Streamlit makes it quite easy and simple to build production ready data science apps (or Data Apps). In this post we will see how to deploy a simple app that uses some NLP packages such as Spacy and TextBlob on Streamlit Sharing. By the end of this post, you will learn How to generate requirements.txt …
In this tutorial we will explore Waypoint – a tool designed by Hashicorp to build,deploy and release your application with ease and simplicity. Waypoint makes it easier for developers to build,deploy and release most applications using a similar and simple workflow. The main requirement includes Your App requirements.txt : For the app you want to …
Build and Deploy Flask App Using Hashicorp’s Waypoint Read More »
Streamlit is such an awesome framework that allows you to build several cool data science apps. In this tutorial we will see how to embed PDF files in our streamlit app. To be able to do so we will need just 2 main things st.markdown() : this will enable us to use the same format …
One of the most common tools used by most Data Scientists when doing a project is a Jupyter Notebook – which can be used primarily with Julia (the Ju in JUpyter) Python (the Pyt In JuPYTer) and R (the R in the JupyteR). It quite useful and has a large community. Let us see another …
One of the cool things about building web applications is the ability to either upload or download files from the web app. In this tutorial we will be exploring streamlit file upload feature. With this feature you can upload any type of files and use them in your app. To work with the file uploads …
Reproducibility and re-usability of your Python projects is an essential aspect of being a software developer or engineer, and one of the ways to do so is to provide means that others can get the same working environments you used for your projects and the same versions of packages and libraries used. This is one …
Pipreqs Tutorial – How to Create Requirements.txt File In Python with Ease Read More »
When working on any python project or data science project it is essential to always work in an environment that makes your project reusable and repeatable without any issues for anyone that picks up your project in the future. This will even help you yourself when you revisit your project in the future. One of …
Streamlit is awesome we several cool and useful features, in this tutorial we will explore yet another additional feature of streamlit – st.beta_columns() and st.beta_expander(). This feature is available from version 0.68 and upwards hence you will need to upgrade your previous version via pip install streamit -U or pip install streamlit –upgrade Let us …
Exploring your dataset is an essential aspect of every data science task or project. As an aspect of the Data Science Life Cycle , it allows you to derive descriptive and practical insight into what your data comprises of. It is essential to know how to derive such insight from your data by creating your …
3 Exploratory Data Analysis Tools In Python For Data Science Read More »
The simplicity that streamlit offers for building apps is so cool and useful. But sometimes you may be constrained by lack of compute or incompatible working environment on your local computer. In most cases Colab solves that problem. But what if you want to share your streamlit app with others from Colab. In this tutorial …