Data Visualization with Python for Beginners

Learn to build a scatter plot in Python with matplotlib.pyplot using home and away goals. Load data from a file, adjust dot size and color, and apply alpha for clarity.

Learn how matplotlib creates default figures and axes, create and size figures with plt.figure and fig, and switch between multiple figures by number or name.

Learn to add and arrange axes inside a matplotlib figure, build multi-axis layouts with add_subplot (3x3 and 3x1 grids), and control or reference axes with set current axis.

Explore the first set of data visualization exercises and review sample solutions after attempting the tasks, using the attached pdf as a guide.

Learn to read data from txt and csv files in Python by building a read_text function, parsing lines, extracting data names, converting values to integers, and storing in a dictionary.

Create a read_csv_file function in Python that reads a CSV, captures headers from the first line, skips the index column, and populates a data dictionary with integer values.

Learn to compute hits per at bat from game data, then plot a green scatter of games vs hits per at bat on an 8x8 figure and display.

Compute hits per at bat from a 2017 baseball data set and visualize year by year with stacked subplots, showing hits per at bat and the at base value.

Learn to customize plots by adjusting x and y limits on separate axes, using set x limits and set y limits, including flipping axis orientation to improve readability.

Learn to add a title and axis labels to your graph, customize their position, font size, color, and rotation, and tailor x and y labels for clear data visualization.

Apply LaTeX-style math formatting to matplotlib plots using a raw string in Python. Insert math environments with dollar signs to render fractions and exponential expressions, like lambda.

Learn to add and format major and minor axis ticks in a Matplotlib plot using multiple locator and formatters for an x axis from 0 to 145.

Explore how to customize x tick labels by retrieving x ticks, editing them (replacing values with strings), and resetting labels, with rotation for readability and major/minor locations.

Practice data visualization with Python through exercise sheet 2, comparing your solutions to the solutions video and learning that multiple approaches can achieve the same end result.

Demonstrate dynamic axis scaling in python data visualization: set y from 0 to 1 for a hit rate, x from first to last year, and apply labels and tick locators.

Learn to derive and format major and minor tick labels for two subplots using loops in Python data visualization.

Learn to add a legend to a Python plot by labeling lines (home team goals) and applying legend after plotting, with positions like upper left or center.

Learn to add text annotations to your charts using the text method, set x and y coordinates, and control horizontal and vertical alignment, rotation, and font styles to label data.

Learn to customize a matplotlib graph by toggling spine visibility on bottom, right, top, and left, and control tick positions to show ticks only on the left or bottom.

Explore matplotlib preset styles to quickly apply consistent graph visuals. View and apply available styles with plt.style.use, and compare options like grayish background, seaborne muted, and white grid.

Save matplotlib figures only after finalizing all edits, using formats like png or pdf and adjusting orientation and dpi. Avoid saving after show to preserve the edits.

Explore data visualization exercise sheet 3 for python beginners, using a new dataset with daytime objects and learn to read free text using the CC functions to use the data.

Learn to read two data files (current prices and onion prices) in CSP and text formats, convert date strings to datetime objects, and prepare data for plotting with Python.

Learn to read csv and text data, convert dates, and plot carrot and onion prices on a single eight-by-eight figure with labels, a title, and a legend.

Learn to identify peak prices for carrot and onion data, annotate the corresponding date and price above each point, and apply light red color using rgba values for clarity.

Discover how to compute the minimum price for onion and carrot data and plot the results using bucket analysis and price change per day.

Learn how to plot the price change per day on a chart by computing x and y coordinates from bucket indices, applying dark red annotations for onion and carrot data.

Adjust font sizes for the title, axes, and annotations, and bold the key price changes to improve readability. Try different styles, then save the final figure with a distinct name.

Explore advanced histogram customization by inspecting return values (n, bins, patches). Learn to tailor individual bars with patches, change colors and widths, and build legends using matplotlib patches.

compare average home and away goals using bar graphs, and learn to customize with width, alignment, colors, axis limits, ticks, and even stack bars for clearer visuals.

Learn to add error bars to bar graphs in matplotlib with x error and y error parameters. Customize per-bar errors, lower and upper values, and colors to clarify data.

Create box and whisker plots in Python using Matplotlib, comparing home and away goals with arrays, showing medians, quartiles, outliers, notches, and optional percentile-based whiskers with confidence intervals.

Create a pie chart of total home goals versus total away goals, label slices as home team and away team, and tune colors, explode, label distance, radius, center, and normalization.

Create two-dimensional histograms with Hist2D to show frequency distributions for home and away goals, then adjust bins and use a color bar to map frequencies.

Explore different types of plotting using onions and carrot prices and price units like euros or dollars. Begin the exercise solo, then view the sample solutions if you need help.

Explore solutions for exercise 1, showing data loading with read CSP or read text functions, figure creation, rounding carrot prices, and histograms for carrot and onion prices with labeled axes.

Learn to add legends to histograms using patches and labels. Set mean bars to blue and green and map means to bins, updating the legend with mean labels.

learn to compute average monthly prices for carrots and onions and visualize them as a stacked bar chart with month labels, legend, and axis titles.

Continue from exercise three to compute monthly minimum and maximum prices for carrots and onions, storing results for a bar graph with y error bars and side-by-side placement.

Explore creating and customizing box and whisker plots in python for beginners, comparing carrot and onion prices with notches, 10th/90th percentile whiskers, and 0.05 confidence bands.

Learn to load images with Matplotlib's image module using imread, then display them in a subplot with imshow, and understand image data as matrices with RGB or RGBA values.

Explore color maps in matplotlib for 2d histograms, using jet, hot, and ocean palettes, and learn to reverse them with _r and cmap for precise bin colors.

Explore adding a color bar to a two-dimensional histogram by plotting with axis, saving counts and edges, configuring extent, accounting for imshow y-axis orientation, and applying a jet color map.

Integrate images, color maps, and color bars from exercise sheet 5; practice solving the problems and follow along with the sample solutions.

Import and display a world map image using matplotlib in Python, loading with the image read method. Remove spines and axes for a clean map presentation.

Explore a practical sample solution in Python to plot country data as bars on a map, set bar heights and colors, and build a color-mapped visualization.

Map prices to a color index for bar charts, color the bars accordingly, and display a custom color bar with min and max price labels.

Learn to create 3d line and scatter plots in Python by importing mpl_toolkits.mplot3d, setting projection=3d, and mapping x, y, z coordinates with optional depth shading.

Explore changing camera angles in 3D plots by using view_init to adjust elevation and rotation, and animate the view with draw and pause for dynamic graph visualization.

Practice animations and timelapse effects in data visualization with Python, applying concepts from previous lectures in this section. This sixth exercise sheet introduces practical animation techniques to visualize dynamic data.

Introduction to building a 3D plot in Python, visualizing carrot and onion data with dates on the x-axis, y offsets, and 3D animation, including tick labels.