MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.ipynb

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        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/label-encoder.png)"
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      "source": [
        "# Label Encoder\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
        "Licensed under the MIT License.<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Data Prep has the ability to encode labels with values between 0 and (number of classes - 1) using `label_encode`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.dataprep as dprep\n",
        "from datetime import datetime\n",
        "dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
        "dflow.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To use `label_encode` from a Dataflow, simply specify the source column and the new column name. `label_encode` will figure out all the distinct values or classes in the source column, and it will return a new Dataflow with a new column containing the labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow = dflow.label_encode(source_column='Primary Type', new_column_name='Primary Type Label')\n",
        "dflow.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To have more control over the encoded labels, create a builder with `dataflow.builders.label_encode`.\n",
        "The builder allows you to preview and modify the encoded labels before generating a new Dataflow with the results. \n",
        "To get started, create a builder object with `dataflow.builders.label_encode` specifying the source column and the new column name.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "builder = dflow.builders.label_encode(source_column='Location Description', new_column_name='Location Description Label')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To generate the encoded labels, call the `learn` method on the builder object:"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "builder.learn()"
      ]
    },
    {
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      "metadata": {},
      "source": [
        "To check the result, access the generated labels through the property `encoded_labels`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "builder.encoded_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To modify the generated results, just assign a new value to `encoded_labels`. The following example adds a missing label not found in the sample data. `builder.encoded_labels` is saved into a variable `encoded_labels`, modified, and assigned back to `builder.encoded_labels`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "encoded_labels = builder.encoded_labels\n",
        "encoded_labels['TOWNHOUSE'] = 6\n",
        "\n",
        "builder.encoded_labels = encoded_labels\n",
        "builder.encoded_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": [
        "dataflow = builder.to_dataflow()\n",
        "dataflow.head(5)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sihhu"
      }
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