Python-Matplotlib visualization (6) - custom axes to make statistical charts clear and easy to understand

Python- Matplotlib visualization (6) - custom axes to make statistical charts clear and easy to understand

• • foreword
  • Control tick spacing
  • Control tick labels
  • • Easier way to set up
    • Advanced Tick Label Control
  • Use a logarithmic scale
  • use polar coordinates
  • series link

foreword

In the series of blog posts, we have learned how to customize the color and style of the drawing to make the drawing more exquisite and meet the aesthetic requirements. You can use Matplotlib to draw complex and exquisite statistical graphs, and also explain the usage of annotations. However, in many cases, we need to customize the coordinate axes to meet the requirements of study or work, so that the statistical graphs become clear and concise , considering this requirement, Matplotlib provides a large number of operations for coordinate axes, using these methods to customize the coordinate axes according to requirements can make the statistical chart more clear.

Control tick spacing

So far, we let Matplotlib automatically handle the position of the ticks on the axes, but sometimes we need to override the default axis tick configuration in order to estimate the coordinates of the points in the graph more quickly. </