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Developing RESTful APIs with Python and Flask

Let's learn how to develop RESTful APIs with Python and Flask.

Last Updated On: September 15, 2022

TL;DR: Throughout this article, we will use Flask and Python to develop a RESTful API. We will create an endpoint that returns static data (dictionaries). Afterward, we will create a class with two specializations and a few endpoints to insert and retrieve instances of these classes. Finally, we will look at how to run the API on a Docker container. The final code developed throughout this article is available in this GitHub repository. I hope you enjoy it!

"Flask allows Python developers to create lightweight RESTful APIs."


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This article is divided into the following sections:

  1. Why Python?
  2. Why Flask?
  3. Bootstrapping a Flask Application
  4. Creating a RESTful Endpoint with Flask
  5. Mapping Models with Python Classes
  6. Serializing and Deserializing Objects with Marshmallow
  7. Dockerizing Flask Applications
  8. Securing Python APIs with Auth0
  9. Next Steps

Why Python?

Nowadays, choosing Python to develop applications is becoming a very popular choice. As StackOverflow recently analyzed, Python is one of the fastest-growing programming languages, having surpassed even Java in the number of questions asked on the platform. On GitHub, the language also shows signs of mass adoption, occupying the second position among the top programming languages in 2021.

Stack Overflow Trends showing Python growth

The huge community forming around Python is improving every aspect of the language. More and more open source libraries are being released to address many different subjects, like Artificial Intelligence, Machine Learning, and web development. Besides the tremendous support provided by the overall community, the Python Software Foundation also provides excellent documentation, where new adopters can learn its essence fast.

Why Flask?

When it comes to web development on Python, there are three predominant frameworks: Django, Flask, and a relatively new player FastAPI. Django is older, more mature, and a little bit more popular. On GitHub, this framework has around 66k stars, 2.2k contributors, ~ 350 releases, and more than 25k forks.

FastAPI is growing at high speed, with 48k stars on Github, 370 contributors, and more than 3.9k forks. This elegant framework built for high-performance and fast-to-code APIs is not one to miss.

Flask, although less popular, is not far behind. On GitHub, Flask has almost 60k stars, ~650 contributors, ~23 releases, and nearly 15k forks.

Even though Django is older and has a slightly more extensive community, Flask has its strengths. From the ground up, Flask was built with scalability and simplicity. Flask applications are known for being lightweight, mainly compared to their Django counterparts. Flask developers call it a microframework, where micro (as explained here) means that the goal is to keep the core simple but extensible. Flask won't make many decisions for us, such as what database to use or what template engine to choose. Lastly, Flask has extensive documentation that addresses everything developers need to start. FastAPI follows a similar "micro" approach to Flask, though it provides more tools like automatic Swagger UI and is an excellent choice for APIs. However, as it is a newer framework, many more resources and libraries are compatible with frameworks like Django and Flask but not with FastAPI.

Being lightweight, easy to adopt, well-documented, and popular, Flask is a good option for developing RESTful APIs.

Bootstrapping a Flask Application

First and foremost, we will need to install some dependencies on our development machine. We will need to install Python 3, Pip (Python Package Index), and Flask.

Installing Python 3

If we are using some recent version of a popular Linux distribution (like Ubuntu) or macOS, we might already have Python 3 installed on our computer. If we are running Windows, we will probably need to install Python 3, as this operating system does not ship with any version.

After installing Python 3 on our machine, we can check that we have everything set up as expected by running the following command:

python --version
# Python 3.8.9

Note that the command above might produce a different output when we have a different Python version. What is important is that you are running at least Python 3.7 or newer. If we get "Python 2" instead, we can try issuing python3 --version. If this command produces the correct output, we must replace all commands throughout the article to use python3 instead of just python.

Installing Pip

Pip is the recommended tool for installing Python packages. While the official installation page states that pip comes installed if we're using Python 2 >= 2.7.9 or Python 3 >= 3.4, installing Python through apt on Ubuntu doesn't install pip. Therefore, let's check if we need to install pip separately or already have it.

# we might need to change pip by pip3
pip --version
# pip 9.0.1 ... (python 3.X)

If the command above produces an output similar to pip 9.0.1 ... (python 3.X), then we are good to go. If we get pip 9.0.1 ... (python 2.X), we can try replacing pip with pip3. If we cannot find Pip for Python 3 on our machine, we can follow the instructions here to install Pip.

Installing Flask

We already know what Flask is and its capabilities. Therefore, let's focus on installing it on our machine and testing to see if we can get a basic Flask application running. The first step is to use pip to install Flask:

# we might need to replace pip with pip3
pip install Flask

After installing the package, we will create a file called and add five lines of code to it. As we will use this file to check if Flask was correctly installed, we don't need to nest it in a new directory.


from flask import Flask

app = Flask(__name__)

def hello_world():
    return "Hello, World!"

These 5 lines of code are everything we need to handle HTTP requests and return a "Hello, World!" message. To run it, we execute the following command:

flask --app hello run

 * Serving Flask app 'hello'
 * Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on
Press CTRL+C to quit

On Ubuntu, we might need to edit the $PATH variable to be able to run flask directly. To do that, let's touch ~/.bash_aliases and then echo "export PATH=$PATH:~/.local/bin" >> ~/.bash_aliases.

After executing these commands, we can reach our application by opening a browser and navigating to or by issuing curl

Hello world with Flask

Virtual environments (virtualenv)

Although PyPA—the Python Packaging Authority group—recommends pip as the tool for installing Python packages, we will need to use another package to manage our project's dependencies. It's true that pip supports package management through the requirements.txt file, but the tool lacks some features required on serious projects running on different production and development machines. Among its issues, the ones that cause the most problems are:

  • pip installs packages globally, making it hard to manage multiple versions of the same package on the same machine.
  • requirements.txt need all dependencies and sub-dependencies listed explicitly, a manual process that is tedious and error-prone.

To solve these issues, we are going to use Pipenv. Pipenv is a dependency manager that isolates projects in private environments, allowing packages to be installed per project. If you're familiar with NPM or Ruby's bundler, it's similar in spirit to those tools.

pip install pipenv

Now, to start creating a serious Flask application, let's create a new directory that will hold our source code. In this article, we will create Cashman, a small RESTful API that allows users to manage incomes and expenses. Therefore, we will create a directory called cashman-flask-project. After that, we will use pipenv to start our project and manage our dependencies.

# create our project directory and move to it
mkdir cashman-flask-project && cd cashman-flask-project

# use pipenv to create a Python 3 (--three) virtualenv for our project
pipenv --three

# install flask a dependency on our project
pipenv install flask

The second command creates our virtual environment, where all our dependencies get installed, and the third will add Flask as our first dependency. If we check our project's directory, we will see two new files:

  1. Pipfile contains details about our project, such as the Python version and the packages needed.
  2. Pipenv.lock contains precisely what version of each package our project depends on and its transitive dependencies.

Python modules

Like other mainstream programming languages, Python also has the concept of modules to enable developers to organize source code according to subjects/functionalities. Similar to Java packages and C# namespaces, modules in Python are files organized in directories that other Python scripts can import. To create a module on a Python application, we need to create a folder and add an empty file called

Let's create our first module on our application, the main module, with all our RESTful endpoints. Inside the application's directory, let's create another one with the same name, cashman. The root cashman-flask-project directory created before will hold metadata about our project, like what dependencies it has, while this new one will be our module with our Python scripts.

# create source code's root
mkdir cashman && cd cashman

# create an empty file

Inside the main module, let's create a script called In this script, we will define the first endpoint of our application.

from flask import Flask
app = Flask(__name__)

def hello_world():
    return "Hello, World!"

As in the previous example, our application returns a "Hello, world!" message. We will start improving it in a second, but first, let's create an executable file called in the root directory of our application.

# move to the root directory
cd ..

# create the file

# make it executable
chmod +x

The goal of this file is to facilitate the start-up of our application. Its source code will be the following:

export FLASK_APP=./cashman/
pipenv run flask --debug run -h

The first command defines the main script to be executed by Flask. The second command runs our Flask application in the context of the virtual environment listening to all interfaces on the computer (-h

Note: we are setting flask to run in debug mode to enhance our development experience and activate the hot reload feature, so we don't have to restart the server each time we change the code. If you run Flask in production, we recommend updating these settings for production.

To check that this script is working correctly, we run ./ to get similar results as when executing the "Hello, world!" application.

 * Serving Flask app './cashman/'
 * Debug mode: on
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on all addresses (
 * Running on
 * Running on
Press CTRL+C to quit

Creating a RESTful Endpoint with Flask

Now that our application is structured, we can start coding some relevant endpoints. As mentioned before, the goal of our application is to help users to manage incomes and expenses. We will begin by defining two endpoints to handle incomes. Let's replace the contents of the ./cashman/ file with the following:

from flask import Flask, jsonify, request

app = Flask(__name__)

incomes = [
    { 'description': 'salary', 'amount': 5000 }

def get_incomes():
    return jsonify(incomes)

@app.route('/incomes', methods=['POST'])
def add_income():
    return '', 204

Since improving our application, we have removed the endpoint that returned "Hello, world!" to users. In its place, we defined an endpoint to handle HTTP GET requests to return incomes and another endpoint to handle HTTP POST requests to add new ones. These endpoints are annotated with @app.route to define routes listening to requests on the /incomes endpoint. Flask provides great documentation on what exactly this does.

To facilitate the process, we currently manipulate incomes as dictionaries. However, we will soon create classes to represent incomes and expenses.

To interact with both endpoints that we have created, we can start our application and issue some HTTP requests:

# start the cashman application
./ &

# get incomes
curl http://localhost:5000/incomes

# add new income
curl -X POST -H "Content-Type: application/json" -d '{
  "description": "lottery",
  "amount": 1000.0
}' http://localhost:5000/incomes

# check if lottery was added
curl localhost:5000/incomes

Interacting with Flask endpoints

Mapping Models with Python Classes

Using dictionaries in a simple use case like the one above is enough. However, for more complex applications that deal with different entities and have multiple business rules and validations, we might need to encapsulate our data into Python classes.

We will refactor our application to learn the process of mapping entities (like incomes) as classes. The first thing that we will do is create a submodule to hold all our entities. Let's create a model directory inside the cashman module and add an empty file called on it.

# create model directory inside the cashman module
mkdir -p cashman/model

# initialize it as a module
touch cashman/model/

Mapping a Python superclass

We will create three classes in this new module/directory: Transaction, Income, and Expense. The first class will be the base for the two others, and we will call it Transaction. Let's create a file called in the model directory with the following code:

import datetime as dt

from marshmallow import Schema, fields

class Transaction(object):
    def __init__(self, description, amount, type):
        self.description = description
        self.amount = amount
        self.created_at =
        self.type = type

    def __repr__(self):
        return '<Transaction(name={self.description!r})>'.format(self=self)

class TransactionSchema(Schema):
    description = fields.Str()
    amount = fields.Number()
    created_at = fields.Date()
    type = fields.Str()

Besides the Transaction class, we also defined a TransactionSchema. We will use the latter to deserialize and serialize instances of Transaction from and to JSON objects. This class inherits from another superclass called Schema that belongs on a package not yet installed.

# installing marshmallow as a project dependency
pipenv install marshmallow

Marshmallow is a popular Python package for converting complex datatypes, such as objects, to and from built-in Python datatypes. We can use this package to validate, serialize, and deserialize data. We won't dive into validation in this article, as it will be the subject of another one. Though, as mentioned, we will use marshmallow to serialize and deserialize entities through our endpoints.

Mapping Income and Expense as Python Classes

To keep things more organized and meaningful, we won't expose the Transaction class on our endpoints. We will create two specializations to handle the requests: Income and Expense. Let's make a file called inside the model module with the following code:

from marshmallow import post_load

from .transaction import Transaction, TransactionSchema
from .transaction_type import TransactionType

class Income(Transaction):
    def __init__(self, description, amount):
        super(Income, self).__init__(description, amount, TransactionType.INCOME)

    def __repr__(self):
        return '<Income(name={self.description!r})>'.format(self=self)

class IncomeSchema(TransactionSchema):
    def make_income(self, data, **kwargs):
        return Income(**data)

The only value that this class adds for our application is that it hardcodes the type of transaction. This type is a Python enumerator, which we still have to create, that will help us filter transactions in the future. Let's create another file, called, inside model to represent this enumerator:

from enum import Enum

class TransactionType(Enum):

The code of the enumerator is quite simple. It just defines a class called TransactionType that inherits from Enum and that defines two types: INCOME and EXPENSE.

Lastly, let's create the class that represents expenses. To do that, let's add a new file called inside model with the following code:

from marshmallow import post_load

from .transaction import Transaction, TransactionSchema
from .transaction_type import TransactionType

class Expense(Transaction):
    def __init__(self, description, amount):
        super(Expense, self).__init__(description, -abs(amount), TransactionType.EXPENSE)

    def __repr__(self):
        return '<Expense(name={self.description!r})>'.format(self=self)

class ExpenseSchema(TransactionSchema):
    def make_expense(self, data, **kwargs):
        return Expense(**data)

Similar to Income, this class hardcodes the type of the transaction, but now it passes EXPENSE to the superclass. The difference is that it transforms the given amount to be negative. Therefore, no matter if the user sends a positive or a negative value, we will always store it as negative to facilitate calculations.

Serializing and Deserializing Objects with Marshmallow

With the Transaction superclass and its specializations adequately implemented, we can now enhance our endpoints to deal with these classes. Let's replace ./cashman/ contents to:

from flask import Flask, jsonify, request

from cashman.model.expense import Expense, ExpenseSchema
from cashman.model.income import Income, IncomeSchema
from cashman.model.transaction_type import TransactionType

app = Flask(__name__)

transactions = [
    Income('Salary', 5000),
    Income('Dividends', 200),
    Expense('pizza', 50),
    Expense('Rock Concert', 100)

def get_incomes():
    schema = IncomeSchema(many=True)
    incomes = schema.dump(
        filter(lambda t: t.type == TransactionType.INCOME, transactions)
    return jsonify(incomes)

@app.route('/incomes', methods=['POST'])
def add_income():
    income = IncomeSchema().load(request.get_json())
    return "", 204

def get_expenses():
    schema = ExpenseSchema(many=True)
    expenses = schema.dump(
        filter(lambda t: t.type == TransactionType.EXPENSE, transactions)
    return jsonify(expenses)

@app.route('/expenses', methods=['POST'])
def add_expense():
    expense = ExpenseSchema().load(request.get_json())
    return "", 204

if __name__ == "__main__":

The new version that we just implemented starts by redefining the incomes variable into a list of Expenses and Incomes, now called transactions. Besides that, we have also changed the implementation of both methods that deal with incomes. For the endpoint used to retrieve incomes, we defined an instance of IncomeSchema to produce a JSON representation of incomes. We also used filter to extract incomes only from the transactions list. In the end we send the array of JSON incomes back to users.

The endpoint responsible for accepting new incomes was also refactored. The change on this endpoint was the addition of IncomeSchema to load an instance of Income based on the JSON data sent by the user. As the transactions list deals with instances of Transaction and its subclasses, we just added the new Income in that list.

The other two endpoints responsible for dealing with expenses, get_expenses and add_expense, are almost copies of their income counterparts. The differences are:

  • instead of dealing with instances of Income, we deal with instances of Expense to accept new expenses,
  • and instead of filtering by TransactionType.INCOME, we filter by TransactionType.EXPENSE to send expenses back to the user.

This finishes the implementation of our API. If we run our Flask application now, we will be able to interact with the endpoints, as shown here:

# start the application

# get expenses
curl http://localhost:5000/expenses

# add a new expense
curl -X POST -H "Content-Type: application/json" -d '{
    "amount": 20,
    "description": "lottery ticket"
}' http://localhost:5000/expenses

# get incomes
curl http://localhost:5000/incomes

# add a new income
curl -X POST -H "Content-Type: application/json" -d '{
    "amount": 300.0,
    "description": "loan payment"
}' http://localhost:5000/incomes

Dockerizing Flask Applications

As we are planning to eventually release our API in the cloud, we are going to create a Dockerfile to describe what is needed to run the application on a Docker container. We need to install Docker on our development machine to test and run dockerized instances of our project. Defining a Docker recipe (Dockerfile) will help us run the API in different environments. That is, in the future, we will also install Docker and run our program on environments like production and staging.

Let's create the Dockerfile in the root directory of our project with the following code:

# Using lightweight alpine image
FROM python:3.8-alpine

# Installing packages
RUN apk update
RUN pip install --no-cache-dir pipenv

# Defining working directory and adding source code
WORKDIR /usr/src/app
COPY Pipfile Pipfile.lock ./
COPY cashman ./cashman

# Install API dependencies
RUN pipenv install --system --deploy

# Start app
ENTRYPOINT ["/usr/src/app/"]

The first item in the recipe defines that we will create our Docker container based on the default Python 3 Docker image. After that, we update APK and install pipenv. Having pipenv, we define the working directory we will use in the image and copy the code needed to bootstrap and run the application. In the fourth step, we use pipenv to install all our Python dependencies. Lastly, we define that our image will communicate through port 5000 and that this image, when executed, needs to run the script to start Flask.

Note: For our Dockerfile, we use Python version 3.8, however, depending on your system configuration, pipenv may have set a different version for Python in the file Pipfile. Please make sure that the Python version in both Dockerfile and Pipfile are aligned, or the docker container won't be able to start the server.

To create and run a Docker container based on the Dockerfile that we created, we can execute the following commands:

# build the image
docker build -t cashman .

# run a new docker container named cashman
docker run --name cashman \
    -d -p 5000:5000 \

# fetch incomes from the dockerized instance
curl http://localhost:5000/incomes/

The Dockerfile is simple but effective, and using it is similarly easy. With these commands and this Dockerfile, we can run as many instances of our API as we need with no trouble. It's just a matter of defining another port on the host or even another host.

Securing Python APIs with Auth0

Securing Python APIs with Auth0 is very easy and brings a lot of great features to the table. With Auth0, we only have to write a few lines of code to get:

For example, to secure Python APIs written with Flask, we can simply create a requires_auth decorator:

# Format error response and append status code

def get_token_auth_header():
    """Obtains the access token from the Authorization Header
    auth = request.headers.get("Authorization", None)
    if not auth:
        raise AuthError({"code": "authorization_header_missing",
                            "Authorization header is expected"}, 401)

    parts = auth.split()

    if parts[0].lower() != "bearer":
        raise AuthError({"code": "invalid_header",
                            "Authorization header must start with"
                            " Bearer"}, 401)
    elif len(parts) == 1:
        raise AuthError({"code": "invalid_header",
                        "description": "Token not found"}, 401)
    elif len(parts) > 2:
        raise AuthError({"code": "invalid_header",
                            "Authorization header must be"
                            " Bearer token"}, 401)

    token = parts[1]
    return token

def requires_auth(f):
    """Determines if the access token is valid
    def decorated(*args, **kwargs):
        token = get_token_auth_header()
        jsonurl = urlopen("https://"+AUTH0_DOMAIN+"/.well-known/jwks.json")
        jwks = json.loads(
        unverified_header = jwt.get_unverified_header(token)
        rsa_key = {}
        for key in jwks["keys"]:
            if key["kid"] == unverified_header["kid"]:
                rsa_key = {
                    "kty": key["kty"],
                    "kid": key["kid"],
                    "use": key["use"],
                    "n": key["n"],
                    "e": key["e"]
        if rsa_key:
                payload = jwt.decode(
            except jwt.ExpiredSignatureError:
                raise AuthError({"code": "token_expired",
                                "description": "token is expired"}, 401)
            except jwt.JWTClaimsError:
                raise AuthError({"code": "invalid_claims",
                                    "incorrect claims,"
                                    "please check the audience and issuer"}, 401)
            except Exception:
                raise AuthError({"code": "invalid_header",
                                    "Unable to parse authentication"
                                    " token."}, 400)

   = payload
            return f(*args, **kwargs)
        raise AuthError({"code": "invalid_header",
                        "description": "Unable to find appropriate key"}, 400)
    return decorated

Then use it in our endpoints:

# Controllers API

# This doesn't need authentication
@cross_origin(headers=['Content-Type', 'Authorization'])
def ping():
    return "All good. You don't need to be authenticated to call this"

# This does need authentication
@cross_origin(headers=['Content-Type', 'Authorization'])
def secured_ping():
    return "All good. You only get this message if you're authenticated"

To learn more about securing Python APIs with Auth0, take a look at this tutorial. Alongside with tutorials for backend technologies (like Python, Java, and PHP), the Auth0 Docs webpage also provides tutorials for Mobile/Native apps and Single-Page applications.

Next Steps

In this article, we learned about the basic components needed to develop a well-structured Flask application. We looked at how to use pipenv to manage the dependencies of our API. After that, we installed and used Flask and Marshmallow to create endpoints capable of receiving and sending JSON responses. In the end, we also looked at how to dockerize the API, which will facilitate the release of the application to the cloud.

Although well structured, our API is not that useful yet. Among the things that we can improve, we are going to cover the following topics in the following article:

Stay tuned!

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