Frosts DataFrames offer various filtering methods, each designed for specific use cases to filter data on a given condition.

Table of Contents

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  1. Table of Contents
  2. Filter Functions
    1. 🔍.filter(key: string, predicate: (value) => boolean, inplace:boolean = false): DataFrame
      1. When to Use .filter()
    2. 💬.query(condition: (row: FrostRow) => boolean): DataFrame
      1. When to Use .query()
    3. ✅.isin(column: string, values: Set<string | number | boolean>): DataFrame
      1. When to use .isin()
  3. Recap - When to Use to Each Filtering Method

Filter Functions

🔍.filter(key: string, predicate: (value) => boolean, inplace:boolean = false): DataFrame

Filters the DataFrame to only include rows where the given column value passes the provided predicate function.

  • key the column name to filter by
  • predicate a function or predicate that returns a boolean that determines whether a row should be include
  • inplace (optional): If true, modifies the current DataFrame directly. Otherwise, only returns a new one. Default is false.

When to Use .filter()

Use .filter() for simple column-based conditions like comparisons (>, <, =, etc.) on individual columns.

.filter() can be used easily with a predicate

const adults = df.filter("Age", age => age > 18);
console.log(adults.values);
/*
[
  { Name: "Alice", Age: 28 },
  { Name: "Bob", Age: 32 },
  { Name: "Charlie", Age: 24 }
]
*/

with any function that takes one value as an input

const data = [
    ["Num"],
    [1],
    [2],
    [3],
    [4]
];
let df = new frosts.DataFrame(data);

function is_even(n: number){
    return n % 2 == 0;
} 

console.log(df.filter("Num", is_even));
/*
[
{Num: 2},
{Num: 4}
]
*/

Or with any of the built-in frosts predicates for filtering (Read More in the Data Cleaning Section)

const alices = df.filter("Name",fr.predicates.equal("Alice"))

💬.query(condition: (row: FrostRow) => boolean): DataFrame

Filters the DataFrame using a custom condition that is applied to each row. The condition can reference multiple columns and use logical operators for complex row-level filtering.

  • condition: A function that receives a FrostRow object and returns true for rows to keep, or false to discard.

When to Use .query()

  • You need to filter by multiple columns at once.
  • You want to use typed getters like get_number() or get_string() for clarity and safety.
  • You’re chaining methods like .encode_headers() or .groupBy() and want expressive logic without messy index access.
const highEarners = df.query(row =>
  row.get_number("Salary") > 100000 && row.get_number("Age") < 50
);

console.log(highEarners.values);
/*
[
  { Name: "Alice", Age: 28, Salary: 120000 },
  { Name: "Bob", Age: 32, Salary: 150000 }
]
*/

const youngTechies = df.query(row =>
  row.get_number("Age") < 30 && row.get_string("Department") === "Engineering"
);

console.log(youngTechies.values);
/*
[
  { Name: "Alice", Age: 28, Department: "Engineering" },
  { Name: "Charlie", Age: 24, Department: "Engineering" }
]
*/

Tip: You can always fall back to row => row.raw["Column"] if needed, but it’s best to use get_string(), get_number(), and get_boolean() for safer logic.

✅.isin(column: string, values: Set<string | number | boolean>): DataFrame

Filters the DataFrame to include only rows where the value in the specified column is found in the provided set.

  • columns: The names of the column to filter by
  • values: values a set containing the values to match against. The set can contain strings, numbers, or booleans.

When to use .isin()

Use .isin() for checking if column values are in a predefined set (e.g., filtering by categories or multiple specific values)

const cities = new Set(["NYC", "LA"]);
const coastal = df.isin("City", cities);
console.log(coastal.values);
/*
[
  { Name: "Alice", City: "NYC" },
  { Name: "Bob", City: "LA" }
]
*/

Recap - When to Use to Each Filtering Method

  • .filter(): Use when you need simple comparisons on a single column (e.g., Age > 30, Salary < 50000).
  • .query(): Use for complex logic involving multiple columns, logical operators (&&, ||), or custom conditions (e.g., Age > 30 && Department === 'HR').
  • .isin(): Use for set membership checks, such as filtering for specific categories, states, or multiple values (e.g., State in [‘CA’, ‘NY’], City in [‘NYC’, ‘LA’]).

Now that we’ve covered how to filter your data to focus on specific rows and columns, we can move on to data aggregation.

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