One Pager Cheat Sheet

• Conveniently explore powerful data visualizations with matplotlib and plotly.
• Matplotlib can be used to graph data on Figures, each containing one or more Axes, using the Object-oriented API and NumPy arrays.
• The Matplotlib figure is composed of an Axes object, containing multiple Axis objects which individually hold the data to be plotted.
• Plotly is a data visualization library that helps you create interactive plots with Graph Objects and traces, while being able to render it in both HTML and Python using ipywidgets.
• Plotly creates interactive web-based plots that can be embedded into websites, apps, and Jupyter notebooks/labs using ipywidgets, allowing users to interact directly with the visualization.
• Types of plots such as matplotlib and plotly are well established for different classes of data.
• We can use matplotlib and plotly to generate scatter plots that represent the relationship between two continuous features.
• Line plots are best to see any kind of function, by which we can easily detect trends and compare multiple functions at the same time with matplotlib.pyplot and plotly.express.
• Bar charts can be used to compare and visualize different kinds of categorical variables and measure the differences over time.
• The histogram is used to graphically represent the distribution of data across a range of bins, which are created using intervals, such as in the example of plotting the age of a population.
• A Pie Chart is a circular graph that is divided into segments or slices of pie (like a pizza) and is used to represent percentage or proportional data.
• Both matplotlib and plotly can display images, annotate them, show their histogram, and many more things, and they are important components of a Convolutional Neural Network (CNN).
• imshow is used across all three platforms (Plotly, Matplotlib, and OpenCV) to display images, taking either an image or an array with an image as its argument to produce a figure.
• Exploratory data visualization can be done using matplotlib and plotly, and will be further explored when looking at a clustering dataset using scatter plot colors.