Frosts

pandas-inspired Excel scripting for Office Scripts

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frosts.DataFrame

🔹 Constructor

new frosts.DataFrame(data: (string | number | boolean)[][]): DataFrame

• First row is treated as the header.
• All other rows become Row objects ({ [key: string]: string | number | boolean }).
• Duplicated headers throw an error.
• Infers column types as either “number”, “string”, or “boolean”.

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Properties

• df.columns: string[] - List of column names.
• df.values: Row[] - Array of Rows Objects (one per observation).
• df.types: {[key:string]: string} - Hashmap of data types of each column.

Example 1: Creating DataFrame and Accessing Properties

// Example data for creating a DataFrame
const data = [
  ["Name", "Age", "IsActive"],
  ["John", 25, true],
  ["Jane", 30, false],
  ["Alice", 22, true]
];

// Create a DataFrame instance
const df = new frosts.DataFrame(data);

// Access the columns
console.log(df.columns); 
// Output: ["Name", "Age", "IsActive"]

// Access the values (rows)
console.log(df.values);
// Output: [
//   { Name: "John", Age: 25, IsActive: true },
//   { Name: "Jane", Age: 30, IsActive: false },
//   { Name: "Alice", Age: 22, IsActive: true }
// ]

// Access the types
console.log(df.types); 
// Output: { Name: "string", Age: "number", IsActive: "boolean" }

// Access all information about the df
console.log(df);
/* Output: 
{
    columns: df.columns, 
    __headers: set from df.columns,
    values: df.values,
    dtypes: df.dtypes
}

Example 2: Handling Duplicate Headers

// Example with duplicated headers
const invalidData = [
  ["Name", "Name", "IsActive"], // Duplicated "Name" column
  ["John", 25, true],
  ["Jane", 30, false],
  ["Alice", 22, true],
  ["Bob", 35, false]  
];

const invalidDf = new fr.DataFrame(invalidData);
// Output: Error: Duplicate headers found

Example 3: Accessing Specific Rows and Their Values

// Accessing a specific row (e.g., first row)
const firstRow = df.values[0];
console.log(firstRow); 
// Output: { Name: "John", Age: 25, IsActive: true }
console.log(firstRow['Age']);
// Output: 25

//Set John's Activity status to false
df.values[0]["IsActive"] = false;

Methods

The methods of the fr.DataFrame class are organized into five main categories, each with its own dedicated page for detailed documentation:

• Basic Operations: Methods for accessing and modifying the DataFrame.
• Filtering: Methods for selecting and filtering rows based on conditions.
• Aggregations: Methods for summarizing and calculating statistics on data.
• Merging: Methods for combining DataFrames.
• Imports/Exports: Ways to import separated values to DataFrames, and export results for PowerAutomate/further scripting

Please refer to the corresponding pages for detailed information and examples for each category.

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