The pyplot module in the matplotlib package is useful for quickly creating a plot. The figures and axes that are a part of the plot are automatically created, without having to explicitly specify them. Using pyplot, we can utilize histograms, line, scatter, 3D, Contour, Polar, and Image plots. 

pyplot module

- Figure object: the window/page that the graphic is drawn on, which can contain one or more axes or plots
- Axes objects: the area on which the data is plotted. Each Axes has a xaxis, yaxis, and a title. The xaxis and yaxis both contain ticks and the ticks' labels

Components of a matplotlib plot

2 useful tips for showing the figure in Jupyter Notebook


* %matplotlib inline


* plt.show()

Show plot in Jupyter Notebook

In Jupyter notebooks, the default image format for matplotlib plots can result in blurry graphics. To achieve crisper, high-quality images, the backend_inline module can be configured to output plots in Scalable Vector Graphics (SVG) format using backend_inline.set_matplotlib_formats('svg').

Matplotlib SVG Display Format

When creating data visualizations, adjusting the dimensions of the plot is often necessary to properly accommodate the data. In matplotlib, the default figure size can be modified globally by updating the rcParams dictionary, specifically by setting d2l.plt.rcParams['figure.figsize'] to a desired width and height tuple.

Matplotlib Figure Size Configuration

A well-formatted plot requires clearly defined axes to be interpretable. In matplotlib, an axes object can be associated with various properties using built-in methods, such as setting the $$x$$-axis and $$y$$-axis labels (set_xlabel, set_ylabel), defining the visible ranges (set_xlim, set_ylim), determining the scaling strategy (set_xscale, set_yscale), and adding legends or grid lines.

Matplotlib Axes Configuration

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.

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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 Documentations

Dive into Deep Learning

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.

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