Data Visualization with Python for Beginners
- 4.3
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Brief Introduction
Learn how to start visualizing all your data directly in your codeDescription
Data and analytics are becoming increasingly important in our world and in modern day businesses. Usually data analytics at one point or another also means including or creating graphics. This can help you get a better sense of the data as well as help you better communicate your findings to others.
Python is a favourite among data professionals, and performing analytics in Python is becoming increasingly more common. Therefore, it's great to be able to also directly create custom graphs alongside all the analytics.
In this course we'll start with some basic setup, and then get into different types of plots that we can create as well as how we can customize them.
We'll start off covering basic line and scatter plots, just to get a hang of the library, and then move further to create a larger variety of graphs. You'll learn how to add error bars, how to use and represent colours for intensities, how to use images in your plots, as well as how to create 3d plots.
Additionally, we'll spend some time looking at the customization options that Matplotlib provides, so that we can change the way our axes and axis ticks and labels look, learn how to add annotations and math formulas, or also how to hide parts of a graph so that we have a reduced and cleaner version.
Requirements
- Requirements
- Basic Python knowledge
- A Python 3 Environment to Code in
Knowledge
- Make line plots in Python
- Make scatter plots in Python
- Make 1-dimensional and 2-dimensional histogram plots
- Customize your plots by adding colour and changing line styles
- Customize your axis by changing the tick labels
- Add custom titles and labels to your plots
- Add custom text to your plots
- Adjust the size of your figures
- Add a legend to your plots
- Be able to save your figures in a desired format to your computer
- Change the scale of the axis to better graph logarithmic data
Maximilian Schallwig
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