{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# One Hot Encoder\n"
      ]
    },
    {
      "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"
    },
    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
  },
  "nbformat": 4,
  "nbformat_minor": 2
}