Working with tables in Azure Machine Learning

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Azure Machine Learning supports a Table type (mltable). This allows for the creation of a blueprint that defines how to load data files into memory as a Pandas or Spark data frame. In this article you learn:

Prerequisites

• An Azure subscription. If you don't already have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning

• The Azure Machine Learning SDK for Python

• An Azure Machine Learning workspace

pip install -U mltable azureml-dataprep[pandas]

Clone the examples repository

The code snippets in this article are based on examples in the Azure Machine Learning examples GitHub repo. To clone the repository to your development environment, use this command:

git clone --depth 1 https://github.com/Azure/azureml-examples

You can find examples relevant to Azure Machine Learning Tables in this folder of the cloned repo:

cd azureml-examples/sdk/python/using-mltable

Introduction

Azure Machine Learning Tables (mltable) allow you to define how you want to load your data files into memory, as a Pandas and/or Spark data frame. Tables have two key features:

1. An MLTable file. A YAML-based file that defines the data loading blueprint. In the MLTable file, you can specify:
   • The storage location or locations of the data - local, in the cloud, or on a public http(s) server.
   • Globbing patterns over cloud storage. These locations can specify sets of filenames, with wildcard characters (*).
   • read transformation - for example, the file format type (delimited text, Parquet, Delta, json), delimiters, headers, etc.
   • Column type conversions (to enforce schema).
   • New column creation, using folder structure information - for example, creation of a year and month column, using the {year}/{month} folder structure in the path.
   • Subsets of data to load - for example, filter rows, keep/drop columns, take random samples.
2. A fast and efficient engine to load the data into a Pandas or Spark dataframe, according to the blueprint defined in the MLTable file. The engine relies on Rust for high speed and memory efficiency.

Azure Machine Learning Tables are useful in these scenarios:

• You need to glob over storage locations.
• You need to create a table using data from different storage locations (for example, different blob containers).
• The path contains relevant information that you want to capture in your data (for example, date and time).
• The data schema changes frequently.
• You want easy reproducibility of your data loading steps.
• You only need a subset of large data.
• Your data contains storage locations that you want to stream into your Python session. For example, you want to stream path in the following JSON lines structure: [{"path": "abfss://[email protected]/my-images/cats/001.jpg", "label":"cat"}].
• You want to train ML models using Azure Machine Learning AutoML.

Azure Machine Learning Tables Quickstart

In this quickstart, you create a Table (mltable) of the NYC Green Taxi Data from Azure Open Datasets. The data has a parquet format, and it covers the years 2008-2021. On a publicly accessible blob storage account, the data files have this folder structure:

/
└── green
    ├── puYear=2008
    │   ├── puMonth=1
    │   │   ├── _committed_2983805876188002631
    │   │   └── part-XXX.snappy.parquet
    │   ├── ...
    │   └── puMonth=12
    │       ├── _committed_2983805876188002631
    │       └── part-XXX.snappy.parquet
    ├── ...
    └── puYear=2021
        ├── puMonth=1
        │   ├── _committed_2983805876188002631
        │   └── part-XXX.snappy.parquet
        ├── ...
        └── puMonth=12
            ├── _committed_2983805876188002631
            └── part-XXX.snappy.parquet

With this data, you need to load into a Pandas data frame:

• Only the parquet files for years 2015-19
• A random sample of the data
• Only rows with a rip distance greater than 0
• Relevant columns for Machine Learning
• New columns - year and month - using the path information (puYear=X/puMonth=Y)

Pandas code handles this. However, achieving reproducibility would become difficult because you must either:

• Share code, which means that if the schema changes (for example, a column name might change) then all users must update their code
• Write an ETL pipeline, which has heavy overhead

Azure Machine Learning Tables provide a light-weight mechanism to serialize (save) the data loading steps in an MLTable file. Then, you and members of your team can reproduce the Pandas data frame. If the schema changes, you only update the MLTable file, instead of updates in many places that involve Python data loading code.

Clone the quickstart notebook or create a new notebook/script

If you use an Azure Machine Learning compute instance, Create a new notebook. If you use an IDE, you should create a new Python script.

Additionally, the quickstart notebook is available in the Azure Machine Learning examples GitHub repo. Use this code to clone and access the Notebook:

git clone --depth 1 https://github.com/Azure/azureml-examples
cd azureml-examples/sdk/python/using-mltable/quickstart

Install the mltable Python SDK

To load the NYC Green Taxi Data into an Azure Machine Learning Table, you must have the mltable Python SDK and pandas installed in your Python environment, with this command:

pip install -U mltable azureml-dataprep[pandas]

Author an MLTable file

Use the mltable Python SDK to create an MLTable file, to document the data loading blueprint. For this, copy-and-paste the following code into your Notebook/Script, and then execute that code:

import mltable

# glob the parquet file paths for years 2015-19, all months.
paths = [
    {
        "pattern": "wasbs://[email protected]/green/puYear=2015/puMonth=*/*.parquet"
    },
    {
        "pattern": "wasbs://[email protected]/green/puYear=2016/puMonth=*/*.parquet"
    },
    {
        "pattern": "wasbs://[email protected]/green/puYear=2017/puMonth=*/*.parquet"
    },
    {
        "pattern": "wasbs://[email protected]/green/puYear=2018/puMonth=*/*.parquet"
    },
    {
        "pattern": "wasbs://[email protected]/green/puYear=2019/puMonth=*/*.parquet"
    },
]

# create a table from the parquet paths
tbl = mltable.from_parquet_files(paths)

# table a random sample
tbl = tbl.take_random_sample(probability=0.001, seed=735)

# filter trips with a distance > 0
tbl = tbl.filter("col('tripDistance') > 0")

# Drop columns
tbl = tbl.drop_columns(["puLocationId", "doLocationId", "storeAndFwdFlag"])

# Create two new columns - year and month - where the values are taken from the path
tbl = tbl.extract_columns_from_partition_format("/puYear={year}/puMonth={month}")

# print the first 5 records of the table as a check
tbl.show(5)

You can optionally choose to load the MLTable object into Pandas, using:

# You can load the table into a pandas dataframe
# NOTE: The data is in East US region and the data is large, so this will take several minutes (~7mins)
# to load if you are in a different region.

# df = tbl.to_pandas_dataframe()

Save the data loading steps

Next, save all your data loading steps into an MLTable file. Saving your data loading steps in an MLTable file allows you to reproduce your Pandas data frame at a later point in time, without need to redefine the code each time.

You can save the MLTable yaml file to a cloud storage resource, or you can save it to local path resources.

# save the data loading steps in an MLTable file to a cloud storage resource
# NOTE: the tbl object was defined in the previous snippet.
tbl.save(path="azureml://subscriptions/<subid>/resourcegroups/<rgname>/workspaces/<wsname>/datastores/<name>/paths/titanic", colocated=True, show_progress=True, overwrite=True)

# save the data loading steps in an MLTable file to a local resource
# NOTE: the tbl object was defined in the previous snippet.
tbl.save("./titanic")

Reproduce data loading steps

Now that you serialized the data loading steps into a file, you can reproduce them at any point in time with the load() method. This way, you don't need to redefine your data loading steps in code, and you can more easily share the file.

import mltable

# load the previously saved MLTable file
tbl = mltable.load("./nyc_taxi/")
tbl.show(5)

# You can load the table into a pandas dataframe
# NOTE: The data is in East US region and the data is large, so this will take several minutes (~7mins)
# to load if you are in a different region.

# load the table into pandas
# df = tbl.to_pandas_dataframe()

# print the head of the data frame
# df.head()
# print the shape and column types of the data frame
# print(f"Shape: {df.shape}")
# print(f"Columns:\n{df.dtypes}")

Create a data asset to aid sharing and reproducibility

You might have your MLTable file currently saved on disk, which makes it hard to share with team members. When you create a data asset in Azure Machine Learning, your MLTable is uploaded to cloud storage and "bookmarked."Your team members can then access the MLTable with a friendly name. Also, the data asset is versioned.

az ml data create --name green-quickstart --version 1 --path ./nyc_taxi --type mltable

subscription_id = "<SUBSCRIPTION_ID>"
resource_group = "<RESOURCE_GROUP>"
workspace = "<AML_WORKSPACE_NAME>"

from azure.ai.ml import MLClient
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes
from azure.identity import DefaultAzureCredential

# set VERSION variable
VERSION="1"

# connect to the AzureML workspace
# NOTE: the subscription_id, resource_group, workspace variables are set
# in the previous code snippet.
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

my_data = Data(
    path="./nyc_taxi",
    type=AssetTypes.MLTABLE,
    description="A random sample of NYC Green Taxi Data between 2015-19.",
    name="green-quickstart",
    version=VERSION,
)

ml_client.data.create_or_update(my_data)

Read the data asset in an interactive session

Now that you have your MLTable stored in the cloud, you and team members can access it with a friendly name in an interactive session (for example, a notebook):

import mltable
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# connect to the AzureML workspace
# NOTE: the subscription_id, resource_group, workspace variables are set
# in a previous code snippet.
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

# get the latest version of the data asset
# Note: The version was set in the previous snippet. If you changed the version
# number, update the VERSION variable below.
VERSION="1"
data_asset = ml_client.data.get(name="green-quickstart", version=VERSION)

# create a table
tbl = mltable.load(f"azureml:/{data_asset.id}")
tbl.show(5)

# load into pandas
# NOTE: The data is in East US region and the data is large, so this will take several minutes (~7mins) to load if you are in a different region.
df = tbl.to_pandas_dataframe()

Read the data asset in a job

If you or a team member want to access the Table in a job, your Python training script would contain:

# ./src/train.py
import argparse
import mltable

# parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('--input', help='mltable to read')
args = parser.parse_args()

# load mltable
tbl = mltable.load(args.input)

# load into pandas
df = tbl.to_pandas_dataframe()

Your job needs a conda file that includes the Python package dependencies:

# ./conda_dependencies.yml
dependencies:
  - python=3.10
  - pip=21.2.4
  - pip:
      - mltable
      - azureml-dataprep[pandas]

You would submit the job using:

# mltable-job.yml
$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json

code: ./src

command: python train.py --input ${{inputs.green}}
inputs:
    green:
      type: mltable
      path: azureml:green-quickstart:1

compute: cpu-cluster

environment:
  image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
  conda_file: conda_dependencies.yml

az ml job create -f mltable-job.yml

from azure.ai.ml import MLClient, command, Input
from azure.ai.ml.entities import Environment
from azure.identity import DefaultAzureCredential

# connect to the AzureML workspace
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

# get the latest version of the data asset
# Note: the VERSION was set in a previous cell.
data_asset = ml_client.data.get(name="green-quickstart", version=VERSION)

job = command(
    command="python train.py --input ${{inputs.green}}",
    inputs={"green": Input(type="mltable", path=data_asset.id)},
    compute="cpu-cluster",
    environment=Environment(
        image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04",
        conda_file="./job-env/conda_dependencies.yml",
    ),
    code="./src",
)

ml_client.jobs.create_or_update(job)

Authoring MLTable Files

To directly create the MLTable file, we suggest that you use the mltable Python SDK to author your MLTable files - as shown in the Azure Machine Learning Tables Quickstart - instead of a text editor. In this section, we outline the capabilities in the mltable Python SDK.

Supported file types

You can create an MLTable with a range of different file types:

For more information, read the MLTable reference resource

Defining paths

For delimited text, parquet, JSON lines, and paths, define a list of Python dictionaries that defines the path or paths from which to read:

import mltable

# A List of paths to read into the table. The paths are a python dict that define if the path is
# a file, folder, or (glob) pattern.
paths = [
    {
        "file": "<supported_path>"
    }
]

tbl = mltable.from_delimited_files(paths=paths)

# alternatively
# tbl = mltable.from_parquet_files(paths=paths)
# tbl = mltable.from_json_lines_files(paths=paths)
# tbl = mltable.from_paths(paths=paths)

MLTable supports these path types:

A note on defining paths for Delta Lake Tables

Compared to the other file types, defining paths to read Delta Lake tables is different. For Delta Lake tables, the path points to a single folder (typically on ADLS gen2) that contains the "_delta_log" folder and data files. time travel is supported. The following code shows how to define a path for a Delta Lake table:

import mltable

# define the cloud path containing the delta table (where the _delta_log file is stored)
delta_table = "abfss://<file_system>@<account_name>.dfs.core.windows.net/<path_to_delta_table>"

# create an MLTable. Note the timestamp_as_of parameter for time travel.
tbl = mltable.from_delta_lake(
    delta_table_uri=delta_table,
    timestamp_as_of='2022-08-26T00:00:00Z'
)

To get the latest version of Delta Lake data, you can pass current timestamp into timestamp_as_of.

import mltable

# define the relative path containing the delta table (where the _delta_log file is stored)
delta_table_path = "./working-directory/delta-sample-data"

# get the current timestamp in the required format
current_timestamp = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
print(current_timestamp)
tbl = mltable.from_delta_lake(delta_table_path, timestamp_as_of=current_timestamp)
df = tbl.to_pandas_dataframe()

Files, folders and globs

Azure Machine Learning Tables support reading from:

• file(s), for example: abfss://<file_system>@<account_name>.dfs.core.windows.net/my-csv.csv
• folder(s), for example abfss://<file_system>@<account_name>.dfs.core.windows.net/my-folder/
• glob pattern(s), for example abfss://<file_system>@<account_name>.dfs.core.windows.net/my-folder/*.csv
• a combination of files, folders, and globbing patterns

Supported data loading transformations

Find full, up-to-date details of the supported data loading transformations in the MLTable reference documentation.

Examples

Examples in the Azure Machine Learning examples GitHub repo became the basis for the code snippets in this article. Use this command to clone the repository to your development environment:

git clone --depth 1 https://github.com/Azure/azureml-examples

This clone repo folder hosts the examples relevant to Azure Machine Learning Tables:

cd azureml-examples/sdk/python/using-mltable

Delimited files

First, create an MLTable from a CSV file with this code:

import mltable
from mltable import MLTableHeaders, MLTableFileEncoding, DataType

# create paths to the data files
paths = [{"file": "wasbs://[email protected]/titanic.csv"}]

# create an MLTable from the data files
tbl = mltable.from_delimited_files(
    paths=paths,
    delimiter=",",
    header=MLTableHeaders.all_files_same_headers,
    infer_column_types=True,
    include_path_column=False,
    encoding=MLTableFileEncoding.utf8,
)

# filter out rows undefined ages
tbl = tbl.filter("col('Age') > 0")

# drop PassengerId
tbl = tbl.drop_columns(["PassengerId"])

# ensure survived column is treated as boolean
data_types = {
    "Survived": DataType.to_bool(
        true_values=["True", "true", "1"], false_values=["False", "false", "0"]
    )
}
tbl = tbl.convert_column_types(data_types)

# show the first 5 records
tbl.show(5)

# You can also load into pandas...
# df = tbl.to_pandas_dataframe()
# df.head(5)

Save the data loading steps

Next, save all your data loading steps into an MLTable file. When you save your data loading steps in an MLTable file, you can reproduce your Pandas data frame at a later point in time, without need to redefine the code each time.

# save the data loading steps in an MLTable file
# NOTE: the tbl object was defined in the previous snippet.
tbl.save("./titanic")

Reproduce data loading steps

Now that file has the serialized data loading steps, you can reproduce them at any point in time with the load() method. This way, you don't need to redefine your data loading steps in code, and you can more easily share the file.

import mltable

# load the previously saved MLTable file
tbl = mltable.load("./titanic/")

Create a data asset to aid sharing and reproducibility

You might have your MLTable file currently saved on disk, which makes it hard to share with team members. When you create a data asset in Azure Machine Learning, your MLTable is uploaded to cloud storage and "bookmarked." Your team members can then access the MLTable with a friendly name. Also, the data asset is versioned.

import time
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes
from azure.identity import DefaultAzureCredential

# Update with your details...
subscription_id = "<SUBSCRIPTION_ID>"
resource_group = "<RESOURCE_GROUP>"
workspace = "<AML_WORKSPACE_NAME>"

# set the version number of the data asset to the current UTC time
VERSION = time.strftime("%Y.%m.%d.%H%M%S", time.gmtime())

# connect to the AzureML workspace
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

my_data = Data(
    path="./titanic",
    type=AssetTypes.MLTABLE,
    description="The titanic dataset.",
    name="titanic-cloud-example",
    version=VERSION,
)

ml_client.data.create_or_update(my_data)

Now that you have your MLTable stored in the cloud, you and team members can access it with a friendly name in an interactive session (for example, a notebook):

import mltable
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# connect to the AzureML workspace
# NOTE:  subscription_id, resource_group, workspace were set in a previous snippet.
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

# get the latest version of the data asset
# Note: The version was set in the previous code cell.
data_asset = ml_client.data.get(name="titanic-cloud-example", version=VERSION)

# create a table
tbl = mltable.load(f"azureml:/{data_asset.id}")

# load into pandas
df = tbl.to_pandas_dataframe()
df.head(5)

You can also easily access the data asset in a job.

Parquet files

The Azure Machine Learning Tables Quickstart explains how to read parquet files.

Paths: Create a table of image files

You can create a table containing the paths on cloud storage. This example has several dog and cat images located in cloud storage, in the following folder structure:

/pet-images
  /cat
    0.jpeg
    1.jpeg
    ...
  /dog
    0.jpeg
    1.jpeg

The mltable can construct a table that contains the storage paths of these images and their folder names (labels), which can be used to stream the images. This code creates the MLTable:

import mltable

# create paths to the data files
paths = [{"pattern": "wasbs://[email protected]/pet-images/**/*.jpg"}]

# create the mltable
tbl = mltable.from_paths(paths)

# extract useful information from the path
tbl = tbl.extract_columns_from_partition_format("{account}/{container}/{folder}/{label}")

tbl = tbl.drop_columns(["account", "container", "folder"])

df = tbl.to_pandas_dataframe()
print(df.head())

# save the data loading steps in an MLTable file
tbl.save("./pets")

This code shows how to open the storage location in the Pandas data frame, and plot the images:

# plot images on a grid. Note this takes ~1min to execute.
import matplotlib.pyplot as plt
from PIL import Image

fig = plt.figure(figsize=(20, 20))
columns = 4
rows = 5
for i in range(1, columns*rows +1):
    with df.Path[i].open() as f:
        img = Image.open(f)
        fig.add_subplot(rows, columns, i)
        plt.imshow(img)
        plt.title(df.label[i])

Create a data asset to aid sharing and reproducibility

You might have your mltable file currently saved on disk, which makes it hard to share with team members. When you create a data asset in Azure Machine Learning, the mltable is uploaded to cloud storage and "bookmarked." Your team members can then access the mltable with a friendly name. Also, the data asset is versioned.

import time
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes
from azure.identity import DefaultAzureCredential

# set the version number of the data asset to the current UTC time
VERSION = time.strftime("%Y.%m.%d.%H%M%S", time.gmtime())

# connect to the AzureML workspace
# NOTE: subscription_id, resource_group, workspace were set in a previous snippet.
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

my_data = Data(
    path="./pets",
    type=AssetTypes.MLTABLE,
    description="A sample of cat and dog images",
    name="pets-mltable-example",
    version=VERSION,
)

ml_client.data.create_or_update(my_data)

Now that the mltable is stored in the cloud, you and your team members can access it with a friendly name in an interactive session (for example, a notebook):

import mltable
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# connect to the AzureML workspace
# NOTE: subscription_id, resource_group, workspace were set in a previous snippet.
ml_client = MLClient(
    DefaultAzureCredential(), subscription_id, resource_group, workspace
)

# get the latest version of the data asset
# Note: the variable VERSION is set in the previous code
data_asset = ml_client.data.get(name="pets-mltable-example", version=VERSION)

# the table from the data asset id
tbl = mltable.load(f"azureml:/{data_asset.id}")

# load into pandas
df = tbl.to_pandas_dataframe()
df.head()

You can also load the data into your job.

Next steps

• Access data in a job
• Create and manage data assets
• Import data assets (preview)
• Data administration