Development
When you create a big python application, you can began to run into challenges with longer scripts that extended beyond the expiration of a single JWT.
To elegantly solve this, You can use decorators to check the token’s expiration and request a new token if necessary. This article goes over the framework I set up so that you can apply a similar mechanism in your own scripts.
Setting the Scene
import time
import requests
class myAPI():
host = None
key = None
secret = None
access_token = None
access_token_expiration = None
def __init__(self,host,key,secret):
# the function that is executed when
# an instance of the class is created
pass
def getAccessToken(self):
# the function that is
# used to request the JWT
pass
class Decorators():
@staticmethod
def refreshToken(decorated):
# the function that is used to check
# the JWT and refresh if necessary
pass
Our class will work by requiring the fields necessary to request the JWT. Without access, there is nothing more our class will do.
When I was creating a one script, which uses JWT for authentication, I began to run into challenges with longer scripts that extended beyond the expiration of a single JWT.
To elegantly solve this, I used decorators to check the token’s expiration and request a new token if necessary. This article goes over the framework I set up so that you can apply a similar mechanism in your own scripts.
Setting the Scene
To get started, I’ve outlined all the parts needed to set up our token-refreshing framework.
Recently, I come across a medium post on announcement off Official Release of bar_chart_race by Ted Petrou. In his article, he provides an excellent tutorial on how to create Bar Chart Race using bar_chart_race package. Check out the official document here .
In our example we use a World Population from 1955 to 2020 dataset from kaggle or you can directly download dataset here .
Installation of Bar Chart Race package
pip3 install bar_chart_race pandas
or using anaconda:
In this tutorial, I would like to introduce a very cool Python hand-painted style visualization package: cutecharts .
Different from the common charts such as Matplotlib and seaborn, this package can be used to generate the following kinds of charts that look like hand drawn, and the effect may be better in some scenarios. Cute charts are also interactive and dynamic. Whenever the mouse is hovering on the chart, the numbers show up.
