Learn to create and customize common matplotlib plots, including line graphs, bar charts, histograms, and pies, and understand axes, tickers, tick labels, and the 2D/3D plotting area.

Learn how matplotlib plots data with y values and a default x axis. Create figures and subplots to compare bar charts, stacked bars, box plots, and scatter plots.

Explore box plots, including the interquartile range, median line, and whiskers, and identify outliers, then compare with a histogram using the same dataset.

Create sine and cosine plots in Python by generating 256 points from -pi to pi with numpy linspace, computing sine and cosine, and customizing axes and labels with LaTeX-style math.

Configure axis lengths, limits, and autoscale in matplotlib, using axis properties like rect, axis bg, and polar, with shared x/y. Add axes, draw lines, enable grid, and understand axis classes.

Learn to customize matplotlib line visuals by adjusting width, color, style, and markers using keyword arguments, setter methods, or setp, with guidance on consistent, accessible color choices for different audiences.

Create and customize figures and subplots in matplotlib, control tick locators and formatters, and format date axes to produce clear, precise data visualizations.

Learn to add legends and annotations to data visualizations using Matplotlib. Position legends and annotate key points to improve clarity and reduce reader misinterpretation.

Move spines to the center by hiding two spines and setting the others to zero, zero in data space. Understand how spines define data boundaries and connect axis tick marks.

Explore 2d histograms to visualize data distributions, learning how bins affect frequency and area normalization, with applications in image analysis like edge detection and image segmentation.

Learn to create bar charts with error bars to visualize measurement uncertainty, display standard deviation or standard error, and indicate confidence intervals, with customizable color, width, and orientation.

Pie charts show numerical proportions summing to 100%, with arc length proportional to value, and this lecture demonstrates creating an exploded pie chart in a square figure with labeled slices.

Visualize data with filled areas under and between curves using matplotlib's fill_between, color settings, and the where condition to highlight specific regions.

Explore advanced matplotlib concepts, including figure and axis structures, labels, transparency, and combining graph types to tackle complex data representations.

Add a data table beside a matplotlib chart to display selected values, using plt.table with optional row and column headers and axis integration.

Learn to customize grids in matplotlib by toggling visibility and adjusting major and minor ticks. Use plt.grid and axis grid modules to style grids for clearer plots.

Explore customizing grids in matplotlib by loading sample data, organizing image grids across axis grids, and plotting multiple images with the standard show call for neat, aligned renders.

Learn to fill under plots in matplotlib using fill, fill_between, and fill_betweenx, with boolean conditions to control the shaded area and visualizing data.

Explore how to create polar plots in matplotlib, converting data to polar coordinates, and configuring polar axes, grids, and labels for radial visualizations using theta and r.

Learn to create three-dimensional visualizations with matplotlib by plotting random sales data for 2017–2020, configuring the three-dimensional axis, choosing a color map, and rendering the power series as spheres.

Explore 3d visualization in matplotlib, including wireframe, surface, and tri surface plots, and learn to render a hyperbolic paraboloid (pringle surface) with numpy and 3d axes.

Explore 3d histograms and 3d bars to visualize the correlation among three variables, using numpy histogram2d and meshgrid, and compare with a scatter plot.

Explore animating figures in matplotlib using the animation base class, time-based, artist, and func animation, and learn how to save animations with FFmpeg or Mencoder.

Learn to create and export animated visualizations with matplotlib by saving frames to PNG files and encoding them with FFmpeg or Mencoder into mp4, using init func, frames, and metadata.

Explore how to work with images and maps in Python for data visualization using Pillow and PIL, applying filters, resizing, and annotating matplotlib charts with image files for geospatial data.

Read all image files from a target folder, resize each to 0.1 of its size, and save the thumbnails to a designated thumbnail folder.

Learn to plot with images in Matplotlib, annotating charts with ship icons proportional to pirate counts, illustrating the link to sea surface temperature and strengthening memory.

Learn to plot with images in matplotlib by annotating data points with pirate images, configuring annotation boxes, arrows, and axis labels to visualize year and temperature data.

Plot years versus temperature and visualize pirate counts by placing scaled pirate ship images at data points using matplotlib annotation boxes, after loading data from a CSV and handling the header.

Learn to plot data on a map by projecting geospatial coordinates with the Basemap toolkit in matplotlib, using a Mercator projection and drawing coastlines, countries, and meridians.

Plot data on a world map using the Google Maps API, using Python to prepare CSV data and render a map and a table with Google Visualization in HTML.

Explore logarithmic and linear plots in python by visualizing exponential y and linear z on a 2x2 subplot grid with log and linear y-scales.

Generate a synthetic audio signal with NumPy by combining sine components and noise, then visualize its time-domain and frequency-domain representations to illustrate spectrograms.

Learn to create and customize stem plots with matplotlib by plotting discrete data as stems from a baseline, configuring line and marker styles, and interpreting the resulting visualization.

Explore how color maps influence data perception and aid reader understanding in visualizations. Learn about matplotlib color maps categories—sequential, diverging, qualitative, cyclic—and practical selection guidance for readability and color blindness.

Learn to set and register color maps in matplotlib, apply cmap to images, pcolor, and scatter plots, and craft readable divergent schemes using color brewer and hex values.

Explore how scatter plots reveal relationships between variables and how histograms summarize distributions, explaining positive and negative correlations and their ideal values.

Explore cross correlation between two data sets to identify matching patterns, visualize with numpy correlate and matplotlib, normalize by zeroth lag, and compare real and artificial trends.

Learn to create and interpret box and whisker plots with matplotlib to compare distributions, medians, and data spread, with optional notches for confidence intervals.

Learn to generate a gantt chart with Python, loading task data, plotting bars via matplotlib, formatting the date axis, and organizing tasks on separate channels.

Explore how to visualize data dispersion with error bars using Matplotlib, computing the mean and a 95% confidence interval, and speeding calculations with NumPy vectorization.

Explore LaTeX 2 techniques in Python visualization with example data and numpy and matplotlib settings. Examine raw strings, Helvetica sizing, and the equation for s with cos and pi.

Explore how Python allows multiple statements on a single line separated by semicolons, with examples like import sys and printing hello, world.

Explore how Python uses header lines and suits to structure code blocks, with header lines starting keywords like if, elif, else and ending with a colon, followed by the suit.

Assign values to variables using the equal sign in Python; variables auto declare and infer type from the assigned value, with integers, floats, and strings as examples.

Explore how K-means clusters unlabeled data into five blobs by finding centers and assigning each instance to the nearest center in this unsupervised learning demonstration.

Train a k-means cluster model with k=5 to find blob centers and assign each instance to the nearest centroid, inspect centroids, and compare hard clustering with soft clustering for predictions.

Explore centroid methods for clustering with k means++ initialization, inertia as the performance metric, and how init hyperparameters influence multiple runs to select the best solution.

Explore clustering as a preprocessing step for dimensionality reduction before supervised learning, using the eight by eight digits dataset, train-test split, and preparing a logistic regression model.

Learn to build a classification pipeline using logistic regression with k-means pre-processing, evaluate accuracy, and optimize the number of clusters with grid search cross-validation to maximize performance.

Demonstrate logistic regression on a dataset of 50 labeled instances where each image represents a cluster, fit the model, and evaluate on test data to achieve about 90.2% accuracy.

Explore dbscan, a density-based clustering method that uses epsilon neighborhoods and min samples to identify dense core regions, form clusters, and flag anomalies, demonstrated with scikit-learn on the moon dataset.

Apply k nearest neighbors classifier to dbscan core samples to predict clusters, identify anomalies, and explore eps, min samples, and density considerations.

Explore gaussian mixture models that generate data from multiple gaussian clusters with varying ellipsoids, weights, means, and covariances, where each point comes from an unknown cluster via a generative process.

Interpret a Gaussian mixture model, where latent variables z select cluster means mu_k and covariances, observed x are drawn from those Gaussians, weighted by phi, with plates indicating repetition.

Explore data frames, a two-dimensional, labeled data structure with columns of different types, created from dicts, lists, series, or numpy arrays, and learn how to set index and columns.

Learn to create data frames from structured or ragged arrays in pandas by specifying column data types, using int, float, and a ten-character string.

Learn to use the pandas DataFrame.assign method to create new columns derived from existing data, and update values by applying lambda-based operations on columns such as a and b.

Define a Python function to choose a visualization type (bar, line, pie), validate user input, and set up basic data visualization workflows.

Explore how to create different chart types using matplotlib, including line and pie charts, by configuring data, labels, markers, colors, and titles, and rendering with plt.show.

Define data labels and a chosen chart type, then visualize the data using that selection. Display the chart in the main program with matplotlib's plt.show.