Python Language Features

While you do not need to know everything about Python syntax, you should know enough to recognize the particular language features that generated code is using.

Modules

One of the most important facets of Python is the ecosystem of modules that people have created to solve a diverse array of problems. You can import these modules into your code so that you don’t have to write your own solution. Python’s standard library has many modules for working with the most common types of problems. Other modules need to be downloaded and installed separately. If you have ArcGIS Pro installed, you already have Python environment with about 200 additional modules installed.

You can import all the functionality of a module with a simple import statement, which will look something like:

import csv

You can also import just a part of a module’s functionality by specifying those parts, which will look something like:

from arcgis import features

You can give a module an alias when you import it. Aliases make it easier to refer to a module, and look something like:

import pandas as pd

You should be able to recognize what it means when the model produces code that imports a module. Because the capabilities of a model cannot be used unless the module is imported, you will also need to recognize when the model has created code that makes use of a module, but has not also written the appropriate import statement.

Data types

Different types of values have different capabilities. For example, you can’t multiply a piece of text by another piece of text. You should recognize the data types created by the model. The table below shows the most common general Python data types.

Data type	Description	Examples
Integer	Whole numbers	`80`, `-2`
Float	Any numeric value	`9.84`, `-1.0`
Boolean	Logical values	`True`, `False`
String	A set of characters, generally representing text values. Enclosed in `"` or `'` quotes.	`"string"`, `'8'`, `"False"`
List	Mutable (changeable) ordered sequence of values. Enclosed in `[]` brackets	`[1, 2, 3, 4, 5, 6]`
Tuple	Immutable (unchangeable) ordered sequence of values. Enclosed in `()` parentheses.	`(1, 2, 3, 4, 5, 6)`
Dictionary	Collection of key: value pairs. Enclosed in `{}` braces. Keys are usually strings or numbers. Values can be anything.	`{"MN": 1, "WI": 2}`

Lists, tuples, and dictionaries are containers for other values. Those values could be any other types, even other lists, tuples, or dictionaries. You can easily have a list of dictionaries, where each value in each dictionary is itself another list of dictionaries.

Variables

Some values, like a long list or heavily nested dictionary, are very complex and hard to work with directly. It can be convenient to give values a name and refer to that value by its name instead. These variable names can also imbue a value with meaning for anyone reading the code. A good name can help us understand better what the code is doing. Values are assigned to variable names using the assignment operator =. For example:

capital = "Saint Paul"
population = 5717000
is_wisconsin = False

Blocks

A block is a chunk of connected code that performs a task. You can recognize a block because the first line will end with a colon (:) and the rest of the block will be indented one level relative to the first line. It is possible to have blocks inside of blocks.

Conditional blocks

Allow you to deal with branching logic in your code. Conditional statements check the truth value of a statement, and if it is true, execute the code in the rest of the block. You can recognize a conditional by the use of the if, elif (else if) and else keywords. Statements typically use the logical operators

Operator	Meaning
`>`	Greater than
`<`	Less than
`==`	Equal to
`>=`	Greater than or equal to
`<=`	Less than or equal to
`!=`	Not equal to

For example:

if capital == "Saint Paul":
    state = "Minnesota"
elif capital == "Madison":
    state = "Wisconsin"
else: 
    state = "Unknown"

This code snippet checks the value of capital against different possibilities. It first checks to see if the value is equal to "Saint Paul". If so, it sets the value of state to "Minnesota". If not, it checks to see if the value is equal to "Madison". If so, it sets the value of state to "Wisconsin". If capital is any other value, it sets the value of state to "Unknown".

For loops

Allow for repeated code execution. You can recognize a for loop by the use of the for and in keywords. For example:

seq = (1, 2, 3, 4, 5, 6)
big_seq = []
for num in seq:
    if num > 4:
        big_seq.append(num)

This code snippet starts with a tuple seq and an empty list big_seq. It looks at every value inside seq in order. If the value is bigger than 4, it appends that value to big_seq. After running this code, the value of big_seq will be [5, 6].

Functions

Encapsulate some process so that the process can easily be repeated without having to write the code to perform that process again. Functions must be defined before they can be used. You can recognize a function definition by the use of the def keyword. A function will typically produce some value, specified by the return keyword. For example:

def dist(x1, y1, x2, y2):
    a_sq = (x2 - x1)**2
    b_sq = (y2 - y1)**2
    return (a_sq + b_sq)**0.5

This code snippet creates a function that calculates the Euclidean distance between two points, given their x,y coordinates. It uses to Pythagorean formula: first it calculates the square of the difference between the two x-coordinates (the a_sq value), then it calculates the square of the difference between the two y-coordinates (the b_sq value). Then it returns the square root of the sum of those squared differences.

After a function has been defined, it must be called in order to execute it. You can recognize a function is being called by the parentheses after its name. There may or may not be any values inside the parentheses. For example:

dist(0, 0, 1, 1)

This code snippet calls the dist function to find the distance between the point at coordinates 0,0 and the point at coordinates 1,1. It will return a value of approximately 1.41.

Files

Sometimes the data we want to work with is stored outside the script, in files. You will need to open those files in order to work with the data inside them. You will recognize code that opens a file by the use of the open function, which specifies the file’s path and a mode ('r' for read, 'w' for overwrite, 'a' for append). As a best practice, opening files should be done in a context manager block, which you can recognize by the use of the with and as keywords. For example:

text_file = r'C:\temp\text.txt'
with open(text_file, 'r') as f:
    for line in f:
        print(line)

This code snippet will open the file located at C:\temp\text.txt and open it for reading. The open file is stored in the f variable. It loops through every line in the file and prints out that line.

Python can read text files, but some file types may require additional modules to read them correctly. For example, the csv module is helpful for reading csv files as tabular data.

import csv
csv_file = r'C:\temp\data.csv'
with open(csv_file, 'r') as f:
    for row in csv.reader(f):
        print(row)

This code works similarly to the code above, but reads a row of table values, instead of a line of text.

Exercise: Identify programming features

Open the Python Feature Quiz in a new browser tab.
Answer the questions about Python features until you either feel confident that you can recognize all the features or you get bored.
Open the source code for the quiz in a new browser tab.
Examine the code. Which features do you recognize? Which features do you not recognize?

Have the model summarize code features

If you have access to an LLM interface, like Copilot or ChatGPT, you can have the model summarize a chunk of code that uses Python features you do not recognize. LLMs tend to be much better at summarizing content than they are at generating it themselves, and the summaries are typically reliable. You can have the model summarize the code it generated for you, or code snippets you have found elsewhere.