Programming ArcGIS 10.1 with Python Cookbook: This book provides the recipes you need to use Python with AcrGIS for more effective geoprocessing. Shortcuts, scripts, tools, and customizations put you in the driving seat and can dramatically speed up your workflow.

To start the IDLE development environment for Python, you can go to Start | Programs | ArcGIS | Python 2.7 | IDLE. Please note that the version of Python installed with ArcGIS will differ depending upon the ArcGIS version that you have installed. For example, ArcGIS 10.0 uses Python 2.6 while ArcGIS 10.1 uses Python 2.7.

A Python shell window similar to the screenshot will be displayed:

The Python shell window is used for output and error messages generated by scripts. A common mistake for beginners is to assume that the geoprocessing scripts will be written in this shell window. That is not the case. You will need to create a separate code window to hold your scripts.

Although the shell window isn't used to write entire scripts, it can be used to interactively write code and get immediate feedback. ArcGIS has a built-in Python shell window that you can use in much the same way. We'll examine the ArcGIS Python window in the next chapter.

Your scripts will be written in IDLE inside a separate window known as the Python script window. To create a new code window, select File | New Window from the IDLE shell window. A window similar to that in the following screenshot will be displayed:

Your Python scripts will be written inside this new code window. Each script will need to be saved to a local or network drive. By default, scripts are saved with a .py file extension.

Existing Python script files can be opened from Windows Explorer by right-clicking on the file and selecting Edit with IDLE, which brings up a new shell window along with the script loaded in the Python script editor. You can see an example of this in the following screenshot:

In this instance, we have loaded the ListFeatureClasses.py script with IDLE. The code is loaded inside the script window:

Now that the code window is open, you can begin writing or editing code. You can also perform some basic script debugging with the IDLE interface. Debugging is the process of identifying and fixing errors in your code.

Once you've written a geoprocessing script in the IDLE code window or opened an existing script, you can execute the code from the interface. IDLE does provide functionality that allows you to check the syntax of your code before running the script. In the code window, select Run | Check Module to perform a syntax check of your code.

Any syntax errors will be displayed in the shell window. If there aren't any syntax errors, you should just see the prompt in the shell window. While the IDLE interface can be used to check for syntax errors, it doesn't provide a way of checking for logical errors in your code nor does it provide more advanced debugging tools found in other development environments, such as PythonWin or Wingware.

Once you're satisfied that no syntax errors exist in your code, you can run the script. Select Run | Run Module to execute the script:

Any error messages will be written to the shell window along with output from print statements and system-generated messages. The print statement simply outputs a string to the shell window. It is often used for updating the status of a running script or for debugging the code.

Python scripts should follow a common structure. The beginning of each script should serve as documentation detailing the script name, author, and a general description of the processing provided by the script. This documentation is accomplished in Python through the use of comments. Comments are lines of code that you add to your script that serve as a documentation of what functionality the script provides. These lines of code begin with a single pound sign (#) or a double pound sign (##), and are followed by whatever text you need to document the code. The Python interpreter does not execute these lines of code. They are simply used for documenting your code. In the next screenshot, the commented lines of code are displayed in red. You should also strive to include comments throughout your script to describe important sections of your script. This will be useful to you (or another programmer) when the time comes to update your scripts.

Although Python includes many built-in functions, you will frequently need to access specific bundles of functionality, which are stored in external modules. For instance, the Math module stores specific functions related to processing numeric values and the R module provides statistical analysis functions. Modules are imported through the use of the import statement. When writing geoprocessing scripts with ArcGIS, you will always need to import the ArcPy module, which is the Python package for accessing GIS tools and functions provided by ArcGIS. import statements will be the first lines of code (not including comments) in your scripts:

import arcpy, os

At a high level, you can think of a variable as an area in your computer's memory reserved for storing values while the script is running. Variables that you define in Python are given a name and a value. The values assigned to variables can then be accessed by different areas of your script as needed, simply by referring to the variable name. For example, you might create a variable that contains a feature class name, which is then used by the Buffer tool to create a new output dataset. To create a variable, simply give it a name followed by the assignment operator, which is just an equal sign (=), and then a value:

fcParcels = "Parcels"
fcStreets = "Streets"

The following table illustrates the variable name and values assigned to the variable using the preceding code example:

There are certain naming rules that you must follow when creating variables, including the following:

There are a few dozen Python keywords that must be avoided including class, if, for, while, and others.

Some examples of legal variable names in Python:

Some examples of illegal variable names in Python:

Python is a case-sensitive language, so pay particular attention to the capitalization and naming of variables in your scripts. Case-sensitivity issues are probably the most common source of errors for new Python programmers, so always consider this as a possibility when you encounter errors in your code. Let's look at an example. The following is a list of three variables; note that although each variable name is the same, the casing is different, resulting in three distinct variables.

If you print these variables, you will get the following output:

print mapsize
>>> 22x34

print MapSize
>>> 8x11

print Mapsize
>>>36x48

Python variable names need to be consistent throughout the script. Best practice is to use camel casing, wherein the first word of a variable name is all lowercase and then each successive word begins with an uppercase letter. This concept is illustrated in the following example with the variable name fieldOwnerName. The first word (field) is all lower case followed by an uppercase letter for the second word (Owner) and third word (Name):

fieldOwnerName

In Python, variables are dynamically typed. Dynamic typing means that you can define a variable and assign data to it without specifically defining that a variable name will contain a specific type of data. Commonly used datatypes that can be assigned to variables include the following:

We will discuss each of these data-types in greater detail in the coming sections.

For instance, in C# you would need to define a variable's name and type before using it. This is not necessary in Python. To use a variable, simply give it a name and value, and you can begin using it right away. Python does the work behind the scenes to figure out what type of data is being held in the variable.

For example, in C# .NET you would need to name and define the datatype for a variable before working with the variable. In the following code example, we've created a new variable called aTouchdown, which is defined as an integer variable, meaning that it can contain only integer data. We then assign the value 6 to the variable:

int aTouchdown;
aTouchdown = 6;

In Python, this same variable can be created and assigned data through dynamic typing. The Python interpreter is tasked with dynamically figuring out what type of data is assigned to the variable:

aTouchdown = 6

Your Python geoprocessing scripts for ArcGIS will often need to reference the location of a dataset on your computer or perhaps a shared server. References to these datasets will often consist of paths stored in a variable. In Python, pathnames are a special case that deserve some extra mention. The backslash character in Python is a reserved escape character and a line continuation character, thus there is a need to define paths using two back slashes, a single forward slash, or a regular single backslash prefixed with the letter r. These pathnames are always stored as strings in Python. You'll see an example of this in the following section.

Illegal path reference:

fcParcels = "c:\Data\Parcels.shp"

Legal path references:

fcParcels = "c:/Data/Parcels.shp"
fcParcels = "c:\\Data\\Parcels.shp"
fcParcels = r"c:\Data\Parcels.shp"

There may be times when you know that your script will need a variable, but don't necessarily know ahead of time what data will be assigned to the variable. In these cases, you could simply define a variable without assigning data to it. Data that is assigned to the variable can also be changed while the script is running.

Variables can hold many different kinds of data including primitive datatypes such as strings and numbers along with more complex data, such as lists, dictionaries and even objects. We're going to examine the different types of data that can be assigned to a variable along with various functions that are provided by Python for manipulating the data.

Python has a number of built-in data-types. The first built-in type that we will discuss is the string data-type. We've already seen several examples of string variables, but these types of variables can be manipulated in a lot of ways, so let's take a closer look at this data-type.

Strings

Strings are ordered collections of characters used to store and represent text-based information. This is a rather dry way of saying that string variables hold text. String variables are surrounded by single or double quotes when being assigned to a variable. Examples could include a name, feature class name, a Where clause, or anything else that can be encoded as text.

String manipulation

Strings can be manipulated in a number of ways in Python. String concatenation is one of the more commonly used functions and is simple to accomplish. The + operator is used with string variables on either side of the operator to produce a new string variable that ties the two string variables together:

shpStreets = "c:\\GISData\\Streets" + ".shp"
print shpStreets

Running this code example produces the following result:

>>>c:\GISData\Streets.shp

String equality can be tested using Python's == operator, which is simply two equal signs placed together. Don't confuse the equality operator with the assignment operator, which is a single equal to sign. The equality operator tests two variables for equality, while the assignment operator assigns a value to a variable:

firstName = "Eric"
lastName = "Pimpler"
firstName == lastname

Running this code example produces the following result:

>>>False

Strings can be tested for containment using the in operator, which returns True if the first operand is contained in the second.

fcName = "Floodplain.shp"
print ".shp" in fcName
>>>True

I briefly mentioned that strings are an ordered collection of characters. What does this mean? It simply means that we can access individual characters or a series of characters from the string. In Python, this is referred to as indexing in the case of accessing an individual character, and slicing in the case of accessing a series of characters.

Characters in a string are obtained by providing the numeric offset contained within square brackets after a string. For example, you could obtain the first string character in the fc variable by using the syntax fc[0]. Negative offsets can be used to search backwards from the end of a string. In this case, the last character in a string is stored at index -1. Indexing always creates a new variable to hold the character:

fc = "Floodplain.shp"
print fc[0]
>>>'F'
print fc[10]
>>>'.'
print fc[13]
>>>'p'

The following image illustrates how strings are an ordered collection of characters with the first character occupying position 0, the second character occupying position 1, and each successive character occupying the next index number:

While string indexing allows you to obtain a single character from a string variable, string slicing enables you to extract a contiguous sequence of strings. The format and syntax is similar to indexing, but with the addition of a second offset, which is used to tell Python how many characters to return.

The following code example provides an example of string slicing. The theString variable has been assigned a value of Floodplain.shp. To obtain a sliced variable with the contents of Flood, you would use the theString[0:5] syntax:

theString = "Floodplain.shp"
print theString[0:5]
>>>Flood

Note

Python slicing returns the characters beginning with the first offset up to, but not including, the second offset. This can be particularly confusing for new Python programmers and is a common source of error. In our example, the returned variable will contain the characters Flood. The first character, which occupies position 0, is F. The last character returned is index 4, which corresponds to the character d. Notice that index number 5 is not included since Python slicing only returns characters up to but not including the second offset.

Either of the offsets can be left off. This in effect creates a wild card. In the case of theString[1:], you are telling Python to return all characters starting from the second character to the end of the string. In the second case, theString[:-1], you are telling Python to start at character zero and return all characters except the last.

Python is an excellent language for manipulating strings and there are many additional functions that you can use to process this type of data. Most of these are beyond the scope of this text, but in general all of the following string manipulation functions are available:

• String length

• Casing functions for conversion to upper and lower case

• Removal of leading and trailing whitespace

• Finding a character within a string

• Replacement of text

• Splitting into a list of words based on a delimiter

• Formatting

import math

fcList = ["Hydrants", "Water Mains", "Valves", "Wells"]
fc = fcList[0]
print fc
>>>Hydrants
fc = fcList[3]
print fc
>>>Wells

fcTuples = ("Hydrants", "Water Mains", "Valves", "Wells")

fcTuples = ("Hydrants", "Water Mains", "Valves", "Wells")
print fcTuples[1]
>>>Water Mains

##create the dictionary
dictLayers = {'Roads': 0, 'Airports': 1, 'Rail': 2}

##access the dictionary by key
dictLayers['Airports']
>>>1
dictLayers['Rail']
>>>2

Classes and objects are a fundamental concept in object-oriented programming. While Python is more of a procedural language, it also supports object-oriented programming. In object-oriented programming, classes are used to create object instances. You can think of classes as blueprints for the creation of one or more objects. Each object instance has the same properties and methods, but the data contained in an object can and usually will differ. Objects are complex datatypes in Python composed of properties and methods, and can be assigned to variables just like any other datatype. Properties contain data associated with an object, while methods are actions that an object can perform.

These concepts are best illustrated with an example. In ArcPy, the Extent class is a rectangle specified by providing the coordinate of the lower-left corner and the coordinate of the upper-right corner in map units. The Extent class contains a number of properties and methods. Properties include XMin, XMax, YMin, and YMax, spatialReference, and others. The minimum and maximum of x and y properties provide the coordinates for the extent rectangle. The spatialReference property holds a reference to a SpatialReference object for the Extent. Object instances of the Extent class can be used both to set and get the values of these properties through dot notation. An example of this is seen in the following code example:

import arcpy
fc = "c:/ArcpyBook/data/TravisCounty/TravisCounty.shp"

# Fetch each feature from the cursor and examine the extent properties and spatial reference
for row in arcpy.da.SearchCursor(fc, ["SHAPE@"]):
  # get the extent of the county boundary
  ext = row[0].extent
  # print out the bounding coordinates and spatial reference
  print "XMin: " + ext.XMin
  print "XMax: " + ext.XMax
  print "YMin: " + ext.YMin
  print "YMax: " + ext.YMax
  print "Spatial Reference: " + ext.spatialReference.name

Running this script yields the following output:

XMin: 2977896.74002
XMax: 3230651.20622
YMin: 9981999.27708
YMax:10200100.7854
Spatial Reference: NAD_1983_StatePlane_Texas_Central_FIPS_4203_Feet

The Extent class also has a number of methods, which are actions that an object can perform. In the case of this particular object, most of the methods are related to performing some sort of geometric test between the Extent object and another geometry. Examples include contains(), crosses(), disjoint(), equals(), overlaps(), touches(),and within().

One additional object-oriented concept that you need to understand is dot notation. Dot notation provides a way of accessing the properties and methods of an object. It is used to indicate that a property or method belongs to a particular class.

The syntax for using the dot notation includes an object instance followed by a dot and then the property or method. The syntax is the same regardless of whether you're accessing a property or a method. A parenthesis, and zero or more parameters, at the end of the word following the dot indicates that a method is being accessed. Here are a couple of examples to better illustrate this concept:

Property: extent.XMin
Method: extent.touches()

Each line of code that you write with Python is known as a statement. There are many different kinds of statements, including those that create and assign data to variables, decision support statements that branch your code based on a test, looping statements that execute a code block multiple times, and others. There are various rules that your code will need to follow as you create the statements that are part of your script. You've already encountered one type of statement: variable creation and assignment.

if fcName == 'Roads':
  gp.Buffer_analysis(fc, "c:\\temp\\roads.shp", 100)
elif fcName == 'Rail':
  gp.Buffer_analysis(fc, "c:\\temp\\rail.shp", 50)
else:
  print "Can't buffer this layer"
)

x = 10
while x < 100:
 print x
 x = x + 10

dictLayers = {"Roads":"Line","Rail":"Line","Parks":"Polygon"}
lstLayers = dictLayers.keys()
for x in lstLayers:
  print dictLayers[x]

import arcpy
import sys

inFeatureClass = arcpy.GetParameterAsText(0)
outFeatureClass = arcpy.GetParameterAsText(1)

try:
  # If the output feature class exists, raise an error
  
  if arcpy.Exists(inFeatureClass):
    raise overwriteError(outFeatureClass)
  else:
    # Additional processing steps
    

except overwriteError as e:
  # Use message ID 12, and provide the output feature class
  #  to complete the message.
  
  arcpy.AddIDMessage("Error", 12, str(e))

import arcpy
from arcpy import env

try:
  if arcpy.CheckExtension("3D") == "Available":
    arcpy.CheckOutExtension("3D")
  else:
    # Raise a custom exception
    raise LicenseError
  
  env.workspace = "D:/GrosMorne"
  arcpy.HillShade_3d("WesternBrook", "westbrook_hill", 300)
  arcpy.Aspect_3d("WesternBrook", "westbrook_aspect")

except LicenseError:
  print "3D Analyst license is unavailable" 
except:
  print arcpy.GetMessages(2)
finally:
  # Check in the 3D Analyst extension
  arcpy.CheckInExtension("3D")

import arcpy

fc = "c:/data/city.gdb/streets"

# For each row print the Object ID field, and use the SHAPE@AREA
# token to access geometry properties

with arcpy.da.SearchCursor(fc, ("OID@", "SHAPE@AREA")) as cursor:
  for row in cursor:
    print("Feature {0} has an area of {1}".format(row[0], row[1]))

You will often find it necessary to retrieve or write information to files on your computer. Python has a built-in object type that provides a way to access files for many tasks. We're only going to cover a small subset of the file manipulation functionality provided, but we'll touch on the most commonly used functions including opening and closing files, and reading and writing data to a file.

Python's open() function creates a file object, which serves as a link to a file residing on your computer. You must call the open() function on a file before reading and/or writing data to a file. The first parameter for the open() function is a path to the file you'd like to open. The second parameter corresponds to a mode, which is typically read (r), write (w), or append (a). A value of r indicates that you'd like to open the file for read only operations, while a value of w indicates you'd like to open the file for write operations. In the event that you open a file that already exists for write operations, this will overwrite any data currently in the file, so you must be careful with write mode. Append mode (a) will open a file for write operations, but instead of overwriting any existing data, it will append the new data to the end of the file. The following code example shows the use of the open() function for opening a text file in a read-only mode:

f = open('Wildfires.txt','r')

After you've completed read/write operations on a file, you should always close the file with the close() method.

After a file has been opened, data can be read from it in a number of ways and using various methods. The most typical scenario would be to read data one line at a time from a file through the readline() method. readline() can be used to read the file one line at a time into a string variable. You would need to create a looping mechanism in your Python code to read the entire file line by line. If you would prefer to read the entire file into a variable, you can use the read() method, which will read the file up to the end of file (EOF) marker. You can also use the readlines() method to read the entire contents of a file, separating each line into individual strings, until the EOF is found.

In the following code example, we have opened a text file called Wildfires.txt in read-only mode and used the readlines() method on the file to read its entire contents into a variable called lstFires, which is a Python list containing each line of the file as a separate string value in the list. In this case, the Wildfire.txt file is a comma-delimited text file containing the latitude and longitude of the fire along with the confidence values for each file. We then loop through each line of text in lstFires and use the split() function to extract the values based on a comma as the delimiter, including the latitude, longitude, and confidence values. The latitude and longitude values are used to create a new Point object, which is then inserted into the feature class using an insert cursor:

import arcpy, os
try:
 
  arcpy.env.workspace = "C:/data/WildlandFires.mdb"
  # open the file to read
  f = open('Wildfires.txt','r')

  lstFires = f.readlines()
  cur = arcpy.InsertCursor("FireIncidents")
  
  for fire in lstFires:
    if 'Latitude' in fire:
      continue
    vals = fire.split(",")
    latitude = float(vals[0])
    longitude = float(vals[1])
    confid = int(vals[2])
    pnt = arcpy.Point(longitude,latitude)
    feat = cur.newRow()
    feat.shape = pnt
    feat.setValue("CONFIDENCEVALUE", confid)
    cur.insertRow(feat)
    except:
  print arcpy.GetMessages()
finally:
	del cur
	f.close()

Just as is the case with reading files, there are a number of methods that you can use to write data to a file. The write() function is probably the easiest to use and takes a single string argument and writes it to a file. The writelines() function can be used to write out the contents of a list structure to a file. In the following code example, we have created a list structure called fcList, which contains a list of feature classes. We can write this list to a file using the writelines() method:

outfile = open('c:\\temp\\data.txt','w')
fcList = ["Streams", "Roads", "Counties"]
outfile.writelines(fcList)