From 33bda032b82e4ea5255e577b78c8cde16f9bd796 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Shan=C3=A9=20Winner?=
 <43390034+swinner95@users.noreply.github.com>
Date: Sun, 28 Jul 2019 00:24:43 -0700
Subject: [PATCH] Delete one-hot-encoder.ipynb

---
 .../how-to-guides/one-hot-encoder.ipynb       | 180 ------------------
 1 file changed, 180 deletions(-)
 delete mode 100644 how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb

diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
deleted file mode 100644
index ad5350ec..00000000
--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
+++ /dev/null
@@ -1,180 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# One Hot Encoder\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep has the ability to perform one hot encoding on a selected column using `one_hot_encode`. The result Dataflow will have a new binary column for each categorical label encountered in the selected column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To use `one_hot_encode` from a Dataflow, simply specify the source column. `one_hot_encode` will figure out all the distinct values or categorical labels in the source column using the current data, and it will return a new Dataflow with a new binary column for each categorical label. Note that the categorical labels are remembered in the Dataflow step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = dflow.one_hot_encode(source_column='Location Description')\n",
-        "dflow_result.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "By default, all the new columns will use the `source_column` name as a prefix. However, if you would like to specify your own prefix, simply pass a `prefix` string as a second parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = dflow.one_hot_encode(source_column='Location Description', prefix='LOCATION_')\n",
-        "dflow_result.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To have more control over the categorical labels, create a builder using `dataflow.builders.one_hot_encode`. The builder allows to preview and modify the categorical labels before generating a new Dataflow with the results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder = dflow.builders.one_hot_encode(source_column='Location Description', prefix='LOCATION_')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To generate the categorical labels, call the `learn` method on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.learn()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To preview the categorical labels, simply access them through the property `categorical_labels` on the builder object:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.categorical_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To modify the generated `categorical_labels`, assign a new value to `categorical_labels` or modify the existing one. The following example adds a missing label not found on the sample data to `categorical_labels`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "builder.categorical_labels.append('TOWNHOUSE')\n",
-        "builder.categorical_labels"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once the desired results are achieved, call `builder.to_dataflow` to get the new Dataflow with the encoded labels."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_result = builder.to_dataflow()\n",
-        "dflow_result.head(5)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sihhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file