PYTHON CHARTS | The definitive Python data visualization site

CHART TYPES

Distribution charts allows visualizing how the data distributes along the support and comparing several groups

Correlation charts are useful for visualizing the relationship between two or more variables

Evolution charts show how a variable or a set or variables evolve, generally through the space or the times

These plots show geographical areas and their relations based on one or several variables associated to those areas

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 charts allows visualizing the classification between the observations of a variable or between different variables

With a syntax similar to Matlab, matplotlib is the most used low-level charting library in Python

seaborn is a matplotlib wrapper. Makes it possible to create beautiful charts with few lines of code

If you prefer dynamic charts over static, then plotly / plotly Express is your best choice