Data Visualization with Python and Matplotlib

Explore matplotlib basics for data visualization, including graph requirements, titles, labels, legends, and common chart types like line, scatter, bar, histogram, stack, and pie charts, plus CSV data loading.

Learn the basics of plotting with Matplotlib in Python by creating a simple line graph with x and y data using plt.plot, exploring figures, subplots, labels, titles, and legends.

Learn to add titles and labels to plots, label x and y axes, and format long titles with line breaks, plus use window controls to pan, zoom, and save.

Master adding and customizing legends in Matplotlib plots, labeling multiple lines with the plot method, and managing legend placement in simple and subplot scenarios.

Learn to create bar charts in Matplotlib by passing x and y data, configuring labels, legends, and colors to produce readable bar graphs.

Explore histograms in matplotlib within python data visualization, compare their distribution insights to bar charts, and learn about bins, bar width, and cumulative histograms for test scores.

Explore scatter plots in Python data visualization, plotting time spent on tests against scores, labeling axes and titles, and comparing multiple datasets with different markers and colors.

Use stack plots in Python with Matplotlib to show how taxes, overhead, and entertainment accumulate to the whole over time. The lecture demonstrates creating, coloring, labeling, and interpreting the stacks.

Explore creating and customizing pie charts in Python with Matplotlib, including labels, colors, start angle, equal aspect, exploded slices, and percentage displays.

Learn to load data from a CSV in Python, parse rows into x and y, and plot with matplotlib, including labels and a legend.

Learn to load data with NumPy's loadtxt, unpack into x and y using a comma delimiter, and accelerate data import for matplotlib visualizations.

Fetch and parse stock prices from the internet in Python 3, decode bytes, split records, filter six-field lines, and load data with NumPy loadtxt for visualization.

Master stock data visualization in Python and Matplotlib by building a date conversion function and plotting a line chart with properly formatted dates.

Plot and customize a stock chart using matplotlib by enabling a grid, adjusting axis labels and tick label rotation, and tweaking figure and subplot settings.

Adjust matplotlib subplots for cleaner visualization, switch the timeframe from ten years to one day, and convert unix time stamps to dates using a date converter with vectorize and datetime.

Customize matplotlib spines by color, thickness, and visibility for each axis, remove top, bottom, left, or right spines, and add horizontal lines to depict gains or losses.

Discover how to build open high low close candlestick charts in Python using Matplotlib finance, prepare open high low close data, customize colors, and format the x-axis dates.

Discover how to create, customize, and apply your own Matplotlib styles to graphs by adjusting defaults, colors, fonts, and grid, using or editing pre-made styles.

Master live updating graphs in Python using Matplotlib animation, reading data from a text file, updating x and y values on a set interval, and clearing before redraw.

Learn to add and place text on a matplotlib plot using text with font dictionaries, customize size and color, and explore dynamic positioning.

Annotate a specific point on a stock plot using x-y coordinates or axes fractions, adding text like 'oil spill happened' with an arrow, and adapt annotations for live charts.

Learn to dynamically annotate the last price on stock charts using Matplotlib, positioning the annotation with x y text, styling with box props and arrows for clear emphasis.

explore extensive customization of the map plot lib module, adjusting colors, spines, live graphs, labels, and subplots, including candlestick charts, and apply premade styles.

Explore creating and customizing subplots in matplotlib, manage figures with add_subplot layouts from one by one to three by one and three by two, and master subplot grid concepts.

Explore subplot2grid in matplotlib, configuring a six-by-one grid to place three axes—a top, middle, and bottom—using start points and row-span and column-span for advanced layouts.

Implement a high minus low indicator on daily price data to visualize volatility, map highs and lows, and extend plotting with simple customization in the plotting library.

Adjust the x-axis dates by using x3 labels and hiding x1/x2 ticks, then illustrate moving averages and fill between regions to highlight trend changes.

Explore how to plot price and volume together using matplotlib by adding a shared x-axis with a second y-axis, using fill_between for volume, and tuning grids, tick labels, and scales.

Add legends to explain plots by labeling age, volume, and moving averages. Adjust legend location, number of columns, font size, and alpha to keep legends readable and reduce overlap.

Explore subplots in Matplotlib, using add_subplot and subplot2grid to arrange multiple charts on one figure and share axes when helpful. Preview moves into base map planning for geographic visualizations.

Download, extract, and install basemap for map plot lib, using Source Forge, 7-zip, and pip install with Python 3.4, then verify import of basemap.

Plot coordinates on a base map by flipping latitude and longitude to x and y, customize markers and colors, and verify land location with is_land.

Learn to connect coordinates on a base map using Matplotlib, plotting a line between points, adding labels and legends, and drawing a curved great-circle path.

Use the base map extension to visualize geographic areas, plot latitude and longitude on globe or flat maps, and zoom into regions, then preview 3-D graphing with map plot lib.

Explore 3D graphing with MAP plot live to add a third axis for richer comparisons, including 3D scatter plots, wireframes, and bar charts.

Explore three-dimensional plotting with Matplotlib, creating a 3D axes, rendering a wireframe, and rotating, labeling, and interacting with the plot through zoom and drag.

Explore 3d visualization with Python and matplotlib by creating 3d scatter plots using x, y, z coordinates, color, markers, depth cues via alpha, and legends.

Learn to create 3d bar charts with python and matplotlib by supplying x, y, z positions and corresponding height, width, and depth for multidimensional data.

Explore 3D representations of data with python's data visualization module Map plot lib, using a vanilla installation to plot dimensions, including scatter plots, bar charts, wire frames, and 3D lines.