Top 10 Data analytics & Data visualization using matplotlib

Explore the figure class as the top-level container for all plot elements, instantiate figures with width, height, and dpi, and configure axes with titles and labels.

Explore how the axis class and figure enable plotting, axis creation, and legend labeling. Learn to customize plots with colors, markers, and line styles for items like TV and smartphone.

Explore the plotting coordinate systems in matplotlib, including data, axis, display, and blended transformations, and learn to adjust x and y limits and place text with axis coordinates.

Learn to plot multiple groups in one canvas with three methods—subplot, figure.add_subplot, and axes objects—using shared or separate axes and customizable axis titles.

Learn to use the subplots function to create a 2x2 grid of plots in matplotlib, access axes, and plot x, x^2, sqrt(x), exp(x), and log(x) in each subplot.

Demonstrate how the subplot2grid function lets axis objects span multiple rows and columns on a 3x3 grid, enabling grouped plots and column merging.

Learn to control grid visibility and styling in Matplotlib using the axis grid function, displaying major and minor grid lines, and customizing color, line style, and width for each axis.

Learn how to control axis limits in matplotlib by using set_xlim and set_ylim to override auto min and max values, with an exponential plot example.

Master how to customize tick marks and labels on the x and y axes in Matplotlib, using xticks, yticks, and tick label functions to change positions and text.

Explore how to create twin axes in matplotlib to plot two datasets with separate y-axes on a shared x-axis, including legends and labeling.

Learn to plot bar charts in Matplotlib to visualize categorical data with bar height representing values, using vertical or horizontal bars. Compare discrete values across multiple series.

Plot stacked bar charts with Matplotlib by stacking two data series using the bottom parameter, and learn to compare contributions and adjust bar positioning.

Learn to build a histogram that shows the distribution of numerical and continuous data using matplotlib's hist function, with bins, counts, range, density, and cumulative options.

Create a pie chart in matplotlib to display a single data series as proportional wedges with labeled percentages, using area values, labels, and colors to show each group's contribution.

Explore how to create scatter plots with Matplotlib, plotting x and y data and differentiating a third variable by color or marker size, using examples like boys and girls grades.

Use contour plots to show how the response z changes with predictors x and y on a 2d grid, with colors representing z values.

Visualize velocity vectors as arrows using a quiver plot, mapping x and y coordinates to arrow locations and u and v components to arrow directions with color options in matplotlib.

Learn to create and interpret a box plot showing min, Q1, median, Q3, and max; draw the box from Q1 to Q3 with whiskers to the extremes using matplotlib.

Compare violin plots with box plots, highlighting full distribution by showing probability density via kernel density estimation across categories, making the data distribution more informative than box plots.

Learn to create three dimensional plots with matplotlib's 3D toolkit, using projection='3D' to plot 3D lines and scatter plots from x, y, z data.

Plot a 3d contour of a three-dimensional sinusoidal function using a 2d regular grid to compute z from x and y, visualizing the surface with a 2d mesh in matplotlib.

Learn to create a three dimensional wireframe plot in matplotlib by using plot_wireframe on a mesh grid with a sinusoidal z, projecting to 3d for clearer 3d visualization.

discover how a 3d surface plot visualizes the relationship between dependent and independent variables, with filled polygons and color maps that reveal topology, as a companion to the contour plot.

Explore how to display and customize text in plots with the Plot Lib library, controlling fonts, color, position, axis labels, titles, subtitles, and Unicode text, including math expressions.

Explore how to render mathematical expressions within text data for graphs in Matplotlib, using dollar signs to embed math, with subscripts, superscripts, and symbols in axis labels and titles.

Read image data with the imread function, display and store it, then rotate, flip, and rescale the image while handling missing files in Google Colab.