Covid-19 : The World Threat
Deadly coronavirus has taken over the world . It has affected the world lot in almost all the fields let it be education, businesses , countries economy , healthcare and much more. How many people have the coronavirus ? This remains the most important question for its people, schools, hospitals, and businesses. It is also still among the hardest to answer.
On looking at the numbers, there are total 17.6 Cr cases all over the world with daily rise in new cases by almost 4–5 L. Total Deaths recorded till now are almost 37.8 L. Though we have good recovery rate as well 16 Cr. Analysis performed reveals that vaccination numbers are also quite good i.e. around 2 billion people worldwide and count is increasing everyday.
Introduction :
This analysis is basically trying to visualize various trends in cases of Covid19 . This report shows various graphs show casing the total confirmed cases, total recovered , total active cases and death rates worldwide. This report also has detailed analysis for Covid-19 cases summary for India.
Exploratory Data Analysis :
In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
The above is the basic approach for any data science project to start with and exploratory analysis is the very basic and most important step .
The very step is Importing Libraries required for the model, followed by reading the dataset and preprocessing it. Here for this analysis I have used two different dataset . These have lot of null values in them, so we need to replace them either with Nan or 0 or we can even delete that rows. This step is must, if not done might affect our visualizations.
Since, we have two datasets there are multiple data frames so for ease we would be combining these into a single data frame and then we would be dropping unwanted columns.
Insights :
Correlation Plot: Correlation plots can be used to quickly find insights. It is used to investigate the dependence between multiple variables at the same time and to highlight the most correlated variables in a data table.
Bar Plots : A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically. One of the axis of the plot represents the specific categories being compared, while the other axis represents the measured values corresponding to those categories.
Scatter Geo Plot : The Plotly Express function
px.scatter_geofor a geographical scatter plot. Thesizeargument is used to set the size of markers from a given column of the Data Frame.
Bar and Line Plots for Daily Rise in Cases:
A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segments.
Vaccination Insights :
Let’s see vaccination trends for top 20 countries . This will have visualizations for number of people vaccinated with one dose and people fully vaccinated.
Vaccine Brands :
There are total 14 brands used by various countries, few of them are :
Choropleth Map : A choropleth map is a type of thematic map in which a set of pre-defined areas is colored or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic within each area, such as population density or per-capita income.
Analysis for India :
India is witnessing a second wave of COVID-19 pandemic. So far, the country has recorded over 2.65 crore confirmed COVID-19 cases, including 2,99,266 deaths. A total of 2,34,25,467 people have recuperated from COVID-19 so far. There are 28,05,399 active cases in the country as of date, which comprises 10.57 percent of the total caseload, the data stated.
But these trends are reducing after taking several precautionary measures. India’s daily Covid count remained below 1 lakh with the country reporting 84,332 new cases , the lowest in 70 days, according to the data updated by the Union health ministry. With this, the country’s total tally has gone up to 29,359,155. On June 8, India reported 86,498 Covid cases, the lowest in 63 days. Deaths due to Covid, however, are on the rise again. With 4,002 more Covid deaths in the last 24 hours, the death toll has climbed to 3,67,081.
Visualizations for India’s Case Trends :
The plot above clearly shows that though active cases are decreasing but there is rise in number of deaths due to covid-19. Reason behind increasing death rates is definitely lack of resources due to more number of cases compared to resources.
Daily vaccinations in India :
Dist Plot : distplot() function provides access to several approaches for visualizing the univariate or bivariate distribution of data, including subsets of data defined by semantic mapping and faceting across multiple subplots.
The above graphs shows us that vaccination in the April and May month was reduced with quite large number due to unavailability of vaccines but later increased after restoration of vaccines. Vaccine brand used in India is Covishield and Covaxin, highest used is Covishield.
Joint plot : jointplot() function provides a convenient interface to the
JointGridclass, with several canned plot kinds. This is intended to be a fairly lightweight wrapper; if you need more flexibility, you should useJointGriddirectly.
Pair Plot : Pair plots are a powerful tool to quickly explore distributions and relationships in a dataset. Seaborn provides a simple default method for making pair plots that can be customized and extended through the Pair Grid class.
Conclusion :
So with that we completed creating COVID-19 report with Python. If you forgot to follow any above-mentioned steps I have provided the full code for this analysis below. Apart from analysis we can create various predicting models using python, numpy, pandas, matplotlib and many more. You can find many useful resources on the internet based on data science in python.
Hope this article was useful and knowledgeable!!!
References :
- Dataset : First and Second
- Libraries : seaborn , plotly , matplotlib and colormaps
- Code : Github and Kaggle Notebook
