It might also be relevant to leave missing values in your data set. Imputing can be useful when you have many variables and don't want to throw away a record for a relatively-low-importance variable. do LOESS smoothing, and take the filtered prediction at that point. Some other best-fit or maximum-liklihood result.Sometimes it's the least-bad option available. Imputing is probably a bad idea: it's possible to introduce bias, introduce fake trends, wash out real trends, …. This is called imputation ( imputing, to impute). If you have missing values (or deleted outliers), you could replace them by calculating a plausible value to replace them. Remove the value at treat it as a missing value (Pandas NaN or similar) in that record.Remove the record entirely because it's invalid.Leave it as-is because it's actually valid.Handling OutliersĪ few things you can do if you identify outliers: Including them in the analysis would overwhelm any other trends. With a basic plot, it seems pretty obvious that some of the “\(n/10\)” ratings aren't really valid. Or a box plot, with and without outliers: Finding Outliers My scatter plot of date vs dog ratings for Exercise 2, without removing outliers: Finding OutliersĪfter removing the crazy outliers, we can actually see the data, and it looks okay: Are those points at 0 and 1 problems? Finding Outliers Get a feeling for how your data is shaped, and look for problems by eye. Wikipedia: Outliersīut that probably won't tell the whole story: different types of problem will show up in different ways. You can identify outliers by looking at how far a point is from the mean, often how many standard deviations from the mean. One is strangely high, but probably a valid measurement. One value is clearly an outlier that measures a different thing. Suppose I am examining the temperature in my house, and take some measurements: Object If it's just a slightly weird value, you have to justify it as an outlier, or keep it. It makes sense to remove data if it's genuinely an error in measurement, or isn't fundamentally a true measurement of what you're looking at. Important point: you can't remove a data point just because you don't like it! That would certainly be be considered an outlier.īut we shouldn't just ignore it: it's a perfectly valid sample and should be included in any analysis. The point at \(x\approx 4\) is four standard deviations from the mean. Outliers can occur in perfectly valid data that has no problems. What you do with outliers is also going to depend… Outliers
OutliersĪn outlier is a data point that's far away from the others.Įxactly how you define far away is going to depend on the data. If you have invalid data in your data set, you can fix, delete, etc, as appropriate. A legal value? length = -10? Date = February 31 2022? Phone number = 6 or 77?.Correct data type? number, boolean, etc.There are a few common problems and solutions we can talk about… Validity bad formatting/not in relational format.difference in scales/inconsistency in description/sometimes include units.Cleaning DataĮxample problems: Data Cleaning & Integration, Chau The process is generally fairly ad hoc: what you need to do depends on the data you get. A data cleaning example in an annotated Notebook It is often a big part of a real-world data analysis task (maybe 50–80%). Taking the data you find and removing/fixing problem data is called data cleaning (or data cleansing). Cleaning Data For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights The data that you get is rarely as accurate and as correct as you'd like.
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