Practical Data Manipulation Techniques in C#

Hello and welcome! Today, we're exploring practical data manipulation techniques in C# . We'll use C# lists to represent our data stream and perform projection, filtering, and aggregation. And here's the star of the show: our operations will be neatly packaged within a C# class! No mess, all clean code.

Introduction to Data Manipulation

Data manipulation is akin to being a sculptor but for data. We chisel and shape our data to get the desired structure. C# lists are perfect for this, and our operations will be conveniently bundled inside a C# class . So, let's get our toolbox ready! Here's a simple C# class, DataStream, that will serve as our toolbox: C# using System; using System.Collections.Generic; using System.Linq; public class DataStream<T> {public List<T> Data { get; set; } public DataStream(List<T> data) {Data = data;}}

Data Projection in Practice

Our first stop is data projection. Think of it like capturing a photo of our desired features. Suppose we have data about people. If we're only interested in names and ages, we project our data to include just these details. We'll extend our DataStream class with a ProjectData method for this: C# using System.Collections.Generic; using System.Linq; public class DataStream<T> {public List<T> Data { get; set; } public DataStream(List<T> data) {Data = data;} public List<dynamic> ProjectData(List<string> keys) {var projectedData = Data.Select(d => {var newObj = new Dictionary<string, object>(); foreach (var key in keys) {var propertyInfo = d.GetType().GetProperty(key); newObj[key] = propertyInfo?.GetValue(d, null);} return (dynamic)newObj;}).ToList(); return projectedData;}} public class Program {public static void Main() {var ds = new DataStream<dynamic>(new List<dynamic> {new { Name = "Alice", Age = 25, Profession = "Engineer" }, new { Name = "Bob", Age = 30, Profession = "Doctor" },}); var projectedDs = ds.ProjectData(new List<string> { "Name", "Age" }); foreach (var item in projectedDs) {Console.WriteLine($"{item["Name"]}, {item["Age"]}");} // Outputs: // Alice, 25 // Bob, 30}} As you can see, we now have a new list with just the names and ages!

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Data Filtering in Practice

Next, we have data filtering, which is like cherry-picking our preferred data entries. We'll extend our DataStream class with a FilterData method that uses a "test" function to filter data:

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    // Existing methods...

    public List<T> FilterData(Func<T, bool> testFunc)
    {
        return Data.Where(testFunc).ToList();
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer" },
            new { Name = "Bob", Age = 30, Profession = "Doctor" },
        });

        Func<dynamic, bool> ageTest = x => x.Age > 26;  // our "test" function
        var filteredDs = ds.FilterData(ageTest);
        foreach (var item in filteredDs)
        {
            Console.WriteLine($"{item.Name}, {item.Age}, {item.Profession}");
        }
        // Outputs:
        // Bob, 30, Doctor
    }
}

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    // Existing methods...

    public List<T> FilterData(Func<T, bool> testFunc)
    {
        return Data.Where(testFunc).ToList();
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer" },
            new { Name = "Bob", Age = 30, Profession = "Doctor" },
        });

        Func<dynamic, bool> ageTest = x => x.Age > 26;  // our "test" function
        var filteredDs = ds.FilterData(ageTest);
        foreach (var item in filteredDs)
        {
            Console.WriteLine($"{item.Name}, {item.Age}, {item.Profession}");
        }
        // Outputs:
        // Bob, 30, Doctor
    }
}

With the filter method, our output is a list with only Bob’s data, as he's the only one who passes the 'age over 26' test.

Data Aggregation in Practice

Last is data aggregation, where we condense our data into a summary. We will add an AggregateData method to our DataStream class for this:

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    // Existing methods...

    public double AggregateData(string key, Func<List<dynamic>, double> aggFunc)
    {
        var values = Data.Select(d =>
        {
            var propertyInfo = d.GetType().GetProperty(key);
            return (dynamic)propertyInfo?.GetValue(d, null);
        }).ToList();

        return aggFunc(values);
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer" },
            new { Name = "Bob", Age = 30, Profession = "Doctor" },
        });

        var averageAge = ds.AggregateData("Age", values => values.Average(v => (double)v));
        Console.WriteLine(averageAge);  // Outputs: 27.5
    }
}

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    // Existing methods...

    public double AggregateData(string key, Func<List<dynamic>, double> aggFunc)
    {
        var values = Data.Select(d =>
        {
            var propertyInfo = d.GetType().GetProperty(key);
            return (dynamic)propertyInfo?.GetValue(d, null);
        }).ToList();

        return aggFunc(values);
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer" },
            new { Name = "Bob", Age = 30, Profession = "Doctor" },
        });

        var averageAge = ds.AggregateData("Age", values => values.Average(v => (double)v));
        Console.WriteLine(averageAge);  // Outputs: 27.5
    }
}

With this script, we get the average age of Alice and Bob, which is 27.5.

Combining Projection, Filtering, and Aggregation

Now, let's combine projection, filtering, and aggregation to see the collective power of these techniques. We'll extend our example to demonstrate this flow:

Data Projection: Choose only the desired fields.
Data Filtering: Filter the data based on certain conditions.
Data Aggregation: Summarize the filtered data.

We'll modify our DataStream class to include all the methods and then use them together in a workflow. On top of that, projection and filtering methods will now return an instance of DataStream<T>, not a list as before, so that we can chain these methods when calling them:

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    public DataStream<dynamic> ProjectData(List<string> keys)
    {
        var projectedData = Data.Select(d =>
        {
            var newObj = new Dictionary<string, object>();
            foreach (var key in keys)
            {
                var propertyInfo = d.GetType().GetProperty(key);
                newObj[key] = propertyInfo?.GetValue(d, null);
            }
            return (dynamic)newObj;
        }).ToList();

        return new DataStream<dynamic>(projectedData);
    }

    public DataStream<T> FilterData(Func<T, bool> testFunc)
    {
        var filteredData = Data.Where(testFunc).ToList();
        return new DataStream<T>(filteredData);
    }

    public double AggregateData(string key, Func<List<dynamic>, double> aggFunc)
    {
        var values = Data.Select(d =>
        {
            var propertyInfo = d.GetType().GetProperty(key);
            return (dynamic)propertyInfo?.GetValue(d, null);
        }).ToList();

        return aggFunc(values);
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer", Salary = 70000 },
            new { Name = "Bob", Age = 30, Profession = "Doctor", Salary = 120000 },
            new { Name = "Carol", Age = 35, Profession = "Artist", Salary = 50000 },
            new { Name = "David", Age = 40, Profession = "Engineer", Salary = 90000 },
        });

        // Step 1: Project the data to include only 'Name', 'Age', and 'Salary'
        var projectedDs = ds.ProjectData(new List<string> { "Name", "Age", "Salary" });

        // Step 2: Filter the projected data to include only those with age > 30
        var filteredDs = projectedDs.FilterData(x => x.Age > 30);

        // Step 3: Aggregate the filtered data to compute the average salary
        var averageSalary = filteredDs.AggregateData("Salary", salaries => salaries.Average(v => (double)v));
        Console.WriteLine(averageSalary);  // Outputs: 70000.0
    }
}

using System;
using System.Collections.Generic;
using System.Linq;

public class DataStream<T>
{
    public List<T> Data { get; set; }

    public DataStream(List<T> data)
    {
        Data = data;
    }

    public DataStream<dynamic> ProjectData(List<string> keys)
    {
        var projectedData = Data.Select(d =>
        {
            var newObj = new Dictionary<string, object>();
            foreach (var key in keys)
            {
                var propertyInfo = d.GetType().GetProperty(key);
                newObj[key] = propertyInfo?.GetValue(d, null);
            }
            return (dynamic)newObj;
        }).ToList();

        return new DataStream<dynamic>(projectedData);
    }

    public DataStream<T> FilterData(Func<T, bool> testFunc)
    {
        var filteredData = Data.Where(testFunc).ToList();
        return new DataStream<T>(filteredData);
    }

    public double AggregateData(string key, Func<List<dynamic>, double> aggFunc)
    {
        var values = Data.Select(d =>
        {
            var propertyInfo = d.GetType().GetProperty(key);
            return (dynamic)propertyInfo?.GetValue(d, null);
        }).ToList();

        return aggFunc(values);
    }
}

public class Program
{
    public static void Main()
    {
        var ds = new DataStream<dynamic>(new List<dynamic>
        {
            new { Name = "Alice", Age = 25, Profession = "Engineer", Salary = 70000 },
            new { Name = "Bob", Age = 30, Profession = "Doctor", Salary = 120000 },
            new { Name = "Carol", Age = 35, Profession = "Artist", Salary = 50000 },
            new { Name = "David", Age = 40, Profession = "Engineer", Salary = 90000 },
        });

        // Step 1: Project the data to include only 'Name', 'Age', and 'Salary'
        var projectedDs = ds.ProjectData(new List<string> { "Name", "Age", "Salary" });

        // Step 2: Filter the projected data to include only those with age > 30
        var filteredDs = projectedDs.FilterData(x => x.Age > 30);

        // Step 3: Aggregate the filtered data to compute the average salary
        var averageSalary = filteredDs.AggregateData("Salary", salaries => salaries.Average(v => (double)v));
        Console.WriteLine(averageSalary);  // Outputs: 70000.0
    }
}

Here:

Projection: We choose only the Name, Age, and Salary fields from our data. The ProjectData method now returns a DataStream<dynamic> object, allowing us to chain multiple operations.
Filtering: We filter the projected data to include only those persons whose age is greater than 30. The FilterData method also returns a DataStream<T> object for chaining.
Aggregation: We calculate the average salary of the filtered data. The final output shows the average salary for those aged over 30, which is 70,000.

By combining these methods, our data manipulation becomes both powerful and concise. Try experimenting and see what you can create!

Practical Data Manipulation Techniques in C#