Data Visualization with Python

Explore the wide range of plot types in map plot lib, from basic line, scatter, bar, stem, and step plots to 3D, histogram, box, and stream plots, including unstructured coordinates.

Set up the python visualization environment, import matplotlib.pyplot as plt, and apply styles like classic and dark background to customize and render line plots in IDEs or browsers.

Set up the plotting environment, create a figure and axis, generate an array with linspace, apply the sine transformation, and render the plot on the axis.

Showcase data visualization with Python by plotting a 100-value array from 0 to 10, applying sine, sign, and cosine functions, and comparing the sign and cosine lines in a plot.

Visualize multiple plots of sin and cos in python by plotting data with a single plot function, adding multiple lines, and color-coding sine and cosine for clear comparison.

Learn to customize line colors in plots by applying color names, short codes, grayscale values, hex codes, rgb tuples, and html color schemes.

Learn to change line type in plots, choosing solid, dashed, dash-dot, or dotted styles, and apply color and axis limits using the plotting library.

Learn to label plots in Python data visualization, including adding titles, axis labels, and legends to identify plots and their axes.

Master legend customization in matplotlib by adding labeled lines, configuring line styles and colors, and positioning legends (including upper left and lower center), removing frames, and using multiple columns.

Explore the object-oriented interface of Matplotlib to reduce repetitive plotting calls by configuring y labels, titles, and legends in a single, efficient approach.

Matplotlib glitches in Python show how to create an axis, plot data, and use the set method to configure x and y limits, labels, and the title in one call.

Explore how scatter plots visualize the relationship between two variables using dots instead of lines. Compare with line plots and learn to write a sketch plot in the plot library.

Generate an array of angles, compute their sine values, and visualize them as a Python scatter plot with blue markers, comparing to a line plot and dashed options.

Visualize data with Python by plotting random values using multiple symbols and colors in a scatterplot, with legends and labels.

Learn how to draw a line inside a scatterplot in Python, combining A and B values with a plot method and styling the line using dash patterns.

Learn to customize a scatterplot inside the map plot lib by changing shape, appearance, and line type, including solid and dashed lines. Follow a console walkthrough to apply these modifications.

Explore how to customize line and scatter plots in python by adjusting colors, marker styles, sizes, line widths, and axis limits using the plot method.

Explore how to create scatterplots with the plot method and the catapult.scatter function, customize markers, color maps, and norms, and preview the resulting scatterplot.

Data visualization with Python contrasts plot and scatter plot, including the built-in scatter function: scatter renders each data point, while plot dot plot clones points once for efficiency.

Learn to create bar plots in Matplotlib, using the bar method to compare discrete categories with horizontal or vertical bars, and plot data from arrays or dictionaries.

Learn to extract dictionary keys and values into separate lists for a matplotlib bar plot, placing keys on the y-axis and values on the x-axis, using Python's dict methods.

Learn to build and label a bar plot in Python, balance data lengths, set figure size, adjust width and color, and add axis labels and a title.

Create a multiple bar plot in matplotlib to compare student enrollment across courses, machine learning, deep learning, data science, and python, using x and y values and a title.

Create a multiple bar plot in matplotlib with numpy datasets, including machine learning, deep learning, data science, configuring a subplots figure and drawing three bars using arange and bar width.

Learn to create bar plots in Python using Matplotlib, with numpy arange data, customizing bars with colors, widths, edge colors, and labels for machine learning, deep learning, and data science.

Add labels to a bar plot by configuring x and y labels, font size and font weight, and legends, then display the chart with enrollment data from 2020 to 2023.

Explore how to visualize student enrollment across years and courses using multiple line plots in Matplotlib, with a legend identifying colors for machine learning, deep learning, and data science.

Create stacked bar plots in Matplotlib by stacking two categories into a single bar, with height representing the combined result from bottom up.

Demonstrate building two class data series in Python using tuples and arrays, compute class totals, and create a stacked bar plot to compare class one and class two.

Demonstrate constructing a stacked bar plot with two classes using matplotlib, configuring subplots and a fixed width of 0.25, and labeling the title, axes, and ticks.

Learn to create a stacked bar plot in Matplotlib by supplying values and labels, configuring legends for class one and class two, and displaying the chart.

Explore how to create histograms in matplotlib with Python, using bin ranges on the x-axis and frequencies on the y-axis to visualize data distributions.

Plot a histogram in Matplotlib using plt.hist, customize bins and orientation, and explore parameters like range, density, cumulative, bar type, and labels.

Explore creating histograms with matplotlib's plt.hist, define bins, add titles and legends, and interpret frequencies to visualize data distributions.

Learn to build a custom data set with NumPy, generating 10,000 values and 20 bins, and visualize a histogram using Matplotlib colors, ticker, and percent formatter.

generate a distribution for a parameter by creating 10,000 random values with numpy, apply a simple transformation, and prepare data to visualize with a histogram.

Create a histogram with matplotlib by configuring figure and axis via plt.subplot, use a 10 by 8 size, plot axis.hist with bins, and overlay two histograms in frame with colors.

Create and compare two histograms in a single plot using matplotlib and numpy random data, exploring color schemes, and customizing colors, alpha, edges, labels, and a legend.