Frequency Tables and Relative Frequency

/**
 * Fintech Math Bootcamp · Lesson 038 of 120
 * Frequency Tables and Relative Frequency
 * Module 04: Location, Ranking, and Exploratory Summaries
 *
 * Scenario: Turning payment status counts into a denominator-aware summary
 * Rule:     relative frequency = category count / total count
 *
 * Try it:   Must shares sum to 100% when a payment can have several labels?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson038() {
  const statuses = ["ok","ok","review","ok","failed"];
  const counts = new Map<string,number>();
  for (const s of statuses) counts.set(s,(counts.get(s)??0)+1);
  const result = [...counts].map(([status,count]) =>
    ({status,count,frequency:count/statuses.length}));
  return result;
}

export const checkedResult = [{"status":"ok","count":3,"frequency":0.6},{"status":"review","count":1,"frequency":0.2},{"status":"failed","count":1,"frequency":0.2}];

// Run this file directly: npx tsx lessons/04-location-ranking-and-exploratory-summaries/038-frequency-tables-and-relative-frequency.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson038(), null, 2));
}

# Fintech Math Bootcamp · Lesson 038 of 120
# Frequency Tables and Relative Frequency
# Module 04: Location, Ranking, and Exploratory Summaries
#
# Scenario: Turning payment status counts into a denominator-aware summary
# Rule:     relative frequency = category count / total count
#
# Try it:   Must shares sum to 100% when a payment can have several labels?
#
# Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
# Free course:    https://courses.thefintechbuilder.com
# Synthetic teaching example, not financial advice or a production library.

import json


def lesson038() -> list:
    statuses = ["ok", "ok", "review", "ok", "failed"]
    counts: dict[str, int] = {}  # dicts keep first-seen order
    for s in statuses:
        counts[s] = counts.get(s, 0) + 1
    return [
        {"status": status, "count": count, "frequency": count / len(statuses)}
        for status, count in counts.items()
    ]


if __name__ == "__main__":
    print(json.dumps(lesson038(), indent=2))

/**
 * Fintech Math Bootcamp · Lesson 038 of 120
 * Frequency Tables and Relative Frequency
 * Module 04: Location, Ranking, and Exploratory Summaries
 *
 * Scenario: Turning payment status counts into a denominator-aware summary
 * Rule:     relative frequency = category count / total count
 *
 * Try it:   Must shares sum to 100% when a payment can have several labels?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

public class Main {
    static List<Map<String, Object>> lesson038() {
        String[] statuses = {"ok", "ok", "review", "ok", "failed"};
        Map<String, Integer> counts = new LinkedHashMap<String, Integer>(); // keeps first-seen order
        for (String s : statuses) {
            Integer seen = counts.get(s);
            counts.put(s, seen == null ? 1 : seen + 1);
        }
        List<Map<String, Object>> rows = new ArrayList<Map<String, Object>>();
        for (Map.Entry<String, Integer> entry : counts.entrySet()) {
            Map<String, Object> row = new LinkedHashMap<String, Object>();
            row.put("status", entry.getKey());
            row.put("count", entry.getValue());
            row.put("frequency", (double) entry.getValue() / statuses.length);
            rows.add(row);
        }
        return rows;
    }

    public static void main(String[] args) {
        System.out.println(toJson(lesson038(), ""));
    }

    // --- Minimal JSON printer: maps keep insertion order, 2-space indent. ---

    static String toJson(Object value, String indent) {
        if (value == null) return "null";
        if (value instanceof String) return "\"" + value + "\"";
        if (value instanceof Double) return formatNumber((Double) value);
        if (value instanceof Number) return value.toString();
        if (value instanceof double[]) {
            List<Object> items = new ArrayList<Object>();
            for (double v : (double[]) value) items.add(v);
            return toJson(items, indent);
        }
        String inner = indent + "  ";
        StringBuilder sb = new StringBuilder();
        if (value instanceof Map) {
            Map<?, ?> map = (Map<?, ?>) value;
            if (map.isEmpty()) return "{}";
            sb.append("{\n");
            int i = 0;
            for (Map.Entry<?, ?> entry : map.entrySet()) {
                sb.append(inner).append('"').append(entry.getKey()).append("\": ")
                  .append(toJson(entry.getValue(), inner))
                  .append(++i < map.size() ? ",\n" : "\n");
            }
            return sb.append(indent).append('}').toString();
        }
        List<?> list = (List<?>) value;
        if (list.isEmpty()) return "[]";
        sb.append("[\n");
        for (int i = 0; i < list.size(); i++) {
            sb.append(inner).append(toJson(list.get(i), inner))
              .append(i + 1 < list.size() ? ",\n" : "\n");
        }
        return sb.append(indent).append(']').toString();
    }

    static String formatNumber(double v) {
        if (Double.isNaN(v) || Double.isInfinite(v)) return "null";
        if (v == Math.rint(v) && Math.abs(v) < 1e15) return Long.toString((long) v);
        return Double.toString(v);
    }
}

// Fintech Math Bootcamp · Lesson 038 of 120
// Frequency Tables and Relative Frequency
// Module 04: Location, Ranking, and Exploratory Summaries
//
// Scenario: Turning payment status counts into a denominator-aware summary
// Rule:     relative frequency = category count / total count
//
// Try it:   Must shares sum to 100% when a payment can have several labels?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.

package main

import (
	"encoding/json"
	"fmt"
)

// FrequencyRow is one line of the frequency table.
type FrequencyRow struct {
	Status    string  `json:"status"`
	Count     int     `json:"count"`
	Frequency float64 `json:"frequency"`
}

func lesson038() []FrequencyRow {
	statuses := []string{"ok", "ok", "review", "ok", "failed"}
	counts := map[string]int{}
	var order []string // Go maps are unordered, so remember first-seen order
	for _, s := range statuses {
		if counts[s] == 0 {
			order = append(order, s)
		}
		counts[s]++
	}
	rows := []FrequencyRow{}
	for _, status := range order {
		rows = append(rows, FrequencyRow{
			Status:    status,
			Count:     counts[status],
			Frequency: float64(counts[status]) / float64(len(statuses)),
		})
	}
	return rows
}

func main() {
	out, _ := json.MarshalIndent(lesson038(), "", "  ")
	fmt.Println(string(out))
}

/**
 * Fintech Math Bootcamp · Lesson 038 of 120
 * Frequency Tables and Relative Frequency
 * Module 04: Location, Ranking, and Exploratory Summaries
 *
 * Scenario: Turning payment status counts into a denominator-aware summary
 * Rule:     relative frequency = category count / total count
 *
 * Try it:   Must shares sum to 100% when a payment can have several labels?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

#include <algorithm>
#include <charconv>
#include <cmath>
#include <iostream>
#include <string>
#include <utility>
#include <vector>

// --- Minimal JSON value and printer: objects keep insertion order, 2-space indent. ---

struct Json {
    enum class Kind { Null, Number, Text, Array, Object };
    Kind kind = Kind::Null;
    double number = 0;
    std::string text;
    std::vector<std::string> keys;  // object keys, parallel to items
    std::vector<Json> items;        // array elements or object values
};

Json num(double v) { Json j; j.kind = Json::Kind::Number; j.number = v; return j; }
Json str(const std::string& s) { Json j; j.kind = Json::Kind::Text; j.text = s; return j; }
Json arr(const std::vector<Json>& values) { Json j; j.kind = Json::Kind::Array; j.items = values; return j; }

Json arr(const std::vector<double>& values) {
    std::vector<Json> items;
    for (double v : values) items.push_back(num(v));
    return arr(items);
}

Json obj(const std::vector<std::pair<std::string, Json>>& fields) {
    Json j;
    j.kind = Json::Kind::Object;
    for (const auto& [key, value] : fields) { j.keys.push_back(key); j.items.push_back(value); }
    return j;
}

std::string formatNumber(double v) {
    if (!std::isfinite(v)) return "null";
    char buf[64];
    auto end = std::to_chars(buf, buf + sizeof buf, v).ptr;  // shortest round-trip form
    return std::string(buf, end);
}

void writeJson(std::ostream& out, const Json& j, const std::string& indent) {
    switch (j.kind) {
    case Json::Kind::Null: out << "null"; return;
    case Json::Kind::Number: out << formatNumber(j.number); return;
    case Json::Kind::Text: out << '"' << j.text << '"'; return;
    default: break;
    }
    bool isObject = j.kind == Json::Kind::Object;
    if (j.items.empty()) { out << (isObject ? "{}" : "[]"); return; }
    std::string inner = indent + "  ";
    out << (isObject ? "{\n" : "[\n");
    for (size_t i = 0; i < j.items.size(); ++i) {
        out << inner;
        if (isObject) out << '"' << j.keys[i] << "\": ";
        writeJson(out, j.items[i], inner);
        out << (i + 1 < j.items.size() ? ",\n" : "\n");
    }
    out << indent << (isObject ? '}' : ']');
}

// --- Lesson ---

Json lesson038() {
    const std::vector<std::string> statuses = {"ok", "ok", "review", "ok", "failed"};
    std::vector<std::pair<std::string, int>> counts;  // first-seen order
    for (const auto& s : statuses) {
        auto it = std::find_if(counts.begin(), counts.end(), [&s](const auto& c) { return c.first == s; });
        if (it == counts.end()) counts.push_back({s, 1});
        else ++it->second;
    }
    std::vector<Json> rows;
    for (const auto& [status, count] : counts) {
        rows.push_back(obj({
            {"status", str(status)},
            {"count", num(count)},
            {"frequency", num(static_cast<double>(count) / statuses.size())},
        }));
    }
    return arr(rows);
}

int main() {
    writeJson(std::cout, lesson038(), "");
    std::cout << '\n';
}

// Fintech Math Bootcamp · Lesson 038 of 120
// Frequency Tables and Relative Frequency
// Module 04: Location, Ranking, and Exploratory Summaries
//
// Scenario: Turning payment status counts into a denominator-aware summary
// Rule:     relative frequency = category count / total count
//
// Try it:   Must shares sum to 100% when a payment can have several labels?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.

// --- Minimal JSON value and printer: objects keep insertion order, 2-space indent. ---

#[allow(dead_code)]
enum Json {
    Null,
    Num(f64),
    Str(String),
    Arr(Vec<Json>),
    Obj(Vec<(String, Json)>),
}

#[allow(dead_code)]
fn nums(values: &[f64]) -> Json {
    Json::Arr(values.iter().map(|&v| Json::Num(v)).collect())
}

#[allow(dead_code)]
fn obj(fields: Vec<(&str, Json)>) -> Json {
    Json::Obj(fields.into_iter().map(|(k, v)| (k.to_string(), v)).collect())
}

fn format_number(v: f64) -> String {
    if !v.is_finite() {
        return "null".to_string();
    }
    if v.fract() == 0.0 && v.abs() < 1e15 {
        return format!("{}", v as i64);
    }
    format!("{:?}", v) // shortest round-trip form
}

impl Json {
    fn render(&self, indent: &str) -> String {
        let inner = format!("{}  ", indent);
        match self {
            Json::Null => "null".to_string(),
            Json::Num(v) => format_number(*v),
            Json::Str(s) => format!("\"{}\"", s),
            Json::Arr(items) if items.is_empty() => "[]".to_string(),
            Json::Obj(fields) if fields.is_empty() => "{}".to_string(),
            Json::Arr(items) => {
                let lines: Vec<String> = items.iter().map(|v| format!("{}{}", inner, v.render(&inner))).collect();
                format!("[\n{}\n{}]", lines.join(",\n"), indent)
            }
            Json::Obj(fields) => {
                let lines: Vec<String> = fields
                    .iter()
                    .map(|(k, v)| format!("{}\"{}\": {}", inner, k, v.render(&inner)))
                    .collect();
                format!("{{\n{}\n{}}}", lines.join(",\n"), indent)
            }
        }
    }
}

// --- Lesson ---

fn lesson038() -> Json {
    let statuses = ["ok", "ok", "review", "ok", "failed"];
    let mut counts: Vec<(&str, usize)> = Vec::new(); // first-seen order
    for &s in &statuses {
        match counts.iter_mut().find(|(status, _)| *status == s) {
            Some((_, n)) => *n += 1,
            None => counts.push((s, 1)),
        }
    }
    let rows: Vec<Json> = counts
        .iter()
        .map(|&(status, count)| {
            obj(vec![
                ("status", Json::Str(status.to_string())),
                ("count", Json::Num(count as f64)),
                ("frequency", Json::Num(count as f64 / statuses.len() as f64)),
            ])
        })
        .collect();
    Json::Arr(rows)
}

fn main() {
    println!("{}", lesson038().render(""));
}

/**
 * Fintech Math Bootcamp · Lesson 038 of 120
 * Frequency Tables and Relative Frequency
 * Module 04: Location, Ranking, and Exploratory Summaries
 *
 * Scenario: Turning payment status counts into a denominator-aware summary
 * Rule:     relative frequency = category count / total count
 *
 * Try it:   Must shares sum to 100% when a payment can have several labels?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

using System;
using System.Linq;
using System.Text.Json;

var options = new JsonSerializerOptions { WriteIndented = true };
Console.WriteLine(JsonSerializer.Serialize(Lesson038(), options));

static object Lesson038()
{
    string[] statuses = { "ok", "ok", "review", "ok", "failed" };
    // GroupBy keeps groups in first-seen order.
    return statuses
        .GroupBy(s => s)
        .Select(g => new
        {
            status = g.Key,
            count = g.Count(),
            frequency = (double)g.Count() / statuses.Length,
        })
        .ToArray();
}