Merge pull request Esri#2239 from Esri/user_feedback_fixes

Small doc fixes to address user feedback and link updates

Author: jyaistMap

guide/03-the-gis/accessing-and-creating-content.ipynb

@@ -1472,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1485,7 +1485,7 @@
     "root_folder = gis.content.folders.get()\n",
     "earthquake_csv_item = root_folder.add(\n",
     "    item_properties=csv_properties, \n",
-    "    data=csv_path,\n",
+    "    file=csv_path,\n",
     "    thumbnail = thumbnail_path\n",
     ").result()"
    ]
@@ -2332,7 +2332,7 @@
    "source": [
     "**Note**: here, we used the `add()` method to add the `FeatureCollection` object in memory as an item on the GIS. Notice we used the `text` property to pass the JSON representation of the feature collection and the `type` property to indicate the item type. You can use the same method to publish web maps and web scenes.\n",
     "\n",
-    "The [sample notebooks for content publishers](https://developers.arcgis.com/python/sample-notebooks/publishing-web-maps-and-web-scenes/) show how the concepts discussed here can be used to publish web maps and scenes to your GIS"
+    "The [sample notebooks for content publishers](https://developers.arcgis.com/python/latest/samples/publishing-web-maps-and-web-scenes/) show how the concepts discussed here can be used to publish web maps and scenes to your GIS"
    ]
   },
   {

guide/05-working-with-the-spatially-enabled-dataframe/part3_data_io_writing_data.ipynb

@@ -2012,7 +2012,7 @@
     "\n",
     "[2] \"Closest Facility Analysis\", https://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/closest-facility.htm, accessed on 09/16/2019\n",
     "\n",
-    "[3] \"Find hospitals closest to an incident\", https://developers.arcgis.com/python/sample-notebooks/finding-hospitals-closest-to-an-incident/, accessed on 09/16/2019"
+    "[3] \"Find hospitals closest to an incident\", https://developers.arcgis.com/python/latest/samples/finding-hospitals-closest-to-an-incident/, accessed on 09/16/2019"
    ]
   }
  ],

guide/11-performing-network-analyses/part4_find_closest_facilities.ipynb

@@ -1,4 +1,4 @@
-# Script: This script accompanies the notebook explained in https://developers.arcgis.com/python/sample-notebooks/clone-portal-users-groups-and-content/
+# Script: This script accompanies the notebook explained in https://developers.arcgis.com/python/latest/samples/clone-portal-users-groups-and-content/
 # Purpose: Run this script to clone users, groups and items from source to a target Portal for ArcGIS.
 # Note: This script does not copy over services, hence the web layer items continue to have the same URL pointing to
 # services in the source server.

samples/03_org_administrators/AdminClonePortal/clone_portal.py

@@ -1,5 +1,5 @@
 # Stand-alone Python script to clone portal users groups and content
-This folder contains the stand-alone Python script `clone_portal.py` for the accompanying sample titled [Clone Portal users, groups and content](https://developers.arcgis.com/python/sample-notebooks/clone-portal-users-groups-and-content/)
+This folder contains the stand-alone Python script `clone_portal.py` for the accompanying sample titled [Clone Portal users, groups and content](https://developers.arcgis.com/python/latest/samples/clone-portal-users-groups-and-content-rn/)
 
 Running the script produces and output similar to below
 

samples/03_org_administrators/AdminClonePortal/readme.md

@@ -167,7 +167,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can download pavement cracks data from the following link: https://developers.arcgis.com/python/sample-notebooks/automate-road-surface-investigation-using-deep-learning/. Extract the downloaded file and run the code below to prepare data in a format that deep learning models expect."
+    "You can download pavement cracks data from the following link: https://developers.arcgis.com/python/latest/samples/automate-road-surface-investigation-using-deep-learning/. Extract the downloaded file and run the code below to prepare data in a format that deep learning models expect."
    ]
   },
   {

samples/04_gis_analysts_data_scientists/automate_road_surface_investigation_using_deep_learning.ipynb

@@ -64,7 +64,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this guide, we will use a pretrained model to get the predictions of energy generation for Solar Photovoltaic power plants using weather variables. We will then see, how using model explainability we can get the explanations for individual predictions. Finally, we will also see global model interpretibility. To get more details of the use case and how to train such a model please refer https://developers.arcgis.com/python/sample-notebooks/solar-energy-prediction-using-weather-variables/"
+    "In this guide, we will use a pretrained model to get the predictions of energy generation for Solar Photovoltaic power plants using weather variables. We will then see, how using model explainability we can get the explanations for individual predictions. Finally, we will also see global model interpretibility. To get more details of the use case and how to train such a model please refer https://developers.arcgis.com/python/latest/samples/solar-energy-prediction-using-weather-variables/"
    ]
   },
   {

samples/04_gis_analysts_data_scientists/model_explainability_using_shap_for_tabular_data.ipynb

@@ -13,7 +13,7 @@
     " \n",
     "Depending on the number of features that are updated, your workflow requirements, you may adopt either or both kinds of update mechanisms.\n",
     "\n",
-    "In the sample [Updating features in a feature layer](https://developers.arcgis.com/python/sample-notebooks/updating-features-in-a-feature-layer/) we explore method 1. In this sample, we explore method 2."
+    "In the sample [Updating features in a feature layer](https://developers.arcgis.com/python/latest/samples/updating-features-in-a-feature-layer/) we explore method 1. In this sample, we explore method 2."
    ]
   },
   {
@@ -74,7 +74,7 @@
     " 1. The name of the file that used to update in step 4 above should match the original file name of the item.\n",
     " 2. The schema -- number of layers (applicable when your original file is a file geodatabase / shape file / service definition), and the name and number of attribute columns should remain the same as before.\n",
     " \n",
-    "The **method 2** explained in this sample is much simpler compared to **method 1** explained in [Updating features in a feature layer](https://developers.arcgis.com/python/sample-notebooks/updating-features-in-a-feature-layer/). However, we cannot make use of the third spreadsheet which has the additional columns for our capitals. To do that, we would first update the features through overwriting, then edit the definition of the feature layer to add new columns and then edit each feature and add the appropriate column values, similar to that explained in method 1."
+    "The **method 2** explained in this sample is much simpler compared to **method 1** explained in [Updating features in a feature layer](https://developers.arcgis.com/python/latest/samples/updating-features-in-a-feature-layer/). However, we cannot make use of the third spreadsheet which has the additional columns for our capitals. To do that, we would first update the features through overwriting, then edit the definition of the feature layer to add new columns and then edit each feature and add the appropriate column values, similar to that explained in method 1."
    ]
   },
   {
@@ -86,13 +86,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# if you've downloaded the samples or are working with your own organization\n",
     "# or Enterprise, you could just read the \n",
-    "# csv data directly instead of copyting the file:\n",
+    "# csv data directly instead of copying the file:\n",
     "#\n",
     "# my_csv = os.path.join(data_path, csv_file)\n",
     "\n",
@@ -997,7 +997,7 @@
    "metadata": {},
    "source": [
     "## Conclusion\n",
-    "Thus, in this sample, we observed how update a feature layer by overwriting it with new content. This method is a lot simpler than method 1 explained in [Updating features in a feature layer](https://developers.arcgis.com/python/sample-notebooks/updating-features-in-a-feature-layer/) sample. However, with this simplicity, we compromise on our ability to add new columns or change the schema of the feature layer during the update. Further, if your feature layer was updated after it was published, then those updates get overwritten when you perform the overwrite operation. To retain those edits, [extract the data](https://developers.arcgis.com/python/guide/checking-out-data-from-feature-layers-using-replicas/#Verify-Extract-capability) from the feature layer, merge your updates with this extract, then overwrite the feature layer."
+    "Thus, in this sample, we observed how update a feature layer by overwriting it with new content. This method is a lot simpler than method 1 explained in [Updating features in a feature layer](https://developers.arcgis.com/python/latest/samples/updating-features-in-a-feature-layer/) sample. However, with this simplicity, we compromise on our ability to add new columns or change the schema of the feature layer during the update. Further, if your feature layer was updated after it was published, then those updates get overwritten when you perform the overwrite operation. To retain those edits, [extract the data](https://developers.arcgis.com/python/guide/checking-out-data-from-feature-layers-using-replicas/#Verify-Extract-capability) from the feature layer, merge your updates with this extract, then overwrite the feature layer."
    ]
   }
  ],

samples/05_content_publishers/overwriting_feature_layers.ipynb

@@ -13,7 +13,7 @@
     " \n",
     "Depending on the number of features that are updated, your workflow requirements, you may adopt either or both kinds of update mechanisms.\n",
     "\n",
-    "In this sample, we explore the first method. For **Method 2**, refer to the sample titled [Overwriting feature layers](https://developers.arcgis.com/python/sample-notebooks/overwriting-feature-layers)\n",
+    "In this sample, we explore the first method. For **Method 2**, refer to the sample titled [Overwriting feature layers](https://developers.arcgis.com/python/latest/samples/overwriting-feature-layers/)\n",
     "\n",
     "**Note**: To run this sample, you need the ``pandas`` library in your conda environment. If you don't have the library, install it by running the following command from cmd.exe or your shell\n",
     "```\n",