WEBVTT 00:00:00.700 --> 00:00:06.780 This lesson converts a small CSV file into a JSON array of active users. 00:00:06.930 --> 00:00:14.405 We will preserve a name containing a comma, convert ages into numbers, and turn active flags into 00:00:14.405 --> 00:00:14.845 Booleans. 00:00:14.995 --> 00:00:18.771 Those details make this more than a file extension change. 00:00:18.921 --> 00:00:25.385 Open fixtures slash input dot CSV, then run the supplied converter from examples. 00:00:25.535 --> 00:00:31.188 The generated JSON and CSV files will appear inside the out directory. 00:00:33.340 --> 00:00:36.988 CSV allows quoted fields containing commas. 00:00:37.138 --> 00:00:43.943 Splitting every input line on a comma would damage Sam Junior's name by treating it as two fields. 00:00:44.093 --> 00:00:48.424 Use parse_csv to interpret records according to the format. 00:00:48.574 --> 00:00:52.734 With header set to true, the first record supplies field names. 00:00:52.884 --> 00:01:01.079 The parser returns a stream of Maps, so subsequent callbacks can access row dot name, row dot age, and 00:01:01.079 --> 00:01:02.804 row dot active explicitly. 00:01:04.950 --> 00:01:07.851 CSV does not infer a schema for us. 00:01:08.001 --> 00:01:13.249 The active field is a String containing the letters true, not a Boolean value yet. 00:01:13.399 --> 00:01:16.770 Our filter therefore compares it with a quoted string. 00:01:16.920 --> 00:01:22.893 This is an easy place to make a silent logic mistake by comparing unlike types. 00:01:23.043 --> 00:01:29.549 Confirm the representation at the input boundary, and decide whether filtering should happen before 00:01:29.549 --> 00:01:33.731 conversion or after a separate validation and conversion stage. 00:01:35.880 --> 00:01:41.021 The projection constructs a fresh Map with exactly the fields we want to publish. 00:01:41.171 --> 00:01:47.401 To_int converts each retained age, and the equality expression produces a real Boolean. 00:01:47.551 --> 00:01:52.713 Collect materializes the stream as a List, which becomes a JSON array. 00:01:52.863 --> 00:01:57.130 This small lesson uses tiny fixtures, so that is appropriate. 00:01:57.280 --> 00:02:04.510 For a very large dataset, think about memory limits and whether your destination can accept records 00:02:04.510 --> 00:02:07.221 incrementally instead of one complete array. 00:02:09.370 --> 00:02:15.002 Encode_json turns ordinary HHY values into JSON text. 00:02:15.152 --> 00:02:19.888 Pretty mode adds readable formatting without changing the data types. 00:02:20.038 --> 00:02:23.110 Save_text writes that one complete String to disk. 00:02:23.260 --> 00:02:29.831 Do not send a lazy Stream or a filesystem object directly to the JSON encoder. 00:02:29.981 --> 00:02:33.671 First consume or project it into supported values. 00:02:33.821 --> 00:02:41.181 Our output is a List of ordinary Maps, and each Map contains only a String, an integer, and a Boolean. 00:02:43.330 --> 00:02:46.871 A round trip is a practical verification step. 00:02:47.021 --> 00:02:51.267 Read the file, parse it back, and inspect important properties. 00:02:51.417 --> 00:02:53.486 There should be two retained users. 00:02:53.636 --> 00:02:59.865 The first name is Ada, the age reports Int, and the second name still includes its comma. 00:03:00.015 --> 00:03:05.349 This checks filtering, conversion, encoding, and quoted CSV handling together. 00:03:05.499 --> 00:03:11.323 Merely seeing that a file exists would not prove any of those requirements were met correctly. 00:03:13.470 --> 00:03:18.803 To encode CSV, convert our collected List back into a Stream explicitly. 00:03:18.953 --> 00:03:26.761 Encode_csv returns text records without line terminators, and save_lines supplies those terminators. 00:03:26.911 --> 00:03:33.781 Reusing the same users List is safe because we are constructing a fresh stream for this operation. 00:03:33.931 --> 00:03:40.032 Do not assume the generated CSV will preserve every formatting choice from the input. 00:03:40.182 --> 00:03:46.006 The purpose is to preserve selected data, with serialization handled by the encoder. 00:03:48.160 --> 00:03:53.131 Malformed JSON should not quietly become a successful empty result. 00:03:53.281 --> 00:03:58.742 Here we deliberately pass invalid text and show a clear failure marker from catch. 00:03:58.892 --> 00:04:06.526 In a production converter, decide whether an invalid record should stop the job or become an explicit 00:04:06.526 --> 00:04:07.873 rejected record report. 00:04:08.023 --> 00:04:12.738 Never substitute a plausible default merely to make the script finish. 00:04:12.888 --> 00:04:19.480 The next lesson shows how modules and per operation Results help express those decisions cleanly. 00:04:21.630 --> 00:04:29.041 For practice, add another active user whose quoted name contains a comma, then check the output count and 00:04:29.041 --> 00:04:29.865 preserved name. 00:04:30.015 --> 00:04:36.009 Next, in a copy of the fixture, replace an active user's age with nonnumeric text. 00:04:36.159 --> 00:04:40.533 Observe the conversion failure and consider where you would validate it. 00:04:40.683 --> 00:04:44.864 Keep the original fixture intact for repeatable comparison. 00:04:45.014 --> 00:04:53.335 You can now move between CSV records and JSON values while controlling selection, field types, and output 00:04:53.335 --> 00:04:53.825 structure.