Define your constants as Enums

 Today, we talk about Enums. More specifically about their uses and why they are useful also in the context of data science projects.

So... let's get started!

What are Enums?

 Enums, or enumerations, are a very useful data type in Python that allow you to define a set of related values that can be used throughout your code. They are useful in a variety of situations, and can make your code more readable, maintainable, and efficient.

General benefits of using Enums in Python

Enums improve the readability of your code

^   back to top   ^

 One of the main benefits of using enums is that they can make your code more readable, especially if you use them to define constants that are used throughout your code. For example, consider the following code:

# Without enums
MAX_RETRIES = 5

def retry_operation(operation, max_retries=MAX_RETRIES):
    for i in range(max_retries):
        try:
            return operation()
        except Exception:
            pass
    raise Exception("Failed after {} retries".format(max_retries))

# With enums
from enum import Enum

class RetryPolicy(Enum):
    MAX_RETRIES = 5

def retry_operation(operation, policy=RetryPolicy.MAX_RETRIES):
    for i in range(policy.value):
        try:
            return operation()
        except Exception:
            pass
    raise Exception("Failed after {} retries".format(policy.value))

 In the first example, the constant MAX_RETRIES is defined at the top of the file, and its value is used in the retry_operation function. This can be confusing to readers of the code, as the value of MAX_RETRIES is not immediately apparent. In contrast, the second example defines an Enum called RetryPolicy, which includes a value called MAX_RETRIES. This value is used in the retry_operation function, making the code much more readable and intuitive.

Enums improve the maintainability of your code

^   back to top   ^

 Enums can also make your code more maintainable, as they allow you to define a set of related values in one place, rather than scattering them throughout your code. This can be especially helpful if you need to change the values of these constants in the future. For example, consider the following code:

# Without enums
SUCCESS = "SUCCESS"
ERROR = "ERROR"

def process_request(request):
    if request.status == SUCCESS:
        return "Request processed successfully"
    elif request.status == ERROR:
        return "Request processing failed"
    else:
        return "Request status unknown"

# With enums
from enum import Enum

class RequestStatus(Enum):
    SUCCESS = "SUCCESS"
    ERROR = "ERROR"

def process_request(request, status=RequestStatus):
    if request.status == status.SUCCESS:
        return "Request processed successfully"
    elif request.status == status.ERROR:
        return "Request processing failed"
    else:
        return "Request status unknown"

 In the first example, the constants SUCCESS and ERROR are defined at the top of the file and used in the process_request function. If you need to change the values of these constants in the future, you will need to update every instance of these constants throughout your code. In contrast, the second example defines an Enum called RequestStatus

Enums improve type safety of your code

^   back to top   ^

 Enums can also improve the type safety of your code, as they allow you to define a fixed set of values that a variable can take on. This can be especially helpful in cases where you need to ensure that a variable is assigned a specific value, or a set of values. For example, consider the following code:

# Without enums
FRUIT_APPLE = "apple"
FRUIT_ORANGE = "orange"
FRUIT_BANANA = "banana"

def get_fruit_price(fruit):
    if fruit == FRUIT_APPLE:
        return 0.5
    elif fruit == FRUIT_ORANGE:
        return 0.25
    elif fruit == FRUIT_BANANA:
        return 0.75
    else:
        raise ValueError("Invalid fruit")

# With enums
from enum import Enum

class Fruit(Enum):
    APPLE = "apple"
    ORANGE = "orange"
    BANANA = "banana"

def get_fruit_price(fruit: Fruit):
    if fruit == Fruit.APPLE:
        return 0.5
    elif fruit == Fruit.ORANGE:
        return 0.25
    elif fruit == Fruit.BANANA:
        return 0.75

 In the first example, the constants FRUIT_APPLE, FRUIT_ORANGE, and FRUIT_BANANA are defined at the top of the file and used in the get_fruit_price function. This can be problematic, as there is nothing to prevent someone from passing an invalid value to the fruit argument. In contrast, the second example defines an Enum called Fruit, which includes values for APPLE, ORANGE, and BANANA. By annotating the fruit argument with the Fruit type, we can ensure that only valid values are passed to the function.

Enums improve performance (in some cases)

^   back to top   ^

 Enums can also offer improved performance compared to other data types, such as strings or integers. This is because enums are stored as integers under the hood, and are much faster to compare than strings or other objects. For example, consider the following code:

# Without enums
def compare_strings(s1, s2):
    return s1 == s2

# With enums
from enum import Enum

class StringEnum(Enum):
    S1 = "s1"
    S2 = "s2"

def compare_strings(s1: StringEnum, s2: StringEnum):
    return s1 == s2

 In the first example, the compare_strings function compares two strings using the == operator. This can be slow, especially if the strings are long or complex. In contrast, the second example defines an Enum called StringEnum, which includes values for S1 and S2. By annotating the arguments with the StringEnum type, we can ensure that only valid values are passed to the function. Additionally, because enums are stored as integers under the hood, the comparison is much faster than if we were comparing two strings.

 In conclusion, enums are a very useful data type in Python that can improve the readability, maintainability, type safety, and performance of your code. They allow you to define a set of related values that can be used throughout your code, and are especially useful when you need to define constants or a fixed set of values. By using enums in your code, you can make your code more intuitive, maintainable, and efficient, and ensure that your variables always have the correct values.

Is there any use of Enums in data science projects?

^   back to top   ^

 Enums are also very useful in the context of data science, because they allow you to define a fixed set of values that can be used throughout your code. This can be especially helpful in cases where you need to ensure that a variable is assigned a specific value, or a set of values.

 For example, consider the code below that processes a dataset of customer data. The dataset includes a column called customer_type, which can take on one of three values: "standard", "premium", or "platinum". Without using enums, you might define these values as constants at the top of your script, like this:

CUSTOMER_TYPE_STANDARD = "standard"
CUSTOMER_TYPE_PREMIUM = "premium"
CUSTOMER_TYPE_PLATINUM = "platinum"

def process_customer_data(customer_type):
    if customer_type == CUSTOMER_TYPE_STANDARD:
        # do something
        pass
    elif customer_type == CUSTOMER_TYPE_PREMIUM:
        # do something
        pass
    elif customer_type == CUSTOMER_TYPE_PLATINUM:
        # do something
        pass

 This approach works, but it can be confusing to readers of the code, as the values of the constants are not immediately apparent, especially if you string along a lot of string descriptors. The becomes even more problematic, the more constants define within different locations of your code. Additionally, as already hinted at above, if you need to change the values of these constants in the future, you will need to update every instance of these constants throughout your code.

A better approach is to use enums to define these values. For example:

from enum import Enum

class CustomerType(Enum):
    STANDARD = "standard"
    PREMIUM = "premium"
    PLATINUM = "platinum"

def process_customer_data(customer_type: CustomerType):
    if customer_type == CustomerType.STANDARD:
        # do something
        pass
    elif customer_type == CustomerType.PREMIUM:
        # do something
        pass
    elif customer_type == CustomerType.PLATINUM:
        # do something
        pass

 This approach is much more readable and intuitive, as the values of the CustomerType enum are clearly defined in one place. Additionally, if you need to change the values of these constants in the future, you only need to update the CustomerType enum, rather than searching for and updating every instance of the constants throughout your code.

 Another advantage of using Enums in data science projects is related to the already previously discussed performance improvements.

Summing up

^   back to top   ^

 Enums are a very useful data type in the context of data science scripts, as they allow you to define a fixed set of values that can be used throughout your code. They can improve the readability, maintainability, and performance of your scripts, and ensure that your variables always have the correct values. By using enums in your data science scripts, you can make your code more intuitive, maintainable, and efficient, and help ensure the quality and accuracy of your results.