mltable 1.0.0

Contains MLTable loading and authoring apis for the mltable package.

# mltable: machine learning table data toolkit MLTable is a Python package that provides fast, flexible data loading functions designed to make accessing “tabular” data easy and intuitive. MLTable will help you to abstract the schema definition for tabular data so that it is easier to materialize the table into a Pandas dataframe. MlTable can be leveraged upon delimited text files, parquet files, delta lake, json-lines files from a cloud object store or local disk.

## Main Features

Here are a few things that mltable does well:

• Flexible sampling and filtering functionality on large data

• Robust IO tools for loading data from  flat files (CSV and delimited), parquet files, delta lake and json-lines files

• Capturing and defining schema contained in flat files

• Fast materialization of data into Pandas DataFrame

## Getting started

You can install MLTable package via pip. `bash pip install mltable `

Please note MLTable package is pre-installed on AzureML compute instances.

## Documentation

The official documentation is hosted on [Create a mltable data asset.](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?tabs=cli#create-a-mltable-data-asset)

MLTable artifact’s metadata file is called  MLTable which adheres to the [AzureML MLTable schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable).

# Release History

## 0.1.0b4 (2022-10-05)

### Features Added - Factory apis(from_paths, from_delimited_files, from_parquet_files, from_json_lines_files). - Authoring apis(keep_columns, drop_columns, take_random_sample, take etc). - Support mltable load from data asset uri

## 0.1.0b3 (2022-06-30)

## 0.1.0b2 (2022-05-23)

## 0.1.0b1 (2022-05-17)

### Features Added - Initial public preview release to load into pandas dataframe