Transform JSON Key into a Polars DataFrame

I was wondering how to read in a JSON file into a polars DataFrame in Rust on the "data" key. However, I believe the structure of the JSON file that I have would be hard to achieve.

Here is the first Structure of the JSON File where it contains dataTypes.

{
  "data": [
    {
      "dataItemName": "TICKER",
      "result": [
        "AAPL",
        "MSFT",
        "TSLA"
      ],
      "dataType": "STRING",
      "error": 0
    },
    {
      "dataItemName": "SALES",
      "result": [ 
        259968,
        143015,
        24578
      ],
      "dataType": "DOUBLE",
      "error": 0
    },
    {
      "dataItemName": "CNAME",
      "result": [
        "Apple Inc.",
        "Microsoft Corporation",
        "Tesla Inc"
      ],
      "dataType": "STRING",
      "error": 0
    },
    {
      "dataItemName": "PRICE",
      "result": [
        115.98,
        214.22,
        430.83
      ],
      "dataType": "DOUBLE",
      "error": 0
    },
    {
      "dataItemName": "ASSETS",
      "result": [
        338516,
        301311,
        34309
      ],
      "dataType": "DOUBLE",
      "error": 0
    }
  ]
}

Here is what I have tried in Rust.

use polars::prelude::*;


fn main() {
    let json_file = std::fs::File::open("data/test_merged.json").unwrap();
    let df = JsonReader::new(json_file).finish().unwrap();
    println!("{:?}", df);
}

Here is the Rust output example which single column/row DataFrame

shape: (1, 1)
┌───────────────────────────────────┐
│ data                              │
│ ---                               │
│ list[struct[63]]                  │
╞═══════════════════════════════════╡
│ [{0.0,0.530558,3.38631,"2023-06-… │
└───────────────────────────────────┘

There are only 3 Data Types Which are Stings Floats and Integers.

Here is a similar question for a Python Version. transform json to polars dataframe

If performance is important, then @BallpointBen's version is not the fastest you can get; here's a more performant version:

pub fn convert(json: &str) -> Result<DataFrame, Box<dyn Error>> {
    use serde::Deserialize;

    #[derive(Debug, Deserialize)]
    #[serde(untagged)]
    enum Values {
        String(Vec<String>),
        Double(Vec<f64>),
    }

    #[derive(Debug, Deserialize)]
    #[serde(rename_all = "UPPERCASE")]
    enum DataType {
        String,
        Double,
    }

    #[derive(Debug, Deserialize)]
    #[serde(rename_all = "camelCase")]
    struct Column {
        data_item_name: String,
        result: Values,
        data_type: DataType,
    }

    #[derive(Debug, Deserialize)]
    struct Data {
        data: Vec<Column>,
    }

    let data = serde_json::from_str::<Data>(json)?;
    let df = data
        .data
        .into_iter()
        .map(|column| match column.data_type {
            DataType::String => {
                let Values::String(values) = column.result else {
                    return Err("column type mismatch");
                };
                Ok(Series::new(&column.data_item_name, values))
            }
            DataType::Double => {
                let Values::Double(values) = column.result else {
                    return Err("column type mismatch");
                };
                Ok(Series::from_vec(&column.data_item_name, values))
            }
        })
        .collect::<Result<DataFrame, _>>()?;

    Ok(df)
}

Benchmark with 1,000 random entries:

BallpointBen            time:   [338.41 µs 340.05 µs 341.85 µs]
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) high mild

Mine                    time:   [195.82 µs 196.79 µs 197.95 µs]
Found 11 outliers among 100 measurements (11.00%)
  8 (8.00%) high mild
  3 (3.00%) high severe