 students, now this module is, in this we will discuss about another very great library of python, I will give you a brief overview of its features and its visualization, when you will see the graphs, you will realize that after some time you will be able to differentiate by looking at the graph and seeing the picture, you will know that most probably 100% obviously, some things overlap, some similarities, but with the experience you will be able to understand that this chart is made from matplotlib, this is made from fusion chart, this is made from high chart, or this is made from seaborn, but again there are pros and cons, so I think I should not use this term, pros and cons, just that which thing is more useful in some particular environment, this matters in this you will see that these, and these actual graphs which are created from seaborn python library, and again they were kept a little so that they can be understood easily, this is also one of your, now one of its back end matplotlib is also being used, but they have done it in a different way, but the difference is that I had told you that the data in matplotlib is in data frames, but this library, this data is maintained within itself, whatever you have, real time data, or any frequency, it maintains the data inside itself, this is common to understand and explore the data, data frames and arrays, this is a little difference that it operates on that, that the data frames and arrays that you have, it operates on that, like in matplotlib, it keeps the data inside the numpy, this data, it keeps the arrays and the data frames in it, this is the library, and then you will see that the different charts of it, all of its own explanation, you can use it, the real thing is that you have these very smart tools available, very smart type of libraries available, and then you can use it, and it happens that the things that you start using, you get used to it so much, then it becomes your second nature and then you keep using the library, in this, if you look at it, it is a very big data set, it is very useful for them, if you have to show multiple variables, or multiple dimensions of the same data, or multiple aspects of the same data, in that, it is very useful as compared to other libraries, in this, you can see that it is giving detailed analysis, it is also doing visualization, and it is also getting data from multiple sources, this is a distinction, or a difference from matplotlib, or from some other libraries, that this library can be used for you in any way, when you design the solution, as a data scientist, you do not have to design the solution in such a way that it will have a front-end or a back-end, in fact, when you design the pipeline, then it is easy for you to make a decision that you have to use the data source, if I have multiple sources, and then after the data wrangling, instead of keeping it in a separate data frame, then I will keep it in the same data frame, that will be at your discretion, and with experience, you will understand and learn this, what I have to do, after that, obviously, when you have to make it a dashboard, or a chart, or multiple charts, then it is also very useful, and you will learn this, that some libraries are like this, in this, you will have to reduce the coding as compared to matplotlib, this is just, it may not have to be done, but normally in general circumstances, you will have to do more coding in it, because you are handling the NAMPAI, you are handling the data frames in it, and then you do other things, whereas in this, these things have been designed in a different way, and may be your one step, may be you have to reduce the coding, that is how you will be able to understand these small distinctions between different libraries, and again, as I said, your entire data pipeline, when you understand it, then it will be easy for you to decide, when you have to use any library, you can use more than one library, if you do not want to visualize it on the front-end, you just have to work for yourself, then you might have a different choice, or when you are doing your ad hoc analysis before handing over to the business, this is a different requirement, so here may be you decide that this is better for me, when you have to give your business, then you have to give it another aspect, that is the time required to process and display the data, so there is a different consideration in that, that I try to make those things, in which my time is at least, so if you have 5 steps of data pipeline, 6 or 5 or 6 steps of data wrangling, then accordingly you will decide, and then when you do ad hoc analysis, then you also get to know that if I did it on sample data, then it took me so much time, now if I do it on the complete data set, then how much processing time can be, or how much my audience has to wait, or how quick it can be, these are some other graphs, there is a little bit of the code attached to it, that you will be able to see how the coding is, now first of all you have to do this, that you import different libraries in it, this is your first step, you have to say import C-Bone as SNS, you can keep something else as SNS, you can keep CNS, ABC, these are some conventions that become a standard, so it is not really technically a standard, but generally it becomes a convention of something, that it becomes a buzzword of something, it becomes a convert, so in this way it is, that I tell Plotlib to do PLT, Plotlib to do PLY, Numpy to do NY, so in this way these are different conventions that have been developed, technically speaking you can change, but because when you have developed a software, it is advisable that you use more and more of the convention, the code that you have written can be used by someone else, it is different, or you work in an environment where your responsibility, your scope is the first two or three steps, someone else has to work ahead, after that someone else has to use that code, so if you use your conventional things in coding, then that will be more appropriate, and it is easier for everyone to understand a common language, this is not, you will not get any syntax error, you can keep anything, but for your other colleague may be they will not appreciate, that is why it is better that you keep this kind of convention, if they say that you keep SNS, then you keep SNS only, so it is better, it is better programming than writing your own, you know this kind of name, you keep it yourself, now we have plotted the time series in this, in this also you see that this is its style, SNS.set, this is its grid style, it has loaded the dataset from the example, and this is long form data, these are different forms of data that you will understand, then you have loaded the dataset, then you have plotted the response different event and regions, then you have loaded it according to the regions, and here you are telling the legend, that what is your region, what is the event, so these are both, and this is your timeline, because we talked that we are plotting the time series, so this is basically your time series, and these are the different values or events, similarly if you have a bi-variate of 2 variables, then how you have to plot it, this is its example, if you look at the code, in this I have tried different versions of the code, if you have different versions of the code, then I have used it as an example, so that you have maximum variety as an example, or as a starting point you have, then you use these things, so similarly you see this, and when you have all the code written, when you have the environment, you will use this code as it is, so if you write this code, then this is what you will get,