From fbff5cd2dd420855685b93398dbe3e0c7b1c8ee4 Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Wed, 25 Apr 2018 14:11:42 -0700 Subject: [PATCH 01/10] updated API overview to reflect 2 new modules --- ...verview-of-the-arcgis-api-for-python.ipynb | 30 +++++++++++------- static/img/guide_api_overview_modules.png | Bin 0 -> 107484 bytes 2 files changed, 18 insertions(+), 12 deletions(-) create mode 100644 static/img/guide_api_overview_modules.png diff --git a/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb b/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb index a5b19ddb83..05f4073711 100644 --- a/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb +++ b/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb @@ -19,19 +19,19 @@ "metadata": {}, "source": [ "## Architecture of the API\n", - "The API is distributed as the `arcgis` conda package. Within the `arcgis` package, which represents the generic GIS model, functionality is organized into a number of different modules that makes it simple to use and understand. Each module has a handful of types and functions that are focused towards one aspect of the GIS.\n", + "The API is distributed as the `arcgis` package via conda and pip. Within the `arcgis` package, which represents the generic GIS model, functionality is organized into a number of different modules that makes it simple to use and understand. Each module has a handful of types and functions that are focused towards one aspect of the GIS.\n", "\n", - "This diagram below depicts the modules present in the API. \n", + "This diagram below depicts the modules present in the API.\n", "\n", - "The gis module is the most important and provides the entry point into the GIS. It lets you manage users, groups and content in the GIS. GIS admins spend a lot of time on this module.\n", + "\n", "\n", - "The modules in green are used to access the various spatial capabilities or geographic datasets in the GIS. These module includes a family of geoprocessing functions, types and other helper objects for working with spatial data of a particular type.\n", + "The `gis` module is the most important and provides the entry point into the GIS. It lets you manage users, groups and content in the GIS. GIS admins spend a lot of time on this module.\n", "\n", - "The modules in blue provide additional functionality for your workflows. They include the geocoding module which is for finding places, the geometry module for representing the geometries of feature data and functions for working with them, and the geoprocessing module that makes it easy to import third party geoprocessing tools and work with them.\n", + "The modules in green are used to access the various spatial capabilities or geographic datasets in the GIS. These module includes a family of geoprocessing functions, types and other helper objects for working with spatial data of a particular type.\n", "\n", - "The modules in orange allow you to visualize GIS data and analysis. This module includes the MapView Jupyter notebook widget for visualizing maps and layers and the mapping module has types and functions for working with web maps and web layers.\n", + "The modules in blue provide additional functionality for your workflows. They include the `geocoding` module which is for finding places, the `geometry` module for representing the geometries of feature data and functions for working with them, the `geoprocessing` module that makes it easy to import third party geoprocessing tools and work with them and the `geoenrichment` module that helps you enrich your datasets with thematic information.\n", "\n", - "![arcgis-api-modules](http://esri.github.io/arcgis-python-api/notebooks/nbimages/guide_api_overview_01.png)\n", + "The modules in orange allow you to visualize and disseminate your GIS data and analysis. The `widgets` module includes the MapView Jupyter notebook widget for visualizing maps and layers, the `mapping` module has types and functions for working with web maps and web layers and the `apps` module helps you create and manage web applications built with ArcGIS.\n", "\n", "### `arcgis.gis` module\n", "The `arcgis.gis` module provides an information model for GIS hosted within ArcGIS Online or Portal for ArcGIS. This module provides functionality to manage (create, read, update and delete) GIS users, groups and content. This module is the most important and provides the entry point into the GIS. \n", @@ -60,6 +60,9 @@ "### `arcgis.geometry` module\n", "The `arcgis.geometry` module defines useful geometry types for working with geographic information and GIS functionality. It provides functions which use geometric types as input and output as well as functions for easily converting geometries between different representations.\n", "\n", + "### `arcgis.geoenrichment` module\n", + "The `arcgis.geoenrichment` module provides the ability to get facts about a location or area. Using GeoEnrichment, you can get information about the people and places in a specific area or within a certain distance or drive time from a location. You can use this data to enhance the accuracy of your predictive models or better explain the inferences you gain from them.\n", + "\n", "### `arcgis.geoprocessing` module\n", "Users can create and share geoprocessing tools in the GIS. The `arcgis.geoprocessing` module provides helper types with dynamically created methods to invoke these tools, and provides simple types that can be used as parameters for these tools along with native Python types.\n", "\n", @@ -70,7 +73,10 @@ "The `arcgis.mapping` module provides components for visualizing GIS data and analysis. This module includes WebMap and WebScene components that enable 2D and 3D mapping and visualization in the GIS. This module also includes mapping layers like MapImageLayer and VectorTileLayer\n", "\n", "### `arcgis.widgets` module\n", - "The `arcgis.widgets` module provides components for visualizing GIS data and analysis. This module includes the MapView Jupyter notebook widget for visualizing maps and layers" + "The `arcgis.widgets` module provides components for visualizing GIS data and analysis. This module includes the MapView Jupyter notebook widget for visualizing maps and layers\n", + "\n", + "### `arcgis.apps` module\n", + "The `arcgis.apps` module allows you to manage some of the web based applications available in ArcGIS." ] }, { @@ -80,16 +86,16 @@ "#### How this website is organized\n", "The **guide** pages explain each individual concept of the API and the best practices. The **sample notebooks** explain how to use the API to write Python scripts, incorporating capabilities such as mapping, query, analysis, geocoding, routing, portal administration, and more to solve real world problems. If you are looking for recipes, you may find them as sample notebooks. \n", "\n", - "The **api reference** provides a reference for each class, its properties and methods in the `arcgis` package. The **forum** is a geonet community where you can share your thoughts about the API, request for features, conduct polls, report bugs, ask questions and engage with the Python community in general." + "The **api reference** provides a reference for each class, its properties and methods in the `arcgis` package. The **community** is a geonet forum where you can share your thoughts about the API, request for features, conduct polls, report bugs, ask questions and engage with the Python community in general." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [geosaurus_gold]", + "display_name": "Python 3", "language": "python", - "name": "Python [geosaurus_gold]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -101,7 +107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/static/img/guide_api_overview_modules.png b/static/img/guide_api_overview_modules.png new file mode 100644 index 0000000000000000000000000000000000000000..daa05dd76231557d31d20f01b1b6d22b86a4428f GIT binary patch literal 107484 zcmZU*1yqz>_Xi3?s~{kVbc3LDHyCtxr*wmKgQ9dZbV_%37<4I}f>P4mAoo1H?|1L_ z|L$6_n3=WbIp_TLKD##|it-Ye_lWKxARu5$Nxo7>KtM_Yzsu;T;FBLr*&W~yqO-EZ 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eszpMSYs%L2kX~lZ07oS7PexKv;+vS^`~L%QNLAMW literal 0 HcmV?d00001 From 6ea614bc4151ca856183938448a6e3491b560cbc Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Thu, 26 Apr 2018 10:03:21 -0700 Subject: [PATCH 02/10] grammar --- .../02-api-overview/overview-of-the-arcgis-api-for-python.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb b/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb index 05f4073711..eabc485c42 100644 --- a/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb +++ b/guide/02-api-overview/overview-of-the-arcgis-api-for-python.ipynb @@ -27,7 +27,7 @@ "\n", "The `gis` module is the most important and provides the entry point into the GIS. It lets you manage users, groups and content in the GIS. GIS admins spend a lot of time on this module.\n", "\n", - "The modules in green are used to access the various spatial capabilities or geographic datasets in the GIS. These module includes a family of geoprocessing functions, types and other helper objects for working with spatial data of a particular type.\n", + "The modules in green are used to access the various spatial capabilities or geographic datasets in the GIS. These modules include a family of geoprocessing functions, types and other helper objects for working with spatial data of a particular type.\n", "\n", "The modules in blue provide additional functionality for your workflows. They include the `geocoding` module which is for finding places, the `geometry` module for representing the geometries of feature data and functions for working with them, the `geoprocessing` module that makes it easy to import third party geoprocessing tools and work with them and the `geoenrichment` module that helps you enrich your datasets with thematic information.\n", "\n", From 6bfefcb4e6e8c7ba3846bc5ffe9de2aeafaa5e95 Mon Sep 17 00:00:00 2001 From: Max Payson Date: Wed, 22 Aug 2018 14:17:04 -0700 Subject: [PATCH 03/10] find places devlab notebook --- labs/find_places.ipynb | 222 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 222 insertions(+) create mode 100644 labs/find_places.ipynb diff --git a/labs/find_places.ipynb b/labs/find_places.ipynb new file mode 100644 index 0000000000..835d04c548 --- /dev/null +++ b/labs/find_places.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Find Places ArcGIS DevLab\n", + "This is a completed solution for the [Find Places](https://developers.arcgis.com/labs/python/find-places/) ArcGIS DevLab. [ArcGIS DevLabs](https://developers.arcgis.com/labs/) are short, introductory tutorials to developing with the [ArcGIS platform](https://developers.arcgis.com/labs/what-is-arcgis/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1.** Model a GIS portal and import the geocode function from the ArcGIS API for Python" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from arcgis.gis import GIS\n", + "from arcgis.geocoding import geocode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2.** Create an anonymous connection to ArcGIS Online. Since the results are not stored, you do not need credentials to call the geocoding service" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gis = GIS()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3.** Pass the relevant parameters into the geocode function and get the results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "geocode_fs = geocode(address=None,\n", + " location=[-118.71511, 34.09042],\n", + " category=\"Coffee shop\",\n", + " out_fields=\"Place_addr, PlaceName\",\n", + " max_locations=25,\n", + " as_featureset=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4.** Convert the results to a dataframe and show the first two locations" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
OBJECTIDPlaceNamePlace_addrSHAPE
01Starbucks26531 Agoura Rd, Calabasas, California, 91302{'x': -118.70030999999994, 'y': 34.14383000000...
12Starbucks26521 Agoura Rd, Calabasas, California, 91302{'x': -118.70023995051699, 'y': 34.14425002668...
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" + ], + "text/plain": [ + " OBJECTID PlaceName Place_addr \\\n", + "0 1 Starbucks 26531 Agoura Rd, Calabasas, California, 91302 \n", + "1 2 Starbucks 26521 Agoura Rd, Calabasas, California, 91302 \n", + "\n", + " SHAPE \n", + "0 {'x': -118.70030999999994, 'y': 34.14383000000... \n", + "1 {'x': -118.70023995051699, 'y': 34.14425002668... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_df = geocode_fs.df\n", + "g_df.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**5.** Create a map to display the results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "35aa51740ad646aca0cba4233d028205", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g_map = gis.map([34.09042, -118.71511], zoomlevel=11)\n", + "g_map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**6.** Once the map has loaded, draw the results" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "g_map.draw(geocode_fs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3e091ff90c8ae1df9dc475c7231d2b84e0d94db6 Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 13:52:03 -0700 Subject: [PATCH 04/10] refined spatially enabled dataframe topic --- ...ntroduction-to-the-spatial-dataframe.ipynb | 108 ++++++++++++------ 1 file changed, 75 insertions(+), 33 deletions(-) diff --git a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb b/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb index 9a02af7f1c..0960deef73 100644 --- a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb +++ b/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb @@ -4,11 +4,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to the Spatial Enabled DataFrame\n", + "# Introduction to the Spatially Enabled DataFrame\n", "\n", - "The [`Spatially Enabled Dataframe`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#spatialdataframe) (SEDF) creates a simple, intutive object that can easily manipulate geometric and attribute data. The Spatially Enabled DataFrame is a custom namespace that is inserted into the popular [Pandas](https://pandas.pydata.org/) [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) structure with spatial abilities, allowing you to use intutive, pandorable operations on both the attribute and spatial columns. Thus the 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", + "The [`Spatially Enabled DataFrame`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#spatialdataframe) (SEDF) creates a simple, intutive object that can easily manipulate geometric and attribute data.\n", "\n", - "The dataframe reads from many **sources**, including shapefiles, [Pandas](https://pandas.pydata.org/) [DataFrames](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe), feature classes, GeoJSON, and Feature Layers.\n", + "
\n", + " New at version 1.5, the Spatially Enabled DataFrame is an evolution of the SpatialDataFrame object that you may be familiar with. While the SDF object is still avialable for use, the team has stopped active development of it and is promoting the use of this new Spatially Enabled DataFrame pattern. The SEDF provides you better memory management, ability to handle larger datasets and is the pattern that Pandas advocates as the path forward.
\n", + "\n", + "The Spatially Enabled DataFrame inserts a custom namespace called `spatial` into the popular [Pandas](https://pandas.pydata.org/) [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) structure to give it spatial abilities. This allows you to use intutive, pandorable operations on both the attribute and spatial columns. Thus, the 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", + "The dataframe reads from many **sources**, including shapefiles, [Pandas](https://pandas.pydata.org/) [DataFrames](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe), feature classes, GeoJSON, and Feature Layers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "This document outlines some fundamentals of using the `Spatially Enabled DataFrame` object for working with GIS data.\n", "\n", @@ -43,22 +54,25 @@ "## Accessing GIS data\n", "GIS users need to work with both published layers on remote servers (web layers) and local data, but the ability to manipulate these datasets without permanentently copying the data is lacking. The `Spatial Enabled DataFrame` solves this problem because it is an in-memory object that can read, write and manipulate geospatial data.\n", "\n", - "The SEDF integrates with Esri's [`ArcPy site-package`](http://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm) as well as the open source [`pyshp`](https://github.com/GeospatialPython/pyshp/), [`shapely`](https://github.com/Toblerity/Shapely) and [`fiona`](https://github.com/Toblerity/Fiona) packages. This means the ArcGIS API for Python SEDF can use either of these geometry engines to provide you options for easily working with geospatial data regardless of your platform. The SEDF transforms data into the formats you desire so you can use Python functionality to analyze and visualize geographic information.\n", - "\n", - "Data can be read and scripted to automate workflows and just as easily visualized on maps in [`Jupyter notebooks`](../using-the-jupyter-notebook-environment/). The SEDF can export data as feature classes or publish them directly to servers for sharing according to your needs.\n", - "\n", - "Let's explore some of the different options available with the versatile `Spatial Enabled DataFrame` namespaces:\n", + "The SEDF integrates with Esri's [`ArcPy` site-package](http://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm) as well as the open source [`pyshp`](https://github.com/GeospatialPython/pyshp/), [`shapely`](https://github.com/Toblerity/Shapely) and [`fiona`](https://github.com/Toblerity/Fiona) packages. This means the ArcGIS API for Python SEDF can use either of these geometry engines to provide you options for easily working with geospatial data regardless of your platform. The SEDF transforms data into the formats you desire so you can use Python functionality to analyze and visualize geographic information.\n", "\n", + "Data can be read and scripted to automate workflows and just as easily visualized on maps in [`Jupyter notebooks`](../using-the-jupyter-notebook-environment/). The SEDF can export data as feature classes or publish them directly to servers for sharing according to your needs. Let's explore some of the different options available with the versatile `Spatial Enabled DataFrame` namespaces:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "### Reading Web Layers\n", "\n", - "[`Feature layers`](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm) hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/) can be easily read into a Spatial DataFrame using the [`from_layer`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.SpatialDataFrame.from_layer) method. Once you read it into a SEDF object, you can create reports, manipulate the data, or convert it to a form that is comfortable and makes sense for its intended purpose.\n", + "[`Feature layers`](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm) hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/) can be easily read into a Spatially Enabled DataFrame using the [`from_layer`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method. Once you read it into a SEDF object, you can create reports, manipulate the data, or convert it to a form that is comfortable and makes sense for its intended purpose.\n", "\n", "**Example: Retrieving an ArcGIS Online [`item`](https://developers.arcgis.com/rest/users-groups-and-items/publish-item.htm) and using the [`layers`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.gis.toc.html#layer) property to inspect the first 5 records of the layer**" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -263,7 +277,7 @@ "[5 rows x 51 columns]" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -273,18 +287,53 @@ "gis = GIS()\n", "item = gis.content.get(\"85d0ca4ea1ca4b9abf0c51b9bd34de2e\")\n", "flayer = item.layers[0]\n", + "\n", + "# create a Spatially Enabled DataFrame object\n", "sdf = pd.DataFrame.spatial.from_layer(flayer)\n", "sdf.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you inspect the `type` of the object, you get back a standard pandas `DataFrame` object. However, this object now has an additional `SHAPE` column that allows you to perform geometric operations. In other words, this `DataFrame` is now geo-aware." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(sdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further, the `DataFrame` has a new `spatial` property that provides a list of geoprocessing operations that can be performed on the object. The rest of the guides in this section go into details of how to use these functionalities. So, sit tight." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading Feature Layer Data\n", "\n", - "As seen above, the SEDF can consume a `Feature Layer` service accessible on the ArcGIS Online platform. Let's take a step-by-step approach to break down the notebook cell above and then extract a subset of records from the feature layer.\n", - "\n", + "As seen above, the SEDF can consume a `Feature Layer` served from either ArcGIS Online or ArcGIS Enterprise orgs. Let's take a step-by-step approach to break down the notebook cell above and then extract a subset of records from the feature layer.\n", "\n", "#### Example: Examining Feature Layer content" ] @@ -298,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -316,7 +365,7 @@ " \n", "
This layer presents the locations of cities within the United States with populations of approximately 10,000 or greater, all state capitals, and the national capital.Feature Layer Collection by esri_dm\n", "
Last Modified: December 21, 2017\n", - "
3 comments, 291,531 views\n", + "
3 comments, 331,873 views\n", " \n", " \n", " " @@ -325,7 +374,7 @@ "" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -543,7 +592,7 @@ "[5 rows x 51 columns]" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -570,15 +619,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can also use sql queries to return a subset of records by leveraging the ArcGIS API for Python's [`Feature Layer`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#featurelayer) object itself. Instantiate a Pandas `DataFrame` directly from the [`FeatureLayer.query()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method and use the data frame's [`head()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.head.html#pandas.core.groupby.GroupBy.head) method to return the first 5 records and a subset of columns from the DataFrame:\n", - "\n", - "#### Example: Feature Layer Query Results to a Spatial DataFrame" + "You can also use sql queries to return a subset of records by leveraging the ArcGIS API for Python's [`Feature Layer`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#featurelayer) object itself. When you run a [`query()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) on a `FeatureLayer`, you get back a `FeatureSet` object. Calling the `sdf` property of the `FeatureSet` returns a Spatially Enabled DataFrame object. We then use the data frame's [`head()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.head.html#pandas.core.groupby.GroupBy.head) method to return the first 5 records and a subset of columns from the DataFrame:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "#### Example: Feature Layer Query Results to a Spatially Enabled DataFrame\n", "We'll use the `AGE_45_54` column to query the dataframe and return a new `DataFrame` with a subset of records. We can use the built-in [`zip()`](https://docs.python.org/3/library/functions.html#zip) function to print the data frame attribute field names, and then use data frame syntax to view specific attribute fields in the output:" ] }, @@ -716,12 +764,12 @@ "\n", "The SEDF can also access local geospatial data. Depending upon what Python modules you have installed, you'll have access to a wide range of functionality: \n", "\n", - "* If the **`ArcPy`** module is installed, meaning you have installed [`ArcGIS Pro`](http://pro.arcgis.com/en/pro-app/) and have installed the ArcGIS API for Python in that same environment, the `SpatialDataFrame` has methods to read a subset of the ArcGIS Desktop [supported geographic formats](http://desktop.arcgis.com/en/arcmap/10.3/manage-data/datatypes/about-geographic-data-formats.htm#ESRI_SECTION1_4835793C55C0439593A46FD5BC9E64B9), most notably:\n", + "* If the **`ArcPy`** module is installed, meaning you have installed [`ArcGIS Pro`](http://pro.arcgis.com/en/pro-app/) and have installed the ArcGIS API for Python in that same environment, the `DataFrame` then has methods to read a subset of the ArcGIS Desktop [supported geographic formats](http://desktop.arcgis.com/en/arcmap/10.3/manage-data/datatypes/about-geographic-data-formats.htm#ESRI_SECTION1_4835793C55C0439593A46FD5BC9E64B9), most notably:\n", " * [`feature classes`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm)\n", " * [`shapefiles`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm), \n", " * [`ArcGIS Server Web Services`](https://enterprise.arcgis.com/en/server/latest/publish-services/windows/what-types-of-services-can-you-publish.htm) and [`ArcGIS Online Hosted Feature Layers`](https://doc.arcgis.com/en/arcgis-online/share-maps/publish-features.htm) \n", " * [`OGC Services`](http://www.opengeospatial.org/standards) \n", - "* If the **ArcPy** module is not installed, the SEDF [`from_featureclass`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.SpatialDataFrame.from_featureclass) method only supports consuming an Esri [`shapefile`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm)\n", + "* If the **ArcPy** module is not installed, the SEDF [`from_featureclass`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method only supports consuming an Esri [`shapefile`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm)\n", "> Please note that you must install the `pyshp` package to read shapefiles in environments that don't have access to `ArcPy`.\n", " \n", "### Example: Reading a Shapefile\n", @@ -882,9 +930,7 @@ } ], "source": [ - "sdf = (pd.DataFrame\n", - " .spatial\n", - " .from_featureclass(\"path\\to\\your\\data\\census_example\\cities.shp\"))\n", + "sdf = pd.DataFrame.spatial.from_featureclass(\"path\\to\\your\\data\\census_example\\cities.shp\")\n", "sdf.tail()" ] }, @@ -931,9 +977,7 @@ } ], "source": [ - "(sdf\n", - " .spatial\n", - " .to_featureclass(location=r\"c:\\output_examples\\census.shp\"))" + "sdf.spatial.to_featureclass(location=r\"c:\\output_examples\\census.shp\")" ] }, { @@ -987,9 +1031,7 @@ ], "source": [ "columns = ['NAME', 'ST', 'CAPITAL', 'STFIPS', 'POP2000', 'POP2007', 'SHAPE']\n", - "(sdf[columns].head()\n", - " .spatial\n", - " .to_featureclass(location=r\"/path/to/your/data/directory/sdf_head_output.shp\"))" + "sdf[columns].head().spatial.to_featureclass(location=r\"/path/to/your/data/directory/sdf_head_output.shp\")" ] } ], @@ -1009,7 +1051,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.6" } }, "nbformat": 4, From a64411ca7e7f27c88636ed55399cff5b7354fbfe Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 13:52:57 -0700 Subject: [PATCH 05/10] refined and renamed SDF intro guide to SeDF intro guide --- ...pynb => introduction-to-the-spatially-enabled-dataframe.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename guide/05-working-with-the-spatial-dataframe/{introduction-to-the-spatial-dataframe.ipynb => introduction-to-the-spatially-enabled-dataframe.ipynb} (100%) diff --git a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb b/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatial-dataframe.ipynb rename to guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb From 10de3f865e964083e1c8039e3f0c70755f43b434 Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 13:54:35 -0700 Subject: [PATCH 06/10] renamed viz with SDF to SeDF --- ...> visualizing-data-with-the-spatially-enabled-dataframe.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename guide/05-working-with-the-spatial-dataframe/{visualizing-data-with-the-spatial-dataframe.ipynb => visualizing-data-with-the-spatially-enabled-dataframe.ipynb} (100%) diff --git a/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatial-dataframe.ipynb b/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatial-dataframe.ipynb rename to guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb From d79aef42e5f21c3c07515ac42703de4305babb97 Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 14:40:50 -0700 Subject: [PATCH 07/10] revised and updated visualization guide --- ...n-to-the-spatially-enabled-dataframe.ipynb | 6 +- ...with-the-spatially-enabled-dataframe.ipynb | 691 +++++++++++++++--- 2 files changed, 575 insertions(+), 122 deletions(-) diff --git a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb b/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb index 0960deef73..76cbc94bbf 100644 --- a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb +++ b/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb @@ -27,10 +27,10 @@ " * [Reading Web Layers](#Reading-Web-Layers)\n", " * [Reading Feature Layer Data](#Reading-Feature-Layer-Data)\n", " * [Examining Feature Layer Content](#Example:-Examining-Feature-Layer-content)\n", - " * [Example: Feature Layer Query Results to a Spatial DataFrame](#Example:-Feature-Layer-Query-Results-to-a-Spatial-DataFrame)\n", + " * [Example: Feature Layer Query Results to a Spatial DataFrame](#Example:-Feature-Layer-Query-Results-to-a-Spatially-Enabled-DataFrame)\n", "* [Accessing local GIS Data](#Accessing-local-GIS-data)\n", " * [Example: Reading a Shapefile](#Example:-Reading-a-Shapefile)\n", - "* [Saving Spatial DataFrames](#Saving-Spatial-DataFrames)\n", + "* [Saving Spatiall Enabled DataFrames](#Saving-Spatially-Enabled-DataFrames)\n", " * [Export Options](#Export-Options)\n", " * [Exporting to a Feature Class](#Export-to-Feature-Class)\n", " * [Example: Export a whole dataset to a Shapefile](#Example:-Export-a-whole-dataset-to-a-shapefile:)\n", @@ -938,7 +938,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Saving Spatial DataFrames\n", + "## Saving Spatially Enabled DataFrames\n", "\n", "The SEDF can export data to various data formats for use in other applications.\n", "\n", diff --git a/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb b/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb index c9ecbe88db..09942afb6f 100644 --- a/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb +++ b/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "## Visualizing Spatial Data\n", - "The Spatially Enabled Dataframe has a `plot()` method that uses a syntax and symbology similar to [`matplotlib`](https://matplotlib.org) for visualizing features on a map. With this functionality, you can easily visualize aspects of your data both on a map and on a matplotlib chart using the same symbology!\n", + "The Spatially Enabled Dataframe has a [`plot()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.plot) method that uses a syntax and symbology similar to [`matplotlib`](https://matplotlib.org) for visualizing features on a map. With this functionality, you can easily visualize aspects of your data both on a map and on a matplotlib chart using the same symbology!\n", "\n", "Some unique characteristics of working with the visualization capabalities on the SDF:\n", "- Uses Pythonic syntax\n", @@ -14,7 +14,7 @@ "- Works on features and attributes simultaneously, eliminating to a great extent the need to iterate over all features (rows)\n", "- Handles reading and writing to multiple formats aiding data conversion\n", "\n", - "Checkout the [Introduction to Spatially Enabled DataFrame](/python/guide/features-module/introduction-to-spatial-dataframe) guide to learn how to create a Spatial DataFrame." + "Checkout the [Introduction to Spatially Enabled DataFrame](/python/guide/features-module/introduction-to-spatially-enabled-dataframe) guide to learn how to create a Spatially Enabled DataFrame." ] }, { @@ -28,6 +28,7 @@ " * [Understanding renderers](#Understanding-renderers)\n", " * [Supported renderers](#Supported-renderers)\n", " * [Visualizing unique values](#Visualizing-unique-values)\n", + " * [Visualizing unique values with Arcade expressions](#Visualizing-Unique-Values-with-Arcade-Expressions)\n", " * [Visualizing classes with different colors](#Visualizing-classes-with-different-colors)\n", " * [ColorMaps and Colors](#Colormaps-and-Colors)\n", " * [Symbology for Simple Renderers](#Symbology-for-Simple-Renderers)\n", @@ -46,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -112,7 +113,7 @@ " 14980\n", " 6\n", " 1074\n", - " null\n", + " {\"x\": -12768343.256613126, \"y\": 3842463.708135...\n", " AZ\n", " 04\n", " 261\n", @@ -136,7 +137,7 @@ " 10239\n", " 6\n", " 2056\n", - " null\n", + " {\"x\": -13613950.337588644, \"y\": 4931686.754090...\n", " CA\n", " 06\n", " 267\n", @@ -160,7 +161,7 @@ " 11869\n", " 6\n", " 2558\n", - " null\n", + " {\"x\": -13066582.116550362, \"y\": 3925650.676616...\n", " CA\n", " 06\n", " 296\n", @@ -184,7 +185,7 @@ " 11195\n", " 6\n", " 761\n", - " null\n", + " {\"x\": -13123874.446103057, \"y\": 4044249.710416...\n", " CA\n", " 06\n", " 86\n", @@ -208,7 +209,7 @@ " 13009\n", " 6\n", " 1763\n", - " null\n", + " {\"x\": -13151212.145276317, \"y\": 4027601.332347...\n", " CA\n", " 06\n", " 88\n", @@ -234,17 +235,24 @@ "3 959 766 514 280 ... 0613560 10866 \n", "4 1112 925 687 477 ... 0614974 12823 \n", "\n", - " POPULATION POP_CLASS RENTER_OCC SHAPE ST STFIPS VACANT WHITE \n", - "0 14980 6 1074 null AZ 04 261 9196 \n", - "1 10239 6 2056 null CA 06 267 8273 \n", - "2 11869 6 2558 null CA 06 296 7530 \n", - "3 11195 6 761 null CA 06 86 5898 \n", - "4 13009 6 1763 null CA 06 88 6930 \n", + " POPULATION POP_CLASS RENTER_OCC \\\n", + "0 14980 6 1074 \n", + "1 10239 6 2056 \n", + "2 11869 6 2558 \n", + "3 11195 6 761 \n", + "4 13009 6 1763 \n", + "\n", + " SHAPE ST STFIPS VACANT WHITE \n", + "0 {\"x\": -12768343.256613126, \"y\": 3842463.708135... AZ 04 261 9196 \n", + "1 {\"x\": -13613950.337588644, \"y\": 4931686.754090... CA 06 267 8273 \n", + "2 {\"x\": -13066582.116550362, \"y\": 3925650.676616... CA 06 296 7530 \n", + "3 {\"x\": -13123874.446103057, \"y\": 4044249.710416... CA 06 86 5898 \n", + "4 {\"x\": -13151212.145276317, \"y\": 4027601.332347... CA 06 88 6930 \n", "\n", "[5 rows x 51 columns]" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +332,7 @@ "metadata": {}, "source": [ "### Plotting the DataFrame\n", - "You can quickly visualize the points by calling the `plot()` method off the dataframes `spatial` accessor and passing the map you created above." + "You can quickly visualize the points by calling the `plot()` method off the `DataFrame`'s `spatial` accessor and passing the map you created above." ] }, { @@ -437,7 +445,7 @@ "\n", "[`Renderers`](https://developers.arcgis.com/documentation/common-data-types/renderer-objects.htm) define how to visually represent a `feature layer` by defining [`symbols`](https://developers.arcgis.com/documentation/common-data-types/symbol-objects.htm) to represent individual features. The `SeDF` provides you with functionality to control the way features appear by choosing the `symbol` the renderer uses.\n", "\n", - "Previous versions of the ArcGIS API for Python provided a method to specify a renderer manually, but you had to know details about the renderer before you drew your data. The [`map.add_layer()`]() method did not provide access to all options avialble for rendering datasets. The new visualization capabilities provided by the SDF allow you to draw spatial data quickly and easily with access to more rendering options." + "Previous versions of the ArcGIS API for Python provided a method to specify a renderer manually, but you had to know details about the renderer before you drew your data. The [`map.add_layer()`]() method did not provide access to all options avialble for rendering datasets. The new visualization capabilities provided by the SEDF allow you to draw spatial data quickly and easily with access to more rendering options." ] }, { @@ -448,7 +456,7 @@ "source": [ "### Supported renderers \n", "\n", - "The renderering options below are documented in further detail in the [`DatFrame Reference`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html). At a high level, you have the following renderers:\n", + "The renderering options below are documented in further detail in the [`DatFrame Reference`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.plot). At a high level, you have the following renderers:\n", "\n", "| Renderer \t| Syntax \t| Explanation \t|\n", "|--------------\t|--------\t|---------------------------------------------------------------------------------------------\t|\n", @@ -660,22 +668,12 @@ "\n", "#### Obtain the Data\n", "\n", - "Access the enterprise to gain access to the FeatureLayer information and query it into a `Spatially Enabled DataFrame`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from arcgis.gis import GIS\n", - "gis = GIS()" + "Access the enterprise to gain access to the FeatureLayer information and query it into a Spatially Enabled DataFrame." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -686,22 +684,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "fs = flayer.query()\n" + "fset = flayer.query()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from arcgis.geometry import Geometry\n", - "g = Geometry(fs.features[0].geometry)\n", - "g.as_arcpy\n" + "g = Geometry(fset.features[0].geometry)\n", + "g.as_arcpy" ] }, { @@ -710,7 +708,7 @@ "source": [ "#### Write out the Arcade Expressions and Stops\n", "\n", - "Arcade expressions require the stops to be manually provided. In this case, we first create an opacity visual variable based on a percent of dominate parties in registered citizens." + "Arcade expressions require the stops to be manually provided. In this case, we first create an opacity visual variable based on a percent of dominant parties in registered citizens." ] }, { @@ -759,18 +757,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff6f41f4452045c99400e39e91a879cb", + "model_id": "520b0b29420f4bdaaae0516b03d1637d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…" + "MapView(layout=Layout(height='400px', width='100%'), zoom=4.0)" ] }, "metadata": {}, @@ -787,49 +785,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualize the Data\n", - "\n", - "Provide the color scheme, and the `arcade_expression` to render the data in a dynamic/rich form." + "![](http://esri.github.io/arcgis-python-api/notebooks/nbimages/guide_sdf_unique_value_arcade.PNG)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "df.spatial.plot(colors='coolwarm',\n", - " map_widget=m3_ua,\n", - " renderer_type='u-a',\n", - " unique_values=uv,\n", - " opacity_stops=opacity_stops,\n", - " opacity_expression=opacity_expression,\n", - " arcade_expression=arcade_expression)" + "#### Visualize the Data\n", + "\n", + "Provide the color scheme, and the `arcade_expression` to render the data in a dynamic/rich form." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'type': 'extent',\n", - " 'xmin': -16105138.185829269,\n", - " 'ymin': 2814150.199672489,\n", - " 'xmax': -6546229.176600808,\n", - " 'ymax': 6727726.047872473,\n", - " 'spatialReference': {'wkid': 102100, 'latestWkid': 3857}}" + "True" ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m3_ua.extent" + "df.spatial.plot(colors='coolwarm',\n", + " map_widget=m3_ua,\n", + " renderer_type='u-a', # 'u-a' stands for uniqe value with arcade expression\n", + " unique_values=uv,\n", + " opacity_stops=opacity_stops,\n", + " opacity_expression=opacity_expression,\n", + " arcade_expression=arcade_expression)" ] }, { @@ -844,20 +835,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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2CACamp Pendleton South11869
3CACitrus11195
4CACommerce13009
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" + ], + "text/plain": [ + " ST NAME POPULATION\n", + "0 AZ Somerton 14980\n", + "1 CA Anderson 10239\n", + "2 CA Camp Pendleton South 11869\n", + "3 CA Citrus 11195\n", + "4 CA Commerce 13009" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df[['ST', 'NAME', 'POPULATION']].head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fc7bb250ccf5447f867844ce227ff6b3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MapView(layout=Layout(height='400px', width='100%'), zoom=4.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "m4 = gis.map('Reno, NV', zoomlevel=4)\n", + "m4 = gis.map('USA', zoomlevel=4)\n", "m4" ] }, @@ -870,9 +951,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.spatial.plot(map_widget=m4,\n", " renderer_type='c', # for class breaks renderer\n", @@ -893,27 +985,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m4.layers" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['type', 'visualVariables', 'rotationType', 'minValue', 'field', 'defaultSymbol', 'defaultLabel', 'classificationMethod', 'classBreakInfos'])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dict(m4.layers[0].layer.layerDefinition.drawingInfo.renderer).keys()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], "source": [ "class_breaks = m4.layers[0].layer.layerDefinition.drawingInfo.renderer.classBreakInfos\n", "print(len(class_breaks))" @@ -928,9 +1030,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7809.0 - 10844.421052631578\n", + "10844.421052631578 - 13879.842105263157\n", + "13879.842105263157 - 16915.263157894737\n", + "16915.263157894737 - 19950.684210526313\n", + "19950.684210526313 - 22986.105263157893\n", + "22986.105263157893 - 26021.526315789473\n", + "26021.526315789473 - 29056.94736842105\n", + "29056.94736842105 - 32092.36842105263\n", + "32092.36842105263 - 35127.78947368421\n", + "35127.78947368421 - 38163.21052631579\n", + "38163.21052631579 - 41198.63157894737\n", + "41198.63157894737 - 44234.05263157895\n", + "44234.05263157895 - 47269.47368421053\n", + "47269.47368421053 - 50304.8947368421\n", + "50304.8947368421 - 53340.31578947368\n", + "53340.31578947368 - 56375.73684210526\n", + "56375.73684210526 - 59411.15789473684\n", + "59411.15789473684 - 62446.57894736842\n", + "62446.57894736842 - 65482.0\n", + "65482.0 - 65482.0\n" + ] + } + ], "source": [ "cbs_list = []\n", "cmap_list = []\n", @@ -949,9 +1078,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Histogram of POPULATION column')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "# build a histogram for the same class breaks\n", @@ -1010,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "hidden": true }, @@ -1030,24 +1180,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { - "hidden": true + "hidden": true, + "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_colormaps()" ] }, - { - "cell_type": "markdown", - "metadata": { - "hidden": true - }, - "source": [ - "![colormap display](http://esri.github.io/arcgis-python-api/notebooks/nbimages/13_display_colormaps.png)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -1059,11 +1212,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accent Accent_r Blues Blues_r BrBG\n", + "BrBG_r BuGn BuGn_r BuPu BuPu_r\n", + "CMRmap CMRmap_r Dark2 Dark2_r GnBu\n", + "GnBu_r Greens Greens_r Greys Greys_r\n", + "OrRd OrRd_r Oranges Oranges_r PRGn\n", + "PRGn_r Paired Paired_r Pastel1 Pastel1_r\n", + "Pastel2 Pastel2_r PiYG PiYG_r PuBu\n", + "PuBuGn PuBuGn_r PuBu_r PuOr PuOr_r\n", + "PuRd PuRd_r Purples Purples_r RdBu\n", + "RdBu_r RdGy RdGy_r RdPu RdPu_r\n", + "RdYlBu RdYlBu_r RdYlGn RdYlGn_r Reds\n", + "Reds_r Set1 Set1_r Set2 Set2_r\n", + "Set3 Set3_r Spectral Spectral_r Wistia\n", + "Wistia_r YlGn YlGnBu YlGnBu_r YlGn_r\n", + "YlOrBr YlOrBr_r YlOrRd YlOrRd_r afmhot\n", + "afmhot_r autumn autumn_r binary binary_r\n", + "bone bone_r brg brg_r bwr\n", + "bwr_r cool cool_r coolwarm coolwarm_r\n", + "copper copper_r cubehelix cubehelix_r flag\n", + "flag_r gist_earth gist_earth_r gist_gray gist_gray_r\n", + "gist_heat gist_heat_r gist_ncar gist_ncar_r gist_rainbow\n", + "gist_rainbow_r gist_stern gist_stern_r gist_yarg gist_yarg_r\n", + "gnuplot gnuplot2 gnuplot2_r gnuplot_r gray\n", + "gray_r hot hot_r hsv hsv_r\n", + "inferno inferno_r jet jet_r magma\n", + "magma_r nipy_spectral nipy_spectral_r ocean ocean_r\n", + "pink pink_r plasma plasma_r prism\n", + "prism_r rainbow rainbow_r seismic seismic_r\n", + "spring spring_r summer summer_r terrain\n", + "terrain_r viridis viridis_r winter winter_r\n" + ] + } + ], "source": [ "from arcgis.mapping import symbol\n", "\n", @@ -1083,24 +1273,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "hidden": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Short Dot" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "show_styles('Line')" ] @@ -1359,7 +1812,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.6" } }, "nbformat": 4, From 13f5c904ed31952e73d154d0baba247b0c646547 Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 14:41:46 -0700 Subject: [PATCH 08/10] renamed SeDF advanced topics to be specific and clear --- ...cs.ipynb => spatially-enabled-dataframe-advanced-topics.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename guide/05-working-with-the-spatial-dataframe/{advanced-topics.ipynb => spatially-enabled-dataframe-advanced-topics.ipynb} (100%) diff --git a/guide/05-working-with-the-spatial-dataframe/advanced-topics.ipynb b/guide/05-working-with-the-spatial-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/advanced-topics.ipynb rename to guide/05-working-with-the-spatial-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb From 1f1f906df1b5ef5cfbab902a9dcb70e585ba041a Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 14:42:55 -0700 Subject: [PATCH 09/10] renamed chapter 5 to reflect migration to Spatially Enabled DataFrame --- .../data.zip | Bin ...duction-to-the-spatially-enabled-dataframe.ipynb | 0 ...patially-enabled-dataframe-advanced-topics.ipynb | 0 ...-data-with-the-spatially-enabled-dataframe.ipynb | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename guide/{05-working-with-the-spatial-dataframe => 05-working-with-the-spatially-enabled-dataframe}/data.zip (100%) rename guide/{05-working-with-the-spatial-dataframe => 05-working-with-the-spatially-enabled-dataframe}/introduction-to-the-spatially-enabled-dataframe.ipynb (100%) rename guide/{05-working-with-the-spatial-dataframe => 05-working-with-the-spatially-enabled-dataframe}/spatially-enabled-dataframe-advanced-topics.ipynb (100%) rename guide/{05-working-with-the-spatial-dataframe => 05-working-with-the-spatially-enabled-dataframe}/visualizing-data-with-the-spatially-enabled-dataframe.ipynb (100%) diff --git a/guide/05-working-with-the-spatial-dataframe/data.zip b/guide/05-working-with-the-spatially-enabled-dataframe/data.zip similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/data.zip rename to guide/05-working-with-the-spatially-enabled-dataframe/data.zip diff --git a/guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb b/guide/05-working-with-the-spatially-enabled-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb rename to guide/05-working-with-the-spatially-enabled-dataframe/introduction-to-the-spatially-enabled-dataframe.ipynb diff --git a/guide/05-working-with-the-spatial-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb b/guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb rename to guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb diff --git a/guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb b/guide/05-working-with-the-spatially-enabled-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb similarity index 100% rename from guide/05-working-with-the-spatial-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb rename to guide/05-working-with-the-spatially-enabled-dataframe/visualizing-data-with-the-spatially-enabled-dataframe.ipynb From 2cc3e630b440236a46bc16f6d4bf7b7f6ec9ecdf Mon Sep 17 00:00:00 2001 From: Atma Mani Date: Tue, 28 Aug 2018 16:58:54 -0700 Subject: [PATCH 10/10] proofed and refined advanced concepts guide --- ...ly-enabled-dataframe-advanced-topics.ipynb | 87 +++++++++++++------ 1 file changed, 60 insertions(+), 27 deletions(-) diff --git a/guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb b/guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb index eed1c4c29d..098fa1e876 100644 --- a/guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb +++ b/guide/05-working-with-the-spatially-enabled-dataframe/spatially-enabled-dataframe-advanced-topics.ipynb @@ -4,15 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Topics\n", + "# Spatially Enabled DataFrames - Advanced Topics\n", "\n", "The information in this section provides a brief introduction to advanced topics with the `Spatially Enabled DataFrame` structure. \n", "\n", "One of the most important tasks for software applications is to quickly retrieve and process information. Enterprise systems, whether storing GIS information or not, all utilize the concept of indexing to allow for quick searching through large data stores to locate and select specific information for subsequent processing. \n", "\n", - "This document will outline how row and column indexing work in the Spatial Dataframe and also demonstrate building a spatial index on dataframe geometries to allow for quick searching, accessing, and processing. The document will also demonstrate spatial joins to combine dataframes.\n", + "This document will outline how row and column indexing work in Spatially Enabled Dataframes and also demonstrate building a spatial index on dataframe geometries to allow for quick searching, accessing, and processing. The document will also demonstrate spatial joins to combine dataframes.\n", "\n", - " * [Dataframe Index](#Dataframe-Index)\n", + " * [DataFrame Index](#DataFrame-Index)\n", + " * [Slicing DataFrames](#Slicing-DataFrames)\n", " * [Spatial Index](#Spatial-Index)\n", " * [Intersection with the Spatial Index](#Intersection-with-the-Spatial-Index)\n", " * [Spatial Joins](#Spatial-Joins)\n", @@ -23,10 +24,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Dataframe Index\n", - "As mentioned in the [Introduction to the spatial dataframe guide](../introduction-to-the-spatial-dataframe), the Pandas [dataframe structure](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) underlies the ArcGIS API for Python Spatial Dataframe. Pandas dataframes are analagous to spreadsheets. They have a row axis and a column axis. Each of these axes are indexed and labeled for quick and easy identification, data alignment, and retrieval and updating of data subsets.\n", + "## DataFrame Index\n", + "As mentioned in the [Introduction to the Spatially Enabled DataFrame guide](../introduction-to-the-spatially-enabled-dataframe), the Pandas [DataFrame structure](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) underlies the ArcGIS API for Python's Spatially Enabled DataFrame. Pandas DataFrames are analagous to spreadsheets. They have a row axis and a column axis. Each of these axes are indexed and labeled for quick and easy identification, data alignment, and retrieval and updating of data subsets.\n", "\n", - "Lets explore the axis labels and indices and how they allow for data exploraation:" + "Let's explore the axes labels and indices and how they allow for data exploraation:" ] }, { @@ -43,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When working with an ArcGIS Online feature layer, the [`query()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method returns a `FeatureSet` object which has a `df` method to instantiate a Spatial Dataframe." + "When working with an ArcGIS Online feature layer, the [`query()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method returns a `FeatureSet` object which has a `sdf` method to instantiate a Spatially Enabled DataFrame." ] }, { @@ -551,6 +552,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Slicing DataFrames\n", "We can access rows, columns and subsets of rows and columns using Python slicing:" ] }, @@ -774,9 +776,9 @@ "metadata": {}, "source": [ "## Spatial Index\n", - "In addition to row and column indices to search a dataframe, we can use a spatial indexes quickly access information based on its location and relationship with other features. They are based on the concept of a minimum bounding rectangle - the smallest rectangle that contains an entire geometric shape. Each of these rectangles are then grouped into `leaf` nodes representing a single shape and `node` structures containing groups of shapes according to whatever algorithm the different types of spatial indexing use. Querying these rectangles requires magnitudes fewer computer resources for accessing and processing geometries relative to accessing the entire feature array of coordinate pairs that compose a shape. Access to points, complex lines and irregularly-shaped polygons becomes much quicker and easier through different flavors of spatial indexing.\n", + "In addition to row and column indices to search a DataFrame, we can use a spatial indexes to quickly access information based on its location and relationship with other features. They are based on the concept of a **minimum bounding rectangle** - the smallest rectangle that contains an entire geometric shape. Each of these rectangles are then grouped into `leaf` nodes representing a single shape and `node` structures containing groups of shapes according to whatever algorithm the different types of spatial indexing use. Querying these rectangles requires magnitudes fewer compute resources for accessing and processing geometries relative to accessing the entire feature array of coordinate pairs that compose a shape. Access to points, complex lines and irregularly-shaped polygons becomes much quicker and easier through different flavors of spatial indexing.\n", "\n", - "The Spatial DataFrame uses an implementation of spatial indexing known as [QuadTree indexing](https://en.wikipedia.org/wiki/Quadtree), which searches nodes when determining locations, relationships and attributes of specific features. QuadTree indexes are the default spatial index, but the SEDF also supports r-tree implementations. In the [**Dataframe Index**](#Datframe-index) section of this notebook, the USA Major Cities feature layer was queried and the `df` method was called on the results to create a data frame. The [`sindex`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.html?highlight=style#arcgis.features.SpatialDataFrame.sindex) method on the `df` creates a quad tree index:" + "The Spatially Enabled DataFrame uses an implementation of spatial indexing known as [QuadTree indexing](https://en.wikipedia.org/wiki/Quadtree), which searches nodes when determining locations, relationships and attributes of specific features. `QuadTree` indexes are the default spatial index, but the SEDF also supports `r-tree` implementations. In the [**DataFrame Index**](#DataFrame-index) section of this notebook, the USA Major Cities feature layer was queried and the `sdf` property was called on the results to create a DataFrame. The [`sindex`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.GeoAccessor.sindex) method on the DataFrame creates a QuadTree index:" ] }, { @@ -792,7 +794,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visually inspect the external frame of the Quadtree index. We'll then plot the spatial dataframe to ensure the spatial index encompasses all our features:" + "Let's visually inspect the external frame of the QuadTree index. We'll then plot the spatial dataframe to ensure the spatial index encompasses all our features:" ] }, { @@ -861,7 +863,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's use the feature we drew above to define a spatial reference variable for use throughout the rest of this guide." + "Let's use the feature we drew earlier to define a spatial reference variable for use throughout the rest of this guide." ] }, { @@ -1477,11 +1479,11 @@ "sym_poly = {\n", " \"type\": \"esriSFS\",\n", " \"style\": \"esriSFSSolid\",\n", - " \"color\": [0,0,0,0],\n", + " \"color\": [0,0,0,0], # hollow, no fill\n", " \"outline\": {\n", " \"type\": \"esriSLS\",\n", " \"style\": \"esriSLSSolid\",\n", - " \"color\": [255,0,0,255],\n", + " \"color\": [255,0,0,255], # red border\n", " \"width\": 3}\n", "}\n", "\n", @@ -1512,11 +1514,11 @@ "sym_poly_aoi = {\n", " \"type\": \"esriSFS\",\n", " \"style\": \"esriSFSSolid\",\n", - " \"color\": [0,0,0,0],\n", + " \"color\": [0,0,0,0], # hollow, no fill\n", " \"outline\": {\n", " \"type\": \"esriSLS\",\n", " \"style\": \"esriSLSSolid\",\n", - " \"color\": [0,255,0,255],\n", + " \"color\": [0,255,0,255], # green border\n", " \"width\": 3}\n", "}\n", "\n", @@ -1783,10 +1785,19 @@ "df.iloc[index_of_features]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us plot these features that intersect on a map:" + ] + }, { "cell_type": "code", "execution_count": 27, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1842,7 +1853,7 @@ "pt_sym = {\n", " \"type\": \"esriSMS\",\n", " \"style\": \"esriSMSDiamond\",\n", - " \"color\": [255,140,0,255], \n", + " \"color\": [255,140,0,255], # yellowish\n", " \"size\": 8,\n", " \"angle\": 0,\n", " \"xoffset\": 0,\n", @@ -1856,18 +1867,25 @@ " m2.draw(shape = df.iloc[pt_index]['SHAPE'], symbol = pt_sym) " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus we were able to use the spatial indexes to query features that fall within an extent." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spatial Joins\n", - "Dataframes are table-like structures comprised of rows and columns. In relational database, SQL `joins` are fundamental operations that combine columns from one or more tables using values that are common to each. They occur in almost all database queries.\n", + "DataFrames are table-like structures comprised of rows and columns. In relational database, SQL `joins` are fundamental operations that combine columns from one or more tables using values that are common to each. They occur in almost all database queries.\n", "\n", "A Spatial join is a table operation that affixes data from one feature layer’s attribute table to another based on a spatial relationship. The spatial join involves matching rows from the Join Features (data frame1) to the Target Features (data frame2) based on their spatial relationship.\n", "\n", - "Let's look at how joins work with dataframes by using subsets of our original dataframe and the pandas `merge` fucntionality. We'll then move onto examining a spatial join to combine features from one dataframe with another based on a common attribute value.\n", + "Let's look at how joins work with dataframes by using subsets of our original DataFrame and the pandas `merge` fucntionality. We'll then move onto examining a spatial join to combine features from one dataframe with another based on a common attribute value.\n", "\n", - "Query the dataframe to extract 3 attribute columns of information from 2 states, Ohio and Michigan:" + "Query the DataFrame to extract 3 attribute columns of information from 2 states, Ohio and Michigan:" ] }, { @@ -2507,7 +2525,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice how all the rows from the left dataframe appear in the result with all the attribute columns and values appended from the right dataframe where the column value of NAME matched. The `POP2010` attribute from the left dataframe is combined with all the attributes from the right dataframe." + "Notice how all the rows from the left `DataFrame` appear in the result with all the attribute columns and values appended from the right `DataFrame` where the column value of `NAME` matched. The `POP2010` attribute from the left `DataFrame` is combined with all the attributes from the right `DataFrame`." ] }, { @@ -2879,18 +2897,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The rows where the on parameter value is the same in both tables have all attributes from both dataframes in the result. The rows from the first dataframe that do not have a matching `NAME` value in the second dataframe have values filled in with NaN values." + "The rows where the on parameter value is the same in both tables have all attributes from both DataFrames in the result. The rows from the first DataFrame that do not have a matching `NAME` value in the second dataframe have values filled in with `NaN` values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A spatial join works similarly on matching attribute values.\n", + "A spatial join works similarly on matching attribute values. However, instead of joining on an attribue field (like you did earlier), you will join based on the spatial relationship between the records in the two tables.\n", "\n", "#### Example: Merging State Statistics Information with Cities\n", "\n", - "The goal is to get Wyoming's city locations and census data joined with Wymoing's state census data.\n", + "The goal is to get Wyoming's city locations and census data joined with Wyoming's state census data.\n", "> If you do not have access to the `ArcPy` site-package from the Python interpreter used to execute the following cells, you must authenticate to an ArcGIS Online Organization or ArcGIS Enterprise portal." ] }, @@ -2948,7 +2966,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use python's list comprehensions to create lists of the attribute columns in the dataframe, then print out the lists to see the names of all the attribute columns." + "We will use python's list comprehensions to create lists of the attribute columns in the DataFrame, then print out the lists to see the names of all the attribute columns." ] }, { @@ -3036,7 +3054,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create a dataframe for the cities in Wyoming:" + "Create a DataFrame for the cities in Wyoming:" ] }, { @@ -3703,6 +3721,13 @@ "sdf2" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice, you retain the geometry type of your left DataFrame (points) in this case, however, you get all the attributes from both the left and right DataFrames. Let us plot the results of the spatial join on a map:" + ] + }, { "cell_type": "code", "execution_count": 47, @@ -3755,6 +3780,14 @@ "for idx, row in sdf2.iterrows():\n", " m3.draw(row['SHAPE'], symbol=pt_sym)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "Spatially Enabled DataFrame give you powerful data analysis and data wrangling capabilities. In addition to performing sql like operations on attribute data, you can perform geographic queries. This guide demonstrated some of these advanced capabilities of the SEDF." + ] } ], "metadata": { @@ -3773,7 +3806,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.6" } }, "nbformat": 4,