CSV Statistical Analyzer (java, written by Gemini Code Assist)

Gemini Code Assist wrote this java project from the task below. It does not run on a clean Ubuntu 22.04 machine as written.

Task given to the agent:

TASK: CSV Statistical Analyzer

Write a program that reads a CSV file and performs comprehensive statistical analysis on every numeric column. It should handle real-world messy data — missing values, mixed types, malformed rows — and produce both a human-readable console report and a machine-readable JSON output.

FUNCTIONAL REQUIREMENTS:
- Accept a CSV file path as a command-line argument
- Auto-detect which columns are numeric vs categorical
- For each numeric column compute: mean, median, standard deviation, variance, min, max, 25th/50th/75th percentiles, and non-missing value count
- Detect outliers using the IQR method (values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR) and list them per column
- For each categorical column compute: unique count, most frequent value, and top 10 value frequencies
- Print a formatted summary table to the console with aligned columns
- Save the complete analysis to report.json including all stats, outlier details, and column type classifications
- If no input file is given, generate a sample CSV with at least 200 rows across 5 numeric and 2 categorical columns, then analyze it
- Handle gracefully: empty files, header-only files, columns with all missing values, single-row files, quoted fields containing commas

Create a complete Java project for a clean Ubuntu 22.04 machine with only JDK 17+ installed. Include:
- Source code
- pom.xml with all dependencies (direct and transitive) pinned to exact versions
- README.md with setup instructions, dependency explanations, build steps, run commands, and expected output

[]

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.csvanalyzer</groupId>
    <artifactId>csv-statistical-analyzer</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>17</maven.compiler.source>
        <maven.compiler.target>17</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <!-- OpenCSV for parsing -->
        <dependency>
            <groupId>com.opencsv</groupId>
            <artifactId>opencsv</artifactId>
            <version>5.9</version>
        </dependency>
        <!-- Apache Commons Math for statistics -->
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-math3</artifactId>
            <version>3.6.1</version>
        </dependency>
        <!-- Jackson for JSON output -->
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.16.1</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.5.1</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <transformers>
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.csvanalyzer.CsvStatisticalAnalyzer</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

# CSV Statistical Analyzer (Java)

A comprehensive tool for statistical analysis of CSV data.

## Setup Instructions

1. Ensure JDK 17+ and Maven are installed.
2. Build the project:
   ```bash
   mvn clean package
   ```

## Run Commands

- Run with a specific CSV:
  ```bash
  java -jar target/csv-statistical-analyzer-1.0-SNAPSHOT.jar data.csv
  ```
- Run with generated sample data:
  ```bash
  java -jar target/csv-statistical-analyzer-1.0-SNAPSHOT.jar
  ```

## Output
- Console: Formatted summary table.
- File: `report.json` containing detailed statistics and outliers.

package com.csvanalyzer;

import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.node.ArrayNode;
import com.fasterxml.jackson.databind.node.ObjectNode;
import com.opencsv.CSVReader;
import com.opencsv.CSVWriter;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;

import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.util.*;

public class CsvStatisticalAnalyzer {

    public static void main(String[] args) {
        String filePath = args.length > 0 ? args[0] : "sample.csv";
        File file = new File(filePath);

        if (!file.exists()) {
            generateSampleCsv(filePath);
        }

        analyzeCsv(filePath);
    }

    private static void generateSampleCsv(String filePath) {
        System.out.println("Generating sample CSV: " + filePath);
        try (CSVWriter writer = new CSVWriter(new FileWriter(filePath))) {
            writer.writeNext(new String[]{"id", "age", "salary", "score", "department"});
            Random rand = new Random(42);
            String[] departments = {"HR", "Engineering", "Sales", "Marketing"};
            for (int i = 1; i <= 200; i++) {
                writer.writeNext(new String[]{
                        String.valueOf(i),
                        String.valueOf(18 + rand.nextInt(62)),
                        String.valueOf(30000 + rand.nextGaussian() * 10000),
                        String.valueOf(rand.nextDouble() * 100),
                        departments[rand.nextInt(departments.length)]
                });
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    private static void analyzeCsv(String filePath) {
        ObjectMapper mapper = new ObjectMapper();
        ObjectNode report = mapper.createObjectNode();
        ObjectNode statisticsNode = report.putObject("statistics");

        try (CSVReader reader = new CSVReader(new FileReader(filePath))) {
            String[] header = reader.readNext();
            if (header == null) return;

            List<String[]> rows = reader.readAll();
            Map<String, List<String>> columnData = new HashMap<>();
            for (String h : header) columnData.put(h, new ArrayList<>());

            for (String[] row : rows) {
                for (int i = 0; i < header.length; i++) {
                    if (i < row.length) columnData.get(header[i]).add(row[i]);
                }
            }

            System.out.printf("%-20s | %-10s | %-15s | %-15s
", "COLUMN", "TYPE", "MEAN/MODE", "STD/FREQ");
            System.out.println("-".repeat(70));

            for (String col : header) {
                List<String> values = columnData.get(col);
                boolean isNumeric = isColumnNumeric(values);

                if (isNumeric) {
                    DescriptiveStatistics stats = new DescriptiveStatistics();
                    for (String v : values) {
                        try {
                            if (!v.isEmpty()) stats.addValue(Double.parseDouble(v));
                        } catch (NumberFormatException ignored) {}
                    }

                    ObjectNode colStats = statisticsNode.putObject(col);
                    colStats.put("type", "numeric");
                    colStats.put("mean", stats.getMean());
                    colStats.put("median", stats.getPercentile(50));
                    colStats.put("std", stats.getStandardDeviation());
                    colStats.put("count", stats.getN());

                    double q1 = stats.getPercentile(25);
                    double q3 = stats.getPercentile(75);
                    double iqr = q3 - q1;
                    ArrayNode outliers = colStats.putArray("outliers");
                    for (double v : stats.getValues()) {
                        if (v < q1 - 1.5 * iqr || v > q3 + 1.5 * iqr) outliers.add(v);
                    }

                    System.out.printf("%-20s | %-10s | %-15.2f | %-15.2f
", col, "Numeric", stats.getMean(), stats.getStandardDeviation());
                } else {
                    Map<String, Integer> freqMap = new HashMap<>();
                    for (String v : values) freqMap.put(v, freqMap.getOrDefault(v, 0) + 1);

                    String mode = freqMap.entrySet().stream().max(Map.Entry.comparingByValue()).map(Map.Entry::getKey).orElse("N/A");
                    int freq = freqMap.getOrDefault(mode, 0);

                    ObjectNode colStats = statisticsNode.putObject(col);
                    colStats.put("type", "categorical");
                    colStats.put("mode", mode);
                    colStats.put("unique_count", freqMap.size());

                    System.out.printf("%-20s | %-10s | %-15s | %-15d
", col, "Categorical", mode, freq);
                }
            }

            mapper.writerWithDefaultPrettyPrinter().writeValue(new File("report.json"), report);
            System.out.println("Report saved to report.json");

        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    private static boolean isColumnNumeric(List<String> values) {
        int count = 0;
        for (String v : values) {
            if (v == null || v.isEmpty()) continue;
            try {
                Double.parseDouble(v);
                count++;
            } catch (NumberFormatException e) {
                return false;
            }
        }
        return count > 0;
    }
}