francerz/php-power-data

There is no license information available for the latest version (v0.1.37) of this package.

PHP data structures for complex data handling

v0.1.37 2024-09-06 16:18 UTC

Unknown License

• Francisco CZ

README

Installation

This package can be installed with composer using following command.

composer require francerz/php-power-data

Using Index class

use Francerz\PowerData\Index;

$index = new Index($data, ['column1', 'column2']);

$col1Values = $index->getColumnValues('column1');
$col2Values = $index->getColumnValues('column2');

foreach ($col1Values as $c1) {
    foreach ($col2Values as $c2) {
        // Retrieves all items that matches $c1 and $c2.
        $items = $index[['column1' => $c1, 'column2' => $c2]];

        $numItems = $index->xCount(['column1' => $c1, 'column2' => $c2]);
        $sumCol3 = $index->sum('column3', ['column1' => $c1, 'column2' => $c2]);
        $first = $index->first(['column1' => $c1, 'column2' => $c2]);
        $last = $index->last(['column1' => $c1, 'column2' => $c2]);
    }
    // Retrieves all items that matches $c1
    $items = $index[['column1' => $c1]];
}

Method groupBy($columns)

The groupBy method allows you to group records from an indexed dataset based on one or more column values. The grouping is performed incrementally, meaning that the method applies filters progressively on the existing index to avoid unnecesary memory and processing overhead. This ensures that the performance remains efficient, even with large datasets.

Parameters:

• $columns (string|array): The name of a single column (string) or an array of column names (array) by which the data should be grouped. The method will return a nested grouping if multiple columns are provided.

Returns:

• array: Returns a nested associative array, where each key corresponds to the unique values in the specified columns. The innermost arrays contain the records that match teh specific grouping of column values.

How it works:

The methods does not create a new index fore each group. Instead, progressively filters the data of each column's unique values, grouping the results step by step. This approach reduces memory consumption and processing time by avoiding redundant operations on the dataset.

Example Usage:

$index = new Index($data, ['country', 'city']);

// Group by a single column
$groupedByCity = $index->groupBy('city');

// Group by multiple columns
$groupedByCountryAndCity = $index->groupBy(['country', 'city']);

print_r($groupedByCity);

Aggregations

Aggregations::concat(array $values, $separator = '')
Aggregations::count(array $values, bool $ignoreNulls = false)
Aggregations::findPercentile(array $values, float $value, int $flags = self::PERCENTILE_FLAGS_MIDDLE)
Aggregations::first(array $values)
Aggregations::frequencies(array $values)
Aggregations::last(array $values)
Aggregations::max(array $values)
Aggregations::mean(array $values, bool $ignoreNulls = false)
Aggregations::median(array $values)
Aggregations::min(array $values)
Aggregations::mode(array $values, bool $strict = false)
Aggregations::percentile(array $values, float $percentile, int $flags = self::PERCENTILE_FLAGS_MIDDLE)
Aggregations::product(array $values, bool $ignoreEmpty = false)
Aggregations::sum(array $values)

Arrays

Arrays::hasNumericKeys(array $array)
Arrays::hasStringKeys(array $array)
Arrays::findKeys(array $array, string $pattern)
Arrays::remove(array &$array, $value)
Arrays::filter($array, $callback = null, $flag = 0)
Arrays::intersect(array $array1, array $array2, ...$_)