Grouped Media function

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 1.2.2 - WIP
+- Adding `medianGrouped()` method for estimating the median of grouped/binned continuous data using interpolation
+
+
 ## 1.2.1 - 2026-02-20
 - Adding `invCdf()` method to normal distribution
 - Adding `getVariance()` method to normal distribution (sigma squared)

README.md

@@ -67,6 +67,7 @@ The various mathematical statistics are listed below:
 | `median()` | median or "middle value" of data |
 | `medianLow()` | low median of data |
 | `medianHigh()` | high median of data |
+| `medianGrouped()` | median of grouped data, using interpolation |
 | `mode()` | single mode (most common value) of discrete or nominal data |
 | `multimode()` | list of modes (most common values) of discrete or nominal data |
 | `quantiles()` | cut points dividing the range of a probability distribution into continuous intervals with equal probabilities |
@@ -192,6 +193,35 @@ $median = Stat::medianHigh([1, 3, 5, 7]);
 // 5
 ```
 
+#### Stat::medianGrouped( array $data, float $interval = 1.0 )
+Estimate the median for numeric data that has been grouped or binned around the midpoints of consecutive, fixed-width intervals.
+The `$interval` parameter specifies the width of each bin (default `1.0`). This function uses interpolation within the median interval, assuming values are evenly distributed across each bin.
+
+```php
+use HiFolks\Statistics\Stat;
+$median = Stat::medianGrouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5]);
+// 3.7
+$median = Stat::medianGrouped([1, 3, 3, 5, 7]);
+// 3.25
+$median = Stat::medianGrouped([1, 3, 3, 5, 7], 2);
+// 3.5
+```
+
+For example, demographic data summarized into ten-year age groups:
+```php
+use HiFolks\Statistics\Stat;
+// 172 people aged 20-30, 484 aged 30-40, 387 aged 40-50, etc.
+$data = array_merge(
+    array_fill(0, 172, 25),
+    array_fill(0, 484, 35),
+    array_fill(0, 387, 45),
+    array_fill(0, 22, 55),
+    array_fill(0, 6, 65),
+);
+round(Stat::medianGrouped($data, 10), 1);
+// 37.5
+```
+
 #### Stat::quantiles( array $data, $n=4, $round=null  )
 Divide data into n continuous intervals with equal probability. Returns a list of n - 1 cut points separating the intervals.
 Set n to 4 for quartiles (the default). Set n to 10 for deciles. Set n to 100 for percentiles which gives the 99 cut points that separate data into 100 equal-sized groups.

TODO.md

@@ -0,0 +1,115 @@
+  Missing Functions
+
+  Python Function: median_grouped(data, interval)
+  Description: Median of grouped/binned continuous data
+  Status: Missing
+  ────────────────────────────────────────
+  Python Function: kde(data, h, kernel)
+  Description: Kernel Density Estimation
+  Status: Missing
+  ────────────────────────────────────────
+  Python Function: kde_random(data, h, kernel)
+  Description: Random sampling from KDE
+  Status: Missing
+
+  Missing Parameters/Variants
+
+  Feature: correlation() with method='ranked'
+  Python: Supports both Pearson and Spearman rank correlation
+  This Package: Only Pearson
+  ────────────────────────────────────────
+  Feature: linear_regression() with proportional=True
+  Python: Supports proportional regression (intercept forced to 0)
+  This Package: No proportional option
+  ────────────────────────────────────────
+  Feature: variance(data, xbar) / pvariance(data, mu)
+  Python: Can pass pre-computed mean to avoid recalculation
+  This Package: No pre-computed mean parameter
+  ────────────────────────────────────────
+  Feature: quantiles() with method='inclusive'
+  Python: Supports both exclusive and inclusive methods
+  This Package: No method parameter
+
+  Summary
+
+  The package is actually very close to full parity with Python's statistics
+  module. The gaps are:
+
+  1. median_grouped - interpolation-based median for grouped/binned data
+  2. kde / kde_random - Kernel Density Estimation (added in Python 3.13,
+  relatively new)
+  3. Spearman rank correlation - via method parameter on correlation()
+  4. Proportional linear regression - forcing intercept through origin
+  5. Minor parameter additions (xbar/mu on variance/stdev, method on quantiles)
+
+  Items 1, 3, and 4 would be the most practical additions to reach near-complete
+   parity with Python's statistics module. The KDE functions (2) are newer and
+  more niche.
+
+
+
+
+  Currently Implemented (for reference)
+
+  Central tendency, variance/stdev, median variants, mode/multimode,
+  geometric/harmonic mean, quantiles, covariance, correlation, linear
+  regression, normal distribution (PDF, CDF, inverse CDF, z-score), frequency
+  tables.
+
+  ---
+  Missing Functions
+
+  Descriptive Statistics
+
+  - Trimmed/Truncated mean - mean after removing outliers (top/bottom x%)
+  - Weighted median - median with weights (like fmean supports weights, but
+  median doesn't)
+  - Skewness - measure of asymmetry of the distribution
+  - Kurtosis - measure of "tailedness" of the distribution
+  - Standard error of the mean (SEM)
+  - Coefficient of variation (CV) - stdev / mean, useful for comparing
+  variability across datasets
+  - Mean absolute deviation (MAD)
+  - Percentile - arbitrary percentile (e.g., 90th percentile) — quantiles()
+  exists but a direct percentile($data, $p) would be convenient
+
+  Correlation & Regression
+
+  - Spearman rank correlation - non-parametric correlation
+  - Kendall tau correlation - another rank-based correlation
+  - Multiple/polynomial regression
+  - R-squared (coefficient of determination)
+
+  Hypothesis Testing
+
+  - T-test (one-sample, two-sample, paired)
+  - Chi-squared test
+  - Z-test
+  - P-value calculation
+  - Confidence intervals
+
+  Other Distributions (beyond Normal)
+
+  - Student's t-distribution
+  - Chi-squared distribution
+  - Binomial distribution
+  - Poisson distribution
+  - Uniform distribution
+  - Exponential distribution
+
+  Outlier Detection
+
+  - IQR-based outlier detection (the building blocks exist with
+  firstQuartile/thirdQuartile, but no dedicated method)
+  - Z-score based outlier detection
+
+  Ranking & Order Statistics
+
+  - Rank - assign ranks to data points
+  - Percentile rank - what percentile a given value falls at
+
+  ---
+  The most impactful additions would likely be skewness, kurtosis, coefficient
+  of variation, percentile, and Spearman correlation — these are commonly needed
+   and align well with the package's existing scope (inspired by Python's
+  statistics module).

src/Stat.php

@@ -143,6 +143,96 @@ public static function median(
         };
     }
 
+    /**
+     * Estimate the median for grouped data that has been binned
+     * around the midpoints of consecutive, fixed-width intervals.
+     *
+     * Uses interpolation within the median interval:
+     * L + interval * (n/2 - cf) / f
+     *
+     * where:
+     * - L is the lower limit of the median interval
+     * - cf is the cumulative frequency of the preceding interval
+     * - f is the number of elements in the median interval
+     *
+     * @param  array<int|float>  $data
+     * @param  float  $interval the width of each bin
+     * @return float the estimated median for grouped data
+     *
+     * @throws InvalidDataInputException if the data is empty
+     */
+    public static function medianGrouped(array $data, float $interval = 1.0): float
+    {
+        sort($data);
+        $n = count($data);
+        if ($n === 0) {
+            throw new InvalidDataInputException("The data must not be empty.");
+        }
+
+        // Find the value at the midpoint (midpoint of the class interval)
+        $x = (float) $data[intdiv($n, 2)];
+
+        // Find where all the x values occur in the sorted data
+        // All x will lie within data[i:j]
+        $i = self::bisectLeft($data, $x);
+        $j = self::bisectRight($data, $x, $i);
+
+        // Lower limit of the median interval
+        $L = $x - $interval / 2.0;
+        // Cumulative frequency of the preceding interval
+        $cf = $i;
+        // Number of elements in the median interval
+        $f = $j - $i;
+
+        return $L + $interval * ($n / 2.0 - $cf) / $f;
+    }
+
+    /**
+     * Binary search: find the leftmost position where $target can be inserted
+     * in $data while keeping it sorted.
+     *
+     * @param  array<int|float>  $data sorted array
+     * @param  float  $target value to locate
+     */
+    private static function bisectLeft(array $data, float $target): int
+    {
+        $lo = 0;
+        $hi = count($data);
+        while ($lo < $hi) {
+            $mid = intdiv($lo + $hi, 2);
+            if ($data[$mid] < $target) {
+                $lo = $mid + 1;
+            } else {
+                $hi = $mid;
+            }
+        }
+
+        return $lo;
+    }
+
+    /**
+     * Binary search: find the rightmost position where $target can be inserted
+     * in $data while keeping it sorted.
+     *
+     * @param  array<int|float>  $data sorted array
+     * @param  float  $target value to locate
+     * @param  int  $lo lower bound for the search
+     */
+    private static function bisectRight(array $data, float $target, int $lo = 0): int
+    {
+        $hi = count($data);
+        while ($lo < $hi) {
+            $mid = intdiv($lo + $hi, 2);
+            if ($data[$mid] <= $target) {
+                $lo = $mid + 1;
+            } else {
+                $hi = $mid;
+            }
+        }
+
+        return $lo;
+    }
+
     /**
      * Return the low median of data.
      * The low median is always a member of the data set.

src/Statistics.php

@@ -173,6 +173,18 @@ public function median(): mixed
         return Stat::median($this->values);
     }
 
+    /**
+     * Estimate the median for grouped data.
+     *
+     * @param  float  $interval the width of each bin
+     *
+     * @see Stat::medianGrouped()
+     */
+    public function medianGrouped(float $interval = 1.0): float
+    {
+        return Stat::medianGrouped($this->numericalArray(), $interval);
+    }
+
     /**
      * Return the first quartile.
      *

tests/StatTest.php

@@ -92,6 +92,40 @@ public function test_calculates_median_high_with_empty_array(): void
         Stat::medianHigh([]);
     }
 
+    public function test_calculates_median_grouped(): void
+    {
+        // Python: median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5]) == 3.7
+        $this->assertEquals(3.7, Stat::medianGrouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5]));
+
+        // Python: median_grouped([52, 52, 53, 54]) == 52.5
+        $this->assertEquals(52.5, Stat::medianGrouped([52, 52, 53, 54]));
+
+        // Python: median_grouped([1, 3, 3, 5, 7]) == 3.25
+        $this->assertEquals(3.25, Stat::medianGrouped([1, 3, 3, 5, 7]));
+
+        // With interval=2: median_grouped([1, 3, 3, 5, 7], interval=2) == 3.5
+        $this->assertEquals(3.5, Stat::medianGrouped([1, 3, 3, 5, 7], 2));
+
+        // Demographics example from Python docs (interval=10)
+        $data = array_merge(
+            array_fill(0, 172, 25),
+            array_fill(0, 484, 35),
+            array_fill(0, 387, 45),
+            array_fill(0, 22, 55),
+            array_fill(0, 6, 65),
+        );
+        $this->assertEquals(37.5, round(Stat::medianGrouped($data, 10), 1));
+
+        // Single element: L = 1 - 0.5 = 0.5, result = 0.5 + 1*(0.5-0)/1 = 1.0
+        $this->assertEquals(1.0, Stat::medianGrouped([1]));
+    }
+
+    public function test_calculates_median_grouped_with_empty_array(): void
+    {
+        $this->expectException(InvalidDataInputException::class);
+        Stat::medianGrouped([]);
+    }
+
     public function test_calculates_mode(): void
     {
         $this->assertEquals(3, Stat::mode([1, 1, 2, 3, 3, 3, 3, 4]));