oracleand so on the sqlite library provides lightweight easy-to-use interface to work with sqlite databasesthough the same can be handled by the other two libraries as well the second way of interacting with databases is the orm or the object relational mapper method this method is synonymous to the object oriented model of datai relational data is mapped in terms of objects and classes sqlalchemy provides high-level interface to interact with databases in the orm fashion we will explore more on these based on the use cases in the subsequent data description in the previous sectionwe discussed various data formats and ways of extracting information from them each of the data formats comprised of data points with attributes of diverse types these data types in their raw data forms form the basis of input features utilized by machine learning algorithms and other tasks in the overall data science workflow in this sectionwe touch upon major data types we deal with while working on different use cases numeric this is simplest of the data types available it is also the type that is directly usable and understood by most algorithms (though this does not imply that we use numeric data in its raw formnumeric data represents scalar information about entities being observedfor instancenumber of visits to web siteprice of productweight of personand so on numeric values also form the basis of vector featureswhere each dimension is represented by scalar value the scalerangeand distribution of numeric data has an implicit effect on the algorithm and/or the overall workflow for handling numeric datawe use techniques such as normalizationbinningquantizationand many more to transform numeric data as per our requirements text data comprising of unstructuredalphanumeric content is one of most common data types textual data when representing human language content contains implicit grammatical structure and meaning this type of data requires additional care and effort for transformation and understanding we cover aspects of transforming and using textual data in the coming categorical this data type stands in between the numeric and text categorical variables refer to categories of entities being observed for instancehair color being blackbrownblonde and red or economic status as lowmediumor high the values may be represented as numeric or alphanumericwhich describe properties of items in consideration based on certain characteristicscategorical variables can be seen asnominalthese define only the category of the data point without any ordering possible for instancehair color can be blackbrownblondeetc but there cannot be any order to these categories ordinalthese define category but can also be ordered based on rules on the context for examplepeople categorized by economic status of lowmediumor high can be clearly ordered/sorted in the respective order