## Teradata Widgets
teradatamlwidgets makes available to Python users a user interface to a collection of analytic functions and plot functions that reside on Teradata Vantage. This package provides Data Scientists and Teradata users a simple UI experience within a Jupyter Notebook to perform analytics and visualization on Teradata Vantage with no SQL coding and limited python coding required.
For documentation and tutorial notebooks please visit [Documentation](https://docs.teradata.com/r/Teradataml-Widgets/March-2024).
For Teradata customer support, please visit [Teradata Support](https://support.teradata.com/csm).
Copyright 2024, Teradata. All Rights Reserved.
### Table of Contents
* [Release Notes](#release-notes)
* [Installation and Requirements](#installation-and-requirements)
* [Using the Teradata Widgets Package](#using-the-teradata-python-package)
* [Documentation](#documentation)
* [License](#license)
## Release Notes:
#### teradatamlwidgets 20.0.0.5
* ##### New Features/Functionality
* Exploratory Data Analysis UI
* BYOM Scoring
* Login UI
* AutoML
* Script Table Operator
* Describe
* Persist
* ###### New APIs:
* `teradatamlwidgets.eda.Ui()`
* `teradatamlwidgets.byom_functions.Ui()`
* `teradatamlwidgets.login.Ui()`
* `teradatamlwidgets.auto_ml.Ui()`
* `teradatamlwidgets.script.Ui()`
* `teradatamlwidgets.describe.Ui()`
* `teradatamlwidgets.persist.Ui()`
* Added support for notebook utilities.
* `tdnb.text()` to create text widget.
* `tdnb.dropdown()` to create dropdown widget.
* `tdnb.multi_select()` to create multiselect widget.
* `tdnb.get()` to get the value of widget.
* `tdnb.get_all()` to get all the values of widgets in current session.
* `tdnb.remove()` to remove the widget.
* `tdnb.remove_all()` to remove all the widgets in current session.
* `tdnb.run_notebook()` to run a notebook from another notebook.
* `tdnb.exit()` to exit the notebook with a message.
* ##### Bug Fixes
* Fully qualified table names are now correctly recognized.
#### teradatamlwidgets 20.0.0.4
* ##### New Features/Functionality
* None
* ###### New APIs:
* None
* ##### Bug Fixes
* Fixed list of SQLE functions
#### teradatamlwidgets 20.0.0.3
* ##### New Features/Functionality
* None
* ###### New APIs:
* None
* ##### Bug Fixes
* Using native dialog boxes
* Parameter name change for Plot (color).
#### teradatamlwidgets 20.0.0.2
* ##### New Features/Functionality
* Updated documentation
* ###### New APIs:
* None
* ##### Bug Fixes
* Initialized default database
#### teradatamlwidgets 20.0.0.1
* ##### New Features/Functionality
* Updated documentation
* ###### New APIs:
* None
* ##### Bug Fixes
* None
#### teradatamlwidgets 20.0.0.0
* ##### New Features/Functionality
* ###### New APIs:
* Analytic functions
* `teradatamlwidgets.analytic_functions.Ui()`
* `teradatamlwidgets.analytic_functions.get_output_dataframe()`
* Plotting
* `teradatamlwidgets.plot.ShowPlots()`
* ##### Bug Fixes
* None
## 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 Widgets Package for Advanced Analytics.
Platform | Command
-------------- | ---
macOS/Linux | `pip install teradatamlwidgets`
Windows | `py -3 -m pip install teradatamlwidgets`
When upgrading to a new version of the Teradata Widgets 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 teradatamlwidgets`
Windows | `py -3 -m pip install --no-cache-dir -U teradatamlwidgets`
## Using the Teradata Python Package
Your Python script must import the `teradatamlwidgets` package in order to use the Teradata Widgets Package:
```
from teradatamlwidgets import login
ui = login.Ui()
```
```
from teradataml import *
from teradatamlwidgets import analytic_functions
Load the example data.
load_example_data("movavg", ["ibm_stock"])
load_example_data("teradataml", "titanic")
inputs = ["ibm_stock"]
outputs = ["Project_OutMovingAverageTest"]
ui = analytic_functions.Ui(
function='MovingAverage',
outputs=outputs,
inputs=inputs)
```
```
from teradataml import *
from teradatamlwidgets import plot
# Load the example data.
load_example_data("movavg", "ibm_stock")
load_example_data("teradataml", "iris_input")
# Plot
plot1 = plot.Ui(
table_name="ibm_stock",
current_plot="Line",
x='period',
series='stockprice',
style='green')
plot2 = plot.Ui(
table_name="iris_input",
current_plot="Scatter",
x='sepal_length',
series='petal_length',
xlabel='sepal_length',
ylabel='petal_length',
grid_color='black',
grid_linewidth=1,
grid_linestyle="-",
style='red',
title='Scatter Plot of sepal_length vs petal_length',
heading= 'Scatter Plot Example')
# Combine Plots
plot.ShowPlots([plot1, plot2], nrows=1, ncols=2)
```
```
from teradatamlwidgets import byom_functions
# BYOM Scoring Functions
byom = byom_functions.Ui(
function = "DataRobotPredict",
byom_location = "mldb",
input_table="iris_test",
model_id="dr_iris_rf",
model_table="byom_models")
```
```
from teradatamlwidgets import auto_ml
ui = auto_ml.Ui(
task="Classification",
training_table=iris_train,
testing_table=iris_test,
predict_table='iris_test',
algorithms=['xgboost', 'knn'],
verbose=0,
max_runtime_secs=300,
max_models=5)
```
```
from teradatasqlalchemy import (CHAR, VARCHAR, CLOB, INTEGER, FLOAT)
from teradatamlwidgets import script
ui = script.Ui(
script_name='ex1pSco.py',
files_local_path='.',
script_command='python3 ./<db_name>/ex1pScoViaDSS.py',
returns=OrderedDict({"Cust_ID": INTEGER(), "Prob_0": FLOAT(), "Prob_1": FLOAT(), "Actual_Value": INTEGER()}))
```
```
from teradatamlwidgets import eda
from teradataml import DataFrame
df = DataFrame("ibm_stock")
ui = eda.Ui(df = df)
```
```
from teradatamlwidgets import describe
from teradataml import DataFrame
df = DataFrame("ibm_stock")
ui = describe.Ui(df = df)
```
```
from teradatamlwidgets import persist
from teradataml import DataFrame
df = DataFrame("ibm_stock")
ui = persist.Ui(df = df)
```
+ Details
+ This package is useful to Data Scientists and Teradata users and provides following:
+ A simple UI experience within Jupyter Notebook.
+ Access to In-DB analytics
+ Visualizations
+ Integration with teradataml
+ Enable simple and easy integration with 3rd party workbenches
+ `teradatamlwidgets.login.Ui` Class
+ Purpose
+ Opens the function UI dialog in the notebook for the functions.
+ Function Output
+ This function will return instance of notebook UI interface.
+ Usage Considerations
+ If you are not already logged in then this will only allow you to log out otherwise the login screen is shown.
+ `teradatamlwidgets.analytic_functions.Ui` Class
+ Purpose
+ Opens the UI dialog in the notebook for the Analytic Functions (subset of the Analytics Database analytic functions, Vantage Analytics Library (VAL) functions, Unbounded Array Framework (UAF) time series functions).
+ Function Output
+ This function will return instance of notebook UI interface for analytic functions.
+ Usage Considerations
+ If you are not already logged in, the first time this is called, the “Login” user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.
+ `teradatamlwidgets.analytic_functions.get_output_dataframe` Method
+ Purpose
+ Gets the DataFrame of the executed function.
+ Function Output
+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.plot.Ui` Class
+ Purpose
+ Allows a user interface for plotting that allows the user to set plotting parameters and then visualize the plots. The internal implementation uses the functionality of TD_PLOT exposed in teradataml DataFrame.
+ Function Output
+ This function will return instance of notebook UI interface for TD_PLOT.
+ Usage Considerations
+ If you are not already logged in, the first time this is called, the “Login” user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.
+ `teradatamlwidgets.plot.ShowPlots` Method
+ Purpose
+ ShowPlots combines multiple plots together into one figure.
+ `teradatamlwidgets.eda.Ui` Class
+ Purpose
+ The Exploratory Data Analysis UI allows the user to take a deeper look into their dataset. The tabs include Data, Analyze, Visualize, Describe, and Persist. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.
+ Function Output
+ This function will return instance of notebook EDA UI interface.
+ Usage Considerations
+ You must login before either using Login UI Class, or using teradataml create_context().
+ `teradatamlwidgets.byom_functions.Ui` Class
+ Purpose
+ Opens the UI dialog in the notebook for the BYOM functions.
+ Function Output
+ This function will return instance of notebook BYOM functions UI interface.
+ Usage Considerations
+ If you are not already logged in, the first time this is called, the “Login” user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.
+ `teradatamlwidgets.byom_functions.get_output_dataframe` Method
+ Purpose
+ Gets the DataFrame of the executed function.
+ Function Output
+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.auto_ml.Ui` Class
+ Purpose
+ Opens the UI dialog in the notebook for the AutoML functions.
+ Function Output
+ This function will return instance of notebook AutoML UI interface.
+ Usage Considerations
+ If you are not already logged in, the first time this is called, the “Login” user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.
+ `teradatamlwidgets.auto_ml.get_prediction_dataframe` Method
+ Purpose
+ To access the predicted output table.
+ Function Output
+ Return Value: teradataml.DataFrame.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.auto_ml.get_auto_ml` Method
+ Purpose
+ To access the AutoML instance.
+ Function Output
+ Return Value: Pandas DataFrame.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.auto_ml.get_leaderboard` Method
+ Purpose
+ To access the leaderboard.
+ Function Output
+ Return Value: teradataml.automl.AutoClassifier or teradataml.automl.AutoRegressor.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.script.Ui` Class
+ Purpose
+ Opens the UI dialog in the notebook for the Script function.
+ Function Output
+ This function will return instance of notebook Script UI interface.
+ Usage Considerations
+ If you are not already logged in, the first time this is called, the “Login” user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.
+ `teradatamlwidgets.script.get_output_dataframe` Method
+ Purpose
+ Gets the DataFrame of the script result.
+ Function Output
+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.
+ Usage Considerations
+ NA
+ `teradatamlwidgets.describe.Ui` Class
+ Purpose
+ Allows user to see the dataFrame description and information, including Shape and Size, Column Statistics, Column Types, Column Summary, Categorical Summary, Futile Columns and Source Query.
+ Function Output
+ This function will return instance of notebook Describe UI interface.
+ Usage Considerations
+ You must login before either using Login UI Class, or using teradataml create_context().
+ `teradatamlwidgets.persist.Ui` Class
+ Purpose
+ Allows user to write records stored in a teradataml DataFrame to Teradata Vantage.
+ Function Output
+ This function will return instance of notebook Persist UI interface.
+ Usage Considerations
+ You must login before either using Login UI Class, or using teradataml create_context().
## Documentation
General product information, including installation instructions, is available in the [Teradata Documentation website](https://docs.teradata.com/search/documents?query=package+python+-lake&filters=category~%2522Programming+Reference%2522_%2522User+Guide%2522*prodname~%2522Teradata+Package+for+Python%2522_%2522Teradata+Python+Package%2522&sort=last_update&virtual-field=title_only&content-lang=)
## License
Use of the Teradata Widgets Package is governed by the *License Agreement for the Teradata Widgets Package for Advanced Analytics*.
After installation, the `LICENSE` and `LICENSE-3RD-PARTY` files are located in the `teradata_widget` directory of the Python installation directory.
Raw data
{
"_id": null,
"home_page": "https://teradata.com",
"name": "teradatamlwidgets",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": null,
"author": "Teradata Corporation",
"author_email": null,
"download_url": null,
"platform": null,
"description": "## Teradata Widgets\n\nteradatamlwidgets makes available to Python users a user interface to a collection of analytic functions and plot functions that reside on Teradata Vantage. This package provides Data Scientists and Teradata users a simple UI experience within a Jupyter Notebook to perform analytics and visualization on Teradata Vantage with no SQL coding and limited python coding required.\n\nFor documentation and tutorial notebooks please visit [Documentation](https://docs.teradata.com/r/Teradataml-Widgets/March-2024).\n\nFor Teradata customer support, please visit [Teradata Support](https://support.teradata.com/csm).\n\nCopyright 2024, Teradata. All Rights Reserved.\n\n### Table of Contents\n* [Release Notes](#release-notes)\n* [Installation and Requirements](#installation-and-requirements)\n* [Using the Teradata Widgets Package](#using-the-teradata-python-package)\n* [Documentation](#documentation)\n* [License](#license)\n\n## Release Notes:\n#### teradatamlwidgets 20.0.0.5\n* ##### New Features/Functionality\n * Exploratory Data Analysis UI\n * BYOM Scoring\n * Login UI\n * AutoML\n * Script Table Operator\n * Describe\n * Persist\n* ###### New APIs:\n * `teradatamlwidgets.eda.Ui()`\n * `teradatamlwidgets.byom_functions.Ui()`\n * `teradatamlwidgets.login.Ui()`\n * `teradatamlwidgets.auto_ml.Ui()`\n * `teradatamlwidgets.script.Ui()`\n * `teradatamlwidgets.describe.Ui()`\n * `teradatamlwidgets.persist.Ui()`\n * Added support for notebook utilities.\n * `tdnb.text()` to create text widget.\n * `tdnb.dropdown()` to create dropdown widget.\n * `tdnb.multi_select()` to create multiselect widget.\n * `tdnb.get()` to get the value of widget.\n * `tdnb.get_all()` to get all the values of widgets in current session.\n * `tdnb.remove()` to remove the widget.\n * `tdnb.remove_all()` to remove all the widgets in current session.\n * `tdnb.run_notebook()` to run a notebook from another notebook.\n * `tdnb.exit()` to exit the notebook with a message.\n\n* ##### Bug Fixes\n * Fully qualified table names are now correctly recognized.\n\n#### teradatamlwidgets 20.0.0.4\n* ##### New Features/Functionality\n * None\n* ###### New APIs:\n * None\n* ##### Bug Fixes\n * Fixed list of SQLE functions\n\n#### teradatamlwidgets 20.0.0.3\n* ##### New Features/Functionality\n * None\n* ###### New APIs:\n * None\n* ##### Bug Fixes\n * Using native dialog boxes\n * Parameter name change for Plot (color).\n\n#### teradatamlwidgets 20.0.0.2\n* ##### New Features/Functionality\n * Updated documentation\n* ###### New APIs:\n * None\n* ##### Bug Fixes\n * Initialized default database\n\n#### teradatamlwidgets 20.0.0.1\n* ##### New Features/Functionality\n * Updated documentation\n* ###### New APIs:\n * None\n* ##### Bug Fixes\n * None\n\n#### teradatamlwidgets 20.0.0.0\n* ##### New Features/Functionality\n* ###### New APIs:\n * Analytic functions\n * `teradatamlwidgets.analytic_functions.Ui()`\n * `teradatamlwidgets.analytic_functions.get_output_dataframe()`\n * Plotting\n * `teradatamlwidgets.plot.ShowPlots()`\n* ##### Bug Fixes\n * None\n\n\n## Installation and Requirements\n\n### Package Requirements:\n* Python 3.5 or later\n\nNote: 32-bit Python is not supported.\n\n### Minimum System Requirements:\n* Windows 7 (64Bit) or later\n* macOS 10.9 (64Bit) or later\n* Red Hat 7 or later versions\n* Ubuntu 16.04 or later versions\n* CentOS 7 or later versions\n* SLES 12 or later versions\n* Teradata Vantage Advanced SQL Engine:\n * Advanced SQL Engine 16.20 Feature Update 1 or later\n* For a Teradata Vantage system with the ML Engine:\n * Teradata Machine Learning Engine 08.00.03.01 or later\n\n### Installation\n\nUse pip to install the Teradata Widgets Package for Advanced Analytics.\n\nPlatform | Command\n-------------- | ---\nmacOS/Linux | `pip install teradatamlwidgets`\nWindows | `py -3 -m pip install teradatamlwidgets`\n\nWhen upgrading to a new version of the Teradata Widgets Package, you may need to use pip install's `--no-cache-dir` option to force the download of the new version.\n\nPlatform | Command\n-------------- | ---\nmacOS/Linux | `pip install --no-cache-dir -U teradatamlwidgets`\nWindows | `py -3 -m pip install --no-cache-dir -U teradatamlwidgets`\n\n## Using the Teradata Python Package\n\nYour Python script must import the `teradatamlwidgets` package in order to use the Teradata Widgets Package:\n\n```\nfrom teradatamlwidgets import login\nui = login.Ui()\n```\n```\nfrom teradataml import *\nfrom teradatamlwidgets import analytic_functions\nLoad the example data.\nload_example_data(\"movavg\", [\"ibm_stock\"])\nload_example_data(\"teradataml\", \"titanic\")\ninputs = [\"ibm_stock\"]\noutputs = [\"Project_OutMovingAverageTest\"]\nui = analytic_functions.Ui(\n\t\tfunction='MovingAverage',\n\t\toutputs=outputs, \n\t\tinputs=inputs)\n```\n```\nfrom teradataml import *\nfrom teradatamlwidgets import plot\n# Load the example data.\nload_example_data(\"movavg\", \"ibm_stock\")\nload_example_data(\"teradataml\", \"iris_input\")\n# Plot\nplot1 = plot.Ui(\n\t\ttable_name=\"ibm_stock\", \n\t\tcurrent_plot=\"Line\", \n\t\tx='period', \n\t\tseries='stockprice', \n\t\tstyle='green')\nplot2 = plot.Ui(\n\t\ttable_name=\"iris_input\", \n\t\tcurrent_plot=\"Scatter\", \n\t\tx='sepal_length', \n\t\tseries='petal_length', \n\t\txlabel='sepal_length',\n\t\tylabel='petal_length',\n\t\tgrid_color='black',\n\t\tgrid_linewidth=1, \n\t\tgrid_linestyle=\"-\",\n\t\tstyle='red', \n\t\ttitle='Scatter Plot of sepal_length vs petal_length',\n\t\theading= 'Scatter Plot Example')\n# Combine Plots\nplot.ShowPlots([plot1, plot2], nrows=1, ncols=2) \n```\n```\nfrom teradatamlwidgets import byom_functions \n# BYOM Scoring Functions\nbyom = byom_functions.Ui(\n\t\tfunction = \"DataRobotPredict\", \n\t\tbyom_location = \"mldb\", \n\t\tinput_table=\"iris_test\", \n\t\tmodel_id=\"dr_iris_rf\", \n\t\tmodel_table=\"byom_models\")\n```\n```\nfrom teradatamlwidgets import auto_ml\nui = auto_ml.Ui(\n\t\ttask=\"Classification\", \n\t\ttraining_table=iris_train, \n\t\ttesting_table=iris_test,\n\t\tpredict_table='iris_test', \n\t\talgorithms=['xgboost', 'knn'],\n\t\tverbose=0,\n\t\tmax_runtime_secs=300,\n\t\tmax_models=5)\n```\n\n```\nfrom teradatasqlalchemy import (CHAR, VARCHAR, CLOB, INTEGER, FLOAT)\nfrom teradatamlwidgets import script \nui = script.Ui(\n\t\tscript_name='ex1pSco.py',\n files_local_path='.', \n script_command='python3 ./<db_name>/ex1pScoViaDSS.py',\n returns=OrderedDict({\"Cust_ID\": INTEGER(), \"Prob_0\": FLOAT(), \"Prob_1\": FLOAT(), \"Actual_Value\": INTEGER()}))\n```\n\n```\nfrom teradatamlwidgets import eda\nfrom teradataml import DataFrame\ndf = DataFrame(\"ibm_stock\")\nui = eda.Ui(df = df)\n```\n\n```\nfrom teradatamlwidgets import describe\nfrom teradataml import DataFrame\ndf = DataFrame(\"ibm_stock\")\nui = describe.Ui(df = df)\n```\n\n```\nfrom teradatamlwidgets import persist\nfrom teradataml import DataFrame\ndf = DataFrame(\"ibm_stock\")\nui = persist.Ui(df = df)\n```\n+ Details\n\n\t+ This package is useful to Data Scientists and Teradata users and provides following:\n\n\t\t+ A simple UI experience within Jupyter Notebook.\n\n\t\t+ Access to In-DB analytics\n\n\t\t+ Visualizations\n\n\t\t+ Integration with teradataml\n\n\t\t+ Enable simple and easy integration with 3rd party workbenches\n\n\n\t+ `teradatamlwidgets.login.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Opens the function UI dialog in the notebook for the functions.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in then this will only allow you to log out otherwise the login screen is shown.\n\n\t+ `teradatamlwidgets.analytic_functions.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Opens the UI dialog in the notebook for the Analytic Functions (subset of the Analytics Database analytic functions, Vantage Analytics Library (VAL) functions, Unbounded Array Framework (UAF) time series functions).\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook UI interface for analytic functions.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in, the first time this is called, the \u201cLogin\u201d user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.\n\n\t+ `teradatamlwidgets.analytic_functions.get_output_dataframe` Method\n\t\t+ Purpose\n\t\t\t+ Gets the DataFrame of the executed function.\n\t\t+ Function Output \n\t\t\t+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.\n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.plot.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Allows a user interface for plotting that allows the user to set plotting parameters and then visualize the plots. The internal implementation uses the functionality of TD_PLOT exposed in teradataml DataFrame.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook UI interface for TD_PLOT.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in, the first time this is called, the \u201cLogin\u201d user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.\n\n\t+ `teradatamlwidgets.plot.ShowPlots` Method\n\t\t+ Purpose\n\t\t\t+ ShowPlots combines multiple plots together into one figure.\n\n\t+ `teradatamlwidgets.eda.Ui` Class\n\t\t+ Purpose\n\t\t\t+ The Exploratory Data Analysis UI allows the user to take a deeper look into their dataset. The tabs include Data, Analyze, Visualize, Describe, and Persist. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook EDA UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ You must login before either using Login UI Class, or using teradataml create_context().\n\n\t+ `teradatamlwidgets.byom_functions.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Opens the UI dialog in the notebook for the BYOM functions.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook BYOM functions UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in, the first time this is called, the \u201cLogin\u201d user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.\n\n\t+ `teradatamlwidgets.byom_functions.get_output_dataframe` Method\n\t\t+ Purpose\n\t\t\t+ Gets the DataFrame of the executed function.\n\t\t+ Function Output \n\t\t\t+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.\n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.auto_ml.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Opens the UI dialog in the notebook for the AutoML functions.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook AutoML UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in, the first time this is called, the \u201cLogin\u201d user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.\n\n\t+ `teradatamlwidgets.auto_ml.get_prediction_dataframe` Method\n\t\t+ Purpose\n\t\t\t+ To access the predicted output table.\n\t\t+ Function Output \n\t\t\t+ Return Value: teradataml.DataFrame.\n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.auto_ml.get_auto_ml` Method\n\t\t+ Purpose\n\t\t\t+ To access the AutoML instance.\n\t\t+ Function Output \n\t\t\t+ Return Value: Pandas DataFrame.\n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.auto_ml.get_leaderboard` Method\n\t\t+ Purpose\n\t\t\t+ To access the leaderboard. \n\t\t+ Function Output \n\t\t\t+ Return Value: teradataml.automl.AutoClassifier or teradataml.automl.AutoRegressor. \n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.script.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Opens the UI dialog in the notebook for the Script function.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook Script UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ If you are not already logged in, the first time this is called, the \u201cLogin\u201d user interface will be displayed so the user can log into a Teradata instance which creates the internal instance.\n\n\t+ `teradatamlwidgets.script.get_output_dataframe` Method\n\t\t+ Purpose\n\t\t\t+ Gets the DataFrame of the script result.\n\t\t+ Function Output \n\t\t\t+ Return Value: teradataml.DataFrame. Returns the output of the function as a teradataml DataFrame.\n\t\t+ Usage Considerations\n\t\t\t+ NA\n\n\t+ `teradatamlwidgets.describe.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Allows user to see the dataFrame description and information, including Shape and Size, Column Statistics, Column Types, Column Summary, Categorical Summary, Futile Columns and Source Query.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook Describe UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ You must login before either using Login UI Class, or using teradataml create_context().\n\n\t+ `teradatamlwidgets.persist.Ui` Class\n\t\t+ Purpose\n\t\t\t+ Allows user to write records stored in a teradataml DataFrame to Teradata Vantage.\n\t\t+ Function Output \n\t\t\t+ This function will return instance of notebook Persist UI interface.\n\t\t+ Usage Considerations\n\t\t\t+ You must login before either using Login UI Class, or using teradataml create_context().\n\n## Documentation\n\nGeneral product information, including installation instructions, is available in the [Teradata Documentation website](https://docs.teradata.com/search/documents?query=package+python+-lake&filters=category~%2522Programming+Reference%2522_%2522User+Guide%2522*prodname~%2522Teradata+Package+for+Python%2522_%2522Teradata+Python+Package%2522&sort=last_update&virtual-field=title_only&content-lang=)\n\n## License\n\nUse of the Teradata Widgets Package is governed by the *License Agreement for the Teradata Widgets Package for Advanced Analytics*. \nAfter installation, the `LICENSE` and `LICENSE-3RD-PARTY` files are located in the `teradata_widget` directory of the Python installation directory.\n\n\n\n\n\n\n\n",
"bugtrack_url": null,
"license": "Teradata License Agreement",
"summary": "Teradataml Widgets",
"version": "20.0.0.5",
"project_urls": {
"Homepage": "https://teradata.com"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "dea32d5710dd8f034f88c8f65785b8eaf6061f93a7b82eef0fe1b48004a1c5e8",
"md5": "9c307c03ef4e4f0bf0438bcf41151366",
"sha256": "2d77980d79b456a294836441db782fdbd58b361405f4c4f5162b2aeadb9f7556"
},
"downloads": -1,
"filename": "teradatamlwidgets-20.0.0.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9c307c03ef4e4f0bf0438bcf41151366",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 581033,
"upload_time": "2025-03-19T05:59:58",
"upload_time_iso_8601": "2025-03-19T05:59:58.509970Z",
"url": "https://files.pythonhosted.org/packages/de/a3/2d5710dd8f034f88c8f65785b8eaf6061f93a7b82eef0fe1b48004a1c5e8/teradatamlwidgets-20.0.0.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-03-19 05:59:58",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "teradatamlwidgets"
}
JSON