Python Data Visualization: Matplotlib & Seaborn Masterclass

Master data visualization principles in Python, using charts to tell clear stories with best practices and the ten second rule to reveal patterns and avoid common errors.

Identify data types: time series, categorical data, numeric data, and hierarchical data; select visuals that compare across time or categories, show composition and distribution, and reveal insights for analysts.

Explore essential visuals analysts use to convey progress and comparisons, including KPI cards, pie charts, heat maps, line and bar charts, scatterplot, area charts, 100% stacked bar charts, and histograms.

Learn to format charts to reduce clutter, label axes, and use annotations and color to highlight key trends. Apply storytelling with Matplotlib and Seaborn to create clear, focused visuals.

Identify common data visualization mistakes in Python, such as using line charts for categorical data and cluttered visuals, then apply clear labels and consistent colors.

Answer the three questions to choose the right visual: data, message, end user. Eliminate noise, tell a data story with titles and call outs, then move to map plot lib.

Master map plot lib basics to build and customize line, bar, pie, scatter, and histogram charts with object oriented plotting and pandas data types.

Compare the pipeline API and object oriented plotting in matplotlib, enabling quick prototypes or detailed customization. Build figures with axes, subplots, and titles using plot and related methods.

Plot pandas data frames with matplotlib by using the index as the x axis and each column as a separate series, with options for single or multi-series plots.

Group hotel bookings by date, sum lodging and other revenue, then resample monthly and plot both series on a single line chart using matplotlib.

Learn to plot data frames in Python with matplotlib, choosing between plotting the entire frame or individual series, then format charts for presentation-ready visuals.

Explore formatting matplotlib figures by starting with a blank canvas, layering axes, and adding figure and axis titles, labels, legends, axis limits, ticks, and spines for clear visualizations.

Learn to add and format chart titles, axis labels, and figure subtitles in matplotlib, including font sizes, for multi-chart figures and clear data communication.

Add a chart legend in matplotlib to identify each plotted series, using labels or legend arguments, and customize its location, orientation, and frame for clearer data.

Learn to customize line styles, colors, and widths in plots using line style, color, and line width arguments, with solid, dashed, and dotted options and practical color choices.

master axis limits in matplotlib by setting x and y bounds, starting y at zero to reveal true magnitude, and adjusting units for clear date-based and value-based comparisons.

Adjust the fig size in the subplots function to control width and height, using centimeters to inches when needed, to create white space and improve axis spacing.

Explore customizing x-axis ticks in Matplotlib by changing tick labels, rotating labels, selecting intervals, and highlighting key dates with annotations, preparing for vertical lines.

Add vertical lines with axvline to highlight key dates in charts. Use the date time library and time delta to plot dates like the start of COVID, showing before-and-after context.

Learn to add text to charts using the text method with x and y coordinates, including labels, figure-level notes, and options for font, color, and line breaks.

Use text annotations to call out data points, like the start of COVID, with the annotate method, arrow props, and connection styles to polish charts.

Learn how to clean up charts by removing borders or spines with set visible(false), then customize colors, line styles, and titles to polish visualizations for presentations.

Polish the assignment chart by removing top and right borders, eliminating scientific notation, and adding a title, y-axis label, legend, and a sum of lodging and other revenue line chart.

Master Matplotlib formatting by enlarging the figure, scaling the y-axis to millions, adding legends, and customizing tick intervals for a clear monthly hotel revenue chart.

Master line charts to visualize trends over time, using dates as the x-axis and pivoted data frame series to compare city inventory and seasonal patterns.

Build a stacked line chart with the stack plot method to show total inventory across markets and each city's contribution, using Los Angeles, San Diego, and San Francisco.

Build dual axis charts with the twin x method to plot San Francisco housing inventory and median active list price on axes, enabling line and bar charts in one visualization.

Create a dual axis line chart to compare quarterly room nights booked (left axis, blue) and bookings cancelled (right axis, orange) using matplotlib, with titles, axis labels, and legend.

Master dual axis line charts by plotting two data series with a secondary y-axis using twinx, resampling quarterly data with sum, and configuring legends, labels, and axis limits.

Master bar charts as a data visualization tool to compare categories, build and customize vertical and horizontal bars using pandas, matplotlib, and seaborn, including grouping, aggregation, and highlighting with color.

Create vertical and horizontal bar charts of total room nights by country and lodging revenue by country for the top ten countries, with axis labels and a title.

Learn to build stacked bar charts in matplotlib by stacking series with the bottom parameter, using a pandas data frame for California, Oregon, and Washington to create 100% stacked charts.

Explore grouped bar charts as a way to avoid stacking, by shrinking and shifting series around x ticks, using seaborn or pandas plotting, with a three-bar example.

Explore combo charts in matplotlib by combining a line chart on the left axis with a bar chart on the right, using alpha transparency to reveal overlapping data.

Master advanced bar charts in matplotlib and seaborn by comparing lodging and other revenue across the top five countries with grouped and 100% stacked charts, using an 80% reference line.

Learn to build advanced bar charts in Python by aggregating hotel data, grouping by nationality, and creating 100% stacked bars for lodging and other revenue with precise axis labeling.

Create pie and donut charts in matplotlib to compare proportions, size slices by values, add labels and percentages, and build donut charts with a center hole.

Create a pie chart and donut chart for revenue by country, featuring the top five countries and an other slice, using a data frame and a center circle.

Master pie and donut charts by grouping data, creating an other category, labeling, and percent formatting, then explore scatterplot and histogram in matplotlib and seaborn.

Explore scatter plots to visualize relationships between two numeric variables with map plot lib, including bubble charts sized by a third variable, and note cautions on causation.

Explore histograms to visualize numeric distributions with matplotlib and seaborn, adjust bins and alpha, enable density for relative frequencies, and compare distributions across datasets like diamond carat groups.

Create a scatterplot of average nightly revenue by country versus average revenue per customer; use bubble size for nightly revenue and add a histogram of ages in Germany and France.

Create a scatterplot of average revenue versus average nights with bubble size by average nightly revenue, and compare histograms. Aggregate by nationality and compute total revenue and revenue per night.

Review the map plot library takeaways, including the Pie Plot API and the object oriented interface for building customizable, multi-chart figures and bar charts.

As consultants for Maven Consulting Group, analyze coffee production, imports, prices, and consumption using bar, line, stack, scatter, pie, and donut charts to derive insights.

Explore data manipulation techniques and five visualization approaches, including bar, stack, pie, donut, and dual-axis charts, to analyze global coffee production and price relationships.

Master customization in map plot lib by building multi-chart figures with subplots and grid spec layouts, tuning colours with custom colour maps, applying style sheets, and saving figures as images.

Create a two by two grid of subplots in a single figure, populate with histograms, bar charts, and scatter plots, and use share X and share Y with tight layout.

Create a single figure with five histograms showing revenue by country for Portugal, Spain, France, Germany, and Great Britain, filtering room nights under 15 and sharing axes.

Create a 2x3 subplot grid with constrained layout, sharex/sharey, and axis off while plotting country-specific histograms from filtered hotel data using f-strings and optional pandas pivot table.

Master grid spec to build multi-chart layouts with variable sizes in matplotlib. Learn to place charts with row-column coordinates, slice notation, and example layouts like histograms, donuts, and line charts.

Build a grid spec layout to visualize hotel revenue with a line chart, a bar chart for top countries, and a donut chart tracking the French revenue goal.

Design a grid spec layout to place a top full-width line chart, bottom-left bar chart, and bottom-right donut chart for monthly revenue data, using an 11 by 8.5 figure.

Learn how to customize chart colors using color lists for bars and lines, apply hex codes for precise hues, and explore color palettes aligned with branding.

Explore how color palettes and color maps determine the default color cycle for charts, with the first series in blue, the second in orange, and the third in green.

Apply the set to color map to the line chart, look up national hex codes for the top five countries, and color the bar and donut charts accordingly.

Learn to customize charts with colors in Matplotlib, using hex codes and color maps to style line, bar, and donut charts, including white non-progress and French blue progress.

Explore style sheets in matplotlib and seaborn to quickly customize charts by applying presets such as 538 or dark grid, then tweak colors or borders as needed.

Explore and apply style sheets to charts in the assignments notebook, preview the available styles, and quickly apply a chosen style to the mesh grid layout to finalize the report.

Apply matplotlib and seaborn styles to charts, compare defaults with dark grid, and explore style sheets to customize attributes across all plots.

Explore how style sheets modify matplotlib rc parameters to control chart aesthetics, including grid color, axis face color, and spines, and learn rc parameter changes via assignment or rc functions.

Learn how to save Matplotlib figures as image files using savefig, choose formats like png, jpeg, svg, or pdf, and adjust dpi for high-resolution presentations.

Explore advanced matplotlib techniques to create multi chart figures with subplots and grid spec, apply color maps or hex colors, and customize aesthetics using styles and rcParams.

Combine and format multiple coffee production charts into a single, color-themed report figure showing Brazil's share and world rankings, using mesh grid, subplots, and data from production and prices datasets.

Walk through building multiple charts with map plot lib including donut charts, a stack plot, horizontal bar and pie charts, and histograms from the production data set for Brazil.

Explore seaborn basics for visualizing pandas data frames, mastering chart types, facet grids, and mapplot lib integration to quickly create bar charts, histograms, and violin plots.

Learn to format seaborn charts with map plot lib arguments, customize spines, colors, and legends, apply confidence intervals, and build line and bar charts with hue and color maps.

Explore creating bar charts and histograms with seaborn, including aggregation by category, mean carat, and axis labeling. Learn to customize with hue, viridis palettes, error bars, bins, and density curves.

Explore lodging revenue distribution with Seabourn by plotting a histogram (percent) for bookings under 1500, then build a bar chart of average room nights by top five countries.

Plot histograms with filtered data (lodging revenue < 1500) using stat=percent, remove the top and right borders, and explore zero-revenue spikes and top five countries with a horizontal bar chart.

Visualize distributions with box plots and violin plots using seaborn, highlighting median and quartiles. Understand interquartile range, outliers, and bimodal patterns, and compare horizontal and vertical layouts.

Build box plots of lodging revenue and violin plots by country, plus violin plots for age distributions of the top five countries, with revenue filters and outlier checks.

Build and interpret box plots and violin plots with seaborn, filtering data to reveal lodging revenue patterns across top countries, age distributions, and room nights.

Explore linear relationships with seaborn visualizations, including regplot, lmplot, jointplot, and pairplot, using carat and price in the diamonds dataset to compare categories by hue, color, and row-column grids.

Explore seaborn's joint plot to visualize the relationship between carat and price with a scatterplot plus histograms, and optional kde or regression line, including alpha tuning and hex density options.

Analyze pair plots to examine relationships among diamond dimensions carat, x, y, and z. Understand diagonals, histograms, KDE options, and how to use hue and corner settings for clearer insights.

Plot lodging revenue versus room nights with regression for all data and for the five countries, and build a six-column pair plot to identify the highest paying country per night.

Visualize regression between lodging revenue and room nights across top countries. Filter outliers, hue by nationality, and explore pair plots for key patterns.

Master heat maps with seaborn to visualize mean prices by clarity and color using pandas pivot tables, annotate cells, and explore correlation matrices for carat and price.

Create heatmaps showing mean lodging revenue by nationality and market segment, and a correlation matrix for the first ten columns of the hotels data using the flag palette.

Explore heatmaps of nationality by market segment with mean lodging revenue using a pivot table and seaborn heatmap, then examine correlations among top features to reveal revenue relationships.

Create FacetGrid visualizations with Seaborn to build a grid of plots by a category (like color), map histograms to each facet, and explore distributions across categories.

Explore how to integrate Seaborn with Matplotlib, using Matplotlib syntax to customize Seaborn charts, create figures and axes, manage subplots, and leverage both libraries for enhanced data visualization.

Explore Seaborn, a user-friendly extension of Matplotlib built for pandas data frames, offering quick data exploration with chart types like violin plots and linear model plots.

Use seaborn and pandas to analyze U.S. used car auction data, exploring price drivers like year, odometer, make, model, body style, and trim, with a Ford F-150 deep dive.

Walk through the final project by modeling relationships with pair plots, heat maps, and regression plots, exploring how odometer, year, condition, and brand drive selling price.