Matplotlib is a Python library for "creating static, animated, and interactive visualizations." Some examples of these visualizations are histograms, bar charts, scatter plots, and image plots.

Matplotlib

https://www.geeksforgeeks.org/difference-between-matplotlib-vs-seaborn/

Difference Between Matplotlib and Seaborn

Seaborn is another Python data visualization library. Seaborn is built on Matplotlib, meaning that it uses matplotlib to create visualizations. This library also works closely with Pandas and NumPy. With Seaborn, we can easily visualize statistical relationships and distributions of data.

Seaborn

The plotly Python library is an interactive, open-source plotting library that supports over unique chart types covering a wide range of statistical, financial, geographic, and scientific graphs. Built on top of the Plotly JavaScript library, plotly enables python users to create interactive web-based visualizations. This library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library. 

Plotly

Bokeh is a data visualization library that helps practitioners to manually inspect datasets. Visualizing data with tools like Bokeh allows developers to identify data quality issues and develop intuitions about the types of problems they need to address before feeding the data into a machine learning model.

Bokeh

Data visualization tools, such as seaborn, Bokeh, and matplotlib, are essential for manually inspecting datasets. By plotting the data, practitioners can visually identify underlying data quality issues—like extreme outliers or recording errors—and develop an intuition for the type of problems they may need to address before feeding data into a model.

Claude

University of Michigan - Ann Arbor

Python's data science libraries can be broken down into three different categories:
- Data Mining
- Data Processing and Modeling
- Data Visualization

Categorizing Python Libraries for DS

Before data can be fed into any machine learning model, its overall quality must be evaluated and improved. Real-world datasets are often plagued by flaws such as extreme outliers, faulty sensor measurements, and human recording errors. Addressing these discrepancies is an essential part of the data preparation process to ensure the model learns from reliable information.

Data Quality

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Python Libraries for Data Science

Dive into Deep Learning

Data Mining Libraries

Data Processing and Modeling

•Data mining: Obtaining data that can be used to deduce conclusions.
•Data processing and modeling: Cleaning extracted data in order to prepare it for analysis and processing this cleaned data in order to reach conclusions. 
•Data visualization: Visually depicting data in various forms allowing for easy interpretation of conclusions.



Defining the Different Library Types for Data Science

The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners.    
  
Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.  

The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. The Transformer was proposed in the paper Attention Is All You Need. It is recommended reading for anyone interested in NLP.

Hugging Face transformers

Data Visualization Libraries

A faulty measurement is an error in a dataset caused by inaccurate readings or malfunctions from the sensors or instruments collecting the data. Along with outliers and recording errors, faulty measurements are a primary source of poor data quality in real-world datasets and must be addressed before the data is fed into a machine learning model.

Faulty Measurement

A recording error is a data quality issue that occurs when information is incorrectly logged, transcribed, or stored during the data collection process. In real-world datasets, recording errors—alongside outliers and faulty sensor measurements—can introduce noise and bias, necessitating thorough data inspection and cleaning before the dataset can be reliably used to train a model.

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