Welcome! On this site you will learn data visualization with Python. You will find code examples of Python graphs made with matplotlib, seaborn, plotly and other packages
CHART TYPES
Distribution
Distribution charts allows visualizing how the data distributes along the support and comparing several groups
Correlation
Correlation charts are useful for visualizing the relationship between two or more variables
Evolution
Evolution charts show how a variable or a set or variables evolve, generally through the space or the times
Spatial
These plots show geographical areas and their relations based on one or several variables associated to those areas
Part of a whole
Part of a whole charts summarize the data in portions or slices. This type of graphs are very useful for representing counts or groups
Ranking
Ranking charts allows visualizing the classification between the observations of a variable or between different variables
GRAPHICS LIBRARIES
Matplotlib
With a syntax similar to Matlab, matplotlib is the most used low-level charting library in Python
Seaborn
seaborn is a matplotlib wrapper. Makes it possible to create beautiful charts with few lines of code
Plotly
If you prefer dynamic charts over static, then plotly / plotly Express is your best choice