Dataframe in rust 🦀

+-----------+------+-----------+
| strangs   | nums | null nums |
+-----------+------+-----------+
| sugar     |    0 |       -10 |
| sweets    |    1 |      Null |
| candy pop |    2 |       200 |
| caramel   |    3 |       400 |
| chocolate |    4 |       777 |
+-----------+------+-----------+

Import

use dataframe::dataframe::*;

Create

Create from rows

using the row! macro

let df = Dataframe::from_rows(
    vec!["id", "name", "score", "val"],
    vec![
        row!(1, "Sally", 23, true),
        row!(2, "Jasper", 41, false),
        row!(3, "Jake", 33, true),
    ],
)
.unwrap();

Create from csv

With ToRow proc-macro

#[derive(Deserialize, ToRow)]
struct MyRow {
    name: String,
    score: i64,
    val: bool,
}

let df = Dataframe::from_csv::<MyRow>("./tests/test.csv").unwrap();

Or implement ToRow manually

impl ToRow for MyRow {
    fn to_row(&self) -> Vec<Cell> {
        vec![self.name.as_str().into(), self.age.into(), self.val.into()]
    }
    fn labels(&self) -> Vec<String> {
        vec!["name".to_string(), "age".to_string(), "val".to_string()]
    }
}

With null values

let df = Dataframe::from_rows(
    vec!["name", "age", "score", "val"],
    vec![
        row!("Sasha", None::<i64>, 160, Some(false)),
        row!("Jane", Some(24), 70, None::<bool>),
        row!("Jerry", None::<i64>, 40, Some(true)),
    ],
)
.unwrap();

With timestamp

let df = Dataframe::from_rows(
    vec!["id", "label", "at"],
    vec![
        row!(2, "Noon", Timestamp(2024, 8, 26, 12, 15, 0)),
        row!(3, "Night", Timestamp(2024, 8, 26, 22, 45, 0)),
        row!(1, "Morning", Timestamp(2024, 8, 26, 8, 5, 0)),
    ],
)
.unwrap();

Supported types

• String
• Int
• Uint
• Float
• Bool
• Option/Null
• chrono::NaiveDateTime

Display

df.print();

Extend

Add column

df.add_col("new column", vec![2, 4, 6]).unwrap();

Add row

df.add_row(vec!["Jane", 44, true]).unwrap();

Concat

df.concat(
    Dataframe::from_rows(
        vec!["id", "name", "score", "val"],
        vec![
            row!(4, "Sam", 23, true),
            row!(5, "Julie", 41, false),
            row!(6, "Jill", 33, true),
        ],
    )
    .unwrap(),
)
.unwrap();

Join

// join(other_df, (left_col, right_col))
let result_df = df
    .join(
        &Dataframe::from_rows(
            vec!["user_id", "score", "rate"],
            vec![
                row!(1, 700, 0.4),
                row!(2, 400, 0.7),
                row!(3, 900, 0.6),
            ],
        )
        .unwrap(),
        ("id", "user_id"),
    )
    .unwrap();

Slice

By index

// to_dataframe copies DataSlice into new Dataframe
df.slice(1, 4).unwrap().to_dataframe();

By column

df.col_slice(["name", "age"].into())
    .unwrap()
    .to_dataframe();

Get cell

// (row_index, col_name)
let cell = df.cell(1, "score").unwrap();

Filter

Simple

let df = df.filter(exp("age", neq(), None::<i64>)).unwrap();

Complex

Nest as many and/or/exp as needed

let df = df
    .filter(or(vec![
        and(vec![exp("id", gt(), 2), exp("score", lt(), 1000)]),
        exp("val", eq(), false),
    ]))
    .unwrap();

Supported expression operations:

• eq() equal
• neq() not equal
• gt() greater than
• lt() less than
• gte() greater or equal than
• lte() less or equal than
• modl(i: i64) mod i is
• regx() matches regex

Mutate

df.col_mut("id").unwrap().iter_mut().for_each(|cell| {
    if let Cell::Int(val) = cell {
        *val *= 2
    }
});

Sort

// sort by, sort dir [asc() | desc()]
df.sort("at", asc()).unwrap();

Iterate

let unames = df
    .iter()
    .map(|row| match row.get("username") {
        Some(Cell::Str(val)) => val,
        _ => "None",
    })
    .collect::<Vec<&str>>();

Store

To csv

df.to_csv("./tests/test.csv").unwrap();

Examples

For more examples, see ./tests/integration_test.rs