Data Visualization in Python Masterclass for Beginners

Learn to represent and work with string data types in python. Discover how to use single or double quotes, escape characters, raw strings, and format with braces.

Explore how to create and manipulate dictionaries in Python, including key value pairs, accessing keys and values, updating entries, and using items for iteration.

Explore boolean values true and false, master Python comparison operators, including == versus =, and >, <, as well as None and string comparisons.

Learn NumPy fundamentals, from arrays and NaN and inf handling to statistical operations, shaping techniques, random numbers, where indexing, concatenation, sorting, and date handling.

Master NumPy arrays in Python by creating 1D and 2D arrays from lists, inspecting shapes, and converting data types such as int, float, and string for flexible data handling.

Learn statistical operations on a NumPy 2D array, including mean, minimum, maximum, variance, standard deviation, and median, with practical steps for feature extraction.

Explore shaping and reshaping a 2d NumPy array, converting from 3x2 to 2x3, and understanding size constraints. Learn how ravel and flatten flatten a 2d array to 1d, behaving similarly.

Learn to write and read numpy arrays using savetxt and loadtxt with comma delimiters, and save multiple arrays with savez and load them from .npz and .npy formats.

Learn how to import pandas, build a dictionary of data, and create a DataFrame with fruits and vegetables as columns. Contrast a Series (one column) with a DataFrame (multiple columns).

Learn to read and write data files in Python using pandas, including reading CSV and Excel files, selecting columns, inspecting with head and tail, setting index, and exporting with to_csv.

Practice inspecting a data frame's columns, using F.dot columns and keyboard shortcuts to reveal column names, then rename columns or update them with a dictionary.

Explore how to use a lambda function in Python to compute mean values from scores, apply row-wise transformations, and check for null values in data.

Learn to create a box plot for data frame columns, interpret min, first quartile, median, third quartile, max, and outliers, and compare mean versus median in horizontal and vertical layouts.

Explore Matplotlib basics by creating line, scatter, box, and pie plots. Build and customize subplots, polar charts, and real-time animations including area-under-curve visualizations.

Create line plots in Python with Matplotlib and NumPy, generating data with linspace and random integers, and customize x and y axes, colors, markers, and line styles.

Master scatter, bar, and histogram plots in Python, tune axis details and bar width, and preview a box plot to analyze data distributions.

Learn to create a pie plot with matplotlib, set labels and colors, and customize with autopct, startangle, and explode to highlight data in the 2025 data visualization masterclass for beginners.

Learn to customize a pie plot by changing text color and size, using extracted text from the plot, and coloring slices, black for all, white for one.

Create a nested pie chart in matplotlib by building outer and inner pies with specified radii and width, using color maps for both layers.

Learn to label a pie plot in Python by combining labels and percentages inside slices, display ingredient names, customize text color, and configure legends for clear data visualization.

Plot 20 bars on a polar axis by using random radii and angles, set a seed for reproducibility, and tune width, color, and alpha.

Show how to build a scatterplot on a polar axis by pairing random angles and radii, coloring points with a continuous colormap and adjusting point area.

Plot the integral curve in matplotlib by defining a function, shading the area under the curve between x=2 and x=9, and annotating the integral expression.

Explore creating real-time matplotlib animations using the animation package and func_animation, converting a 2d static CPU usage plot into a dynamic visualization.

Learn to animate a real time Matplotlib plot in a notebook, using a sliding window of 200 data points to visualize CPU usage as new data arrives.

Plot date-time data with line and scatter plots by blending pandas plotting and matplotlib, customize labels and titles, and switch styles to produce clear, publication-ready temperature visuals.

Explore Seaborn data visualization in Python, mastering numerical plots (scatter, line), categorical plots (box, violin, bar), and distribution plots (joint, rug) to select effective figures for modeling.

Control the line plot size and axes, plot the time axis and signal values, and enable a confidence interval to show mean estimates across time points and sensors.

Plot multiple signals with line plots in Python using a relational plot, color by coherence, and style by T1 and T2 with adjustable palette and thickness.

Learn to create subplots by distributing plots across a canvas using columns and rows, splitting by time (lunch/dinner) and smoker status, with wrap options for readable grids.

Learn to create a seaborn box plot of diamond prices by color, using the diamonds dataset, and explore how hue for cut and dodge settings affect readability.

Learn to control plotted figure aesthetics in Python by adjusting styles, color palettes, and axis properties, and customize multiple sine plots for clear visualization.

Generate a 100-row, three-column data frame of random numbers, compute cumulative sums, and plot an interactive line chart to compare columns with plotly.

Generate cufflink data to visualize a 3d bubble plot with x, y, z coordinates and bubble size, and build a heat map from a correlation matrix with spectral color scale.

Explore the Boston dataset with 506 instances and 30 predictive attributes to predict median house value, and perform exploratory data analysis and plot styling in Python.

Explore how pd.describe() provides a full statistical snapshot of each column, including count, mean, variance, standard deviation, min, max, and quartiles, to compare distributions.

Explore how a seaborn pair plot reveals correlations among 14 variables, using a 7x7 matrix of scatter plots and diagonal distributions to assess relationships with the price target.

Explore box plots and relational plots in seaborn to analyze house prices by proximity to the Charles River, using hue, style, and size to reveal distributions, medians, and outliers.

Explore joint plots to examine the relationship between house size and price, comparing scatter, hexbin, and kernel density estimates to reveal distributions and handle outliers.

Create a joint plot with a scatter overlay to visualize room size versus price, customize color, marker size, and alpha, and switch to a kde plot for pure density visualization.