in [ ]del(city_data['country']in [ ]city_data merge(country_data'inner'head(out[ ]city city_ascii lat lng pop iso iso qal eh-ye now qal eh-ye af afg chaghcharan chaghcharan af afg lashkar gah lashkar gah af afg zaranj zaranj af afg tarin kowt tarin kowt af afg province country badghis afghanistan ghor afghanistan hilmand afghanistan nimroz afghanistan uruzgan afghanistan here we had common column in both the dataframesiso which the merge function was able to pick up automatically in case of the absence of such common nameswe can provide the column names to join onby using the parameter on of the merge function the merge function provides rich set of parameters that can be used to change its behavior as and when required we will leave it on you to discover more about the merge function by trying out few examples scikit-learn scikit-learn is one of the most important and indispensable python frameworks for data science and machine learning in python it implements wide range of machine learning algorithms covering major areas of machine learning like classificationclusteringregressionand so on all the mainstream machine learning algorithms like support vector machineslogistic regressionrandom forestsk-means clusteringhierarchical clusteringand many many moreare implemented efficiently in this library perhaps this library forms the foundation of applied and practical machine learning besides thisits easy-to-use api and code design patterns have been widely adopted across other frameworks toothe scikit-learn project was initiated as google summer of code project by david cournapeau the first public release of the library was in late it is one of the most active python projects and is still under active development with new capabilities and existing enhancements being added constantly scikit-learn is mostly written in python but for providing better performance some of the core code is written in cython it also uses wrappers around popular implementations of learning algorithms like logistic regression (using liblinearand support vector machine (using libsvmin our introduction of scikit-learn we will first go through the basic design principles of the library and then build on this theoretical knowledge of the package we will implement some of the algorithms on sample data to get you acquainted with the basic syntax we leverage scikit-learn extensively in subsequent so the intent here is to acquaint you with how the library is structured and its core components