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

Learn to create a line graph that shows the relationship between two variables and how a variable evolves over time. Set the x and y ranges and compare multiple lines.

Explore data visualization in Python using MATLAB, Leap Seabourne, and bloodily to perform data exploration on multiple datasets, then build dashboards with the Dash tool from the bloodily library.

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.

Examine SQL and NoSQL data sources, cloud stores like DynamoDB, and Python web scraping. Analyze flowers using sepal and petal measurements to classify species with World Bank or Eurostat data.

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.

Learn how scatter plots map endurance on the x axis and strength on the y axis, with point size showing hours trained per week and color signaling demographic group.

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 how box plots reveal the range and distribution of petal length and width, using the iris dataset to identify outliers and verify iris samples.

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.

See how bar charts compare related values, illustrated by Johnny’s exam results and conditioning tests. Bar height indicates relative performance, and charts can be horizontal or vertical.

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.

Compute the average values for each variable across species, then create a 2x2 subplot figure with shared axes to visualize these means as colored bar diagrams using a magma palette.

determine if an image is grayscale or color by its channel count. convert color images to grayscale with a weighted red, green, blue average to produce single channel and plot.

Explore how color map palettes transform images and charts, apply six color maps including plasma and inferno to a composite figure, and add color bars, titles, and layout adjustments.

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.

Create color histograms for an image by plotting red, green, and blue channel frequencies with gridspec and three subplots.

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.

Save the current figure to a local file using the save function, specify the file name, and choose a valid image extension such as png or jpeg.

Explore seaborn's simple interface to create a wide range of graphs with just one line of code, accelerating data exploration and working with arrays or data tables.

Learn how seaborn offers figure-level and axes-level graphs to build multiple plots in a single figure or one plot at a time, using simple code.

Explore pandas data frames, a Python library for tabular data, and see how rows, columns, and an index organize a flight data set to enable rich visualizations.

Visualize Titanic passenger demographics by age, gender, class, and embarkation port using seaborn and matlab-style plots; most passengers are men aged 20–30, many in third class, embarking from Southampton.

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.

Discover the pairgrid class to explore relationships between variable pairs in a dataset. Instantiate a pairgrid or use the pairplot function to automatically generate scatterplots and diagonals showing single-variable distributions.

Compare penguin species by analyzing four biometric variables with a bear plot graph from the Seabourne Library, coloring points by species and noting density plots on the diagonal.

Explore how a joint grid presents the relationship between two variables alongside their marginal distributions, using either the JointGrid object or the joint plot function.

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).

Explore Plotly to create polished graphs with a single line of code, including data, layout, and frames. Leverage JavaScript objects under the hood for easy web embedding.

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.