Walkthrough: Processing CSV Data

This walkthrough demonstrates the core concepts of the library by building a practical data processing pipeline.

The Goal

Our objective is to process a string of CSV data. We will parse the data, skip the header, transform it into a more usable format, filter it based on a condition, and finally, collect the results.

The Pipeline

Here is the complete pipeline:

use function Pipeline\take;

// Sample CSV data with a header row
$csv = <<<CSV
name,age,city
Alice,30,New York
Bob,25,Los Angeles
Charlie,35,Chicago
David,28,New York
CSV;

// Build the pipeline
$users = take(explode("\n", $csv))
    ->map(str_getcsv(...))        // 1. Parse each line into an array
    ->slice(1)                    // 2. Skip the header row
    ->map(fn($row) => [          // 3. Transform to an associative array
        'name' => $row[0],
        'age' => (int)$row[1],
        'city' => $row[2]
    ])
    ->filter(fn($user) => $user['age'] >= 30)  // 4. Keep users aged 30 or over
    ->toList();                   // 5. Execute the pipeline and collect results

// The final result:
// [
//   ['name' => 'Alice', 'age' => 30, 'city' => 'New York'],
//   ['name' => 'Charlie', 'age' => 35, 'city' => 'Chicago']
// ]

Step-by-Step Explanation

1. take(explode("\n", $csv)): We begin by creating a pipeline from the CSV data. explode() splits the string into an array of lines.

2. map(str_getcsv(...)): The map() method is used to apply str_getcsv() to each line, converting each CSV string into an array of values.

3. slice(1): This method skips the first element of the pipeline, which in this case is the header row.

4. map(fn($row) => ...): We use map() again to transform the indexed array for each row into a more readable associative array.

5. filter(fn($user) => ...): The filter() method is used to apply our business logic, keeping only the users who are 30 years of age or older.

6. toList(): This is a terminal operation. It triggers the execution of all the previous (lazy) operations and collects the final results into an array.

Key Concepts

This example illustrates several core principles of the library:

• Lazy Evaluation: No processing occurs until a terminal method like toList() is called.
• Method Chaining: Each operation returns the pipeline object, allowing for a fluent and expressive syntax.
• Transformation: map() is used to change the structure and format of the data.
• Filtering: filter() and slice() are used to selectively remove data.

Next Steps

• Explore the Cookbook for more practical examples.
• Consult the API Reference for detailed information on each method.