Covid-19 Data Analysis Project Using Python And Tableau | Covid Data Analysis Tutorial | Simplilearn

hello everyone and welcome to this interesting video tutorial by simply learn today we are going to perform two hands-on projects on covet data analysis using python and tableau this is going to be a really interesting and fun session where i'll be asking you a few generic quiz questions related to coronavirus please make sure to answer them in the comment section of the video we'll be happy to hear from you covet or coronavirus is an ongoing global pandemic of coronavirus disease that emerged in 2019 and was first identified in wuhan china it is defined as an illness caused by a novel coronavirus called severe acute respiratory syndrome 2 commonly known as sars cove 2 on march 11 2020 the who declared coven 19 a global health emergency the virus has so far infected over 22 crore people and killed more than 4.5 million innocents in india there have been over 3.3 crore confirmed cases and nearly 4 lakh 41 000 deaths have been reported so far this data is according to official figures released by the union ministry of health and family welfare as the world tries to cope up with this deadly virus we request all our viewers and their family members to follow all the necessary precautions to avoid getting infected quiz time now let's see our first quiz in this project what does corona in corona virus mean here are the options is it a beer b respiratory is it c crown or is it d sun this is a very general question i'm sure a lot of you may already know some of you may not please let us know your answers in the comment section of the video we'll be glad to hear from you now in this video we will use three different kuvin19 datasets and perform data analysis using python and tableau the project will give you hands-on experience working on real-world data sets and how you can use the different python libraries to analyze and visualize data and draw conclusions you will learn how to create different plots in tableau and then make a dashboard from the visuals the project will give you an idea about the impact of coronavirus globally in terms of confirmed cases that's reported the number of recoveries as well as active cases we will also see how india has been affected since the pandemic started and dive into the different states and union territories to learn more about the covet 19 influence and the vaccination status first let me show you the two data sets that we'll be using so for our first project using python we'll be using the first two data sets kovid 19 india and covet vaccine statewise let me open the two data sets okay so this is the first data set you can see here we have the date we have the time then we have the different state names but scroll down you can see we have kerala tamil nadu delhi haryana rajasthan sladhak punjab telangana and other states then we have something called as confirmed indian national so actually this two data sets confirmed indian national and confirmed foreign national we won't be using so in the demo itself we'll be dropping these two columns what we are concerned about are the last three columns the cured cases or the recoveries the number of deaths reported and then we have the total number of confirmed cases let me just sort the b column that is the date column so that you have an idea about the recent data we have i'll continue with the current selection and sort it you can see here this is till 11th of august 2021 so this data was collected from kegel it has some discrepancies that we will see in the demo the data is available for free we will provide the link to the data sets in the description of the video so please go ahead and download them the visualizations and results that you will see in the demo are based on the datasets that we'll be using we haven't pre-processed the data to remove outliers or any missing values now before i jump into the demo let me show you the second data set that we are going to use okay so coveted vaccine state wise is my second data set let me open it there you go so this is the second data set that we'll be using in the python project you can see we have a column called updated on then we have the state again you can see here there are a few discrepancies here it has taken the country name and not the state name below you can see there are the different state names and you can also see we have information about the total doses administered we have the sessions sites first dose administered then we have the second dose then we have information about male and female doses you can see here the different vaccines administered covaxian and cova shield you have sputnik v and here are the different age groups as well and finally if you see we have male individuals vaccinated the total number of female individuals vaccinated for a particular day we have information about transgender individuals vaccinated and finally we have the total individuals vaccinated each day all right before we jump into the hands-on part let's have a look at the second quiz in this project so here is the second quiz question which is the first country to start covid vaccination for toddlers is it a japan b israel c portugal or is it d cuba this is a very recent development that took place if you watch daily news updates on coronavirus you will be definitely able to answer the question please give it a try and put your answers in the comments section of the video it is really important for our viewers to know the right answer all right so now let's begin with our demo so i am on my jupyter notebook so the first project we are going to use python jupyter notebook i'll just rename this notebook as kovid data analysis project and click on rename all right so first and foremost we need to import all the necessary libraries that we are going to use so first i am importing pandas as pd this is for data manipulation then we have numpy as np numpy is used for numerical computation then we are importing matplotlib c bond and plotly these three libraries will be used for plotting our data and creating interesting visualizations finally i'm also importing my date time function all right so i'll hit shift enter to run the first cell now it's time to load our first data set which is related to the kovate 19 cases in india for the different states and union territories so i'll create a variable called covet underscore df df is for data frame i'll use my pandas library and then give the read underscore csv function since our data sets are csv files and inside double quotes i'll pass in the location where my data sets are present so i'll just copy this location i'll paste it here we'll change the backslash to forward slash and after that i'm going to give my file name followed by the extension of the file i'll say 19 india dot csv let's go ahead and run it all right now to see the first few rows of the data frame i'm going to use the head function i'll say head and within brackets let's say i pass in 10 which means i want to see the first 10 rows of data if i run it there you go here you can see from 0 till 9 so we have 10 rows of information and these are the different column names you have s number date time state or union territory then you have confirmed indian national confirmed foreign national cured cases deaths reported and the confirmed cases all right now moving ahead let's use the info function to get some idea about a data set if i run it you can see here it gives us the total number of columns we have nine columns the total number of entries or the rows we have eighteen thousand hundred and ten rows of information starting from zero till eighteen thousand one hundred and nine you see here the different types of variables or column names that we have then it has information about the memory usage as well and this side you can see the data types cool now we'll use another very important function which is to get some idea about statistical analysis the basic statistics about your data set for that i will be using the describe function okay so if you can see here the describe function is for numerical columns only and you have the measures such as count the mean standard deviation maximum minimum the 25th percentile 50th percentile and the 75th percentile value okay now let's move ahead and import the second data set which is related to vaccination so i'll create a variable called vaccine underscore df i'll write pd dot read underscore csv function which is present in pandas library i'll move to the top and i'll copy the file location and we'll change the name of the file copy this and let me paste it here and instead of covet underscore 19 underscore india i'm going to say governed underscore vaccine underscore statewide okay so this is the data set that we saw covered underscore vaccine underscore state wise all right let me run it cool and let's display the first seven rows of information from this data frame i'll be using the head function and inside the function i'll pass in seven there you go so here you can see we have from 0 till 6 there are total 24 columns a lot of them have null values you can see here all right now from the first data set which is the covered underscore df data frame we'll be dropping a few unnecessary columns such as the time column confirmed indian national and confirmed foreign national as well as the s number we do need these columns so it's better to learn how to drop the columns for our analysis so i'll say kovid underscore df dot i'll use the drop function and within square brackets i'll pass in the column names the first is s number i'll give a comma within double quotes i'll say the next column is time my third column that i want to drop is confirmed indian national give another comma within double quotes i'll say confirmed foreign national outside the square brackets i'll give another comma and pass in my next argument that is in place equal to i'll say true i'll give another comma and say access equal to 1 let's run it ok it has thrown an error let's debug the error says confirmed indian okay it should be indian national and not indian nation i'll just mention it as national will run it again okay now we have removed these four columns let me show you the data set now there you go so we have only the date column state or union territory cured that's and confirmed now let's see how you can change the format of the date column for that you have the function called to datetime i'll say covid underscore df i'll pass in my column name that is date i'll say equal to pd dot i'll use the pandas function that is to underscore date time i'll say covet underscore df which is my data frame name pass in my variable which is date give a comma and i'll use my argument that is format equal to i'll say percentage y give a dash say percentage m give another dash and say percentage d let's run it and i'll print the head of the data frame cool now moving ahead now we will see how to find the total number of active cases so active case is nothing but the total number of confirmed cases minus the sum of cure cases plus that's reported so let's find the active cases i'll give a comment okay i'll first write my data frame name that is covered underscore df within square brackets i'll give my new column which is active underscore cases i'll say equal to kubit underscore df and my first column would be the confirmed cases column minus i'll say kovid underscore df then i'll pass in my cured column plus i'll again say kovid underscore df and add my deaths column this time let's print the last five rows from the new data frame that we have created okay let's run it it has thrown an error let's debug it says dataframe object has no attribute tails this should be teal there you go you can see here we have added a new column called active cases which is the confirmed cases minus the sum of cured and deaths reported column now we will learn how to create a pivot table using the pandas library so in this table we'll be summing all the confirmed deaths and cured cases for each of the states and union territories so we'll be using the pivot underscore table function for this i'll create a variable called statewise and say pd dot i'll use the pivot underscore table function i'll pass in my data frame that is kobit underscore df and then i'll give my values parameter inside the square brackets i'll pass in my columns confirmed deaths and then we'll have the cure column give a comma and next argument would be index which is going to be my state slash the union territory column let me bring this to the next line so it is more readable i'll say union territory i will give a comma now and pass in my last argument which is agg function that means aggregate function and this function would be max all right let's run it okay now i'm going to find out the recovery rate so recovery rate is basically the total number of qr cases divided by the total number of confirmed cases into 100 so i'll say statewise and within square brackets i'll pass in my variable that i want to create which is recovery rate this will be equal to [Music] the cured cases multiplied by 100 by the total number of confirmed cases within square brackets i'll give my column as confirmed let's run this okay i'll just copy this column paste it here so this time we are going to find out the mortality rate so mortality rate is nothing but the total number of deaths divided by the total number of confirmed cases into hundred so i'm just going to replace the names here i'll say mortality all right and then instead of cured i'll say my deaths column in 200 divided by the confirmed cases let's run it okay now we are going to sort the values based on the confirmed cases column and we will sort it in descending order so let me show you how to do it i'll say statewide equal to i'll use the function short underscore values so i'll pass in my variable statewise dot and use the short underscore values function i'll say by i want to sort it by my confirmed cases column give a comma and i'll say ascending equal to false let's run it now we are going to plot our pivot table using a nice visual so for that i am going to use my background underscore gradient function and inside that function we'll pass in our cmap parameter i'll show you how to do it say style dot background underscore gradient and inside this will pass in a parameter called cmap so c map stands for color maps this is present inside the matplotlib library so here you can see there is a nice documentation on choosing color maps in matplotlib this is provided by matplotlib.org if i scroll down you can see there are a number of c maps that you can use purples blues you have something called reds there are other things like magma you have summer autumn spring winter cool all these you can use whichever color map that you want and here you can see the different shades or the gradients okay so i am going to use my color map as cube helix let me run it and show you the pivot table there you go here you can see we have our pivot table ready now as i said in the beginning there are a few discrepancies in the data set so here you can see there's one called maharashtra and there's also maharashtra triple star this you can ignore even if i scroll down you have madhya pradesh followed by three asterisks you can ignore this value as well even for bihar we have so these have been duplicated and here you can see the different state names and union territories then on the top you have the confirmed cases cured cases the debts reported and the new columns that we created these are calculated columns recovery rate and mortality rate and we have ordered it in descending order of confirmed cases so so far our data says that maharashtra has the highest number of cases followed by kerala karnataka tamil nadu andhra pradesh and uttar pradesh so these are the top five states which have the highest number of confirmed cases even if you see the mortality rate is also high for maharashtra and if i scroll down the mortality rate is also high for uttarakhand if you see here if i scroll further your punjab the mortality rate is also high all right so this was our first visual that we created in the kobe data analysis project now moving ahead we'll see the top 10 states based on the number of active cases so we'll start i'll give a comment top 10 active cases states okay so we are going to explore another very important pandas function in this which is known as group by so i'll first pass in my data frame which is kovid underscore df dot followed by the group by function i am going to group my data based on state slash union territory column then i'll say dot max which is to find the maximum value from the states that have the highest active cases so i have passed in my active cases column and we're also going to group it based on the date column after that we are going to sort the values so i'll use my function that is sort underscore values i'll say short by my column that is active cases let me bring this to the next line underscore cases i'll give a comma and say ascending equal to false say dot and then reset my index for that i'll use reset underscore index function ok let's check if everything is fine i have missed a square bracket here let me give another square bracket here okay and all this we are going to store in our variable called top underscore 10 underscore active underscore cases okay now let me go ahead and run this cell okay there is a syntax error here now let's run it okay now i'll create another variable called fig here we'll pass in the plt which is for matplotlib library and we'll give the figure size using the fig size argument it's equal to and within a tuple and passing the size let's say 16 comma 9 we'll run it okay and let's give a title to our plot so here we are going to create a bar plot so using plt.title we'll pass in the title let's say top 10 states with most active cases in india i'll give a comma and pass in the size of my title let's say 25 okay you can see we are slowly being able to create our graph the most important thing is to pass in the x-axis and the y-axis i'll say ax which is for axis to pass in the axis i am going to use the bar plot function that is present inside the c bond library i'll say sns.bar plot i'll say data equal to my variable which is top underscore 10 underscore states this is actually active states all right and now i'm going to use the i lock function and take the first 10 states so i'm using a colon and then giving 10 as my value iloc is for index location i'll give a comma and say my y axis to be active cases give another comma and say my x-axis to be state slash union territory give another comma we are going to pass in the line width to 2 i'll say line width equal to 2 and will give an edge color let's say the edge color is red okay so i have my access defined now let's run it there's an error here it says stopped in active states not found let's go to the top and see the exact okay so this is top 10 active cases let me change it to cases here now run it okay the x axis also has a mistake this should be state slash union territory now let me run it there you go we have our plot created but as you can see here the labels of the different states and union territories are overlapping so for that let me first pass in the x labels i'll say plt dot x label and my x label would be states then i'll say plt dot by label my y label will have the total active cases and finally i'll write plt dot show before i run it let's collate all the lines of code that we have written for our top 10 states for most active cases in india into one cell so what i'll do is i'll just copy this and we'll keep adding in this cell itself okay i'll go to the top next we'll copy my figure size and we'll paste it here next let's take the title and put it here give us peace and we are going to copy this cell and i'll paste it here all right now it's time to run it there you go you see it here we have a nice bar plot ready on the top you can see the title top 10 states with most active cases and you see the edges are in red color for all the bars on the x-axis you have the different state names maharashtra karnataka kerala you also have andhra pradesh gujarat west bengal and chatham so as you can see maharashtra has the highest number of active cases based on our data followed by karnataka caroline tamilnadu at second third and fourth place respectively and in the ninth place we have west bengal in the tenth place we have chattisgarh on the y-axis you can see the total active cases which are in lakhs okay now moving ahead now we'll see the top 10 states based on the total number of deaths reported so i'll give a comment top states with highest debts okay so i'll first create my variable saying top 10 underscore deaths this will be very similar to what we did here just that we need to change a few column names so instead of active cases we'll be using deaths okay so i'll start with my data frame that is covered underscore df followed by using the group by function and i want to group my data based on the state slash union territory column then i'll choose the max function and within double square brackets i'll pass in my column names deaths and date let's make it consistent i'll be using single quotes okay i'll say dot i'm gonna short my result therefore i am using the sort underscore values function i want to sort it by deaths column i'll give another comma and say ascending equal to false which means i want to order my result in descending order then i'll say dot reset underscore index okay after that i'll give my figure size i'll say plt dot figure and within brackets i'll pass in my argument which is fig size equal to using a tuple i'll say 18 comma let's say 5 now let's give a title for that i'll use the title function which is present in the matplotlib library i'll say top 10 states with most deaths and let's give a size to the title 25 now it's time for us to give the access labels i'll just scroll down okay so i'll say ax equal to again this will be a bar plot so i'm using my c bond library followed by the bar plot function my data will be top underscore 10 underscore depths which is this variable i'll give my index location iloc i'm going to choose 12 states the reason being there are some discrepancies in the data i'll show you once i plot this result so in the y axis we'll have depths column in the x axis we'll take state slash union territory okay i'll give another comma and say line width equal to 2 and will give an edge color to our bars like we did here so i'll say edge color equal to let's say black okay now finally we'll give the x label the y label so i'll say plt dot x label x label would be states my y label will be total death cases then i'll write plt dot show now let's run it there you go you can see here we have a nice bar plot on the top we have the title top 10 states with most deaths now what i specifically wanted you to see was these discrepancies in the data you can see here maharashtra is repeated twice even in our data that we collected from kegel karnataka spelling has an error you can see here we have a few rows of information where karnataka is spelled as k e r a a n instead of k e r n a all right so to remove these two results or to ignore these two results i had given my index location till 12 so we have maharashtra karnataka tamil nadu delhi then uttar pradesh west bengal kerala punjab andhra pradesh and chhattisgard with the states that have the most number of deaths reported okay now we'll create a line plot to see the growth or the trend of active cases for top five states with most number of confirmed cases so the states are maharashtra karnataka kerala tamil nadu and uttar pradesh i can show you so these are the states with highest number of active cases okay i'll just give a comment saying growth trend all right so for that we'll start with our figure size as plt dot figure i'll use my parameter fixed size equal to 12 comma 6. i'll say my axis as sns dot this time we are going to create a line plot my data would be the original data frame that is covered underscore df and within square brackets i'll say covad underscore df and we are going to pass in the state or the union territory column i'll give a dot and use the is in function so the states should include maharashtra will give a comma the next state should be karnataka next we have kerala give another comma my fourth state is tamil nadu give a comma and my fifth state would be uttar pradesh all right so i'll give a comma here and then say x equal to the date column y will be active cases column and i'm going to use another parameter called hue which is to give different colors to the different states the five states that we are looking for so here will be my state slash union territory column okay after this i'll say ax dot set title my title will be top five affected states in india we'll give a size let's say 16 all right okay this should be size equal to 16 and to make sure everything is fine let me just move it to the above line okay now we'll run it okay there is some error again it says invalid syntax let's see where we went wrong let me bring this to the above line okay this should be covered underscore df okay make sure you keep these small errors in check let's try and run it again all right if i scroll down there you go here you can see we have the top five affected states in india you see the different states here and the colors so this violet or the purple color is for maharashtra you can see how the active cases searched around april and may and then you can see even for karnataka the active cases surged very rapidly around may and june then it has decreased luckily similarly you can see the plots for even uttar pradesh as well as for tamil nadu so all the five states you can see one common trend that after march so around april the cases started to emerge very rapidly and later after july they started dipping okay now we are going to use our second data set which is related to vaccination all right so first and foremost let me go ahead and print the data frame for you so that you know the data that we are going to use so this is the data we are going to use for our next set of analysis even this data has a few errors and you can see there are a lot of missing values it makes sense because not all the days you would have vaccination for people and very crucial error that we are going to deal with is this state column here you can see in the state column we have india in a few rows so we are going to ignore these rows all right first let's rename are updated on column to just vaccine date so you can use the rename function present in the pandas library to do it so i'll say vaccine underscore df dot rename i'll say columns equal to within curly braces i'll write updated on which is my original column name give a colon i want to change this to vax vaccine underscore date i'll give a comma and say in place equal to true so it will change my column name from updated on to vaccine underscore date in the original data frame let's run it now let me go ahead and print the first 10 rows in the data set you can see here we have successfully changed the column name to vaccine underscore date the rest of the columns remain the same all right now you can check the total number of columns rows the data types and the memory usage using the info function here you can see it we have total 24 columns 7845 entries and these are the columns that are present and if i scroll further you see here says the memory usage and here on the right you can see the data type cool now as you saw the data has a lot of missing values let's find the sum of all the missing values for each column so i'll say vaccine underscore df dot is null and then i'm going to use the sum function over it if i run it there you go so almost all the columns have missing values but columns such as male individuals vaccinated female individuals vaccinated even for columns like the age groups there are a lot of missing values now in the next step we are going to drop a few missing value columns so i'll say a new variable called vaccination i'll give the data frame name vaccine underscore df and use the drop function and inside the drop function we are going to write columns equal to i'll pass in the column names that i want to drop i'll say doses administered so this is the column that i want to delete sputnik v doses administered i'll give a comma the second column is e e f i the next column is 18 to 44 years within bracket it has doses administered give a comma i'll say 45 to 60 years doses administered to another comma the final column that we are going to drop is 60 plus which is this one 60 plus years doses administered okay so these are the columns that we want to drop and finally i'll give my parameter access equal to one okay let me just recheck this column names first one is sputnik v doses administered then we have efi followed by 18 to 44 years dose is administered then we have 45 to 60 years doses admin is stored finally we have 60 plus years i've added a s here which was missing and there should be a space as well because there's a space here in the column now let's run it all right so we have successfully dropped the columns that we wanted to drop now i'll say vaccination dot head you can see here we have dropped those columns and we are left with these columns now okay now we are going to create a plot that is a pie plot to see the vaccination for male and female individuals so i'll give a comment as male versus female vaccination to create this pie plot i am going to use the plotly library that we had imported which is this one plotly okay so first of all we need to filter out the male individuals from our data frame i'll say male equal to vaccination meal and passing in the column individuals vaccinated we need to recheck the variable name whether it is correct or not you see here mail individuals vaccinated then i'll say dot sum similarly we will filter out the female individuals i'll say female equal to vaccination which is my new data frame within square brackets i'll pass in the column name female individual vaccinated i'll say dot sum okay okay these should be individuals as per the column name present in the data frame cool now i'm going to say px dot pi function which is present in the plotly library i am going to give the names of the pies so first one is mail and the second one is female give a comma and say values equal to male comma female then i'll give a comma and pass in my title which is male and female vaccination okay this should be names all right now let's go ahead and run this cell so it'll take some time to create the pie chart here we go see here i have my title male and female vaccination so these are the two pies or the areas you have the label female and the value here you have the label meal and the value so from our data you can see 53 percent male individuals have been vaccinated compared to 47 percent for females okay now we are going to drop all those rows where our state was mentioned as india in the original data set i had shown this to you in the beginning so we are going to remove rows where state is india okay so to do that i'll say vaccine i'm creating another variable called vaccine and we'll use our original data frame that we had imported which is vaccine underscore df i'll say vaccine underscore df dot state not equal to india now let's print vaccine okay you can see here we have our data frame printed and from this data frame we are taking only those values or the rows where state is not equal to india cool now what we are going to do is we'll rename the last column which is total individuals vaccinated to just total so i'll say vaccine dot rename we'll use the parameter columns equal to within curly braces i'll say total individuals vaccinated give a colon and we'll change this to just total give a comma and say in place equal to true and finally i'll print my head of the new data frame this should be columns equal to okay now you can see here if i scroll to the right and if i check we have successfully renamed our column all right now moving on to the last two visualizations that we'll see in this first project using python that is to find the states with most number of vaccinated individuals and the state with the least number of vaccinated individuals okay just give a comment most vaccinated state okay for this i'll first create a variable called wax underscore vac which is for maximum vaccination i'm going to use my data frame vaccine followed by the group by function i want to grip it by state column and we'll find the total sum of vaccinated individuals for each state i'll convert this to frame there should be a dot to underscore frame and i'll pass in my column name which is total after that i'll say max underscore vac equal to max underscore vac dot short underscore values i'm going to sort it by my total column and say ascending equal to false which means i want to sort it in descending order so that we have the states with the highest number of individuals vaccinated i want the top five states now let's go ahead and print it so this will create a nice table for us there you go here you can see we have maharashtra uttar pradesh rajasthan gujarat in west bengal as the top five states with most number of vaccinated individuals now we are going to use this table and convert it into a chart now i had already written my code for the bar plot i'm just going to paste it so here i have defined my figure size as 10 comma 5 then i'm giving a title as top 5 vaccinated states in india and the size is 20 after that i have created a variable called x which is going to store my bar plot this bar plot we have created using the c bond library and you see here i have my y-axis defined then i have the line width and the edge color as well i have given my x-label as states by label as vaccination and i'm finally going to display my plot so let's go ahead and run it okay you can see it here we have converted this table into a bar plot now so first we have maharashtra with the highest number of vaccination then we have uttar pradesh rajasthan gujarat and west bengal now i want the viewers watching this video to find the states with least number of vaccinated individuals and convert that table into a bar plot that will show the bottom five vaccinated states in india which means the states which have least number of individuals vaccinated so please go ahead and put your codes in the comment section of the video it will be very similar to these two cells just that now we want to know the least five vaccinated states in india so please put the code snippets in the comments section of this video we'll be more than happy to know from you in case you are not able to do it we'll definitely keep the answers in the comments itself all right so this brings us to the end of the first project on kovit data analysis using python so in this project we used two data sets one was on the covet 19 india data set for different states and union territories and the second data set that we used was for analyzing the status of vaccination in the different states in india now we'll move on to our second project okay so before moving on to our tableau project let's see the final quiz which of the following is not a common coronavirus symptom so this question is to make you aware of the correct coveted symptoms here are the options is it a fever b vision loss c loss of taste and smell or is it d cup please put your answers in the comments section of the video and let us know what you think now we'll move on to our second govi data analysis project for this project we'll be using the tableau software and a global coronavirus data set first we look at the data set and the fields we have okay so i am in my data set folder we have already seen the first two data sets in our python project this time we are going to use the global covet data let me open it all right so here you can see we have the different country names there are 192 countries in our data set let me go to the top so we have country or region we also have the code for each of the countries then we have column called last updated so here you can see our data is still 12th of march 2021 and there are two columns which don't have any information that is people hospitalized and people tested then we have a column called active cases then we have a column for confirmed cases in the different countries we have the number of deaths and then we have the incident rate the latitude and the longitude for the countries then we have the mortality rate this is in fractions then we have the number of recovered cases and finally we have a column called uid so we'll be mostly using the country column active confirmed deaths and the recovered cases column and we'll be using the tableau software to create interesting visualizations and we'll convert those visuals and put it in the form of a dashboard towards the end of the project okay so let me close this file or let it be open i'll search for tableau public you can see here i have tableau public installed you can use the desktop version of tableau or the public version of tableau so for this project we are going to use the tableau public edition so here you can see i have created a few visuals already if you want you can clear them out okay now the first thing we need to do is to connect our csv data set to tableau for that under connect we have to a file i'm going to use text file and under chrome downloads so i'll search for my folder covered india and here i have my global covet data file let me open it this will take some time to load there you can see so on the tableau you can see the view here it shows the country or the region you have the last update you have columns like active confirmed that's mortality rate and recovered and here you can see the symbols for the different columns so this is a globe which means this field is a region or a geographic field abc means it's a text field then we have the calendar which is for a date field abcr text field and this hash represents numeric fields all right so now that we have loaded our data let me just click on sheet1 so this is the canvas where we are going to create our different reports all right so on the left you can see there are two panes the data pane and the analytics pane in the data pane you can see the name of the data set global covet data and here you can see the different columns that we have in the data set so on the top the ones marked in blue are called as dimensions and at the bottom whatever you see are marked in green and are called as measures so dimensions are always represented with blue icons and measures are in green icons so tableau has automatically detected the different data types for each of the columns all right so first of all we are going to create a simple table to see the total number of confirmed cases for each of the countries so for that i'm going to drag my country or the region column onto rows here you have rows i'll just drag it and drop it here there you go it has populated all my country names starting from afghanistan so it has ordered in ascending order of alphabets now to see the total confirmed cases for each country i am going to drag my confirmed column onto text so here under marks card you have the text option i'll just drag it and drop it here there you go so tableau how easily has shown me a table where you can see the different country names and on the right you have the total number of cases that have emerged from these countries now this is not the most recent data as i told you this data was till march 2021. cool now you can go ahead and short the table based on the total confirmed cases here on the top you can see here there are two options to sort in ascending order or in descending order let's first sort in ascending order by sum of confirmed cases within country and region there you go so here you can see these are the countries which have very few cases and if i scroll down you have france uk russia brazil india and u.s with the highest number of kovite 19 cases if you want you can also short it in descending order so that the countries with the highest number of confirmed cases appear at the top you can see it here and these are the countries that have very few covet cases now you can convert this simple table into different charts and graphs to do that here on the right top corner you have an option called show me now i can just click on this horizontal bar graph so that will automatically convert my table into a bar graph again you can go ahead and short the data there you go so here you have u.s india brazil russia uk france spain and italy this is a horizontal bar graph sorted in descending order of confirmed cases now suppose you want to display only the top few countries with the highest number of confirmed cases for that you can select these bars so i'll select till let's say germany or let's say to argentina okay and i'll click on keep only you can see it here it says 12 items selected that is the sum of confirmed cases i'll just click on keep only there you go so this horizontal bar graph has information only for these few countries that have the highest number of confirmed cases now you can go ahead and play with the color formatting so here under colors let me choose a green color okay now you can mark here since we selected only the top 12 countries with the highest number of confirmed cases so what we did was we just filtered it to the top 12 countries so tableau has automatically added country region in the filters card now if you want you can go ahead and edit this filter as well suppose you want to remove argentina you can just uncheck this click on apply and okay you see here argentina has been removed i click on undo so ctrl z to get it back cool now you can convert this horizontal bar plot into a vertical bar plot let me show you how to do it i'll just remove these fields from my chart now what i'm going to do is i'll drag country or region onto columns and i'll have confirmed column under rows there you go here you can see we have a vertical bar plot now and again you can use the access now to short your data this is in descending order similarly you can sort it in ascending order here you have the option to sort your data okay cool and below you can see the different country names all right now you can go ahead and give a name to this sheet let's say top 10 confirmed countries to supply and click on ok you have the title to this plot similarly you can change it here as well top 10 confirmed countries okay now let's move on to our second visualization where we will create a global map so this global map will show the different countries present in the map and the cases that they have so to create the map first i'll drag my longitude generated field onto columns and then i'll drag latitude on to rows you see here we have the map ready and now i'll drag country region onto detail so this will give you the different country names you can see here we have canada the united states mexico here you have india sri lanka there's australia this is brazil and similarly we have spain france belgium there's italy here we have also greece similarly we have the african countries like central african republic this congo angola and on the top you have russia and this is iceland so on and so forth now i'm going to drag the active cases column onto detail again now if i hover the cursor you can see the active numbers for brazil you can see for india you have countries like china then here you have thailand algeria now i'm going to drag the confirmed cases column on to label so that you have the labels here you can see here the confirmed labels cool now if you only want to focus on a few countries present in a particular region you can highlight that region for example suppose for europe you want to focus on this region if you select it it will highlight only those countries and if you hover your mouse over these points it will show you the active cases and the total number of confirmed cases all right now if you want you can go ahead and change the color of the circles let's see i'll keep it as red okay so this is how you can create a map to represent the different active and confirmed cases i'll rename the title to global cases i'll just click on apply and okay similarly i'll change the sheet name as well to global cases okay now from this map you can see the countries such as usa brazil you have russia and india with the highest number of kovit 19 cases even in countries such as spin the numbers are really high and a lot of the african countries were also impacted and these small countries like solomon islands you have fizzy islands with few number of cases even for new zealand as well all right now moving to our next visual now we are going to see the top 10 countries where highest debts have been reported so far due to the pandemic for this we will create a tree map now so first of all i'll choose my country column and put it under columns and then i'll select the deaths column and put it under rows i have the bar plot ready next i'll go to show me and here i have the option to select a tree map you see here for a tree map you need one or more dimensions and one or two measures so i'll just click on this tree map okay here you can see i have my tree map ready so tableau has automatically adjusted my fields so it has removed my fields from rows and columns and put it under size color and label next i will drag my country column and put it under filters and from here i am going to choose top i'll filter by field i'm going to select the top 10 countries based on the total number of deaths just click on apply and click on ok so these are the countries us brazil mexico india united kingdom italy russia spain france germany which have the highest number of deaths reported now you can also give the label i'll put the debts column under label so that you have the labels here all right so this was a tree map that we created to show the top 10 countries with the highest number of deaths i'll just change the title top 10 deaths cases now you can also edit the title in terms of the font color and everything that's i'll keep it taboo bold and i'll choose this as 14 click on apply and okay there you go similarly let's rename the sheet to death cases cool now moving to the next visualization where we'll see about the recoveries so before we move on to create a visualization for the top 10 or let's say the bottom 10 countries in terms of recoveries let me show you how to change the color of the stream app so here by default tableau select the automatic palette if you want you can change it to different palettes let's see i'll go with gold purple diverging click on apply and i'll click on ok if this is not looking that great you can select another one let's say red green white diverging cool now let's go to our fourth visualization which is about 10 recoveries i'll click on apply and ok first i'll drag my country or the region column under columns and then i'll take the recovered column under rows this time instead of sum we are going to choose average okay and here i'll select my packed bubble chart after that i am going to drag my country or region onto colors and then i'll also put country under filters so i'll go to the top menu and i'll filter it by field i want to show the top 10 countries based on the average number of recoveries so click on apply and okay you can see here these are the countries that have the highest number of average recoveries you have india brazil turkey italy argentina germany colombia russia poland and mexico i'll just rename this sheet to recovery cases cool now let's move on to our next visual okay so here we are going to create a scatter plot to analyze the total confirmed cases versus the total number of deaths so whenever you have two numeric fields it is better to create a scatter plot to visualize the cases so for that i am going to drag my confirmed cases to columns and then i will drag my depth column onto rows okay so by default the shape is a circle and to see all the countries i am going to drag country under color i'll say add all members there you go here you can see the legend for the different countries and the top you have us with the highest number of confirmed cases and the highest number of deaths as per our data set then you also have brazil here which has the second highest number of deaths then you have mexico followed by india so this data is not a very recent data this data was last updated on march now from this we are going to filter out the top 10 countries with the highest number of confirmed cases and the deaths so for that i'll drag my country column onto filters and i'll go to top filter by field i'll say top 10 and i'll click on apply and ok so here you can see we have siberia sweden belgium netherlands spain italy france russia uk and you have brazil so these are the countries which have the highest number of active cases so instead of active let's select deaths click on apply and okay okay now if you see we have us here brazil india then mexico uk russia france italy germany and spain which have the highest number of deaths reported so far now if you want you can change the size of the circles based on the highest number of active cases so i can just drag my active column under size so you can see here spin france uk have the highest number of active cases now let's say instead of active i'll just have my confirmed cases under size okay and then you can also change the shape from circle to any other shape let's say square or this asterisk okay let's go with the triangle cool i'll rename this sheet to confirmed versus depths click on apply and ok i'll rename this to confirmed versus deaths alright now moving on to our next visualization where we will see the mortality for each of the countries and figure out which countries have the highest mortality rate okay so first i'll drag mortality rate onto column and then i'll have my country column under rows so there you go now let me go ahead and sort it in descending order so you can see here you have yemen there is mexico syria sudan egypt equator china and other countries which have the highest mortality rate and you can also select the country region under colors let's go ahead and edit the color instead of automatic let's select summer or you can go for some other palette let's say tableau classic medium i'll assign this palette yes i'll apply and okay all right finally we'll move ahead to our last visualization where we'll create a dual access chart so the dual axis chart in tableau will have one column and two different visualizations on the rows so here you have one column and on the right also you'll have one column with their own access values so this dual axis chart will be for recovered cases and the death cases let me show you how to do it so i'll drag my country field onto columns and then i'll choose recovered column onto rows so here i have got my line chart and the second column i'm going to choose is the depths column i'll hold my cursor and i'll place it when i get this green color rectangle i'll place it here there you go so now we have created two measure names deaths and recovered and here you have the access values you can synchronize the access values if you want the recovered cases are in millions and the deaths are in thousands now let's go ahead and change the recovered line to bars and we're going to short it okay now let's change the color we'll select let's say blue i'll assign the palette click on apply and okay and if you want you can also change the color for depths from blue to let's say winter i'll assign the palette click on apply and okay you can have whichever color you want and to make the access seam you can just right click on this axis and click on synchronize access so here you can see the values let's keep it in thousands only cool i'll rename this sheet to dual access okay now before we move ahead and create our final dashboard i want to show you one more feature in tableau that is to format the labels so you can see here instead of writing 5 lakh 30 821 we can change this in terms of millions or thousands and write it as let's say 530k to do that what i'll do is i'll go to format and here we have the option to select font and in font i'll go to fields and click on sum of deaths and here in this pane i'll choose number custom decimal places will be 0 and display units i'll select thousands you can see here the values have been changed to 531k 193k 158k so on and so forth cool now let's go ahead and create our final dashboard here is the option to create a new dashboard so i'll just click on new dashboard and we are going to increase the size here so that it fits our view so we have selected the default view and the size i am going to choose here is automatic so now we can drop our sheets one by one first let's drag the global cases sheet here okay you can see we have our map now i'm going to choose the top 10 confirmed cases all right and after that we'll choose the death cases sheet so tableau will prompt you to place the sheet at the right location okay and now i'll choose my fifth visual which is confirmed versus deaths and if you want you can choose any one of these let's say i'll take the dual axis chart now you can just minimize the sheets and adjust the sizes to make it look better okay so you can see here we have our dashboard ready now you can still go ahead and customize the dashboards in terms of adding different filters to it we are not going to show that now you are getting some messages here to unknown for nulls you can remove them as well and here on the top you have the option to click on the presentation mode the hugo so this is our final dashboard that we have created all right so this brings us to the end of the second coronavirus data analysis project using tableau now you can just click on escape to go back all right now you can save this and this will be published in the tableau public platform under your profile so that brings us to the end of this video tutorial on kobe data analysis project using python and tableau i hope it was useful and informative please try to answer the quizzes that you saw in this video thank you for watching and keep learning hi there if you like this video subscribe to the simply learn youtube channel and click here to watch similar videos to nerd up and get certified click here

Channel: Simplilearn

Views: 8,561

Rating: 4.8990536 out of 5

Keywords: covid-19 data analysis project using python and tableau, covid data analysis tutorial, covid 19 data analysis, covid 19 data analysis project using python, covid 19 dashboard, covid 19 data analysis and visualization project, covid 19 data analysis project, covid 19 data analysis python, covid 19 data analytics project, covid project, covid-19 data analysis, covid project in python, python covid 19 project, covid 19, coronavirus, python, tableau report, simplilearn

Id: DJofs2JyIVM

Length: 95min 15sec (5715 seconds)

Published: Thu Sep 09 2021