Skip to main content

Python Basics- hands on with strings

In our first exercise we will do some hands on with Python's in built methods.

         Store your own version of the message "Hello World" in a variable, and print it.


       Store your first name, in lowercase, in a variable.Using that one variable, print your name in lowercase, Titlecase, and UPPERCASE.



Choose a person you look up to. Store their first and last names in separate variables.
Use concatenation to make a sentence about this person, and store that sentence in a variable.-
Print the sentence.



Store your first name in a variable, but include at least two kinds of whitespace on each side of your name.
Print your name as it is stored.
Print your name with whitespace stripped from the left side, then from the right side, then from both sides.


So keep practicing by your own with above examples in your notebook and comment if you face any issue.

Comments

Post a Comment

Popular posts from this blog

Learn the fastest way to build data apps

Another post starts with you beautiful people! I hope you have enjoyed and learned something new from my previous three posts about machine learning model deployment. In one post we have learned  How to deploy a model as FastAPI?  I n the second post, we have learned  How to deploy a deep learning model as RestAPI ? and in the third post, we have also learned  How to scale your deep learning model API?   If you are following my blog posts, you have seen how easily you have transit yourselves from aspiring to a mature data scientist. In this new post, I am going to share a new framework-  Streamlit which will help you to easily create a beautiful app with Python only. I will show here how had I used the Streamlit framework to create an app for my YOLOv3 custom model. What is Streamlit? Streamlit’s open-source app framework is the easiest way for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours!...
48 comments
Read more

Machine Learning-Cross Validation & ROC curve

Another post starts with you beautiful people! Hope you enjoyed my previous post about improving your model performance by  confusion metrix . Today we will continue our performance improvement journey and will learn about Cross Validation (k-fold cross validation) & ROC in Machine Learning. A common practice in data science competitions is to iterate over various models to find a better performing model. However, it becomes difficult to distinguish whether this improvement in score is coming because we are capturing the relationship better or we are just over-fitting the data. To find the right answer of this question, we use cross validation technique. This method helps us to achieve more generalized relationships. What is Cross Validation? Cross Validation is a technique which involves reserving a particular sample of a data set on which we do not train the model. Later, we test the model on this sample before finalizing the model. Here are the steps involved in...
28 comments
Read more

How to use opencv-python with Darknet's YOLOv4?

Another post starts with you beautiful people 😊 Thank you all for messaging me your doubts about Darknet's YOLOv4. I am very happy to see in a very short amount of time my lovely aspiring data scientists have learned a state of the art object detection and recognition technique. If you are new to my blog and to computer vision then please check my following blog posts one by one- Setup Darknet's YOLOv4 Train custom dataset with YOLOv4 Create production-ready API of YOLOv4 model Create a web app for your YOLOv4 model Since now we have learned to use YOLOv4 built on Darknet's framework. In this post, I am going to share with you how can you use your trained YOLOv4 model with another awesome computer vision and machine learning software library-  OpenCV  and of course with Python 🐍. Yes, the Python wrapper of OpenCV library has just released it's latest version with support of YOLOv4 which you can install in your system using below command- pip install opencv-pyt...
84 comments
Read more