teradataml 16.20.0.6

Teradata Vantage Python package for Advanced Analytics

Teradata Python package for Advanced Analytics.

teradataml makes available to Python users a collection of analytic functions that reside on Teradata Vantage. This allows users to perform analytics on Teradata Vantage with no SQL coding. In addition, the teradataml library provides functions for scaling data manipulation and transformation, data filtering and sub-setting, and can be used in conjunction with other open-source python libraries.

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Table of Contents

• Release Notes
• Installation and Requirements
• Using the Teradata Python Package
• Documentation
• License

Release Notes:

teradataml 16.20.00.06

Fixed the DataFrame data display corruption issue observed with certain analytic functions.

teradataml 16.20.00.05

Compatible with Vantage 1.1.1.
The following ML Engine (teradataml.analytics.mle) functions have new and/or updated arguments to support the Vantage version:

• AdaBoostPredict
• DecisionForestPredict
• DecisionTreePredict
• GLMPredict
• LDA
• NaiveBayesPredict
• NaiveBayesTextClassifierPredict
• SVMDensePredict
• SVMSparse
• SVMSparsePredict
• XGBoostPredict

teradataml 16.20.00.04

• Improvements

  • DataFrame creation is now quicker, impacting many APIs and Analytic functions.
  • Improved performance by reducing the number of intermediate queries issued to Teradata Vantage when not required.
    • The number of queries reduced by combining multiple operations into a single step whenever possible and unless the user expects or demands to see the intermediate results.
    • The performance improvement is almost proportional to the number of chained and unexecuted operations on a teradataml DataFrame.
  • Reduced number of intermediate internal objects created on Vantage.

• New Features/Functionality

  • General functions

    • New functions
      • show_versions() - to list the version of teradataml and dependencies installed.
      • fastload() - for high performance data loading of large amounts of data into a table on Vantage. Requires teradatasql version 16.20.0.48 or above.
      • Set operators:
        • concat
        • td_intersect
        • td_except
        • td_minus
      • case() - to help construct SQL CASE based expressions.
    • Updates
      • copy_to_sql
        • Added support to copy_to_sql to save multi-level index.
        • Corrected the type mapping for index when being saved.
      • create_context() updated to support 'JWT' logon mechanism.

  • Analytic functions

    • New functions
      • NERTrainer
      • NERExtractor
      • NEREvaluator
      • GLML1L2
      • GLML1L2Predict
    • Updates
      • Added support to categorize numeric columns as categorical while using formula - as_categorical() in the teradataml.common.formula module.

  • DataFrame

    • Added support to create DataFrame from Volatile and Primary Time Index tables.
    • DataFrame.sample() - to sample data.
    • DataFrame.index - Property to access index_label of DataFrame.
    • Functionality to process Time Series Data
      • Grouping/Resampling time series data:
        • groupby_time()
        • resample()
      • Time Series Aggregates:
        • bottom()
        • count()
        • describe()
        • delta_t()
        • mad()
        • median()
        • mode()
        • first()
        • last()
        • top()
    • DataFrame API and method argument validation added.
    • DataFrame.info() - Default value for null_counts argument updated from None to False.
    • Dataframe.merge() updated to accept columns expressions along with column names to on, left_on, right_on arguments.

  • DataFrame Column/ColumnExpression methods

    • cast() - to help cast the column to a specified type.
    • isin() and ~isin() - to check the presence of values in a column.

• Removed deprecated Analytic functions

  • All the deprecated Analytic functions under the teradataml.analytics module have been removed. Newer versions of the functions are available under the teradataml.analytics.mle and the teradataml.analytics.sqle modules. The modules removed are:
    • teradataml.analytics.Antiselect
    • teradataml.analytics.Arima
    • teradataml.analytics.ArimaPredictor
    • teradataml.analytics.Attribution
    • teradataml.analytics.ConfusionMatrix
    • teradataml.analytics.CoxHazardRatio
    • teradataml.analytics.CoxPH
    • teradataml.analytics.CoxSurvival
    • teradataml.analytics.DecisionForest
    • teradataml.analytics.DecisionForestEvaluator
    • teradataml.analytics.DecisionForestPredict
    • teradataml.analytics.DecisionTree
    • teradataml.analytics.DecisionTreePredict
    • teradataml.analytics.GLM
    • teradataml.analytics.GLMPredict
    • teradataml.analytics.KMeans
    • teradataml.analytics.NGrams
    • teradataml.analytics.NPath
    • teradataml.analytics.NaiveBayes
    • teradataml.analytics.NaiveBayesPredict
    • teradataml.analytics.NaiveBayesTextClassifier
    • teradataml.analytics.NaiveBayesTextClassifierPredict
    • teradataml.analytics.Pack
    • teradataml.analytics.SVMSparse
    • teradataml.analytics.SVMSparsePredict
    • teradataml.analytics.SentenceExtractor
    • teradataml.analytics.Sessionize
    • teradataml.analytics.TF
    • teradataml.analytics.TFIDF
    • teradataml.analytics.TextTagger
    • teradataml.analytics.TextTokenizer
    • teradataml.analytics.Unpack
    • teradataml.analytics.VarMax

teradataml 16.20.00.03

• Fixed the garbage collection issue observed with remove_context() when context is created using a SQLAlchemy engine.
• Added 4 new Advanced SQL Engine (was NewSQL Engine) analytic functions supported only on Vantage 1.1:
  • Antiselect, Pack, StringSimilarity, and Unpack.
• Updated the Machine Learning Engine NGrams function to work with Vantage 1.1.

teradataml 16.20.00.02

• Python version 3.4.x will no longer be supported. The Python versions supported are 3.5.x, 3.6.x, and 3.7.x.
• Major issue with the usage of formula argument in analytic functions with Python3.7 has been fixed, allowing this package to be used with Python3.7 or later.
• Configurable alias name support for analytic functions has been added.
• Support added to create_context (connect to Teradata Vantage) with different logon mechanisms. Logon mechanisms supported are: 'TD2', 'TDNEGO', 'LDAP' & 'KRB5'.
• copy_to_sql function and DataFrame 'to_sql' methods now provide following additional functionality:
  • Create Primary Time Index tables.
  • Create set/multiset tables.
• New DataFrame methods are added: 'median', 'var', 'squeeze', 'sort_index', 'concat'.
• DataFrame method 'join' is now updated to make use of ColumnExpressions (df.column_name) for the 'on' clause as opposed to strings.
• Series is supported as a first class object by calling squeeze on DataFrame.
  • Methods supported by teradataml Series are: 'head', 'unique', 'name', '__repr__'.
  • Binary operations with teradataml Series is not yet supported. Try using Columns from teradataml.DataFrames.
• Sample datasets and commands to load the same have been provided in the function examples.
• New configuration property has been added 'column_casesenitive_handler'. Useful when one needs to play with case sensitive columns.

teradataml 16.20.00.01

• New support has been added for Linux distributions: Red Hat 7+, Ubuntu 16.04+, CentOS 7+, SLES12+.
• 16.20.00.01 now has over 100 analytic functions. These functions have been organized into their own packages for better control over which engine to execute the analytic function on. Due to these namespace changes, the old analytic functions have been deprecated and will be removed in a future release. See the Deprecations section in the Teradata Python Package User Guide for more information.
• New DataFrame methods shape, iloc, describe, get_values, merge, and tail.
• New Series methods for NA checking (isnull, notnull) and string processing (lower, strip, contains).

teradataml 16.20.00.00

• teradataml 16.20.00.00 is the first release version. Please refer to the Teradata Python Package User Guide for a list of Limitations and Usage Considerations.

Installation and Requirements

Package Requirements:

• Python 3.5 or later

Note: 32-bit Python is not supported.

Minimum System Requirements:

• Windows 7 (64Bit) or later
• macOS 10.9 (64Bit) or later
• Red Hat 7 or later versions
• Ubuntu 16.04 or later versions
• CentOS 7 or later versions
• SLES 12 or later versions
• Teradata Vantage Advanced SQL Engine:
  • Advanced SQL Engine 16.20 Feature Update 1 or later
• For a Teradata Vantage system with the ML Engine:
  • Teradata Machine Learning Engine 08.00.03.01 or later

Installation

Use pip to install the Teradata Python Package for Advanced Analytics.

Platform	Command
macOS/Linux	pip install teradataml
Windows	py -3 -m pip install teradataml

When upgrading to a new version of the Teradata Python Package, you may need to use pip install's --no-cache-dir option to force the download of the new version.

Platform	Command
macOS/Linux	pip install --no-cache-dir -U teradataml
Windows	py -3 -m pip install --no-cache-dir -U teradataml

Using the Teradata Python Package

Your Python script must import the teradataml package in order to use the Teradata Python Package:

>>> import teradataml as tdml
>>> from teradataml import create_context, remove_context
>>> create_context(host = 'hostname', username = 'user', password = 'password')
>>> df = tdml.DataFrame('iris')
>>> df

   SepalLength  SepalWidth  PetalLength  PetalWidth             Name
0          5.1         3.8          1.5         0.3      Iris-setosa
1          6.9         3.1          5.1         2.3   Iris-virginica
2          5.1         3.5          1.4         0.3      Iris-setosa
3          5.9         3.0          4.2         1.5  Iris-versicolor
4          6.0         2.9          4.5         1.5  Iris-versicolor
5          5.0         3.5          1.3         0.3      Iris-setosa
6          5.5         2.4          3.8         1.1  Iris-versicolor
7          6.9         3.2          5.7         2.3   Iris-virginica
8          4.4         3.0          1.3         0.2      Iris-setosa
9          5.8         2.7          5.1         1.9   Iris-virginica

>>> df = df.select(['Name', 'SepalLength', 'PetalLength'])
>>> df

              Name  SepalLength  PetalLength
0  Iris-versicolor          6.0          4.5
1  Iris-versicolor          5.5          3.8
2   Iris-virginica          6.9          5.7
3      Iris-setosa          5.1          1.4
4      Iris-setosa          5.1          1.5
5   Iris-virginica          5.8          5.1
6   Iris-virginica          6.9          5.1
7      Iris-setosa          5.1          1.4
8   Iris-virginica          7.7          6.7
9      Iris-setosa          5.0          1.3

>>> df = df[(df.Name == 'Iris-setosa') & (df.PetalLength > 1.5)]
>>> df

          Name  SepalLength  PetalLength
0  Iris-setosa          4.8          1.9
1  Iris-setosa          5.4          1.7
2  Iris-setosa          5.7          1.7
3  Iris-setosa          5.0          1.6
4  Iris-setosa          5.1          1.9
5  Iris-setosa          4.8          1.6
6  Iris-setosa          4.7          1.6
7  Iris-setosa          5.1          1.6
8  Iris-setosa          5.1          1.7
9  Iris-setosa          4.8          1.6

Documentation

General product information, including installation instructions, is available in the Teradata Documentation website

License

Use of the Teradata Python Package is governed by the License Agreement for the Teradata Python Package for Advanced Analytics. After installation, the LICENSE and LICENSE-3RD-PARTY files are located in the teradataml directory of the Python installation directory.