The development of data analytics pipelines by Data Scientists requires several skills. Having an easy, intuitive, and interactive development environment is critical. Jupyter Notebook is an open source web application that allows you to create and share interactive textual documents, containing objects such as equations, graphs and executable source code in different languages. Let’s discover its main features.
Chatbots are a technology that allows you to automate interaction with users. Leveraging the latest artificial intelligence technologies, conversations turn out to be more and more real. Examples of chatbots evolution are virtual assistants like Alexa, Cortana and Siri. Let’s find out how you can develop a simple chatbot in Python using the Chatterbot library.
Analyzing data also requires graphing the data or the results from the analysis performed. Many libraries in Python provide useful tools for visualization, but the plots produced are static. The Pandas Bokeh library is a great alternative for creating interactive plots and including them in web projects. Let’s find out how to use it and the results we can achieve through some examples.
Pandas is the most used library by data scientists to analyze data. But if you are not an expert programmer or simply want to explore your data in a simple and intuitive way you can use PandasGUI. This is a library that allows you to view and interact with Pandas dataframes with a simple mouse click.
Nowadays, data processing and analysis is increasingly required within web applications. Unfortunately, the required execution time can sometimes be too large to handle requests asynchronously. In this tutorial we discover how to use Celery in a Django project to create asynchronous tasks for our needs.
Choosing a web framework for developing a web application is always important. The best Python frameworks are, according to developers, Django and Flask. Let’s find out their peculiarities and try to understand which one is the best according to the needs of our project.