Hands-on Data Visualization With Python

Explore Matplotlib, Seabourne and Plomley to compare their pros and cons, and see how each enables quick, powerful visualizations with minimal code for storytelling.

Explore Google Colab as a cloud-based notebook for data visualization with Python. Learn to mount Google Drive, set working directories, run code, and manage libraries.

Differentiate numerical data from categorical data and apply cleaning and preprocessing to prepare data for visualizations. Use pandas frames and nan handling to prepare data for matplotlib visualizations.

Learn to identify and handle NaNs in data frames by applying methods such as filling, dropping, mean imputation, and forward or backward propagation to prepare data for visualization.

Encode categorical data into numerical representations using pandas get_dummies to prepare inputs for machine learning models and visualizations. Apply encoding to remove bias and convert categories into structured features.

Convert data types in a data frame using as type, check dtypes, and ensure compatible formats for visualization with pandas and numpy, preventing errors in Python projects.

Learn to build simple visualizations with Matplotlib in Python, using figures and axes, plotting data on x and y coordinates, including 3D plots and object-oriented and pyplot workflows.

Create a simple Matplotlib plot to compare three neural networks on the same graph. Label axes, add a legend, and set a title to illustrate distribution.

Build a bar chart with matplotlib using plt.bar on simple data, exploring range, length, and color options to visualize values.

Learn to create a stacked bar chart in matplotlib by plotting two groups, price and tax, and customize width, style, and tick rotation to improve readability.

Learn to build subplots in matplotlib, arranging a one-by-four grid that mixes bar, scatter, and line plots from a data dictionary, adjust figure size and shared axes, and improve readability.

Use matplotlib to create a scatterplot of year, day, and length to reveal patterns in bitcoin ransomware data from the UCI dataset, using pandas to handle first hundred thousand rows.

Master practical data visualization with Python by building a scatter plot from csv using pandas and matplotlib, exploring 100000 rows with year, day, length, color mapping, preparing for seaborn.

Discover Seaborn, a Python library built on Matplotlib that integrates with pandas to create statistical graphs. Emphasize plot elements with a dataset-oriented declarative API for insightful visualizations.

Learn to use Seaborn's catplot with the tips data set to visualize day versus total bill. See Seaborn's architecture on matplotlib and its built-in data sets.

Explore Seaborn real plot visualizations using penguins data to compare flipper length and bill length with scatter and line options, incorporating island, sex, species, and theme customization.

Visualize financial data over a year by generating random four-column data and plotting with seaborn and matplotlib, using a white grid theme.

Plot the closing prices of real stock data using seaborn line plot, fetching data with pandas data reader from Yahoo for Google, Microsoft, Apple, and Tesla.

Visualize 2015 US flight delays with Seaborn relplot, mapping distance to x, air time to y, hue by flight number, and size by departure delay, using a cube helix palette.

Apply seaborn in a practical challenge by building a joint plot to explore how training hours, company type, and city development index relate to job change in data scientists.

Learn to clean data and create a Seaborn joint plot of city development index versus training hours, colored by company type, using kde and univariate/bivariate insights.

Explore saving a seaborn box plot figure: create a new data frame, visualize pulse by diet (low fat vs no fat), and save the figure for download in Colab.

Explore interactive data visualization with plotline express by building a bar graph from the Gapminder Italy data, highlighting year and population.

Learn to customize a Plotly bar graph in Python, using hover data, color scales, labels, and height to explore Italy’s life expectancy over time.

Visualize age vs BMI with a Plotly Express scatter plot, coloring points by diabetes outcome to reinforce the plot structure and data interpretation.

Explore plotly graph objects to visualize stock data, building a time-series scatterplot of Apple stock open price and plotting open, high, and low using go.

Explore plotting open, high, and low prices for Tesla stock with Plotly in a three-line visualization, including hover text, layout updates, and date-sorted traces.

Explore interactive three-dimensional visualizations with Plotly in Python by building a Mesh3D plot using x, y, and z data, with reproducible random data and customizable color and axis ticks.

Build chloroplasts maps to visualize May 2020 county employment using FIPS codes in Plotly, with emphasis on data quality, documentation reference, and solving missing states.

Build data visualization with python using Plotly express chloroplasts Mapbox to plot May 2020 employment by county with FIPS codes. Learn to check NaNs and duplicates and adjust color scale.

Apply a lambda function in Python to fix missing or misformatted FIPS area codes and plot corrected unemployment data using Plotly, exploring color scales and figure factory alternatives.

Visualize covid data across counties in New York and New Jersey with Plotly animations, using a scatter plot where bubble size shows confirmed cases and color encodes the state.