Koala

What is the library?

Python's pandas implemented for fast, type safe programming in Rust.

Available Functions & Attributes

• CSV

  .read_csv(path: &str) -> CSV

  returns CSV struct reading file from given path

        let mut content = String::new();
        let csv : CSV = dataframe::df::read_csv("test.csv", &mut content); // CSV { headers, values, matrix }

  .get_df() -> DataFrame

  returns DataFrame from a CSV struct

         let mut df = csv.get_df(); // DataFrame { columns, dataset, values }

• DataFrame

  .columns -> Vec<&str>

  returns array of strings, containing column names

         df.columns; // ["name","age"]

  .dataset -> Vec<Vec<&str>>

  returns dataset matrix

         df.dataset; // [["bob","30"]
                        ["richard", "25"]]

  .values -> Vec<Pair(&str, Vec<&str>)>

  returns vector of pairs, containing column name, and all column values

         df.values; // [("name", ["bob", "richard"]), ("age", ["30", "25])]

  .max(column: &str) -> f64

  returns max from all values inside a column

         df.max("age"); // 30 as f64

  .max(column: &str) -> f64

  return min from all values inside a column

         df.min("age"); // 25 as f64

  .mean(column: &str) -> f64

  return mean from all values inside a column

         df.mean("age"); // 27.5 as f64

  .sum(column: &str) -> f64

  returns sum of all non N/A values from column

         df.sum("age"); // 55 as f64

  [&str] -> Vec<&str>

  string index for DataFrame, returns all values from a given column

         df["age"]; // ["30", "25"]

  [usize] -> Vec<&str>

  usize index for DataFrame, returns given row with all columns

         df[0]; // ["bob", "30"]

  .iloc(Vec<Range, Range>) -> Vec<Vec<&str>>

  returns sliced dataset matrix from given range

         df.iloc([0..2, 0..1].to_vec()); // [["richard"], ["bob"]]

  .is_na_col(column: &str) -> bool

  returns if given column on DataFrame has a missing value

         df.is_na_col("age"); // false

  .is_na() -> bool

  returns matrix containing missing value bool for each value

         df.is_na(); // [[false, false], [false, false]]

  .push(value: Vec<&str>)

  returns matrix containing missing value bool for each value

         df.push(["ann", "20"]);
         df.dataset; // [["richard", "30"], ["bob", "25"], ["ann", "20"]]

  .pop(value: Vec<&str>) -> Vec<&str>

  returns matrix containing missing value bool for each value

        df.pop(); // ["ann", "20"]
        df.pop(); // ["bob", "25"]
        df.dataset; // [["richard", "30"]]

  .n_uniques(column: &str) -> usize

  returns matrix containing missing value bool for each value

       df.n_uniques("age"); // 2 as usize

  .uniques(column: &str) -> Vec<&str>

  returns matrix containing missing value bool for each value

      df.uniques("age"); // ["30", "25"]

  .apply(column: &str, function: for<'r> fn(&'r str) -> &'a str)

  applies closure function to each value on given column

      fn in_my_twenties<'r>(age: &str) -> &'r str { "20" }   
      df.apply("age", in_my_twenties);
      df.dataset; // [["richard", "20"], ["bob", "20"]]

  .fillna(column: &str, value: &str)

  assigns given value to each N/A value on column

     df.fillna("age", df.mean("age")); // [["richard", "26"], ["bob", "26"]] given bob had no prior age

  .dtypes -> HashMap<&str, &str>

  returns type of each column

    df.dtypes // {"age": "numeric", "name": "str" }