Pandas: data analysis with Python [part 2].

Pandas is a Python library that allows us to analyze data from a variety of sources. Among the most useful features we surely find several functions to clean our data and extract some statistics about the distribution of values of various attributes. In addition, we can create aggregations with different logics and graph the data to extract more information. Let’s find out how to do all this with just a few lines of code!

Pandas: data analysis with Python [part 1].

Data scientists continually need to read, manipulate, and analyze data. In many cases they use specific tools, but sometimes they need to develop their own code. To do this, the Pandas library comes to our aid. Let’s learn about its data structures, how we can read data from different sources and manipulate it for our purposes.

Gradio: web applications in Python for AI [Part 3]

With Gradio, it is possible to create web applications for our machine learning and AI models in just a few lines of code. Through some examples, we will see the advanced features available, such as authentication, caching, and input file processing. We will also build a chatbot and an image classifier from pre-trained models. Finally we will discuss how to deploy our project in a few simple steps.

Gradio: web applications in python for AI [part2]

Gradio is a python library that allows us to create web applications quickly and intuitively for our machine learning and AI models. Our applications always require user interaction and layout customization. Let us find out, through examples, how to improve our applications.

Gradio: web applications in python for AI [part1]

Writing web applications for our machine learning and/or artificial intelligence models can take a lot of time and skills that we do not possess. To streamline and speed up this task we are helped by Gradio, a Python library designed to create web applications with just a few lines of code. Let’s discover its basic functionality with some examples.

The best Python frontend libraries for data science

Developing web and desktop applications requires knowledge of several programming languages. Defining user interfaces is a fundamental aspect of providing a good user experience (UX) and viewing information in an effective and appealing manner. For those involved in analysing data or collecting it, however, this aspect is critical. In fact, many developers specialise in one language and are reluctant to expand their knowledge to other languages or tasks that are not their core business. For this reason, in this article we present five libraries in python that can facilitate and speed up the development of user interfaces.

Plotly Go: advanced visualization in Python

Visualizing data is critical to better understand the data and analysis performed. There are several tools, free and paid, that allow you to create fantastic dashboards. However, it is possible to write a few riches in Python to get great results and be more flexible depending on the project of interest. Let’s find out how to create interactive Scatter Bubble charts with Plotly Go on a real project.

Clustering: a real project to explore data

Clustering is a very powerful tool for grouping data. There are many algorithms that can be applied, so the choice is always difficult. In addition, all clustering algorithms require parameters to work. By means of a real case study, applied to real estate data, we will combine PCA, hierarchical clustering and K-means to provide optimal clustering solutions.

OpenCV and Streamlit: create a photo editing app

Manipulating images is a task that is very useful in several application fields. OpenCV, a Python library, easily allows us to modify images according to our needs. In this tutorial we discover how to build a simple web app using Streamlit to apply some effects to our photos.

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