1624603716
A light-weight python wrapper for the Datawrapper API (v3). While it is not developed by Datawrapper officially, you can use it with your API credentials from datawrapper.de
pandas.DataFrame
in one call!pip install -U datawrapper
or install with Poetry
poetry add datawrapper
Makefile
contains many functions for fast assembling and convenient work.
1. Download Poetry
make download-poetry
2. Install all dependencies and pre-commit hooks
make install
If you do not want to install pre-commit hooks, run the command with the NO_PRE_COMMIT flag:
make install NO_PRE_COMMIT=1
3. Check the security of your code
make check-safety
This command launches a Poetry
and Pip
integrity check as well as identifies security issues with Safety
and Bandit
. By default, the build will not crash if any of the items fail. But you can set STRICT=1
for the entire build, or you can configure strictness for each item separately.
make check-safety STRICT=1
or only for safety
:
make check-safety SAFETY_STRICT=1
multiple
make check-safety PIP_STRICT=1 SAFETY_STRICT=1
List of flags for
check-safety
(can be set to1
or0
):STRICT
,POETRY_STRICT
,PIP_STRICT
,SAFETY_STRICT
,BANDIT_STRICT
.
4. Check the codestyle
The command is similar to check-safety
but to check the code style, obviously. It uses Black
, Darglint
, Isort
, and Mypy
inside.
make check-style
It may also contain the STRICT
flag.
make check-style STRICT=1
List of flags for
check-style
(can be set to1
or0
):STRICT
,BLACK_STRICT
,DARGLINT_STRICT
,ISORT_STRICT
,MYPY_STRICT
.
5. Run all the codestyle formaters
Codestyle uses pre-commit
hooks, so ensure youāve run make install
before.
make codestyle
6. Run tests
make test
7. Run all the linters
make lint
the same as:
make test && make check-safety && make check-style
List of flags for
lint
(can be set to1
or0
):STRICT
,POETRY_STRICT
,PIP_STRICT
,SAFETY_STRICT
,BANDIT_STRICT
,BLACK_STRICT
,DARGLINT_STRICT
,ISORT_STRICT
,MYPY_STRICT
.
8. Build docker
make docker
which is equivalent to:
make docker VERSION=latest
More information here.
9. Cleanup docker
make clean_docker
or to remove all build
make clean
More information here.
You can see the list of available releases on the GitHub Releases page.
We follow Semantic Versions specification.
We use Release Drafter
. As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when youāre ready. With the categories option, you can categorize pull requests in release notes using labels.
For Pull Request this labels are configured, by default:
Label | Title in Releases |
---|---|
enhancement , feature |
š Features |
bug , refactoring , bugfix , fix |
š§ Fixes & Refactoring |
build , ci , testing |
š¦ Build System & CI/CD |
breaking |
š„ Breaking Changes |
documentation |
š Documentation |
dependencies |
ā¬ļø Dependencies updates |
You can update it in release-drafter.yml
.
GitHub creates the bug
, enhancement
, and documentation
labels for you. Dependabot creates the dependencies
label. Create the remaining labels on the Issues tab of your GitHub repository, when you need them.
@misc{datawrapper,
author = {chekos},
title = {A light-weight python wrapper for the Datawrapper API (v3). While it is not developed by Datawrapper officially, you can use it with your API credentials from datawrapper.de},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/chekos/datawrapper}}
}
This project was generated with python-package-template
.
Author: chekos
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/chekos/Datawrapper
License: This project is licensed under the terms of the MIT
license. See LICENSE for more details.
#data-visualization #python #api
1619518440
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
ā¦
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
1619510796
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
1595396220
As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.
Typical prevention mechanisms like Captchas and browser fingerprinting wonāt work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.
Most APIs provide access to resources that are lists of entities such as /users
or /widgets
. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:
First Call: GET /items?skip=0&take=10
Second Call: GET /items?skip=10&take=10
However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped
A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:
skip = 0
while True: response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip), headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]}) print("Fetched 10 items") sleep(randint(100,1000)) skip += 10
To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as ātouched 1,000,000 items in a one hour periodā. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.
Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.
The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.
APIs are used in a way that increases the probability credentials are leaked:
If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if itās your own and help them by adding guards that prevent accidental key exposure.
The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.
The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)
APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.
The magical part about APIs is almost every access requires an API Key. If a request doesnāt have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response.
There are a variety of algorithms to do this such as leaky bucket and fixed window counters.
APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.
Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs
APIs provide access to dynamic data thatās scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you donāt cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.
#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys
1601381326
Weāve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.
The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:
With regards to how the APIs are shared:
#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway
1604399880
Iāve been working with Restful APIs for some time now and one thing that I love to do is to talk about APIs.
So, today I will show you how to build an API using the API-First approach and Design First with OpenAPI Specification.
First thing first, if you donāt know whatās an API-First approach means, it would be nice you stop reading this and check the blog post that I wrote to the Farfetchs blog where I explain everything that you need to know to start an API using API-First.
Before you get your hands dirty, letās prepare the ground and understand the use case that will be developed.
If you desire to reproduce the examples that will be shown here, you will need some of those items below.
To keep easy to understand, letās use the Todo List App, it is a very common concept beyond the software development community.
#api #rest-api #openai #api-first-development #api-design #apis #restful-apis #restful-api