Data Visualization in Python (Mplib, Seaborn, Plotly, Dash)

Explore the anatomy of a figure from the Matlock Library, where the figure object contains all diagrams and axis objects control each diagram with labels, ticks, titles, grids, and legends.

Shows how to build a multi-line plot using the imp interface and the object-oriented interface, plotting linear, cosine, and sine curves with customizable colors, width, style, labels, title, and legend.

Learn to plot with MATLAB's object oriented interface by creating a figure and axes, using subplot, and plotting multiple functions with object methods, labels, and legends.

Draw circles at the intersections of x^2 and 2x to highlight points on the graph, using an ellipse object from batches, and customize position, size, color, alpha, and borders.

Add horizontal and vertical lines to mark a point on the graph using a gray, 0.5 transparency; adjust line style and axis position, then consult the documentation for accepted parameters.

Begin data exploration by loading the iris dataset from the library, inspect its dictionary structure with data, target, and feature labels, and split rows by species using numpy.

illustrate proportions with a pie chart by showing each element's share of the total, using percentages or counts, and customize readability with shadows, colors, and exploded slices.

Convert the pie chart to a doughnut chart, use text props to set font size 14, add a centered circle, and set the chart title class distribution at 18.

Use a scatter plot of petal length vs petal width to differentiate species, color by class, label axes, and explore a 3D scatterplot to improve separation.

Visualize iris data in 3d by plotting sepal length, sepal width, and petal length with a scatter plot, labeled axes and legend to test if sepal length adds discriminative power.

Explore how box plots display a variable's distribution, using medians, quartiles, and whiskers to reveal spread and outliers across subgroups such as men, women, and children.

Explore feature value distribution with a customized violin plot, adding title, horizontal orientation, labeled axes, blue violins with transparency, and median circles plus quartile markers to compare variable distributions.

Learn to use multiple graphs, including bar charts, point diagrams, and line diagrams, to display related customer data with a shared y axis and clear figure titles.

Learn to build complex figure layouts by arranging multiple graphs in a grid, spanning rows and columns, customizing axes, labeling each subplot, and combining images with related histograms.

Explore techniques to increase the resolution of low-resolution images reduced by a factor of 15, comparing nearest-neighbor and bicubic interpolation, and observe how upscaling improves detail while remaining imperfect.

Visualize how much guests paid with a stacked histogram, revealing most passengers paid up to about $35, with third class dominating lower prices and first class at higher prices.

Examine whether paying more for a ticket affects survival odds by comparing survived and didn't survive across first, second, and third class using box plots with a logarithmic y-scale.

Visualize Titanic passengers by deck using a magma color palette and hue-based coloring to reveal that first-class passengers largely occupy decks a–c, with higher fares on higher decks.

Visualize flight evolution with a heat map of months by years in wide format, using the B-But method and integer formatting to show rising summer counts (1949–1960).

Learn how Plotly Express provides a simple, one-line interface to create plots with sensible defaults, automatic axis labeling and styling, and easy parameter overrides, using data frames for web-ready plots.

Import the wind dataset with the bloodily library and seaborn, load and display it, and examine wind direction, intensity, and frequency for future polar chart visualization.