diff --git a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb b/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
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--- a/how-to-use-azureml/work-with-data/dataprep/how-to-guides/impute-missing-values.ipynb
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-{
-  "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/impute-missing-values.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Impute missing values\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML Data Prep has the ability to impute missing values in specified columns. In this case, we will attempt to impute the missing _Latitude_ and _Longitude_ values in the input data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# loading input data\n",
-        "dflow = dprep.read_csv(path= '../data/crime-spring.csv')\n",
-        "dflow = dflow.keep_columns(['ID', 'Arrest', 'Latitude', 'Longitude'])\n",
-        "dflow = dflow.to_number(['Latitude', 'Longitude'])\n",
-        "dflow.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The third record from input data has _Latitude_ and _Longitude_ missing. To impute those missing values, we can use `ImputeMissingValuesBuilder` to learn a fixed program which imputes the columns with either a calculated `MIN`, `MAX` or `MEAN` value or a `CUSTOM` value. When `group_by_columns` is specified, missing values will be imputed by group with `MIN`, `MAX` and `MEAN` calculated per group."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Firstly, let us quickly see check the `MEAN` value of _Latitude_ column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dflow_mean = dflow.summarize(group_by_columns=['Arrest'],\n",
-        "                       summary_columns=[dprep.SummaryColumnsValue(column_id='Latitude',\n",
-        "                                                                 summary_column_name='Latitude_MEAN',\n",
-        "                                                                 summary_function=dprep.SummaryFunction.MEAN)])\n",
-        "dflow_mean = dflow_mean.filter(dprep.col('Arrest') == 'FALSE')\n",
-        "dflow_mean.head(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `MEAN` value of _Latitude_ looks good. So we will impute _Latitude_ with it. As for `Longitude`, we will impute it using `42` based on external knowledge."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# impute with MEAN\n",
-        "impute_mean = dprep.ImputeColumnArguments(column_id='Latitude',\n",
-        "                                          impute_function=dprep.ReplaceValueFunction.MEAN)\n",
-        "# impute with custom value 42\n",
-        "impute_custom = dprep.ImputeColumnArguments(column_id='Longitude',\n",
-        "                                            custom_impute_value=42)\n",
-        "# get instance of ImputeMissingValuesBuilder\n",
-        "impute_builder = dflow.builders.impute_missing_values(impute_columns=[impute_mean, impute_custom],\n",
-        "                                                   group_by_columns=['Arrest'])\n",
-        "# call learn() to learn a fixed program to impute missing values\n",
-        "impute_builder.learn()\n",
-        "# call to_dataflow() to get a dataflow with impute step added\n",
-        "dflow_imputed = impute_builder.to_dataflow()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# check impute result\n",
-        "dflow_imputed.head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As the result above, the missing _Latitude_ has been imputed with the `MEAN` value of `Arrest=='false'` group, and the missing _Longitude_ has been imputed with `42`."
-      ]
-    }
-  ],
-  "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
-}
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