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.

Create and display a figure with the figure function, then save it to a variable. Retrieve the current figure and current axes to access the single axis.

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.

Learn to create a two-function plot and annotate points at (0,0) and (2,4) with markers, arrows, and a bounding box using the annotate function.

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.

Learn to manipulate the axes of a Python graph by setting xlim and ylim, customizing tick labels as multiples of pi, enabling grid, and applying a pi-based LaTeX formatter.

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.

Compute the number of records per class using the unique function, revealing three classes with 50 observations each, and visualize the distribution with a labeled pie chart showing percentages.

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.

Explore three dimensional graphs by adding a depth axis to display surface elevation, terrain with longitude and latitude, using color scales for a fourth variable or a 3D scatterplot.

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 violin plots to compare distributions across subgroups, revealing medians, quartiles, density peaks, and the frequency of values for variables such as height across groups.

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.

Explore how global styles in the MATLAB lip library alter graph aesthetics, including dark themes and seaborn-inspired palettes, and apply a random style to see font, background, and legend changes.

Reset the base style of the Matloff library, convert an image to a numeric array, load a sample flower, and display it with the show function.

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.

Modify the appearance of charts with Seaborn by adjusting context, color palettes, styles, and theme to suit notebooks, papers, slides, or posters.

Analyze the Titanic dataset by examining survived and alive indicators, embarked port, fare, sex, siblings and parents aboard, demographic group, deck, and traveling alone, while noting missing values.

Explore the Titanic dataset with seaborn: load the data into a pandas DataFrame, inspect 891 passengers across 15 properties, and prepare to analyze survival and demographics.

Explore density plots to visualize the distribution of a variable using a smooth function, showing how common values are, and compare with histograms and the cumulative distribution.

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.

Explore Titanic passengers' age distributions by class using density plots and cumulative distributions, adjust visual styling, and compare solo travel with stacked bar charts.

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.

Demonstrates building a histogram and stacked bar charts to visualize survival odds by passenger class (first, second, third) and demographic group (women, children, men), with clear titles and color coding.

Load the penguins dataset with seaborn's load_dataset to inspect the first five rows, explore features such as species and biometric measurements, and identify variables that differentiate penguin species.

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.

Create a demography column from species and gender, drop unknown genders, then use a joint plot of bill length vs weight colored by demography to separate penguin species and gender.

We load the flight dataset, create a flight variable, and inspect its three columns: year, month, and number of passengers.

Compare long and wide data formats using a passenger dataset by month and year; long format keeps one row per year-month, while wide format aggregates into year columns.

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.

Explore polar charts by plotting a categorical variable around a circle using polar coordinates, with a radial variable like wind intensity to create radar-like visuals, bars, or lines.

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.

Visualize wind direction and strength with polar scatter, line, and bar plots, color-coded by frequency, to show most winds are low intensity and blow from south to west.