diff --git a/work-with-data/dataprep/how-to-guides/data-profile.ipynb b/work-with-data/dataprep/how-to-guides/data-profile.ipynb
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--- a/work-with-data/dataprep/how-to-guides/data-profile.ipynb
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@@ -1,179 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/dataprep/how-to-guides/data-profile.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Data Profile\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A DataProfile collects summary statistics on each column of the data produced by a Dataflow. This can be used to:\n",
-        "- Understand the input data.\n",
-        "- Determine which columns might need further preparation.\n",
-        "- Verify that data preparation operations produced the desired result.\n",
-        "\n",
-        "`Dataflow.get_profile()` executes the Dataflow, calculates profile information, and returns a newly constructed DataProfile."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.dataprep as dprep\n",
-        "\n",
-        "dflow = dprep.auto_read_file('../data/crime-spring.csv')\n",
-        "\n",
-        "profile = dflow.get_profile()\n",
-        "profile"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A DataProfile contains a collection of ColumnProfiles, indexed by column name. Each ColumnProfile has attributes for the calculated column statistics. For non-numeric columns, profiles include only basic statistics like min, max, and error count. For numeric columns, profiles also include statistical moments and estimated quantiles."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['Beat']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also extract and filter data from profiles by using list and dict comprehensions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "variances = [c.variance for c in profile.columns.values() if c.variance]\n",
-        "variances"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "column_types = {c.name: c.type for c in profile.columns.values()}\n",
-        "column_types"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If a column has fewer than a thousand unique values, its ColumnProfile contains a summary of values with their respective counts."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['Primary Type'].value_counts"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Numeric ColumnProfiles include an estimated histogram of the data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['District'].histogram"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To configure the number of bins in the histogram, you can pass an integer as the `number_of_histogram_bins` parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile_more_bins = dflow.get_profile(number_of_histogram_bins=5)\n",
-        "profile_more_bins.columns['District'].histogram"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For columns containing data of mixed types, the ColumnProfile also provides counts of each type."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "profile.columns['X Coordinate'].type_counts"
-      ]
-    }
-  ],
-  "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
-}
\ No newline at end of file