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Python for Data Visualization
Learn different Python tools for visualizing dataPreview the first lesson free — get full access to all 20 lessons.
Intermediate Provider : Brian Higgins 8 chapters · 20 Lessons · 1h 33m in Arabic, German, English, Spanish, French, Portuguese, Chinese
Course Description
The course will focus on using the Python Matplotlib library to visualize data. Python coding will be done using Python notebooks (.ipynb files) in the JupyterLab tool, where you’ll learn how to draw line graphs, histograms, heatmaps, and box plots using Matplotlib. We’ll show you how to use HVPlot library to add interactive widgets to plots to gain deeper insights into the data being visualized.
You’ll also use the Python Numpy library to save values in arrays and to generate random number data for visualization examples. You’ll see how the Pandas library can be used to store data in a DataFrame, handle time series data, perform mathematical calculations and plot the calculated data. Then you’ll learn how the Seaborn library can add features to a heatmap, while the Scipy library generates a KDE line to plot above a histogram. Finally, we’ll show you how to style visualizations by adding colors, labels, and grids.
What You'll Learn
- Create line graphs, histograms, heatmaps, and box plots using the Python Matplotlib library
- Import, generate, and manipulate data with NumPy and Pandas for visualization
- Build interactive plots and widgets using the HVPlot library
- Style visualizations by adding colors, labels, and grids
- Use the Seaborn library to enhance heatmaps and the SciPy library to generate a KDE line above a histogram
- Plot time series and calculated temporal data stored in a Pandas DataFrame
Key Takeaways
- The course uses the Python Matplotlib library to visualize data through line graphs, histograms, heatmaps, and box plots.
- Python coding is done in Python notebooks (.ipynb files) using the JupyterLab tool.
- The HVPlot library is used to add interactive widgets to plots for deeper insight into the data.
- The NumPy library stores values in arrays and generates random number data, while the Pandas library stores data in a DataFrame, handles time series data, and performs calculations.
- The Seaborn library adds features to a heatmap and the SciPy library generates a KDE line to plot above a histogram.
Frequently Asked Questions
What Python libraries does this course cover?
The course covers Matplotlib for visualizing data, HVPlot for interactive widgets, NumPy for arrays and random number data, Pandas for storing data in a DataFrame and handling time series, Seaborn for adding features to heatmaps, and SciPy for generating a KDE line above a histogram.
What tools will I use to write the code?
Python coding is done using Python notebooks (.ipynb files) in the JupyterLab tool.
What types of visualizations will I learn to create?
You will learn to draw line graphs, histograms, heatmaps, and box plots, including interactive line plots and interactive heatmaps using HVPlot.
How does the course handle interactive visualizations?
It shows how to use the HVPlot library to add interactive widgets to plots, including toolbar widgets and a filter widget, to gain deeper insights into the data being visualized.
Does the course cover working with time-based data?
Yes. It covers plotting temporal data with a line graph, plotting calculated temporal data, and creating a box plot to describe temporal data, with Pandas used to handle time series data.
