it is important to note that standard mathematical operations likeadditionsubtractionmultiplicationetc do not carry meaning for categorical variables even though that may be allowed syntactically (categorical variables represented as numbersthus is it important to handle categorical variables with care and we will see couple of ways of handling categorical data in the coming section different data types form the basis of features that are ingested by algorithms for analysis of data at hand in the coming sections and especially feature engineering and selectionyou will learn more on how to work with specific data types data wrangling so far in this we discussed data formats and data types and learned about ways of collecting data from different sources now that we have an understanding of the initial process of collecting and understanding datathe next logical step is to be able to use it for analysis using various machine learning algorithms based upon the use case at hand but before we reach the stage where this "rawdata is anywhere close to be useable for the algorithms or visualizationswe need to polish and shape it up data wrangling or data munging is the process of cleaningtransformingand mapping data from one form to another to utilize it for tasks such as analyticssummarizationreportingvisualizationand so on understanding data data wrangling is one of most important and involving steps in the whole data science workflow the output of this process directly impacts all downstream steps such as explorationsummarizationvisualizationanalysis and even the final result this clearly shows why data scientists spend lot of time in data collection and wrangling there are lot many surveys which help in bringing this fact out that more than oftendata scientists end up spending of their time in data processing and wranglingso before we get started with actual use cases and algorithms in the coming it is imperative that we understand and learn how to wrangle our data and transform it into useable form to begin withlet' first describe the dataset at hand for the sake of simplicitywe prepare sample dataset describing product purchase transactions by certain users since we already discussed ways of collecting/extracting datawe will skip that step for this section figure - shows snapshot of dataset figure - sample dataset