python for data python has several built-in data structuresincluding listsdictionariesand setsthat we use to build customized objects in additionthere are number of internal librariessuch as collections and the math objectwhich allow us to create more advanced structures as well as perform calculations on those structures finallythere are the external libraries such as those found in the scipy packages these allow us to perform range of advanced data tasks such as logistic and linear regressionvisualizationand mathematical calculations such as operations on matrixes and vectors external libraries can be very useful for an outof-the-box solution howeverwe must also be aware that there is often performance penalty compared to building customized objects from the ground up by learning how to code these objects ourselveswe can target them to specific tasksmaking them more efficient this is not to exclude the role of external libraries and we will look at this in design techniques and strategies to beginwe will take an overview of some of the key language features that make python such great choice for data programming the python environment feature of the python environment is its interactive console allowing you to both use python as desktop programmable calculator and also as an environment to write and test snippets of code the read-evaluate-print loop of the console is very convenient way to interact with larger code basesuch as to run functions and methods or to create instances of classes this is one of the major advantages of python over compiled languages such as / +or javawhere the write-compile-test-recompile cycle can increase development time considerably compared to python' read evaluate print loop being able to type in expressions and get an immediate response can greatly speed up data science tasks there are some excellent distributions of python apart from the official cpython version two of the most popular are anaconda (canopy (their own developer environments both canopy and anaconda include libraries for scientificmachine learningand other data applications most distributions come with an editor there are also number of implementations of the python consoleapart from the cpython version most notable amongst these is the ipython/jupyter platform that includes webbased computational environment