data visualization is thus the process of visually representing information in the form of chartsgraphspicturesand so on for better and universally consistent understanding we mention universally consistent understanding to point out very common issue with human languages human languages are inherently complex and depending upon the intentions and skills of the writerthe audience may perceive the written information in different ways (causing all sorts of problemspresenting data visually thus provides us with consistent language to present and understand information (though this as well is not free from misinterpretation yet it provides certain consistencyin this sectionwe begin by utilizing pandas and its capabilities to visually understand data through different visualizations we will then introduce visualizations from the matplotlib perspective ##note data visualization in itself is popular and deep field of study utilized across domains this and section only presents few topics to get us started this is by no means comprehensive and detailed guide on data visualization interested readers may explore furtherthough topics covered here and in coming should be enough for most common tasks related to visualizations visualizing with pandas data visualization is diverse field and science on its own though the selection of the type of visualization highly depends on the datathe audienceand morewe will continue with our product transaction dataset from the previous section to understand and visualize just as quick recapthe dataset at hand consisted of transactions indicating purchase of products by certain users each transaction had the following attributes datethe date of the transaction pricethe price of the product purchased product idproduct identification number quantity purchasedthe quantity of product purchased in this transaction serial nothe transaction serial number user ididentification number of user performing the transaction user typethe type of user we wrangle our dataset to clean up the column namesconvert attributes to correct data typesand derive additional attributes of user_class and purchase_weekas discussed in the previous section pandas is very popular and powerful libraryexamples of which we have been seeing throughout the visualization is another important and widely used feature of pandas it exposes its visualization capabilities through the plot interface and closely follows matplotlib style visualization syntax line charts we begin with first looking at the purchase patterns of user who has maximum number of transactions (we leave this as an exercise for you to identify such usera trend is best visualized using the line chart simply subsetting the dataframe on the required fieldsthe plot(interface charts out line chart by default the following snippet shows the price-wise trend for the given user df[df user_id =max_user_id][['price']plot(style='blue'plt title('price trends for particular user'