10 ways to visualize data in Python

　　How can I visualize in python?

There are not many things involved! Python has made it easy for you to visualize-just by visualizing the two exclusive libraries (libraries), commonly known as matplotlib and Seaborn. Have you heard of it?

Matplotlib: The Python-based drawing library provides complete 2D and limited 3D graphics support for matplotlib. This is useful for publishing high-quality images in a cross-platform interactive environment. It can also be used for animations.

Seaborn:seaborn is a python used to create informative and attractive statistical graphics libraries. This library is based on the matplotlib. Seaborn provides a variety of features, such as built-in themes, palettes, functions, and tools, to visualize the single-factor, two-factor, linear regression, data matrix, Statistical time series, and more, so that we can further build complex visualizations.

　　What are the different visualizations I can make?

The most commonly used visualization technology is described in the "A Comprehensive Guide to Data visualization", which was recently published. If you have not read this book before further further study, we recommend that you refer to this book.

The following is the Python code and its output results. I just use the following datasets to create these visualizations.

　　

　　Import data sets

　　

　　1. Histogram

　　

　　

　　2. Box Line diagram

　　

　　3. Violin Chart

　　

　　4. Bar chart

　　

　　5. Line chart

　　

　　6. Stacked Column Chart

　　

　　7. Scatter chart

　　

　　8. Bubble chart

　　

　　9. Pie chart

　　

　　10. Heat Map

You can try to draw a heat map based on two variables, such as the x-axis is gender, the y-axis is BMI, and the data points are sales values.

　　

　　

　　Conclusion

Now, you must have realized the beauty of data visualization, why not try it yourself? In future articles, we will also explore the use of Python for map visualization and word cloud.

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10 ways to visualize data in Python