Add comments to this test file (#27836)

[reviewed by @jabraham17]

I went through this file for slides and made some notes for myself to
understand it, so figured it would be useful to add them as comments to
the test itself.

Double checked that the test still ran okay

Author: lydia-duncan

test/library/packages/Python/examples/parquet/readParquet.chpl

@@ -4,13 +4,18 @@ config const filename = "data.parquet";
 
 
 proc getArray(type eltType, ref data_chunks, num_rows) {
+  // Creates an array to store all the unified chunks
   var arr: [0..<num_rows] eltType;
   var i = 0;
+  // Traverse the list of Python `Value`s provided to us
   for chunk in data_chunks {
+    // Call `to_numpy` on the individual Python `Value`s
+    // This returns a `PyArray` of the specified element type
     var chunk_arr =
       chunk!.call(
         owned PyArray(eltType, 1),
         "to_numpy", kwargs=["zero_copy_only"=>false, "writable"=>true]);
+    // (Continue to) fill in `arr` with the contents of that numpy array
     arr[i..#chunk_arr.size] = chunk_arr.array();
     i += chunk_arr.size;
   }
@@ -24,23 +29,30 @@ proc main() {
   var pa = interp.importModule("pyarrow");
   var pq = interp.importModule("pyarrow.parquet");
 
+  // Open a Parquet file
   var parquet_file = pq.call("ParquetFile", filename);
   var columns = parquet_file.get("schema").get("names"):list(string);
   var num_rows = parquet_file.get("metadata").get("num_rows"):int;
+  // Create an array of lists to store the result
   var data_chunks: [0..<columns.size] list(owned Value?);
 
+  // Iterate over the Parquet file
   for batch in parquet_file.call("iter_batches", kwargs=["batch_size"=>300]) {
+    // In each batch, get each column and store it into a list in the array
     for (col, idx) in zip(columns, 0..) {
       data_chunks[idx].pushBack(batch.call("__getitem__", col));
     }
   }
 
+  // schema_arrow is used to get the type stored in the column
   var schema_arrow = parquet_file.get('schema_arrow');
   for (col, idx) in zip(columns, 0..) {
     write("Column: ", col);
 
     var rowType = schema_arrow.call('field', col).get('type');
     if pa.call("int64") == rowType {
+      // getArray traverses the list stored at each idx, using numpy to gather
+      // chunks into a single array
       var arr = getArray(int(64), data_chunks[idx], num_rows);
       writeln(" Sum: ", + reduce arr);
     } else if pa.call("float64") == rowType {