installing libraries we will not be covering the basics of pythonas we assume you are already acquainted with basic python syntax feel free to check out any standard course or book on python programming to pick up on the basics we will cover one very basic but very important aspect of installing additional libraries in python the preferred way to install additional libraries is using the pip installer the basic syntax to install package from python package index (pypiusing pip is as follows pip install required_package this will install the required_package if it is present in pypi we can also use other sources other than pypi to install packages but that generally would not be required the anaconda distribution is already supplemented with plethora of additional librarieshence it is very unlikely that we will need additional packages from other sources another way to install packageslimited to anacondais to use the conda install command this will install the packages from the anaconda package channels and usually we recommend using thisespecially on windows why python for data scienceaccording to survey by stackoverflow (python is world' th most used language it is one of the top three languages used by data scientists and one of the most "wantedlanguage among stackoverflow users in factin recent poll by kdnuggets in python got the maximum number of votes for being the leading platform for analyticsdata scienceand machine learning based on the choice of users (choice when it comes to the practices of data science we will now try to illustrate these advantages and argue our case for "why python is language of choice for data scientists?powerful set of packages python is known for its extensive and powerful set of packages in fact one of the philosophies shared by python is batteries includedwhich means that python has rich and powerful set of packages ready to be used in wide variety of domains and use cases this philosophy is extended into the packages required for data science and machine learning packages like numpyscipypandasscikit-learnetc which are tailor-made for solving variety of real-world data science problemsand are immensely powerful this makes python go-to language for solving data science related problems easy and rapid prototyping python' simplicity is another important aspect when we want to discuss its suitability for data science python syntax is easy to understand as well as idiomaticwhich makes comprehending existing code relatively simple task this allows the developer to easily modify existing implementations and develop his own ones this feature is especially useful for developing new algorithms which may be experimental or yet to be supported by any external library based on what we discussed earlierpython development is independent of time consuming build and link processes using the repl shellidesand notebooksyou can rapidly build and iterate over multiple research and development cycles and all the changes can be readily made and tested