Complete Course on Data Visualization, Matplotlib and Python

Add two lines using the Line2D artist in matplotlib by supplying x and y data, applying auto scaling, and using the lines container to customize colors.

Manipulate Line2D properties by adjusting opacity, line width, and markers. Modify clipping and axis boundaries, and save figures with DPI settings.

Learn to create and add rectangle patches in matplotlib by instantiating rectangle artists and adding them to an axis with add_patch, while noting a bar container as an efficient alternative.

Manipulate rectangle artist properties in Matplotlib by importing patches, configuring x, y, and width, adjusting opacity, border width, and colors, and managing z-order to layer rectangles.

Learn to manipulate fancy box patch properties in matplotlib, applying the box style parameter to create rounded rectangles and style frames around axes, legends, and other patches.

Learn how matplotlib uses text artists and their surrounding box, how to set text and box properties, and how to add, inspect, and delete text on the axis.

Create text artists and add them to the axis, then fine-tune color, size, opacity, font, and rotation, and customize the wrapping box with a dictionary.

Explore how matplotlib annotations work with text, adding text at coordinates and arrows between points, and understand the annotation artist, including the fancy arrow patch and text instance.

Explore annotation properties in Matplotlib by customizing the annotation artist text, box, and arrow, including line width, arrow style, and connection style.

Learn to create legends in matplotlib from scratch and with handy shortcuts, assign labels to colored patches, and use axis.legend for quick results.

Learn to customize matplotlib legends by tweaking text and patch colors, opacities, and the legend box—including padding and corner radius—and create custom legends with patch and line2D handles.

Explore how to customize Matplotlib axis spines and labels, adjusting color, style, width, padding, and visibility, and learn practical techniques for managing x and y axis labels and the legend.

Learn to manipulate x and y axis text and tick labels in matplotlib, including tick lines, axis labels, and grid lines, with ticks on both ends.

Explore how to control axis ticks in matplotlib with tick formatters and locators, including auto, max, linear, multiple, fixed, and null, plus string, scalar, percent, and index formatters.

Create line charts in matplotlib from arrays or pandas dataframes, including csv input, and use the dataframe index as x-values when needed; add legends, markers, and min/max annotations.

Learn to build bar charts with matplotlib using a US music sales by genre dataset, customize legends, titles, spines, and annotate bars for clarity.

Create grouped bar charts with matplotlib and pandas to place rock and jazz side by side using the US music sales data by genre, adjusting x values and bar widths.

Learn to create stacked bar charts in matplotlib with and without pandas, define bottom values, and customize legends using the Line2D artist on a dataset of country, jazz, and rock.

Create and customize scatter plots with Matplotlib and pandas, using unemployment rate and interest rate data to reveal relationships and adjust colors, sizes, and axis styling.

Create and customize pie charts using Matplotlib and pandas. Instantiate figure and axis, set equal aspect, assign labels and values, adjust wedge colors, display percentage distribution, and hide the legend.

Create donut charts by overlaying an empty circle on a pie chart in Matplotlib, using the divorce rate dataset, and explore adding artist patches to customize visuals.

Create histograms from a DataFrame using the histogram method on an axis, customize bins, opacity, and labels for clear data visualization.

Learn to create polar charts in matplotlib by converting linear axes to polar, using theta as x and radius as y, with segments and ranges on a divorce rate dataset.

Create dual axis charts with and without pandas by overlaying axes on a shared x axis, then add legends and use a secondary y for the pandas version.

Explore autocorrelation, a core method in time series analysis that uses past values to predict future price. Build and interpret an autocorrelation chart from a csv dataset.

Explore kernel density estimator plots, a smoothing technique to infer population distributions from finite data. Use pandas to plot density estimator with a title, dollar x-axis, zoom -100 to 600.

Learn to build and customize box plots with Matplotlib using Bitcoin data, exploring boxes, caps, flyers, outliers, medians, whiskers, and orientation, with annotations for outliers.

Learn to create and customize violin plots with Matplotlib, using cryptocurrency price data to visualize probability density alongside box plot features, including bodies, caps, and the mean line.

Create a heat map of price correlations among altcoins from a cryptocurrency dataset, dropping missing values, with a colorbar from 0.3 to 1 and optional overlays for precision.

Create a Seaborn regression plot with a scatter and linear fit, adjusting markers, line style, and the confidence interval. Use logistic regression for binary outcomes and tweak bootstrap settings.

Learn to create count plot for categorical data with seaborn using the Titanic dataset, counting male and female passengers and comparing groups by hue and class.

Learn to create and customize bar plots in seaborn, comparing total bill by gender, by weekday, and other groupings, with mean, median, sum, and count estimators, plus confidence intervals.

Create a vertical box plot with Seaborn from the tips dataset, grouping by smoker and plotting total bill by day to compare distributions.

Learn how to create violin and swarm plots in seaborn, compare with box plots, and adjust parameters like dodge, split, scale, and overlaying swarm points using the tips dataset.

Learn how to create and customize vector plots with factor plot, replacing bar and count plots, exploring facet grids, axes, and multi-axes layouts.

Explore univariate distributions with seaborn distplot, including histogram and KDE plots, using the tips dataset, and learn how to toggle ROC plot and normalization between probabilities and frequencies.

Learn to create a seaborn joint plot with three axes, including the joint and marginal plots, and customize scatter points, colors, and the regression line.

Explore creating pair plots with seaborn to visualize feature relationships in the iris dataset. Customize hue by type, diagonal histograms or kde, and legends to assess feature separability.

Learn to customize matplotlib legends in a bar chart: round legend patches, adjust background color, move the legend below the x-axis, and color the first label and handle.

Explore tick customization in matplotlib, including y-axis tick labels, rotation, currency formatting with USD, and x-axis placement via fixed locator and tick params.

Apply patch coloring in a grouped bar chart by turning blue patches red and refreshing the legend in matplotlib with Python. Annotate orange bars with values in a Jupyter notebook.

learn to style lines in matplotlib by loading the iris dataset, creating a joint plot, and applying maroon color, dotted line, and markers while adjusting axis limits and clip behavior.

Explore Matplotlib annotations by customizing bounding boxes with rounded corners, radii, padding, and face color, then adjust arrow style and connection angles in interactive plots.

Explore path collections by creating a scatter plot with the iris dataset in a Jupyter notebook, then adjust marker sizes and set alpha to 0.3 with orange and maroon colors.

Learn to manipulate matplotlib axes spines by removing the top spine, styling the right spine with width two and red color, and removing all spines to streamline iris line chart.