Visualization with Python: Matplotlib All In One

Learn how to install Matplotlib with Anaconda, use Conda to create virtual environments, switch between them, and verify the installation across operating systems.

Activate and manage conda environments, clone roots, and install matplotlib in a dedicated environment. Verify installations with conda list and install required packages.

Explore data with map plot lib through exploratory data analysis on the adult census dataset, using histograms, bar charts, and scatter plots, plus a simple linear regression with numpy polyfit.

Master using map plot lib inside a Jupyter notebook to visualize data inline, import the library, plot ranges, and leverage IPython kernel and keyboard shortcuts for efficient workflows.

Explore the three interfaces for map plot lib— pi lab, pi plot, and pipeline— from high-level to object oriented API, and learn when to use each.

Learn the stateful PI plot interface, its interactive and non-interactive modes, and how to plot data with titles, axis labels, and focused, step-by-step updates.

Explore the plot function's flexible interface for plotting data as lines, with optional format strings, colors, markers, and multiple lines.

Explore plotting functions with real-world iris data, using box plots, scatter plots, and stacked histograms to reveal distributions and relationships, while learning color mapping and legend proxies.

Learn to build a parallel coordinates visualization in Matplotlib to explore high-dimensional data, color lines by a target variable, and identify clusters using the iris dataset.

Learn to display multiple plots in a single figure with matplotlib's subplot and subplots, enabling side-by-side comparison of feature pairs to predict species.

Learn to fine-tune subplot spacing and readability by using tight_layout and subplots_adjust for a 3 by 2 grid.

Explore creating a scatterplot matrix with Matplotlib, showing pairwise feature relationships and color-coded classes from the iris dataset, with diagonal histograms and optional target handling.

Master using the subplot to grid function in matplotlib to build flexible, multi-plot layouts, including two by two and three by two grids, with row span and column span.

Explore Map Lib architecture, separating figure creation from rendering across scripting, artist, and backend layers, and use the object oriented API to build a histogram with lower layers only.

Discover the anatomy of a Matplotlib graphic by examining the figure, axes, and text classes, and learn how a figure acts as an object tree coordinating plots.

Explore how the pyplot pipeline saves figures, manages the current figure and axes, and traces function calls with callable objects and a logging decorator.

Explore how pyplot uses a figure stack to manage the current figure and how bypassing it can trigger memory issues and warnings.

Learn to customize the default appearance of plots by editing map plot lib RC params and using the RC function to adjust line width, style, and markers.

Explore Matplotlib style sheets to share presets, and modify rc params globally or temporarily. Learn default styles, apply with style.use and context managers, and combine multiple styles for consistent visuals.

Create a custom stylesheet for matplotlib to match specific looks, including xkcd-style graphs. Use introspection, config directories, and style application to save and reuse styles.

Learn to enable built-in interactivity in map plot lib using the event handling API to attach callbacks, plot data with left clicks, and use keyboard input for regression in notebooks.

Create interactive interfaces for Matplotlib plots using ipywidgets Interact to adjust investment parameters—initial investment, interest rate, compounding, and time—and visualize future value in a dynamic notebook.

Learn to refine an ipywidgets interface with the API widgets library to build polished, interactive plots, including custom widgets, multi-line comparisons, and a reset button.

Explore how three-dimensional earth becomes two-dimensional on maps, revealing the trade-offs of map projections. Observe how the Mercator projection preserves angles but distorts size, demonstrated by distortion indicators.

Learn to use the basemap library to draw a world map with the Robinson projection, transforming longitude and latitude to x and y, and rendering coastlines, borders, and parallels.

Create a choropleth with matplotlib by building a core uplift map of the U.S. county unemployment rate, using FIPS codes, shapefiles, and a heat mapper for color normalization.

Create a 1-to-1 dot density map of San Francisco crime data using the ArcGIS image method, projecting with World Mercator 3857, and render translucent red squares to reveal concentrations.

Explore seaborn as a powerful companion to matplotlib, enabling attractive statistical graphics with minimal code. Create histograms, scatter plots with regression, and pair plots to reveal relationships.

Learn how the pandas data analysis library provides a simple interface to the map plot library, using dataframes, histograms, bar charts, and color maps on census and iris datasets.

Translate map plot loop figures to interactive web graphics using Bokeh, Plotly, and D3 in Jupyter notebooks. Explore interactivity and offline rendering to choose the right library for web visuals.