Introduction to `active-imports`

`Active-imports` is a dynamic and powerful library designed to facilitate efficient data manipulation and importing processes. With dozens of useful APIs, `active-imports` caters to various data handling needs and ensures seamless integration into your data pipeline. This tutorial provides an overview of the library, a suite of API examples, and a comprehensive app example to illustrate its potential.

API Examples

1. Importing Data from Various Sources

  from active_imports import import_from_csv, import_from_excel, import_from_json

  # Import from a CSV file
  data_csv = import_from_csv('path/to/your/file.csv')

  # Import from an Excel file
  data_excel = import_from_excel('path/to/your/file.xlsx')

  # Import from a JSON file
  data_json = import_from_json('path/to/your/file.json')

2. Transforming Data

  from active_imports import transform_data

  # Define a transformation function
  def upper_case(record):
      return record.upper()

  # Apply transformation to data
  transformed_data = transform_data(data_csv, upper_case)

3. Filtering Data

  from active_imports import filter_data

  # Define a filter function
  def filter_condition(record):
      return record['age'] > 30

  # Apply filter to data
  filtered_data = filter_data(data_csv, filter_condition)

4. Validating Data

  from active_imports import validate_data

  # Define validation rules
  def validation_rules(record):
      return 'name' in record and 'age' in record

  # Validate data
  valid_data = validate_data(data_csv, validation_rules)

Complete App Example

Building a Simple Data Processing App with `active-imports`

  from active_imports import (
      import_from_csv, transform_data,
      filter_data, validate_data, export_to_csv
  )

  # Step 1: Import data from CSV
  data = import_from_csv('path/to/your/data.csv')

  # Step 2: Transform data
  def capitalize_name(record):
      if 'name' in record:
          record['name'] = record['name'].capitalize()
      return record

  data = transform_data(data, capitalize_name)

  # Step 3: Filter data
  def filter_adults(record):
      return record.get('age', 0) >= 18

  data = filter_data(data, filter_adults)

  # Step 4: Validate data
  def validate_record(record):
      return 'name' in record and isinstance(record['age'], int)

  valid_data = validate_data(data, validate_record)

  # Step 5: Export valid data to a new CSV file
  export_to_csv(valid_data, 'path/to/your/output.csv')

Conclusion

The `active-imports` library provides a comprehensive toolkit for data importing, transforming, filtering, and validating. By utilizing these APIs, developers can streamline their data workflow and enhance productivity. The provided app example demonstrates how `active-imports` can be used to build efficient data processing applications.

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