diff --git a/Getting_Started/Teradataml_Widgets/AutoML_Tutorial.ipynb b/Getting_Started/Teradataml_Widgets/AutoML_Tutorial.ipynb new file mode 100644 index 00000000..25291428 --- /dev/null +++ b/Getting_Started/Teradataml_Widgets/AutoML_Tutorial.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "371d27f5", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " AutoML with Teradatamlwidgets\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "56d7d758", + "metadata": {}, + "source": [ + "

Introduction

\n", + "\n", + "

The Teradataml Widgets (teradatamlwidgets) enhances teradataml’s built-in interaction capabilities with the Teradata Vantage™ Data and Analytics Platform. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.

\n", + "\n", + "

With these components, in this notebook you will be able to:

\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "6a011d3f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### For ClearScape:\n", + "

Install/update packages - Do this if you have any issues executing the cells in this notebook.

\n", + "

NOTE: If you update the teradatamlwidgets library, you will need to restart the Kernel.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cd01e2e", + "metadata": {}, + "outputs": [], + "source": [ + "#pip --upgrade teradatamlwidgets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca12836", + "metadata": {}, + "outputs": [], + "source": [ + "#from teradataml import create_context, get_context, remove_context\n", + "#%run -i ../../UseCases/startup.ipynb\n", + "#eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "#print(eng)" + ] + }, + { + "cell_type": "markdown", + "id": "175ba860", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "801b06ad", + "metadata": {}, + "source": [ + "After running the notebook cell below, a login screen shows. This login screen has a place for the Host, Username and Password.\n", + "\n", + "Once you type in this information, click Login." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6eb48ee4", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import login\n", + "ui = login.Ui()" + ] + }, + { + "cell_type": "markdown", + "id": "84f2ea00", + "metadata": { + "tags": [] + }, + "source": [ + "## Setting up the test train data\n", + "\n", + "### Load Data and DataFrame Objects\n", + "\n", + "In this example we will load some tables using teradataml. As we have already logged in, we can call teradataml load functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9e67adf", + "metadata": {}, + "outputs": [], + "source": [ + "from teradataml import load_example_data\n", + "from teradataml import DataFrame\n", + "load_example_data(\"teradataml\", \"iris_input\")\n", + "iris = DataFrame.from_table(\"iris_input\")\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ae6c0466", + "metadata": {}, + "source": [ + "### Test Train Split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fc773e0", + "metadata": {}, + "outputs": [], + "source": [ + "iris_sample = iris.sample(frac = [0.8, 0.2])\n", + "# Fetching train and test data\n", + "iris_train= iris_sample[iris_sample['sampleid'] == 1].drop('sampleid', axis=1)\n", + "iris_test = iris_sample[iris_sample['sampleid'] == 2].drop('sampleid', axis=1)\n", + "from teradataml import copy_to_sql\n", + "copy_to_sql(df = iris_train, table_name = \"iris_train\", if_exists=\"replace\")\n", + "copy_to_sql(df = iris_test, table_name = \"iris_test\", if_exists=\"replace\")" + ] + }, + { + "cell_type": "markdown", + "id": "e38b8ba4", + "metadata": { + "tags": [] + }, + "source": [ + "#### Code Explanation\n", + "Below is the basic set up of the notebook with the mandatory parameters\n", + "\n", + "Import the notebook using the code :\n", + "\n", + "`from teradatamlwidgets import auto_ml`\n" + ] + }, + { + "cell_type": "markdown", + "id": "92193dc4", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Example: AutoML Classification Standalone\n", + "\n", + "Run the cell to open the AutoML UI. Then follow these steps:\n", + "\n", + "1. In Initialize tab, set Target Column to species and click Fit\n", + "2. After Fit has completed, click the Close button.\n", + "3. Then click Leaderboard to see the top leaders. Then click the Close button.\n", + "4. In the Prediction tab, click Execute to calculate the predicted output. Clicking the Close button will return you to the AutoML configuration options." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9076b46b", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import auto_ml\n", + "\n", + "ui = auto_ml.Ui(\n", + " task=\"Classification\", \n", + " training_table=iris_train, \n", + " predict_table='iris_input',\n", + " algorithms=['xgboost', 'knn'],\n", + " verbose=0,\n", + " max_runtime_secs=300,\n", + " max_models=5)" + ] + }, + { + "cell_type": "markdown", + "id": "36a451b3", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Predict Dataframe\n", + "In order to access the predicted output table, run the below command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80e0cb77", + "metadata": {}, + "outputs": [], + "source": [ + "ui.get_prediction_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b66670", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Leaderboard\n", + "In order to access the leaderboard, run the below command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b62c5a3", + "metadata": {}, + "outputs": [], + "source": [ + "ui.get_leaderboard()" + ] + }, + { + "cell_type": "markdown", + "id": "617ca0b6", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## AutoML instance\n", + "In order to access the AutoML instance, run the below command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09fa057c", + "metadata": {}, + "outputs": [], + "source": [ + "ui.get_auto_ml()" + ] + }, + { + "cell_type": "markdown", + "id": "bc345b04-3c59-43db-855b-1d2ddd254883", + "metadata": { + "tags": [] + }, + "source": [ + "## Example: AutoML Classification Integrated Function UI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aea4135-db32-4731-8fee-dd3153292974", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import analytic_functions\n", + "\n", + "inputs = ['iris_train']\n", + "ui = analytic_functions.Ui(function=\"AutoML\", inputs=inputs)" + ] + }, + { + "cell_type": "markdown", + "id": "72c55c42-fec8-4fd3-b394-22cfc03da87c", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Getting_Started/Teradataml_Widgets/BYOM_Tutorial.ipynb b/Getting_Started/Teradataml_Widgets/BYOM_Tutorial.ipynb new file mode 100644 index 00000000..f49cc636 --- /dev/null +++ b/Getting_Started/Teradataml_Widgets/BYOM_Tutorial.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c5f93b60", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " BYOM with Teradatamlwidgets\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "219acebd", + "metadata": {}, + "source": [ + "

Introduction

\n", + "\n", + "

The Teradataml Widgets (teradatamlwidgets) enhances teradataml’s built-in interaction capabilities with the Teradata Vantage™ Data and Analytics Platform. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.

\n", + "\n", + "

With these components, in this notebook you will be able to:

\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "6a011d3f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### For ClearScape:\n", + "

Install/update packages - Do this the first time running this (or Tutorial) Notebook ONLY.

\n", + "

NOTE: You will have to SHUTDOWN (file->shutdown) and reopen to run Demo.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cd01e2e", + "metadata": {}, + "outputs": [], + "source": [ + "#pip install teradatamlwidgets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca12836", + "metadata": {}, + "outputs": [], + "source": [ + "#from teradataml import create_context, get_context, remove_context\n", + "#%run -i ../../UseCases/startup.ipynb\n", + "#eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "#print(eng)" + ] + }, + { + "cell_type": "markdown", + "id": "175ba860", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "801b06ad", + "metadata": {}, + "source": [ + "#### After Running the Cell\n", + "\n", + "After running the notebook cell below, a login screen shows. This login screen has a place for the Host, Username and Password.\n", + "\n", + "Once you type in this information, click Login." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6eb48ee4", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import login\n", + "\n", + "ui = login.Ui()" + ] + }, + { + "cell_type": "markdown", + "id": "84f2ea00", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Setting up the Model\n", + "\n", + "### Load Data and DataFrame Objects\n", + "\n", + "In this example we will load some tables using teradataml. As we have already logged in, we can call teradataml load functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9e67adf", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required libraries / functions.\n", + "import os, teradataml\n", + "from teradataml import get_connection, DataFrame\n", + "from teradataml import save_byom, retrieve_byom, load_example_data\n", + "from teradataml import configure, display_analytic_functions, execute_sql\n", + "\n", + "# Load example data.\n", + "load_example_data(\"byom\", \"iris_test\")\n", + "\n", + "# Create teradataml DataFrame objects.\n", + "iris_test = DataFrame.from_table(\"iris_test\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae6c0466", + "metadata": {}, + "source": [ + "### Load Model into Vantage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fc773e0", + "metadata": {}, + "outputs": [], + "source": [ + "# Load model file into Vantage.\n", + "model_file = os.path.join(os.path.dirname(teradataml.__file__), \"data\", \"models\", \"dr_iris_rf\")\n", + "try: \n", + " save_byom(\"dr_iris_rf\", model_file, \"byom_models\")\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "e38b8ba4", + "metadata": {}, + "source": [ + "#### Code Explanation\n", + "Below is the basic set up of the notebook with the mandatory parameters\n", + "\n", + "Import the notebook using the code :\n", + "\n", + "`from teradatamlwidgets import byom_functions`" + ] + }, + { + "cell_type": "markdown", + "id": "92193dc4", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Example: DataRobotPredict" + ] + }, + { + "cell_type": "markdown", + "id": "4c279e9d", + "metadata": {}, + "source": [ + "Score data in Vantage with a model that has been created outside the Vantage by removing all the all cached models.\n", + "\n", + "The parameters for the DataRobotPredict example are as follows:\n", + "\n", + "- Set Accumulate to 'id', 'sepal_length', 'petal_length'\n", + "- Set OverwriteCachedModel to \"*\"\n", + " \n", + "- Function is already set to DataRobotPredict\n", + "- BYOM Install is already set to \"mldb\"\n", + "- InputTable is already set to \"iris_test\"\n", + "- ModelID is already set to \"dr_iris_rf\"\n", + "- ModelTable is already set to \"byom_models\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9076b46b", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import byom_functions\n", + "\n", + "ui = byom_functions.Ui(function = \"DataRobotPredict\", \n", + " byom_location = \"mldb\", \n", + " data=\"iris_test\", \n", + " model_id=\"dr_iris_rf\", \n", + " model_table=\"byom_models\")" + ] + }, + { + "cell_type": "markdown", + "id": "36a451b3", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Output Dataframe\n", + "In order to access the full output table, run the below command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80e0cb77", + "metadata": {}, + "outputs": [], + "source": [ + "ui.get_output_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "639eaaa2-5228-411c-afd5-cc90cfecf051", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Getting_Started/Teradataml_Widgets/EDA.ipynb b/Getting_Started/Teradataml_Widgets/EDA.ipynb new file mode 100644 index 00000000..ec3edd76 --- /dev/null +++ b/Getting_Started/Teradataml_Widgets/EDA.ipynb @@ -0,0 +1,391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5005165d-678a-4967-aee6-74f0c3ca4e80", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Teradataml Widgets EDA\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4ec1d34c-1610-4ce1-8e89-b56cfeb83090", + "metadata": {}, + "source": [ + "

Introduction

\n", + "\n", + "

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.\n", + "This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.

\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "f26f5e9c-041e-4903-98a8-cb5ccd81072d", + "metadata": {}, + "source": [ + "#### Code Explanation\n", + "Below is the basic set up of the notebook to call the EDA UI:\n", + "1. Create Connection\n", + "\n", + "2. Call the DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fec0b072-430d-4637-a8c3-191af6752b25", + "metadata": {}, + "outputs": [], + "source": [ + "from teradataml import *\n", + "import getpass\n", + "conn = create_context(host=getpass.getpass(\"Hostname: \"), \n", + " username=getpass.getpass(\"Username: \"),\n", + " password=getpass.getpass(\"Password: \"))" + ] + }, + { + "cell_type": "markdown", + "id": "7f388904-2a84-4705-baec-45940d93e1a3", + "metadata": {}, + "source": [ + "### Load Tables\n", + "\n", + "In this example we will load some tables using teradataml. As we have already logged in, we can call teradataml load functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6eb7821d-fc71-437a-8266-e842fce9b385", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the example data.\n", + "load_example_data(\"movavg\", \"ibm_stock\")" + ] + }, + { + "cell_type": "markdown", + "id": "0aeb5a95-f932-4837-a0e8-49bd42b42cc8", + "metadata": {}, + "source": [ + "### Set the DataFrame\n", + "By setting the DataFrame and calling it, the EDA UI will appear with the 5 different tabs (Data, Describe, Visualize, Analyze, and Persist)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3af4dd05-7896-4153-9b5a-64993ae203d6", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(\"ibm_stock\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a64c980-8193-4dd7-8d5a-5e98c1479112", + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "06d098e1-7c4e-4fae-a5e4-36d0d38a4896", + "metadata": {}, + "source": [ + "### Pipeline Example" + ] + }, + { + "cell_type": "markdown", + "id": "6594bb1e-6523-4eb1-9380-d95de564f8b9", + "metadata": {}, + "source": [ + "Pipelining is possible within the Analyze tab. \n", + "1. Click on the **Analyze** tab\n", + "2. Type in and choose \"Linear Regression\"\n", + "3. Under Required tab for *Input Columns* select \"age\", \"years_with_bank\", and \"nbr_children\". For *Response Column* choose \"income\"\n", + "4. Click **Execute** button\n", + "5. Click **Add to Pipeline** button\n", + "6. Type in and choose \"Linear Regression Predict\"\n", + "7. Under Optional tab for *Index Columns* select \"age\", \"years_with_bank\". For *Response Column* choose \"income\". For *Acumulate* choose \"nbr_children\"\n", + "8. Click **Execute** button" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b806fc4-f9a9-4b09-b1eb-4534b5734553", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(\"Customer\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "328572f6-2d57-4559-9c9c-16e5d6287f1a", + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "7ef92fe0-9e15-4f1c-a5f1-fc375705128a", + "metadata": {}, + "source": [ + "### Analyze Tab - Access Output DataFrame\n", + "After executing in the Analyze, it is possible to access the last output dataframe by calling the method below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3b459a6-50b1-49af-9650-13c2f2e19cef", + "metadata": {}, + "outputs": [], + "source": [ + "df.get_output()" + ] + }, + { + "cell_type": "markdown", + "id": "690e4990-1461-4e8f-9573-f8e5d9ea55aa", + "metadata": {}, + "source": [ + "### Also possible with query input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0b56ba2-fd89-4683-b9bd-b0af9897b37b", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(\"customer\")\n", + "if True:\n", + " for i in range(30):\n", + " df = df.assign(**{\"column_long_xyz_{}\".format(i): i})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "cb1dc9b3-f244-40dd-aac0-2a646bc956a3", + "metadata": {}, + "source": [ + "## Approach 2\n", + "The EDA UI is also callable within teradatamlwidgets, using `from teradatamlwidgets import eda`\n", + "\n", + "#### Code Explanation\n", + "Below is the basic set up of the notebook to call the EDA UI :\n", + "1. Create a connection using teradataml create_context or log in using\n", + "\n", + " `from teradatamlwidgets import login`\n", + "\n", + " `ui = login.Ui(val_location=\"VAL\")`\n", + "\n", + "3. Import the notebook using the code : \n", + "\n", + " `from teradatamlwidgets import eda`\n", + "\n", + "4. Set up the input DataFrame and call code using :\n", + " \n", + " `ui = eda.Ui(df = df)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82ede802-edf1-4786-bed1-3c348e7d86d1", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import eda\n", + "\n", + "df = DataFrame(\"ibm_stock\")\n", + "\n", + "ui = eda.Ui(df = df)" + ] + }, + { + "cell_type": "markdown", + "id": "0f66716a-f186-4503-8f51-b73f39919482", + "metadata": {}, + "source": [ + "##### Each tab from the EDA UI is also callable separately within teradatamlwidgets.\n", + "\n", + "Analyze: `from teradatamlwidgets import analytic_functions` \n", + "\n", + "Visualize: `from teradatamlwidgets import plot` \n", + "\n", + "Describe: `from teradatamlwidgets import describe` \n", + "\n", + "Persist: `from teradatamlwidgets import persist` \n", + "\n", + "\n", + "*The explanation for **Analyze** (analytic_functions) is available in separate notebook \"Tutorial.ipynb\".*\n", + "\n", + "*The explanation for **Visualize** (plot) is available in separate notebook \"Plot.ipynb\".*\n", + "\n", + "\n", + "## Describe\n", + "#### Code Explanation\n", + "Below is the basic set up of the notebook to call the Describe UI :\n", + "1. Create a connection using teradataml create_context or log in using\n", + " \n", + " `from teradatamlwidgets import login`\n", + "\n", + " `ui = login.Ui(val_location=\"VAL\")`\n", + "\n", + "3. Import the notebook using the code : \n", + "\n", + " `from teradatamlwidgets import describe`\n", + "\n", + "4. Set up the input DataFrame and call code using :\n", + " \n", + " `ui = describe.Ui(df = df)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5445f1ad-06c7-48b9-81cd-b1e3159e2e17", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import describe\n", + "\n", + "df = DataFrame(\"ibm_stock\")\n", + "\n", + "ui = describe.Ui(df = df)" + ] + }, + { + "cell_type": "markdown", + "id": "9aa09f9c-6f76-4465-b0e4-f4c32720c323", + "metadata": {}, + "source": [ + "## Persist\n", + "#### Code Explanation\n", + "Below is the basic set up of the notebook to call the Persist UI :\n", + "1. Create a connection using teradataml create_context or log in using\n", + " \n", + " `from teradatamlwidgets import login`\n", + "\n", + " `ui = login.Ui(val_location=\"VAL\")`\n", + "\n", + "3. Import the notebook using the code : \n", + "\n", + " `from teradatamlwidgets import persist`\n", + "\n", + "4. Set up the input DataFrame and call code using :\n", + " \n", + " `ui = persist.Ui(df = df)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab52689-8842-496e-9d9a-b9d37301a0c1", + "metadata": {}, + "outputs": [], + "source": [ + "from teradatamlwidgets import persist\n", + "\n", + "df = DataFrame(\"ibm_stock\")\n", + "\n", + "ui = persist.Ui(df = df)" + ] + }, + { + "cell_type": "markdown", + "id": "2e6cf1ff-2ff7-4e96-bee4-a632cf7167d2", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Getting_Started/Teradataml_Widgets/Plot_Notebook.ipynb b/Getting_Started/Teradataml_Widgets/Plot_Notebook.ipynb index ec1eef92..a3567dbb 100644 --- a/Getting_Started/Teradataml_Widgets/Plot_Notebook.ipynb +++ b/Getting_Started/Teradataml_Widgets/Plot_Notebook.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "markdown", - "id": "79fd4b3c-6c41-484f-93be-248f6330f552", + "id": "de32dad3", "metadata": {}, "source": [ "
\n", "

\n", - " Plotting with Teradataml Widgets\n", + " Plot with Teradatamlwidgets\n", "
\n", " \"Teradata\"\n", "

\n", @@ -16,97 +16,85 @@ }, { "cell_type": "markdown", - "id": "47369844-f8f9-41c9-bb1f-21e8ab3862a2", + "id": "ae1f9877", "metadata": {}, "source": [ - "

Introduction

\n", + "

Introduction

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The Teradataml Widgets (teradatamlwidgets) enhances teradataml’s built-in interaction capabilities with the Teradata Vantage™ Data and Analytics Platform. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.

\n", + "

The Teradataml Widgets (teradatamlwidgets) enhances teradataml’s built-in interaction capabilities with the Teradata Vantage™ Data and Analytics Platform. This provides visual components for scaled, in-Database Analytics with data that you keep in the Teradata Vantage Analytics Database within a notebook.

\n", "\n", - "

With these components, in a notebook you will be able to:

\n", + "

With these components, in this notebook you will be able to:

\n", "\n", - "