Plotly Documentation

The plotly.express module contains functions that can create entire figures at once. It is usually imported as px and is a built-in part of the plotly library. Thus to use this module, simply use the code snippet 
```
import plotly.express as px
```

Fundamentals Using Plotly Express

- Linear regression with scikit-learn and plotly
- Ordinary Least Square(OLS) with plotly.express
- 3D regression surface with px.scatter3d and go.Surface
- Prediction Error Plots

Machine Learning with Plotly

The plotly Python library ("plotly.py") is an interactive, open-source plotting library supporting a wide range of unique chart types covering statistical, financial, geographic, and scientific graphs. It is built on top of the Plotly JavaScript library, enabling Python users to create interactive, web-based visualizations. "plotly.py" is used to distinguish this Python library from the underlying Plotly JavaScript library.

University of Michigan - Ann Arbor

Google

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.

Data Visualization Libraries

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

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.

Learn Before

Related

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