mporting ummarizing and isualizing data this describes where to find useful data setshow to load them into pythonand how to (re)structure the data we also discuss various ways in which the data can be summarized via tables and figures which type of plots and numerical summaries are appropriate depends on the type of the variable(sin play readers unfamiliar with python are advised to read appendix first introduction data comes in many shapes and formsbut can generally be thought of as being the result of some random experiment -an experiment whose outcome cannot be determined in advancebut whose workings are still subject to analysis data from random experiment are often stored in table or spreadsheet statistical convention is to denote variables -often called features -as columns and the individual items (or unitsas rows it is useful to think of three types of columns in such spreadsheet the first column is usually an identifier or index columnwhere each unit/row is given unique name or id certain columns (featurescan correspond to the design of the experimentspecifyingfor exampleto which experimental group the unit belongs often the entries in these columns are deterministicthat isthey stay the same if the experiment were to be repeated other columns represent the observed measurements of the experiment usuallythese measurements exhibit variabilitythat isthey would change if the experiment were to be repeated there are many data sets available from the internet and in software packages wellknown repository of data sets is the machine learning repository maintained by the university of california at irvine (uci)found at features