Does this help?
I am experimenting with python and data analytics. I collected tweets, counted the distinct users, and summed them ,grouped by their locations. Then i have calculated the percentage of users per country population. To make my graphs look better i have standardised my data using the z-score formula. Now i observe that i have a few outliers that ruin my graphs, so i will exclude them. My question is, do i have to exlude them from the original dataset and then re standardise my data, or is it correct to just exclude the standardised form from my analysis and proceed with the values i have already calculated?