My First Encounter with arcpy.da.UpdateCursor

I have been using arcpy intermittently over the past year and a half mainly for automating and chaining batch processing to save myself countless hours of repetition. This week, however, I had to implement a facet of arcpy that I had not yet had the opportunity to utilise – the data access module.

Data Cursor

The Scenario
A file geodatabase with 75 feature classes each containing hundreds to thousands of features. These feature classes were the product of a CAD (Bentley Microstation) to GIS conversions via FME with data coming from 50+ CAD files. As a result of the conversion each feature class could contain features with various attributes from one or multiple CAD files but each feature class consisted of the same schema which was helpful.


The main issue was that the version number for a chunk of the CAD files had not been corrected. Two things needed to be fixed: i) the ‘REV_NUM’ attribute for all feature classes needed to be ‘Ver2’, there would be a mix of ‘Ver1’ and ‘Ver2’,  and ii) in the ‘MODEL_SUMMARY’ if ‘Ver1’ was found anywhere in the text it needed to be replaced with ‘Ver2’. There was one other issue and this stemmed from creating new features and not attributing them, this would have left a ‘NULL’ value in the ‘MODEL’ field (and the other fields). All features had to have standardised attributes. The script would not fix these but merely highlight the feature classes.

OK so a quick recap…
1. Set the ‘REV_NUM’ for every feature to ‘Ver2’
2. Find and replace ‘Ver1’ with ‘Ver2’ in the text string of ‘MODEL_SUMMARY’ for all features.
3. Find all feature classes that have ‘NULL’ in the ‘MODEL’ field.

The Script
Let’s take a look at the thirteen lines of code required to complete the mission.

import arcpy

arcpy.env.workspace = r"C:\Users\*****\Documents\CleanedUp\Feature_Classes.gdb"
fc_list = arcpy.ListFeatureClasses()
fields = ["MODEL", "MODEL_SUMMARY", "REV_NUM"]

for fc in fc_list:
 with arcpy.da.UpdateCursor(fc, fields) as cursor:
  for row in cursor:
   if row[0] == None or row[0] == "":
    print fc + ": Null value found for MODEL"
   if row[1] != None:
    row[1] = row[1].replace("Ver1", "Ver2")
   row[2] = "Ver2"

The Breakdown
Import the arcpy library (you need ArcGIS installed and a valid license to use)

import arcpy

Set the workspace path to the relevant file geodatabase

arcpy.env.workspace = r"C:\Users\*****\Documents\CleanedUp\Feature_Classes.gdb"

Create a list of all the feature classes within the file geodatabase.

fc_list = arcpy.ListFeatureClasses()

We know the names of the fields we wish to access so we add these to a list.

fields = ["MODEL", "MODEL_SUMMARY", "REV_NUM"]

For each feature class in the geodatabase we want to access the attributes of each feature for the relevant fields.

for fc in fc_list:
 with arcpy.da.UpdateCursor(fc, fields) as cursor:
  for row in cursor:

If the ‘MODEL’ attribute has a None (NULL) or empty string value then print the feature class name to the screen. Once one is found we can break out and move onto the next feature class.

   if row[0] == None or row[0] == "":
    print fc + ": Null value found for MODEL"

We know have a list of feature classes that we can fix the attributes manually.

Next we find any instance of ‘Ver1’ in ‘MODEL_SUMMARY’ text strings and replace it with ‘Ver2’….

   if row[1] != None:
    row[1] = row[1].replace("Ver1", "Ver2")

…and update all ‘REV_NUM’ attributes to ‘Ver2’ regardless of what is already attributed. This is like using the Field Calculator to update.

   row[2] = "Ver2"

Perform and commit the above updates for each feature.


Very handy to update the data you need and this script can certainly be extended to handle more complex operations using the arcpy.da.UpdateCursor module.

Check out the documentation for arcpy.da.UpdateCursor

Book Review: Learning ArcGIS Geodatabases [eBook]

Title: Learning ArcGIS Geodatabases
Author: Hussein Nasser
Publisher: Packt Publishing
Year: 2014
Aimed at: ArcGIS – beginner to advanced
Purchased from:

Learning ArcGIS Geodatabases

After using MapInfo for four years my familiarity with ArcGIS severely declined. The last time I utilised ArcGIS in employment shapefiles were predominantly used but I knew geodatabases were the way forward. If they were going to play a big part in future employment it made sense to get more intimate with them and learn their inner secrets. This compact eBook seemed like a good place to start…

The first chapter is short and sweet and delivered at a beginner’s level with nice point to point walkthroughs and screenshots to make sure you are following correctly. You are briefed on how to design, author, and edit a geodatabase. The design process involves designing the schema and specifying the field names, data types, and the geometry types for the feature class you wish to create. This logical design is then implemented as a physical schema within the file geodatabase. Finally, we add data to the geodatabase through the use of editing tools in ArcGIS and assign attribute data for each feature created. Very simple stuff so far that provides a foundation for getting set-up for the rest of the book.

The second chapter is a lot bulkier and builds upon the first. The initial task in Chapter 2 is to add new attributes to the feature classes followed by altering field properties to suit requirements. You are introduced to domains, designed to help you reduce errors while creating features and preserve data integrity, and subtypes. We are shown how to create a relationship class so we can link one feature in a spatial dataset to multiple records in a non-spatial table stored in the geodatabase as an object table. The next venture in this chapter takes a quick look at converting labels to an annotation class before ending with importing other datasets such as shapefiles, CAD files, and coverage classes and integrating them into the geodatabase as a single point of spatial reference for a project.

Chapter 3 looks at improving the rough and ready design of the geodatabase through entity-relationship modelling, which is a logical diagram of the geodatabase that shows relationships in the data. It is used to reduce the cost of future maintenance. Most of the steps from the first two chapters are revisited as we are taken through creating a geodatabase based on the new entity relationship model. The new model reduces the number of feature classes and improves efficiency through domains, subtypes and relationship classes. Besides a new train of thought on modelling a geodatabase for simplicity the only new technical feature presented in the chapter is enabling attachments in the feature class. It is important to test the design of the geodatabases through ArcGIS, testing includes adding a feature, making use of the domains and subtypes, and test the attachment capabilities to make sure that your set-up works as it should.

Chapter 4 begins with the premise of optimizing geodatabases through tuning tools. Three key optimizing features are discussed; indexing, compressing, and compacting. The simplicity of the first three chapters dwindles and we enter a more intermediate realm. For indexing, how to enable attribute indexing and spatial indexing in ArcGIS is discussed along with using indexes effectively. Many of you may have heard about database indexing before, but the concept of compression and compacting in a database may be foreign. These concepts are explored and their effective implementation explained.

The first part of the fifth chapter steps away from the GUI of ArcGIS for Desktop and ArcCatalog and switches to Python programming for geodatabase tasks. Although laden with simplicity, if you have absolutely no experience with programming or knowledge of the general concepts well then this chapter may be beyond your comprehension, but I would suggest performing the walkthroughs as it might give you an appetite for future programming endeavours. We are shown how to programmatically create a file geodatabase, add fields, delete fields, and make a copy of a feature class to another feature class. All this is achieved through Python using the arcpy module. Although aimed at highlighting the integration of programming with geodatabase creation and maintenance the author also highlights how programming and automation improves efficiency.

The second part of the chapter provides an alternative to using programming for geoprocessing automation in the form of the Model Builder. The walkthrough shows us how to use the Model Builder to build a simple model to create a file geodatabase and add a feature class to it.

The final chapter steps up a level from file geodatabases to enterprise geodatabases.

“An enterprise geodatabase is a geodatabase that is built and configured on top of a powerful relational database management system. These geodatabases are designed for multiple users operating simultaneously over a network.”

The author walks us through installing Microsoft SQL Server Express and lists some of the benefits of employing an enterprise geodatabase system. Once the installation is complete the next step is to connect to the database from a local and remote machine. Once connections are established and tested an enterprise geodatabase can be created to and its functionality utilised. You can also migrate a file geodatabase to and enterprise geodatabase. The last part of Chapter 6 shows how privileges can be used to grant users access to data that you have created or deny them access. Security is an integral part of database management.

Overall Verdict: for such a compact eBook (158 pages) it packs a decent amount of information that provides good value for money, and it also introduces other learning ventures that come part and parcel with databases in general and therefore geodatabases. Many of the sections could be expanded based on their material but the pagination would then increase into many hundreds (and more) and beyond the scope of this book. The author, Hussein Nasser, does a great job with limiting the focus to the workings of geodatabases and not veering off on any unnecessary tangents. I would recommend using complimentary material to bolster your knowledge with regards to many of the aspects such as entity-relationship diagrams, indexing (both spatial and non-spatial), Python programming, the Model Builder, enterprise geodatabases and anything else you found interesting that was only briefly touched on. Overall the text is a foundation for easing your way into geodatabase life, especially if shapefiles are still the centre of you GIS data universe.

[An Introduction to] Hotspot Analysis Using ArcGIS

Make sure to read the What is Hotspot Analysis? post before proceeding with this tutorial. This tutorial will serve as an introduction to hotspot analysis with ArcGIS Desktop. You will find links at the bottom of the post that will provide information for further research.

Get the Data

It is often difficult to find real data for use with tutorials so first of all a hat tip to Eric Pimpler, the author of ArcGIS Blueprints, for pointing me towards accessing crime data for Seattle. To follow this tutorial you will need the neighborhoods of Seattle Shapefile which you can download from here and burglary data for 2015 which I have provided a link to here. Use the Project tool from Data Management Tools > Projections and Transformations to project the data into a Projected Coordinate System. For this tutorial I have used UTM Zone 10N. Open, view and if you want style the data in ArcMap.

HSA Vector Data

Spatial Autocorrelation: Is there clustering?

The presence of spatial clustering in the data is a requisite for hotspot analysis. Moran’s I is a measure of spatial autocorrelation that returns a value ranging from -1 to 1. Perfect dispersion at -1, complete random arrangement at 0, and a north/south divide at +1 indicating perfect correlation.

Moran's I Visual

For statistical hypothesis testing, Moran’s I value can be transformed to a z-zcore in which values greater than 1.96 or smaller than -1.96 indicate spatial autocorrelation that is significant at the 5% level.

We first need to prepare the data. At the moment each point represent one incident, we need to aggregate the data in some way so that each feature has an attribute with a value in a range. Open the Copy Features tool from Data Management Tools > Features. Create a copy of the burglary point layer. Run the tool and add the new layer to the map.

Copy Features Tool

Open the Integrate tool from Data Managemant Tools > Feature Class. Select the copy of the burglary layer as the Input Features and set an XY Tolerance of 90 or 100 meters. Run the tool. This will relocate points within 90m (or 100m) or whatever you set in XY Tolerance field, of each other and stack them on top of one another.

Integrate Tool

At this moment each point sits on top of another. We need to merge coincident points and make a count of how many were merged at each point. Open the Collect Events tool from Spatial Statistics Tools > Utilities. Set the copy of the burglary layer as the Input Incident Features and set a filepath and name for the Output Weighted Point Feature Class. Run the tool.

Collect Events Tool

The data will be added to the map with graduated symbols, however, we are interested in running further analysis using Moran’s I. If you open the attribute table for the layer you will see a field has been added called ICOUNT. This field holds the count of cooincident points from the Intergrate layer. Open the Spatial Autocorrelation (Moran’s I) from Spatial Statistics Tools > Analyzing Patterns. Set the aggregated burglary layer as the Input Feature Class and ICOUNT as the Input Field. I have left the default setting for the other parameters (see below).

Spatial Autocorrelation Tool

Run the tool by clicking on OK. A summary will display with statistical findings.

Moran's I Values

We return a value close to 0.2 and a high z-score. This indicates that clustering exists within the data for high positive values. We are now confident that clustering exists within the dataset and can continue with performing the hotspot analysis.

Optimized Hotspot Analysis

Remove all layers from map the except the two original layers with the burglary data and the neighborhoods. From the Toolbox navigate to Spatial Statistics Tools > Mapping Clusters and open the Optimized Hotspot Analysis tool. This tool allows for quick hotspot analysis using minimal input parameters and sets/calculates default parameters for those you have no control over. For more control over the statistical elements you can use the Hotspot Analysis (Getis-Ord GI*) tool. For now we will use the optimized approach.

Set the burglary points as the Input Features, name your Output Features (here I have named them ohsa_burg_plygns), select COUNT_INCIDENTS_WITHIN_AGGREGATION_POLYGONS for the Incident Data Aggregation Method and choose the neighborhoods features for the Polygons For Aggregating Incidents Into Counts.

Optimized HSA - Polygons

OHSA: Aggregating Point Data to Polygon Features

Click OK to run the tool. The ohsa_burg_plygns layer will automatically be added as a layer to the map, if not, add it and turn off all other layers. So what has happened here? The tool has aggregated the point data into the neighborhood polygons. If you open the attribute table for the newly created layer you will see a field names Count_Join which is a count of burglaries per neighborhood. A z-score and a p-score is calculated which enables the detection of hot and cold spots in the data. Remember, a high z-score and a low p-value for a feature indicates a significant hotspot. A low negative z-score and a small p-value indicates a significant cold spot. The higher (or lower) the z-score, the more intense the clustering. A z-score near 0 means no spatial clustering.

HSA Attribute Table

The Gi_Bin field classifies the data into a range from -3 (Cold Spot – 99% Confidence) to 3 (Hot Spot – 99% Confidence), with 0 being non-significant, just take a look at your Table of Contents.

Optimized - Confidence Levels

The map should look similar to below. There are several neighborhoods that are statistically significant hotspots. It is important to note that you may need to factor in other data or normalise your data to refine results. Some of the neighborhoods might be densely populated with suburban housing while in others housing may be sparse and bordering towards rural. This may affect findings and you may need to create ratios before analysing. We won’t delve into this here as this tutorial is introductory level (and because I don’t have the data to do so).

OHSA Polygon Map

OHSA: Aggregating Point Data to Fishnet Features

Close any attribute tables and turn off all layers in your map. Re-open the Optimized Hotspot Analysis tool and set the input as seen below. This time we will create a fishnet/grid to aggregate the point data to.

Optimized - Fishnet

Click OK to run the tool. The tool removes any locational outliers, calculates a cell size, and aggregates the point data to the cells in the grid. Similar to aggregating to polygons the fishnet attribute table will have a join count, z-score, p-score and bin value with the same confidence levels.

OHSA Fishnet MapShould attention be entirely focused on the red areas? Copy the fishnet layer and paste it into the data frame. Rename the copy as fishnet_count. Open the properties and navigate to the Symbology tab. Change the Value field to Join_Count, reduce the Classes to 5 and set the classification to Equal Count. Click OK.

Fishnet SymbologyThere will be one red cell and one light red cell in the northern half of the map. Use the zoom tool to zoom-in closer to both features. Turn on the labels for the feature for the Join_Count attribute. Notice that the light-red cell has a count of 19 but in the Hotspot Analysis this was a non-significant area. With the second highest burglary count for a 300m x 300m area surely this area requires some attention. Perhaps all areas outside of significant hotspots with values greater that 15 are a priority? I am not a expert in crime analysis so I’ll leave it up to those sleuth’s.

OHSA Fishnet Labels

This just serves to note to make sure that you use all the analysis techniques at your disposal from simple to more advanced, from visual and labels to statistical.

OHSA: Create Weighted Points by Snapping Nearby Incidents

Zoom out to the full extent of the neighborhoods layer and turn off all layers in the map. Re-open the Optimized Hotspot Analysis tool and set the input as seen below. Notice this time we will also create a Density Surface.

Optimized - Points

Click OK and run the tool. The tool calculates a distance value and converges points that fall within that distance in relation to each other. It then runs the hotspot analysis similar to the previous two examples producing an attribute table with an ICOUNT field, z-score, p-score and bin value for confidence level. The ICOUNT field denotes how many incidents the one point references.

OHSA Points Map

Let’s clip the density raster to the neighborhoods layer. Open the Clip tool from Data Management Tools > Raster > Raster Processing. Set the Input Raster as the density raster, use the neighborhoods layer as the Output Extent, make sure Use Input Features for Clipping Geometry is checked, set and name the Output Raster Dataset.

Density Raster Clip

Click OK and run the tool. Add the newly created raster to the map if it hasn’t automatically been added. Make it the only visible layer. Open the properties for the layer and go to the Symbology tab. Select Classified and generate a histogram if asked to. Change the Classes to 7 and the colour ramp to match previous colour schemes. You might need to flip the colour  ramp to achieve this.

Density Clip Symbology

Open the Display tab and select Bilinear Interpolation from Resample during display dropdown menu. This will smoothen the contour look of the raster. Click OK to view the density surface. Turn on the neighborhoods and make the fill transparent with a black outline.

Density Raster


The Optimized Hotspot Analysis tool is a great place to start but it limits the analysis to default parameters set by the tool or calculated by the tool. For more advanced user control you can use the Hotspot Analysis (Getis-Ord Gi*) tool. You will need to use other tools such as Spatial Join to aggregate your data to polygons and create a Join_Count field, or the Create Fishnet tool to define a grid and then use Spatial Join. Remember to delete any grid cells that have a value of zero prior to running the hotspot analysis.

Getis-Ord Tool

See the resources below for more information on using Getis-Ord Gi* and what the parameters do especially in relation to the Conceptualization of Spatial Relationships parameter.

Hotspot Analysis with ArcGIS Resources

ArcGIS Optimized Hotspot Analysis
ArcGIS Mapping Cluster Toolset: Hot Spot Analysis

ArcGIS How Hot Spot Analysis Works
ArcGIS – Selecting a Conceptualization of Spatial Relationships: Best Practices

Crime Data for Seattle

Crime data was accessed using the ArcGIS REST API and the Socrata Open Data API from the website. I highly recommend getting your hands on Eric Pimplers ArcGIS Blueprints eBook for a look at exciting workflows with ArcPy and the ArcGIS REST API.

Haversine Distances with Python, EURO 2016 & Historic Results

Irish JerseyIreland kick off their European Championship 2016 Group E on the 13th of June at the Stade de France, Saint-Denis, in the northern suburbs of Paris at 6pm local time (5pm on the Emerald Isle). Our opponents that evening will be Sweden, followed by a trek down south to Bordeaux to face Belgium on the 18th, and then a northern journey to Lille to take on Italy in the final group game on the 22nd.

Euro 2016 Overview

Click on map to enlarge

I have a flight from Dublin to Paris, a train from Paris to Bordeaux, Bordeaux to Lille, Lille to Paris, and a flight back to Dublin. Let’s use the simple Haversine formula to calculate approximately how far I will be travelling to follow the boys in green this summer. Just to note, I am not being pessimistic by not sticking around for the knockout stages, I have every faith that we will get out of the group, a two week holiday in France is going to take its toll on the bank account! But who knows…

The Haversine formula calculates the distance between two points on a sphere, also known as the Greater Circle distance, here’s the Python code similar to

from math import radians, sin, cos, sqrt, asin

def haversine(coords_1, coords_2):

 lon1 = coords_1[0]
 lat1 = coords_1[1]
 lon2 = coords_2[0]
 lat2 = coords_2[1]
 # Earth radius in kilometers
 earth_radius = 6372.8

 # convert from degrees to radians
 dLon = radians(lon2 - lon1)
 dLat = radians(lat2 - lat1) 
 lat1 = radians(lat1)
 lat2 = radians(lat2)

 # mathsy stuff for you to research...if you're so inclined to
 a = sin(dLat/2)**2 + cos(lat1)*cos(lat2)*sin(dLon/2)**2
 c = 2*asin(sqrt(a))

 # return the distance rounded to 2 decimal places
 return round((earth_radius * c),2)

Now to use the function to calculate my approximate travelling distance. First define the coordinates as lists.

# lon - lat
dub_airport = [-6.2700, 53.4214]
cdg_airport = [2.5478, 49.0097]
paris = [2.3831, 48.8390]
bordeaux = [-0.5800, 44.8400]
lille = [3.0583, 50.6278]

And then use the coordinates as parameters for the haversine function. Print the distance for each leg and the total distance.

leg_1 = haversine(dub_airport, cdg_airport)
print "Leg 1: " + str(leg_1) + " Km"

leg_2 = haversine(paris, bordeaux)
print "Leg 2: " + str(leg_2) + " Km"

leg_3 = haversine(bordeaux, lille)
print "Leg 3: " + str(leg_3) + " Km"

leg_4 = haversine(lille, paris)
print "Leg 4: " + str(leg_4) + " Km"

leg_5 = haversine(cdg_airport, dub_airport)
print "Leg 5: " + str(leg_5) + " Km"

total_dist = leg_1 + leg_2 + leg_3 + leg_4 + leg_5
print "Total Distance " + str(total_dist) + " Km"

The output from running the complete script is…

Leg 1: 785.3 Km
Leg 2: 498.57 Km
Leg 3: 698.71 Km
Leg 4: 204.79 Km
Leg 5: 785.3 Km
Total Distance 2972.67 Km

Just over 2,970 Km! Ok so I could have been more accurate with getting the road length from my house to the airport, using the Haversine to find the distance from Dublin Airport to Charles De Gaulle, and then using road and rail networks to calculate my internal travel in France but the idea here was to introduce you to the Haversine formula.

And here’s a little map to show my up coming travels. These maps were made by exporting data from ArcGIS as an emf and imported in CorelDraw, a graphic design package for styling.

Euro 2016 Travel

Click on the map to enlarge

That’s it for the Haversine, Python and GIS in general. If you have no interest in football you can stop reading here. From here down it’s just historic results against our opponents and opinions.



We have faced Sweden in two world cup qualifying campaigns, in qualification for one European Championship, and four times in a friendly match. It doesn’t bode well that we have never beaten Sweden in a competitive game, losing four times and drawing twice.

vrs Sweden Competitive

Our dominance over Sweden in friendlies see us with a 75% win percentage, only losing once in four meetings.

v Sweden Friendlies

Overall: P10 W3 D2 L5 F13 A16 – WIN% 30



Similar to our record against Sweden, Ireland have never beaten Belgium in a competitive fixture, although in the seven competitive games we have only been beaten twice, drawing the other five.

v Belgium Competitive

We have to look to the friendly results for Ireland to get the better of Belgium, with Ireland edging the score at four wins to three.

v Belgium Friendlies

Overall: P14 W4 D5 L5 F24 A25 – WIN% 28.57



Well would you look at that!! We have beaten at least one of our upcoming opponents in a competitive match. Ok so one win out of eight but I am every the optimist and from 1994 onwards our record against Italy is pretty decent both competitively and in friendlies.

v Italy Competitive

Our overall record against Italy looks dismal but recent results give Ireland hope. Since and including our meeting with Italy at World Cup 94 the head-to-head is two wins each and three draws. If you offered my a draw now against Italy I’d take it. Remember there are now twenty-four teams in the competition meaning the best placed third team in each group qualifies for the second round.

v Italy Friendlies

Overall: P13 W2 D3 L8 F9 A20 – WIN% 16.67

Do we stand a chance of getting out of the group?

Let me know your thoughts. With three teams qualifying from four out of the six groups Ireland have every chance of progressing. A win and a draw will more than likely be the minimum requirement to achieve passage to the next phase, but let’s not forget that Ireland reached the Quarter Finals of Italia 90 having not won a single match! Beat Sweden in the opener and get a draw against Italy. If only it was that simple 🙂


Data used to create maps was downloaded from Natural Earth.
Historic results collate from the FAI and Wikipedia.

$5 New Year Skill Up with Packt Publishing Ends Soon! (Jan 8)

Packt Skill Up Event

Don’t miss out on this fantastic eBook and video sale from Packt Publishing where everything is $5 and there are plenty of GIS and geospatial books to choose from. There are books dedicated to ESRI software ArcGIS such as ArcGIS for Desktop, ArcGIS Pro, ArcGIS for Server, and ArcPy. There are also books for your favourite open source geospatial software and programming activities such as QGIS, Python Programming, R Programming, GeoServer, and JavaScript libraries such as OpenLayers and Leaflet. There are a lot of great titles from authors well established in their respective industries.

You are not limited to upgrading geospatial skills, there are over 3000 book titles and 150 videos for web development, application development, databases and data analysis, game development, programming skills and so much more.

My New Years resolution is to put down the TV remote and focus on upgrading my current skill set, Packt has provided a very cost effective way for me to achieve my goals. I have purchased from Packt in the past and recently purchased 8 titles in their Skill Up sale to keep me on my toes in the New Year. These are…

Some of these are pre-order and will hopefully give me a bit of time to get through a few of the other titles before their release. You can see my book review for Learning QGIS (Second Edition) here. One down seven to go.

For those of you interested in trends Packt have also created a Year in Review 2015 pdf that will guide users through the biggest topics to come out of 2015 and show you what’s going to be defining 2016 and beyond – so they can learn intelligently and strategically.

Just to note…Packt divides the learning curve into six titles…

Learning Curve

Learning: As a new user, these step-by-step tutorial guides will give you all the practical skills necessary to become competent and efficient.
Beginner’s Guide: Friendly, informal tutorials that provide a practical introduction using examples, activities, and challenges.
Essentials: Fast paced, concentrated introductions showing the quickest way to put the tool to work in the real world.
Cookbook: A collection of practical self-contained recipes that all users of the technology will find useful for building more powerful and reliable systems.
Blueprints: Guides you through the most common types of project you’ll encounter, giving you end-to-end guidance on how to build your specific solution quickly and reliably.
Mastering: Take your skills to the next level with advanced tutorials that will give you confidence to master the tool’s most powerful features.

Go forth and SKILL UP!