DEV Community

Praveen Raghuvanshi
Praveen Raghuvanshi

Posted on • Edited on

COVID-19 EDA and Prediction using .Net Dataframe and ML.Net (C#) - Exploratory Data Analysis

#dotnet #datascience #csharp #machinelearning

COVID-19

  • As per Wiki Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally, resulting in the ongoing 2019–20 coronavirus pandemic.
  • The virus had caused a pandemic across the globe and spreading/affecting most of the nations.
  • The purpose of notebook is to visualize the trends of virus spread in various countries and explore features present in ML.Net such as DataFrame.

Acknowledgement

Dataset

Introduction

DataFrame: DataFrame is a new type introduced in .Net. It is similar to DataFrame in Python which is used to manipulate data in notebooks. It's a collection of columns containing data similar to a table and very helpful in analyzing tabular data. It works flawlessly without creating types/classes mapped to columns in a table which we used to do with ML.Net. It has support for GroupBy, Sort, Filter which makes analysis very handy. It's a in-memory representation of structured data.

In this tutorial we'll cover below features

  • Load a CSV
  • Metadata
    • Description
    • Info
  • Display records
    • Head
    • Sample
  • Filtering
  • Grouping
  • Aggregate

For overview, please refer below links

Part-2 covers time series analysis and prediction using ML.Net

Summary

Below is the summary of steps we'll be performing

  1. Define application level items

    • Nuget packages
    • Namespaces
    • Constants

  2. Utility Functions

    • Formatters

  3. Load Dataset

  4. Analyze Data

    • Date Range
    • Display Dataset - display(dataframe)
    • Display Top 5 Rows - dataframe.Head(5)
    • Display Random 6 Rows - dataframe.Sample(6)
    • Display Dataset Statistics - dataframe.Description()
    • Display Dataset type information - dataframe.Info()

  5. Data Cleaning

    • Remove Invalid cases

  6. Data Visualization

    • Global
      • Confirmed Vs Deaths Vs Recovered
      • Top 5 Countries with Confirmed cases
      • Top 5 Countries with Death cases
      • Top 5 Countries with Recovered cases
    • India
      • Confirmed Vs Deaths Vs Recovered

Note : Graphs/Plots may not render in GitHub due to security reasons, however if you run this notebook locally/binder they will render.

1. Define Application wide Items

Nuget Packages 

// ML.NET Nuget packages installation
#r "nuget:Microsoft.ML"
#r "nuget:Microsoft.Data.Analysis"

// Install XPlot package
#r "nuget:XPlot.Plotly"

// CSV Helper Package for reading CSV
#r "nuget:CsvHelper"
Enter fullscreen mode Exit fullscreen mode 
Installed package XPlot.Plotly version 3.0.1
Installed package Microsoft.Data.Analysis version 0.4.0
Installed package CsvHelper version 15.0.5
Installed package Microsoft.ML version 1.5.0
Enter fullscreen mode Exit fullscreen mode

Namespaces 

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.Data.Analysis;
using Microsoft.AspNetCore.Html;
using System.IO;
using System.Net.Http;
using System.Globalization;
using CsvHelper;
using CsvHelper.Configuration;
using XPlot.Plotly;
Enter fullscreen mode Exit fullscreen mode

Constants 

// Column Names
const string FIPS = "FIPS";
const string ADMIN = "Admin2";
const string STATE = "Province_State";
const string COUNTRY = "Country_Region";
const string LAST_UPDATE = "Last_Update";
const string LATITUDE = "Lat";
const string LONGITUDE = "Long_";
const string CONFIRMED = "Confirmed";
const string DEATHS = "Deaths";
const string RECOVERED = "Recovered";
const string ACTIVE = "Active";
const string COMBINED_KEY = "Combined_Key";

// File
const string DATASET_FILE = "05-27-2020";
const string FILE_EXTENSION = ".csv";
const string NEW_FILE_SUFFIX = "_new";
const char SEPARATOR = ',';
const char SEPARATOR_REPLACEMENT = '_';
const string DATASET_GITHUB_DIRECTORY = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/";

// DataFrame/Table
const int TOP_COUNT = 5;
const int DEFAULT_ROW_COUNT = 10;
const string VALUES = "Values";
const string INDIA = "India";
Enter fullscreen mode Exit fullscreen mode

2. Utility Functions

Formatters

By default the output of DataFrame is not proper and in order to display it as a table, we need to have a custom formatter implemented as shown in next cell.

// Formats the table

Formatter<DataFrame>.Register((df, writer) =>
{
    var headers = new List<IHtmlContent>();
    headers.Add(th(i("index")));
    headers.AddRange(df.Columns.Select(c => (IHtmlContent) th(c.Name)));
    var rows = new List<List<IHtmlContent>>();
    var take = DEFAULT_ROW_COUNT;
    for (var i = 0; i < Math.Min(take, df.Rows.Count); i++)
    {
        var cells = new List<IHtmlContent>();
        cells.Add(td(i));
        foreach (var obj in df.Rows[i])
        {
            cells.Add(td(obj));
        }
        rows.Add(cells);
    }

    var t = table(
        thead(
            headers),
        tbody(
            rows.Select(
                r => tr(r))));

    writer.Write(t);
}, "text/html");
Enter fullscreen mode Exit fullscreen mode

Copy dataset csv and replace Separator in cells 

// Replace a characeter in a cell of csv with a defined separator
private void CreateCsvAndReplaceSeparatorInCells(string inputFile, string outputFile, char separator, char separatorReplacement)
{
    var culture = CultureInfo.InvariantCulture;
    using var reader = new StreamReader(inputFile);
    using var csvIn = new CsvReader(reader, new CsvConfiguration(culture));
    using var recordsIn = new CsvDataReader(csvIn);
    using var writer = new StreamWriter(outputFile);
    using var outCsv = new CsvWriter(writer, culture);

    // Write Header
    csvIn.ReadHeader();
    var headers = csvIn.Context.HeaderRecord;
    foreach (var header in headers)
    {
        outCsv.WriteField(header.Replace(separator, separatorReplacement));
    }
    outCsv.NextRecord();

    // Write rows
    while (recordsIn.Read())
    {
        var columns = recordsIn.FieldCount;
        for (var index = 0; index < columns; index++)
        {
            var cellValue = recordsIn.GetString(index);
            outCsv.WriteField(cellValue.Replace(separator, separatorReplacement));
        }
        outCsv.NextRecord();
    }
}
Enter fullscreen mode Exit fullscreen mode

3. Load Dataset

Download Dataset from Johns Hopkins CSSE

We'll be using COVID-19 dataset from Johns Hopkins CSSE. The csse_covid_19_data directory has .csv file for each day and we'll be performing analysis on latest file present. Latest file present at the time of last modification of this notebook was 05-27-2020.csv. If you wish to use a different file, update DATASET_FILE constant in Constants cell above.

We'll download file to current directory.

// Download csv from github
var originalFileName = $"{DATASET_FILE}{FILE_EXTENSION}";
if (!File.Exists(originalFileName))
{
    var remoteFilePath = $"{DATASET_GITHUB_DIRECTORY}/{originalFileName}";
    display(remoteFilePath);
    var contents = new HttpClient()
        .GetStringAsync(remoteFilePath).Result;

    File.WriteAllText(originalFileName, contents);
}
Enter fullscreen mode Exit fullscreen mode

https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports//05-27-2020.csv

Load dataset in DataFrame

Issue: We can load csv using LoadCsv(..) method of DataFrame. However, there is an issue of not allowing quotes and separator(comma in this case) in a cell value.
The dataset, we are using has both of them and LoadCsv fails for it.
As a workaround, we'll use CSVHelper to read the csv file and replace command separator with underscore, save the file and use it to load in DataFrame LoadCsv(..) method.

Invalid Character

// Load and create a copy of dataset file
var newFileName = $"{DATASET_FILE}{NEW_FILE_SUFFIX}{FILE_EXTENSION}";
display(newFileName);
CreateCsvAndReplaceSeparatorInCells(originalFileName, newFileName, SEPARATOR, SEPARATOR_REPLACEMENT);
Enter fullscreen mode Exit fullscreen mode

05-27-2020_new.csv

var covid19Dataframe = DataFrame.LoadCsv(newFileName);
Enter fullscreen mode Exit fullscreen mode

4. Data Analysis

Data analysis is a critical activity in the field of Data science. It provides ways to uncover the hidden attributes of a dataset which can't be analyzed or predicted by simply looking at the data source. DataFrame makes the analysis simple by providing great API's such as GroupBy, Sort, Filter etc. Jupyter notebook is great tool for this kind of activity which maintains values of variables executed in a cell and providing it to other cells.

Finding the range in the records in Dataset

In DataFrame, Columns property allows access to values within a column by specifying column name. we'll use Last_Update column to get the date and sort it to get the start and end date

// Gets the data range

var dateRangeDataFrame = covid19Dataframe.Columns[LAST_UPDATE].ValueCounts();
var dataRange = dateRangeDataFrame.Columns[VALUES].Sort();
var lastElementIndex = dataRange.Length - 1;

var startDate = DateTime.Parse(dataRange[0].ToString()).ToShortDateString();
var endDate  = DateTime.Parse(dataRange[lastElementIndex].ToString()).ToShortDateString(); // Last Element

display(h4($"The data is between {startDate} and {endDate}"));
Enter fullscreen mode Exit fullscreen mode

The data is between 5/28/2020 and 5/28/2020

Display 10 records

Here we have 12 columns which includes Country, State, Confirmed, Deaths, Recovered and Active cases

display(covid19Dataframe)
Enter fullscreen mode Exit fullscreen mode
index FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ Confirmed Deaths Recovered Active Combined_Key
0 45001 Abbeville South Carolina US 2020-05-28 02:32:31 34.223335 -82.46171 35 0 0 35 Abbeville_ South Carolina_ US
1 22001 Acadia Louisiana US 2020-05-28 02:32:31 30.295065 -92.4142 397 22 0 375 Acadia_ Louisiana_ US
2 51001 Accomack Virginia US 2020-05-28 02:32:31 37.76707 -75.63235 780 12 0 768 Accomack_ Virginia_ US
3 16001 Ada Idaho US 2020-05-28 02:32:31 43.452656 -116.241554 798 22 0 776 Ada_ Idaho_ US
4 19001 Adair Iowa US 2020-05-28 02:32:31 41.330757 -94.47106 7 0 0 7 Adair_ Iowa_ US
5 21001 Adair Kentucky US 2020-05-28 02:32:31 37.1046 -85.281296 96 19 0 77 Adair_ Kentucky_ US
6 29001 Adair Missouri US 2020-05-28 02:32:31 40.190586 -92.600784 46 0 0 46 Adair_ Missouri_ US
7 40001 Adair Oklahoma US 2020-05-28 02:32:31 35.88494 -94.65859 82 3 0 79 Adair_ Oklahoma_ US
8 8001 Adams Colorado US 2020-05-28 02:32:31 39.87432 -104.33626 3006 118 0 2888 Adams_ Colorado_ US
9 16003 Adams Idaho US 2020-05-28 02:32:31 44.893337 -116.45452 3 0 0 3 Adams_ Idaho_ US
Display Top 5 records 
covid19Dataframe.Head(5)
Enter fullscreen mode Exit fullscreen mode
index FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ Confirmed Deaths Recovered Active Combined_Key
0 45001 Abbeville South Carolina US 2020-05-28 02:32:31 34.223335 -82.46171 35 0 0 35 Abbeville_ South Carolina_ US
1 22001 Acadia Louisiana US 2020-05-28 02:32:31 30.295065 -92.4142 397 22 0 375 Acadia_ Louisiana_ US
2 51001 Accomack Virginia US 2020-05-28 02:32:31 37.76707 -75.63235 780 12 0 768 Accomack_ Virginia_ US
3 16001 Ada Idaho US 2020-05-28 02:32:31 43.452656 -116.241554 798 22 0 776 Ada_ Idaho_ US
4 19001 Adair Iowa US 2020-05-28 02:32:31 41.330757 -94.47106 7 0 0 7 Adair_ Iowa_ US
Display Random 6 records 
covid19Dataframe.Sample(6)
Enter fullscreen mode Exit fullscreen mode
index FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ Confirmed Deaths Recovered Active Combined_Key
0 18025 Crawford Indiana US 2020-05-28 02:32:31 38.288143 -86.44519 23 0 0 23 Crawford_ Indiana_ US
1 51065 Fluvanna Virginia US 2020-05-28 02:32:31 37.84158 -78.27715 87 6 0 81 Fluvanna_ Virginia_ US
2 13241 Rabun Georgia US 2020-05-28 02:32:31 34.883896 -83.403046 17 1 0 16 Rabun_ Georgia_ US
3 12003 Baker Florida US 2020-05-28 02:32:31 30.3306 -82.284676 29 3 0 26 Baker_ Florida_ US
4 21133 Letcher Kentucky US 2020-05-28 02:32:31 37.123066 -82.85346 4 0 0 4 Letcher_ Kentucky_ US
5 12077 Liberty Florida US 2020-05-28 02:32:31 30.23766 -84.88293 209 0 0 209 Liberty_ Florida_ US
Display Dataset Statistics such as Total, Max, Min, Mean of items in a column 
covid19Dataframe.Description()
Enter fullscreen mode Exit fullscreen mode
index Description FIPS Lat Long_ Confirmed Deaths Recovered Active
0 Length (excluding null values) 3009 3346 3346 3414 3414 3414 3414
1 Max 99999 71.7069 178.065 370680 37460 391508 229780
2 Min 0 -52.368 -164.03539 0 0 0 -364117
3 Mean 27622.38 35.3851 -78.927086 1667.1909 104.16784 688.3679 882.6784
Display Dataset type information for each column 
covid19Dataframe.Info()
Enter fullscreen mode Exit fullscreen mode
index Info FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ Confirmed Deaths Recovered Active Combined_Key
0 DataType System.Single System.String System.String System.String System.String System.Single System.Single System.Single System.Single System.Single System.Single System.String
1 Length (excluding null values) 3009 3414 3414 3414 3414 3346 3346 3414 3414 3414 3414 3414

5. Data Cleaning

Data Cleaning is another important activity in which remove the irrelevant data present in our dataset. This irrelevant data can be due missing values, invalid values or an outlier. The columns with less significance is removed for better analysis and prediction of our data. In order to keep this notebook simple, we'll use one of the techniques to remove invalid data. In this we are going to remove invalid Active cases such as the ones having negative values. The other techniques we can apply on data could be DropNull to remove rows with null values, FillNull to fill null values with other such as mean, average. We can transform DataFrame and remove some of the unnecessary columns.

Remove invalid Active cases 

covid19Dataframe.Description()
Enter fullscreen mode Exit fullscreen mode
index Description FIPS Lat Long_ Confirmed Deaths Recovered Active
0 Length (excluding null values) 3009 3346 3346 3414 3414 3414 3414
1 Max 99999 71.7069 178.065 370680 37460 391508 229780
2 Min 0 -52.368 -164.03539 0 0 0 -364117
3 Mean 27622.38 35.3851 -78.927086 1667.1909 104.16784 688.3679 882.6784

From the above description table, we could see negative value for Active cases which seems to be incorrect as number of active cases is cases is calculated by the below formula

Active = Confirmed - Deaths - Recovered

In order to check for invalid active cases, we'll use DataFrame Filter to retrieve active column values whose value is less than 0.0

// Filter : Gets active records with negative calues

PrimitiveDataFrameColumn<bool> invalidActiveFilter = covid19Dataframe.Columns[ACTIVE].ElementwiseLessThan(0.0);
var invalidActiveDataFrame = covid19Dataframe.Filter(invalidActiveFilter);
display(invalidActiveDataFrame)
Enter fullscreen mode Exit fullscreen mode
index FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ Confirmed Deaths Recovered Active Combined_Key
0 90004 Unassigned Arizona US 2020-05-28 02:32:31 <null> <null> 0 2 0 -2 Unassigned_ Arizona_ US
1 90018 Unassigned Indiana US 2020-05-28 02:32:31 <null> <null> 0 159 0 -159 Unassigned_ Indiana_ US
2 90022 Unassigned Louisiana US 2020-05-28 02:32:31 <null> <null> 84 105 0 -21 Unassigned_ Louisiana_ US
3 90024 Unassigned Maryland US 2020-05-28 02:32:31 <null> <null> 0 67 0 -67 Unassigned_ Maryland_ US
4 90032 Unassigned Nevada US 2020-05-28 02:32:31 <null> <null> 0 6 0 -6 Unassigned_ Nevada_ US
5 90033 Unassigned New Hampshire US 2020-05-28 02:32:31 <null> <null> 10 51 0 -41 Unassigned_ New Hampshire_ US
6 90038 Unassigned North Dakota US 2020-05-28 02:32:31 <null> <null> 0 8 0 -8 Unassigned_ North Dakota_ US
7 90056 Unassigned Wyoming US 2020-05-28 02:32:31 <null> <null> 0 13 0 -13 Unassigned_ Wyoming_ US
8 <null> C. Valenciana Spain 2020-05-28 02:32:31 39.484 -0.7533 11089 1332 9970 -213 C. Valenciana_ Spain
9 <null> Cantabria Spain 2020-05-28 02:32:31 43.1828 -3.9878 2283 202 2287 -206 Cantabria_ Spain

If we take any record(index 5) and apply above formula to calculate

Active(-13) = Confirmed(10) - Deaths(51) - Recovered(0)

We could see invalid active cases.

In order to remove it, we'll apply a Filter to DataFrame to get active values greater than or equal to 0.0.

// Remove invalid active cases by applying filter

PrimitiveDataFrameColumn<bool> activeFilter = covid19Dataframe.Columns[ACTIVE].ElementwiseGreaterThanOrEqual(0.0);
covid19Dataframe = covid19Dataframe.Filter(activeFilter);
display(covid19Dataframe.Description());
Enter fullscreen mode Exit fullscreen mode
index Description FIPS Lat Long_ Confirmed Deaths Recovered Active
0 Length (excluding null values) 3001 3338 3338 3395 3395 3395 3395
1 Max 99999 71.7069 178.065 370680 37460 142208 229780
2 Min 0 -52.368 -164.03539 0 0 0 0
3 Mean 27564.82 35.48979 -79.35968 1664.4899 103.38468 505.34668 1055.7584

As seen above, negative active cases have been removed

6. Visualization

Visualization of data helps business owners make better decisions. The DataFrame maintains data in a tabular format. In order to prepare data for different plots, I have used DataFrame features such as Sum, GroupBy, OrderBy, OrderByDescending etc.

For visualization, I have used open source library called as XPlot.Plotly. Different plots have been used such as Bar, Pie and Line/Scatter Graph.

Global

Collect Data 
//  Gets the collection of confirmed, deaths and recovered

var confirmed = covid19Dataframe.Columns[CONFIRMED];
var deaths = covid19Dataframe.Columns[DEATHS];
var recovered = covid19Dataframe.Columns[RECOVERED];

// Gets the sum of collection by using Sum method of DataFrame
var totalConfirmed = Convert.ToDouble(confirmed.Sum());
var totalDeaths = Convert.ToDouble(deaths.Sum());
var totaRecovered = Convert.ToDouble(recovered.Sum());
Enter fullscreen mode Exit fullscreen mode
Confirmed Vs Deaths Vs Recovered cases 
display(Chart.Plot(
    new Graph.Pie()
    {
        values = new double[]{totalConfirmed, totalDeaths, totaRecovered},
        labels = new string[] {CONFIRMED, DEATHS, RECOVERED}
    }
));
Enter fullscreen mode Exit fullscreen mode

Confirmed Vs Deaths Vs Recovered

Top 5 Countries with Confirmed cases

In order to get top 5 countries data, I have used DataFrame's GroupBy, Sum, OrderByDescending methods

// The data for top 5 countries is not present in the csv file.
// In order to get that, first DataFrame's GROUPBY is used aginst the country.
// Then it was aggregated using SUM on Confirmed column.
// In the last, ORDERBYDESCENDING is used to get the top five countries.

var countryConfirmedGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(CONFIRMED).OrderByDescending(CONFIRMED);
var topCountriesColumn = countryConfirmedGroup.Columns[COUNTRY];
var topConfirmedCasesByCountry = countryConfirmedGroup.Columns[CONFIRMED];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> confirmedCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    confirmedCases.Add(Convert.ToInt64(topConfirmedCasesByCountry[index]));
}
Enter fullscreen mode Exit fullscreen mode 
var title = "Top 5 Countries : Confirmed";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = confirmedCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);
Enter fullscreen mode Exit fullscreen mode

Top 5 Confirmed Countries Global

Top 5 Countries with Deaths 
// Get the data
var countryDeathsGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(DEATHS).OrderByDescending(DEATHS);
var topCountriesColumn = countryDeathsGroup.Columns[COUNTRY];
var topDeathCasesByCountry = countryDeathsGroup.Columns[DEATHS];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> deathCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    deathCases.Add(Convert.ToInt64(topDeathCasesByCountry[index]));
}
Enter fullscreen mode Exit fullscreen mode 
var title = "Top 5 Countries : Deaths";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = deathCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);
Enter fullscreen mode Exit fullscreen mode

Top-5 Countries: Deaths

Top 5 Countries with Recovered cases 
// Get the data
var countryRecoveredGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(RECOVERED).OrderByDescending(RECOVERED);
var topCountriesColumn = countryRecoveredGroup.Columns[COUNTRY];
var topRecoveredCasesByCountry = countryRecoveredGroup.Columns[RECOVERED];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> recoveredCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    recoveredCases.Add(Convert.ToInt64(topRecoveredCasesByCountry[index]));
}
Enter fullscreen mode Exit fullscreen mode 
var title = "Top 5 Countries : Recovered";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = recoveredCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);
Enter fullscreen mode Exit fullscreen mode

Top-5 Countries : Recovered

World Confirmed Cases Map 
var world = countryConfirmedGroup;
countries.Clear();
List<string> worldConfirmedCases = new List<string>();
for (int index = 0; index < world.Columns[COUNTRY].Length; index++)
{
    countries.Add(world.Columns[COUNTRY][index].ToString());
    worldConfirmedCases.Add(world.Columns[CONFIRMED][index].ToString());
}

var locations = countryConfirmedGroup.Columns[COUNTRY];

var worldConfirmedMap = Chart.Plot(
    new Graph.Choropleth()
    {
        locations = countries.ToArray(),
        z = worldConfirmedCases.ToArray(),
        locationmode = "country names",
        text = countryConfirmedGroup.Columns[COUNTRY],
        colorscale = "active",
        hoverinfo = COUNTRY,
        autocolorscale = true,

    });
worldConfirmedMap.WithTitle("World Confirmed Cases");
display(worldConfirmedMap);
Enter fullscreen mode Exit fullscreen mode

World Confirmed Cases Map

India

Confirmed Vs Deaths Vs Recovered cases

Filtering on Country column with INDIA as value

// Filtering on Country column with INDIA as value

PrimitiveDataFrameColumn<bool> indiaFilter = covid19Dataframe.Columns[COUNTRY].ElementwiseEquals(INDIA);
var indiaDataFrame = covid19Dataframe.Filter(indiaFilter);

var indiaConfirmed = indiaDataFrame.Columns[CONFIRMED];
var indiaDeaths = indiaDataFrame.Columns[DEATHS];
var indiaRecovered = indiaDataFrame.Columns[RECOVERED];

var indiaTotalConfirmed = Convert.ToDouble(indiaConfirmed.Sum());
var indiaTotalDeaths = Convert.ToDouble(indiaDeaths.Sum());
var indiaTotaRecovered = Convert.ToDouble(indiaRecovered.Sum());
Enter fullscreen mode Exit fullscreen mode 
display(Chart.Plot(
    new Graph.Pie()
    {
        values = new double[]{indiaTotalConfirmed, indiaTotalDeaths, indiaTotaRecovered},
        labels = new string[] {CONFIRMED, DEATHS, RECOVERED}
    }
));
Enter fullscreen mode Exit fullscreen mode

India - Confirmed Vs Recovered Vs Deaths

Conclusion

I hope you have enjoyed reading the notebook, and might have got some idea on the powerful features of DataFrame in .Net. Data science capabilities are emerging fast in the .Net ecosystem which abstracts lot of complexity present in the field. The focus of this notebook is data analysis and there is nothing present from a Machine Learning perspective such as making a prediction. In Part-2, I have done time series analysis and predictions using ML.Net

Notebook: https://github.com/praveenraghuvanshi1512/covid-19

If you liked it, please like/comment at Comments. It'll encourage me to write more.

Contact

LinkedIn : https://in.linkedin.com/in/praveenraghuvanshi

Github : https://github.com/praveenraghuvanshi1512

Twitter : @praveenraghuvan

I am running an unofficial telegram group for ML.Net enthusiasts, please feel free to join it at https://t.me/joinchat/IifUJQ_PuYT757Turx-nLg

References

Be Safe

Top comments (0)

Read next

hana_sato profile image

Talend vs. Apache Kafka: Which Data Tool Drives Better Business Insights?

Hana Sato -

power_gi_1122 profile image

Microsoft Robotic Process Automation

Power GI -

craftedwithintent profile image

Enhancing Observability in Machine Learning with OpenTelemetry: InsightfulAI Update

Philip Thomas -

foyzulkarim profile image

Understanding Machine Learning Model Types: A Practical Guide (3)

Foyzul Karim -