Update part1_introduction_to_dataengineering.ipynb

Updated per Atma's feedback

Author: mohi9282

guide/16-data-engineering-guides/part1_introduction_to_dataengineering.ipynb

@@ -29,7 +29,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -50,7 +49,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -65,7 +63,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -95,7 +92,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -105,7 +101,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -137,7 +132,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -159,7 +153,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -292,15 +285,14 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [`Spatially Enabled DataFrame`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#spatialdataframe) (SEDF) inserts _\"spatial abilities\"_ into popular Pandas [DataFrame](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html). This allows users to use intuitive Pandas operations on both the attribute and spatial columns. With SEDF, you can easily manipulate geometric and attribute data.\n",
+    "The [`Spatially Enabled DataFrame`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#spatialdataframe) (SEDF) inserts _\"spatial abilities\"_ into the popular [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html). This allows users to use intuitive Pandas operations on both the attribute and spatial columns. With SEDF, you can easily manipulate geometric and attribute data. SEDF is a capability that is added to the Pandas DataFrame structure, by the ArcGIS API for Python, to give it spatial abilities. \n",
     "\n",
     "SEDF is based on data structures inherently suited to data analysis, with natural operations for the filtering and inspecting of subsets of values, which are fundamental to statistical and geographic manipulations.\n",
     "\n",
-    "Let's quickly look at how data can be imported and exported using __Spatially Enabled DataFrame__. We will take a deeper dive into working with Spatially Enabled DataFrame in the later parts of this guide series."
+    "Let's quickly look at how data can be imported and exported using __Spatially Enabled DataFrame__. The details shown below are a high level overview and we will take a deeper dive into working with Spatially Enabled DataFrame in the later parts of this guide series."
    ]
   },
   {
@@ -1045,7 +1037,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1124,7 +1115,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1454,7 +1444,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1508,7 +1497,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1564,7 +1552,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [