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Documentation for Microsoft Azure Machine Learning Data Preparation
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README.md

Azure Machine Learning Data Prep SDK

You will find in this repo:

Installation

Here are the SDK installation steps.

Documentation

Here is more information on how to use the new Data Prep SDK:

Release Notes

2019-08-05 (version 1.1.10)

New features

  • You can now request to execute specific inspectors (e.g. histogram, scatter plot, etc) on specific columns.
  • Added a parallelize argument to append_columns. If True, data will be loaded into memory but execution will run in parallel; if False, execution will be streaming but single-threaded.

2019-07-25 (version 1.1.9)

New features

  • Added support for reading a file directly from a http or https url.

Bug fixes and improvements

  • Improved error message when attempting to read a Parquet Dataset from a remote source (which is not currently supported).
  • Fixed a bug when writing to Parquet file format in ADLS Gen 2, and updating the ADLS Gen 2 container name in the path.

2019-07-09 (version 1.1.8)

New features

  • Dataflow objects can now be iterated over, producing a sequence of records. See documentation for Dataflow.to_record_iterator.

Bug fixes and improvements

  • Increased the robustness of DataPrep SDK.
  • Improved handling of pandas DataFrames with non-string Column Indexes.
  • Improved the performance of to_pandas_dataframe in Datasets.
  • Fixed a bug where Spark execution of Datasets failed when run in a multi-node environment.

2019-07-01 (version 1.1.7)

We reverted a change that improved performance, as it was causing issues for some customers using Azure Databricks. If you experienced an issue on Azure Databricks, you can upgrade to version 1.1.7 using one of the methods below:

  1. Run this script to upgrade: %sh /home/ubuntu/databricks/python/bin/pip install azureml-dataprep==1.1.7
  2. Recreate the cluster, which will install the latest Data Prep SDK version.

2019-06-24 (version 1.1.6)

New features

  • Added summary functions for top values (SummaryFunction.TOPVALUES) and bottom values (SummaryFunction.BOTTOMVALUES).

Bug fixes and improvements

  • Significantly improved the performance of read_pandas_dataframe.
  • Fixed a bug that would cause get_profile() on a Dataflow pointing to binary files to fail.
  • Exposed set_diagnostics_collection() to allow for programmatic enabling/disabling of the telemetry collection.
  • Changed the behavior of get_profile(). NaN values are now ignored for Min, Mean, Std, and Sum, which aligns with the behavior of Pandas.

2019-06-10 (version 1.1.5)

Bug fixes and improvements

  • For interpreted datetime values that have a 2-digit year format, the range of valid years has been updated to match Windows May Release. The range has been changed from 1930-2029 to 1950-2049.
  • When reading in a file and setting handleQuotedLineBreaks=True, \r will be treated as a new line.
  • Fixed a bug that caused read_pandas_dataframe to fail in some cases.
  • Improved performance of get_profile.
  • Improved error messages.

2019-05-28 (version 1.1.4)

New features

  • You can now use the following expression language functions to extract and parse datetime values into new columns.
    • RegEx.extract_record() extracts datetime elements into a new column.
    • create_datetime() creates datetime objects from separate datetime elements.
  • When calling get_profile(), you can now see that quantile columns are labeled as (est.) to clearly indicate that the values are approximations.
  • You can now use ** globbing when reading from Azure Blob Storage.
    • e.g. dprep.read_csv(path='https://yourblob.blob.core.windows.net/yourcontainer/**/data/*.csv')

Bug fixes

  • Fixed a bug related to reading a Parquet file from a remote source (Azure Blob).

2019-05-08 (version 1.1.3)

New features

Bug fixes and improvements

  • Fixed issues with column type conversion:
    • Now correctly converts a boolean or numeric column to a boolean column.
    • Now does not fail when attempting to set a date column to be date type.
  • Improved JoinType types and accompanying reference documentation. When joining two dataflows, you can now specify one of these types of join:
    • NONE, MATCH, INNER, UNMATCHLEFT, LEFTANTI, LEFTOUTER, UNMATCHRIGHT, RIGHTANTI, RIGHTOUTER, FULLANTI, FULL.
  • Improved data type inferencing to recognize more date formats.

2019-04-17 (version 1.1.2)

Note: Data Prep Python SDK will no longer install numpy and pandas packages. See updated installation instructions.

New features

  • You can now use the Pivot transform.
  • You can now use regular expressions in native functions.
    • Examples:
      • dflow.filter(dprep.RegEx('pattern').is_match(dflow['column_name']))
      • dflow.assert_value('column_name', dprep.RegEx('pattern').is_match(dprep.value))
  • You can now use to_upper and to_lower functions in expression language.
  • You can now see the number of unique values of each column in a data profile.
  • For some of the commonly used reader steps, you can now pass in the infer_column_types argument. If it is set to True, Data Prep will attempt to detect and automatically convert column types.
    • inference_arguments is now deprecated.
  • You can now call Dataflow.shape.

Bug fixes and improvements

  • keep_columns now accepts an additional optional argument validate_column_exists, which checks if the result of keep_columns will contain any columns.
  • All reader steps (which read from a file) now accept an additional optional argument verify_exists.
  • Improved performance of reading from pandas dataframe and getting data profiles.
  • Fixed a bug where slicing a single step from a Dataflow failed with a single index.

2019-04-08 (version 1.1.1)

New features

  • You can read multiple Datastore/DataPath/DataReference sources using read_* transforms.
  • You can perform the following operations on columns to create a new column: division, floor, modulo, power, length.
  • Data Prep is now part of the Azure ML diagnostics suite and will log diagnostic information by default.
    • To turn this off, set this environment variable to true: DISABLE_DPREP_LOGGER

Bug fixes and improvements

  • Improved code documentation for commonly used classes and functions.
  • Fixed a bug in auto_read_file that failed to read Excel files.
  • Added option to overwrite the folder in read_pandas_dataframe.
  • Improved performance of dotnetcore2 dependency installation, and added support for Fedora 27/28 and Ubuntu 1804.
  • Improved the performance of reading from Azure Blobs.
  • Column type detection now supports columns of type Long.
  • Fixed a bug where some date values were being displayed as timestamps instead of Python datetime objects.
  • Fixed a bug where some type counts were being displayed as doubles instead of integers.

2019-03-25 (version 1.1.0)

Breaking changes

  • The concept of the Data Prep Package has been deprecated and is no longer supported. Instead of persisting multiple Dataflows in one Package, you can persist Dataflows individually.

New features

  • Data Prep can now recognize columns that match a particular Semantic Type, and split accordingly. The STypes currently supported include: email address, geographic coordinates (latitude & longitude), IPv4 and IPv6 addresses, US phone number, and US zip code.
  • Data Prep now supports the following operations to generate a resultant column from two numeric columns: subtract, multiply, divide, and modulo.
  • You can call verify_has_data() on a Dataflow to check whether the Dataflow would produce records if executed.

Bug fixes and improvements

  • You can now specify the number of bins to use in a histogram for numeric column profiles.
  • The read_pandas_dataframe transform now requires the DataFrame to have string- or byte- typed column names.
  • Fixed a bug in the fill_nulls transform, where values were not correctly filled in if the column was missing.

2019-03-11 (version 1.0.17)

New features

  • Now supports adding two numeric columns to generate a resultant column using the expression language.

Bug fixes and improvements

  • Improved the documentation and parameter checking for random_split.

2019-02-27 (version 1.0.16)

Bug fix

  • Fixed a Service Principal authentication issue that was caused by an API change.

2019-02-25 (version 1.0.15)

New features

  • Data Prep now supports writing file streams from a dataflow. Also provides the ability to manipulate the file stream names to create new file names.

Bug fixes and improvements

  • Improved performance of t-Digest on large data sets.
  • Data Prep now supports reading data from a DataPath.
  • One hot encoding now works on boolean and numeric columns.
  • Other miscellaneous bug fixes.

2019-02-11 (version 1.0.12)

New features

  • Data Prep now supports reading from an Azure SQL database using Datastore.

Changes

  • Significantly improved the memory performance of certain operations on large data.
  • read_pandas_dataframe() now requires temp_folder to be specified.
  • The name property on ColumnProfile has been deprecated - use column_name instead.

2019-01-28 (version 1.0.8)

Bug fixes

  • Significantly improved the performance of getting data profiles.
  • Fixed minor bugs related to error reporting.

2019-01-14 (version 1.0.7)

New features

  • Datastore improvements (documented in Datastore how-to-guide)
    • Added ability to read from and write to Azure File Share and ADLS Datastores in scale-up.
    • When using Datastores, Data Prep now supports using service principal authentication instead of interactive authentication.
    • Added support for wasb and wasbs urls.

2019-01-09 (version 1.0.6)

Bug fixes

  • Fixed bug with reading from public readable Azure Blob containers on Spark.

2018-12-19 (version 1.0.4)

New features

  • to_bool function now allows mismatched values to be converted to Error values. This is the new default mismatch behavior for to_bool and set_column_types, whereas the previous default behavior was to convert mismatched values to False.
  • When calling to_pandas_dataframe, there is a new option to interpret null/missing values in numeric columns as NaN.
  • Added ability to check the return type of some expressions to ensure type consistency and fail early.
  • You can now call parse_json to parse values in a column as JSON objects and expand them into multiple columns.

Bug fixes

  • Fixed a bug that crashed set_column_types in Python 3.5.2.
  • Fixed a bug that crashed when connecting to Datastore using an AML image.

2018-12-07 (version 0.5.3)

Fixed missing dependency issue for .NET Core2 on Ubuntu 16.

2018-12-03 (version 0.5.2)

Breaking changes

  • SummaryFunction.N was renamed to SummaryFunction.Count.

Bug fixes

  • Use latest AML Run Token when reading from and writing to datastores on remote runs. Previously, if the AML Run Token is updated in Python, the Data Prep runtime will not be updated with the updated AML Run Token.
  • Additional clearer error messages
  • to_spark_dataframe() will no longer crash when Spark uses Kryo serialization
  • Value Count Inspector can now show more than 1000 unique values
  • Random Split no longer fails if the original Dataflow doesn’t have a name

2018-11-19 (version 0.5.0)

New features

  • Created a new DataPrep CLI to execute DataPrep packages and view the data profile for a dataset or dataflow
  • Redesigned SetColumnType API to improve usability
  • Renamed smart_read_file to auto_read_file
  • Now includes skew and kurtosis in the Data Profile
  • Can sample with stratified sampling
  • Can read from zip files that contain CSV files
  • Can split datasets row-wise with Random Split (e.g. into test-train sets)
  • Can get all the column data types from a dataflow or a data profile by calling .dtypes
  • Can get the row count from a dataflow or a data profile by calling .row_count

Bug fixes

  • Fixed long to double conversion
  • Fixed assert after any add column
  • Fixed an issue with FuzzyGrouping, where it would not detect groups in some cases
  • Fixed sort function to respect multi-column sort order
  • Fixed and/or expressions to be similar to how Pandas handles them
  • Fixed reading from dbfs path.
  • Made error messages more understandable
  • Now no longer fails when reading on remote compute target using AML token
  • Now no longer fails on Linux DSVM
  • Now no longer crashes when non-string values are in string predicates
  • Now handles assertion errors when Dataflow should fail correctly
  • Now supports dbutils mounted storage locations on Azure Databricks

2018-11-05 (version 0.4.0)

New features

  • Type Count added to Data Profile
  • Value Count and Histogram is now available
  • More percentiles in Data Profile
  • The Median is available in Summarize
  • Python 3.7 is now supported
  • When you save a dataflow that contains datastores to a Data Prep package, the datastore information will be persisted as part of the Data Prep package
  • Writing to datastore is now supported

Bug fixes

  • 64bit unsigned integer overflows are now handled properly on Linux
  • Fixed incorrect text label for plain text files in smart_read
  • String column type now shows up in metrics view
  • Type count now is fixed to show ValueKinds mapped to single FieldType instead of individual ones
  • Write_to_csv no longer fails when path is provided as a string
  • When using Replace, leaving “find” blank will no longer fail

Known Issues

  • If running version 0.1.0: To fix "Error Message: Cannot run the event loop while another loop is running", downgrade Tornado version to 4.5.3. Restart any running kernels for the change to take effect.
pip install -U tornado==4.5.3

Datasets License Information

IMPORTANT: Please read the notice and find out more about this NYC Taxi and Limousine Commission dataset here: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml

IMPORTANT: Please read the notice and find out more about this Chicago Police Department dataset here: https://catalog.data.gov/dataset/crimes-2001-to-present-398a4

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