Data Visualization using Python on Jupyter Notebook

hey everyone welcome to this second video in which we are going to do data visualization using python so first of all your system should have python and jupyter notebook installed in your system if you haven't installed these softwares then please install this software first there's a link in this video description that is about how to install python and jupyter notebook in your system please check out that video then after installing these softwares you are ready to go you are ready to do the data visualization so let's start now we have to open command prompt inside the command prompt we have to write a command which is jupyter notebook now you can see jupyter notebook opens in your pc now you can see that the files and folders of the system are visible inside the jupiter notebook now you have to create a works workplace where you will create your python file or where you have your resources so a workplace is where all the files are stored python files and all your resource files now all you need is a data set on which you are going to work so you can refer a website called kaggle.com it's a very famous website for data sets cable.com is a website that has lots of data or you can use your own data right now from kaggle website you can download any data set as your wish but make sure the data set you are going to download is a csv file format data set i have already chosen a data set from the kaggle website and my data set on which i am going to work on which this video is based for visualization the data set is weather data set so my data set is having different different parameters so let me first show you how my data set is look like so my data set is having total 15 or 14 15 16 parameters that is columns so basically i am going to work on this tables you can see this table so after all the requirements satisfied we are ready to do visualization now we have to go to our workplace and create a file python first of all we have to import library inside the python files so first we have to import some libraries so the first library i will import that is pandas so i am importing the pandas as pd so pd is representing a library that is pandas so the second line is import c1 as sns so sns is representing c1 now c1 is uh sns dot set color codes is equal to true so why the color code should be true for representing the data so that the data should look attractive the visualization of data should look attractive so that is why color codes is equal to true now now we are making a variable that is weather and it is taking the data set that is test.csv so so weather is having the our data set and how how this data set is going to be in weather because of this read csv function which is the function of panda library okay now now weather dot head so whether dot head is using for checking the the headings that is column name of the data set so we are uh we we can see our data's data columns by this line now the next line is weather.info so whether or not info is using for checking all the data types and the data values or data like column values and rows values of a particular column so like we have data time is 199 column that is not none and it is an object column so we are toting total 199 entries are there in date time and same as for the other columns now the next thing is now we can do our plotting so how you can do our plotting so we uh in c bond we have different types of functions so the first function which is univariate function is bar plot so by this line sns dot bar plot weather humidity weather temperature so weather humidity means weather is a is the data set which is having humidity as a column comma where there is a data set again and which is having temperature as a column so we are we are plotting a bar bar graph uh using this two columns to uh to check uh to to do the uh data visualization on this two columns so how you can do this you can do while writing this line so so bar graph is a histogram graph it shows the histograms like right now the next and next next type of plotting is dist plot so this plot is having only one column it is for continuously data representation so in this disk plot you can see that there is only one column that is humidity so for single column distribution we can use the dist plot by variant distribution so in by video distribution we are having more than one columns so more than one columns means we are uh we are visualizing the data using more than one column so the function for bivariate is joint plot so as c1 is having joint plot so by this line you can see uh weather is having humidity and weather is having temperature so we are visualizing the data using these two columns or um with this joint plot now now the next is uh having this kind is equal to hex so kind is equal to hex means uh the data it will be visualizing uh so instead of this points it will have these hexagon points right so after that we are having sns kind is equal to kde so kd is for kds for kernel density estimate so we have two uh two kinds like uh kind is equal to x and kind is equal to kde now we have pair plot so fair plot is always also is also but pair plot is using for visualizing pairwise relationship in a data set so if we uh if we want to show pairwise relationship in a data set so we can use pair plot so pair plate is have a pair plot is having three three variables three column name that is humidity temperature and air pollution so it is showing you the three data accordingly now now the next plotting type is plotting with catagorical data so using category according to category you can plot the data so like in this we are um for categorize categoriological data we have the strip plot in c bond so in c one we have a plot so according to this strip plot we we can give the column name that is weather weather type and another one is weather temperature so it will show you the relationship between these two columns so weather type and weather temperature so this is how it going to be work and this is how the data is visualizing now you can see the rain mesh drizzle clouds clear haze according to the temperature right now the next thing is you can give a jitter so why this jitter is used so in strip plot this scatter plot points will usually overlap so this makes it difficult to see the full distribution of data so one easy solution is to adjust the positions uh using some random jitter so and this is why the jitter is true now the next thing is box plot we have box plot so box plot is also in uh categorical data so box plot is also having a two uh two columns and the hue is equal to weather so u means u uh u is uh differentiating uh like x axis is humidity and y axis is temperature and the he and the according to this uh hue it will show you or it will visualize the data so this is how this box plot is uh working and it is also in sns now the next thing is bar plot so it is very familiar a familiar style of plot that complies this goal in bar plot in c1 bar plot function operates on full data set and show an arbitrary estimate using the mean by default okay so when there are multiple observation in each category it also use bootstrapping to compute a confidence interval after the estimate and plots that using error bars so like in bar plots we are having three two columns and another one is hue so it is showing you the bar column so it is a very familiar now the next is count count plot so count plot is just used for single single column so it counts the value of of single column so count part is also very useful in data visualization so i am i am using this count part on weather type column now the next thing is point plot so point plot is making a relationship between two between more than one i can say more than one uh more than one columns so u is equal to whether so according to this weather type it is uh showing you the relationship between humidity and temperature of the weather right now the next and the and the second last is lm plot so lm plot is for is for linear regression like it shows the lm plot is one of the most widely used function to quickly plot the linear relationship between two variables so i i am showing with a linear relationship between humidity and temperature and data is weather so data is whether and the x axis is humidity and y axis is temperature so lm plot is using for linear regression so it is showing you the linear regression now ah and this is another type of lm plot it is just having another parameter that is weather type that is equal to hue the hue is according to this hue it is showing you the accesses and y axis okay so guys uh these were all the types that i have seen that i have showed you for analyzing the data or data visualization i hope you all like the this data visualization using python and if you have any problem in this video then you can just tell me by giving a comment where you are facing the problem till then i would like to say goodbye to you 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Channel: Think And Build

Views: 108,021

Keywords: data analytics, data analysis, data analyst, data analysis with python, data visualization, data visualization in python, python, data visualization using matplotlib, python data visualization, visualization, python visualization, data visualization tutorial, data visualization python seaborn, python data visualization tutorial, data visualization in python using matplotlib, data visualisation and analysis using python, data visualization using pyplot, python matplotlib tutorial

Id: tse_8LLWtfY

Length: 12min 27sec (747 seconds)

Published: Fri May 22 2020