After data cleansing is well done, during subsequent processing or when viewing final results, we often need to browse data. Sometimes, the result of a program may be found to be incorrect. At this time, you might get stubborn with the program, feeling overwhelmed with sorrow and unable to fall asleep. However, mistakes might have existed in data, actually during your previous data processing. Even worse, mistakes might exist even when you input the initial data or imported them. Thus, to avoid such tragedies, let's waste no time in seeing what data display can do in Python. After previous processing, as we see, the data have already got a very clear structure with its own columns and its own index. The meanings of these data are also very clear. Then, why do we need to display data? It is for better and clearly viewing the data structure and the contents for selection. We generally display such things, like, view the row index, view the column index or view the value of data, view the description of data, etc. As for the row index, we all know the "index" attribute. And, "columns", for the column index. To view the value, use "values". To view the basic description of data, we use "describe". The link of data display enables us to better view whether there's any problem in previous data cleansing. For example, in such a data frame of our "djidf", as we know, it has a column "lasttrade". Its current format is the "float" type. Previously, at the website, its format, as we acquired, was a string. We processed it with a program, and converted such data into a "float" type, into a list. Then, use the list to generate a DataFrame. After processing with such a method, the data become the "float" type. This would make subsequent processing like calculation and sorting more conveniently and efficiently. For this reason, data display happens to be a re-check process. Besides, we can view more data details, say, to view the basic information of the first 5 and last 5 stocks of Dow Jones Industrial Average. It's indeed the row display in DataFrame. We may use the original traditional slicing method. Sure, this dedicated method is also OK. As we see, here are two methods. We can understand their literal meanings. So easy. To add an argument 5 in "head()" means to view the first 5. To add an argument 5 in "tail()" means to view the last 5. Sure, we may still use the original traditional slicing method. Apart from row display, it's also possible to conduct column display. Give it a go. With the traditional slicing method, we select the first 5 records in this DataFrame of "djidf". Sure we may also use its head method to select the first 5 records. If we're to select one row in it, say, lasttrade, we may write it like this. Besides, we may view, say, its dimensions and its element quantity etc. If data display is correct, generally, we select them or part of them for use. In next section, we'll focus on dedicated selections. We generally conduct some basic display operations and then conduct further selection operations. Selections also simultaneously include display.