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Example 04
beginner
04
DataFrame
Basics

DataFrame Basics

DataFrames in Deepbox provide a tabular API for data analysis. This example covers creating DataFrames from objects, selecting columns with .select(), filtering rows with .filter(), sorting by column values with .sort(), and accessing individual columns as Series with .get().

Deepbox Modules Used

deepbox/dataframe

What You Will Learn

  • Create DataFrames from column-oriented JavaScript objects
  • Select columns, filter rows, sort by values
  • Use .head() / .tail() for quick previews
  • Access shape and column metadata

Source Code

04-dataframe-basics/index.ts
1import { DataFrame } from "deepbox/dataframe";23const expectNumber = (value: unknown): number => {4  if (typeof value !== "number") {5    throw new Error("Expected number");6  }7  return value;8};910console.log("=== DataFrame Basics ===\n");1112// Create a DataFrame from an object with column arrays1314// Creating a DataFrame from an object15const df = new DataFrame({16  name: ["Alice", "Bob", "Charlie", "David", "Eve"],17  age: [25, 30, 35, 28, 32],18  salary: [50000, 60000, 75000, 55000, 70000],19  department: ["IT", "HR", "IT", "Sales", "HR"],20});2122// Display the complete DataFrame23console.log("Full DataFrame:");24console.log(df.toString());2526// Display DataFrame dimensions27console.log(`\nShape: ${df.shape[0]} rows × ${df.shape[1]} columns`);2829// Access individual columns30console.log("\nColumn Access:");31console.log(`Columns: ${df.columns.join(", ")}\n`);3233// Accessing columns34// Get a single column as a Series35const ages = df.get("age");36console.log("Age column:");37console.log(`${ages.toString()}\n`);3839// Selecting specific columns40// Select multiple columns at once41const subset = df.select(["name", "salary"]);42console.log("\nSelecting Multiple Columns:");43console.log("Selected columns (name, salary):");44console.log(`${subset.toString()}\n`);4546// Filtering rows47// Filter for high earners (salary > 60000)48const highEarners = df.filter((row) => expectNumber(row.salary) > 60000);49console.log("Employees with salary > 60000:");50console.log(`${highEarners.toString()}\n`);5152// Sorting53// Sort by salary in descending order54const sortedBySalary = df.sort("salary", false);55console.log("\nSorting:");56console.log("Sorted by salary (descending):");57console.log(`${sortedBySalary.toString()}\n`);5859// Head and tail60console.log("\nFiltering Rows:");61console.log("First 3 rows:");62console.log(`${df.head(3).toString()}\n`);6364console.log("Last 2 rows:");65console.log(`${df.tail(2).toString()}\n`);6667console.log("✓ DataFrame basics complete!");

Console Output

$ npx tsx 04-dataframe-basics/index.ts
=== DataFrame Basics ===

Full DataFrame:
      name  age  salary  department
0    Alice   25   50000          IT
1      Bob   30   60000          HR
2  Charlie   35   75000          IT
3    David   28   55000       Sales
4      Eve   32   70000          HR

Shape: 5 rows × 4 columns

Column Access:
Columns: name, age, salary, department

Age column:
0  25
1  30
2  35
3  28
4  32
Name: age, Length: 5


Selecting Multiple Columns:
Selected columns (name, salary):
      name  salary
0    Alice   50000
1      Bob   60000
2  Charlie   75000
3    David   55000
4      Eve   70000

Employees with salary > 60000:
      name  age  salary  department
2  Charlie   35   75000          IT
4      Eve   32   70000          HR


Sorting:
Sorted by salary (descending):
      name  age  salary  department
2  Charlie   35   75000          IT
4      Eve   32   70000          HR
1      Bob   30   60000          HR
3    David   28   55000       Sales
0    Alice   25   50000          IT


Filtering Rows:
First 3 rows:
      name  age  salary  department
0    Alice   25   50000          IT
1      Bob   30   60000          HR
2  Charlie   35   75000          IT

Last 2 rows:
    name  age  salary  department
3  David   28   55000       Sales
4    Eve   32   70000          HR

✓ DataFrame basics complete!
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