Python中的 plotly.express.scatter_polar()函数

Python的 Plotly 库对于数据可视化和简单轻松地理解数据非常有用。 Plotly 图形对象是一个易于使用的高级界面。

plotly.express.scatter_polar()函数

该函数用于创建极坐标图。极坐标图表示沿径向和角轴的数据。

Syntax: plotly.express.scatter_polar(data_frame=None, r=None, theta=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, title=None, template=None, width=None, height=None)

Parameters:

data_frame: DataFrame or array-like or dict needs to be passed for column names.

r, theta: This parameter is either a name of a column in data_frame, or a pandas Series or array_like object. It is used to position marks along the radial and angular axis in polar coordinates respectively.

color: This parameters assign color to marks.

symbol: This parameter is used to assign symbols to marks. It is either a name of a column in data_frame, or a pandas Series or array_like object.

size: This parameter is used to assign mark sizes. It is either a name of a column in data_frame, or a pandas Series or array_like object.

hover_name:  Values from this column or array_like appear in bold in the hover tooltip.

hover_data: This parameter is used to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip.

title: The figure title

width: The figure width in pixels.

height: The figure height in pixels.

示例 1：

Python3

import plotly.express as px
  
df = px.data.tips()
  
plot = px.scatter_polar(df, r = 'day', 
                        theta = 'total_bill')
plot.show()

输出：

示例 2：使用颜色参数

Python3

import plotly.express as px
  
df = px.data.tips()
  
plot = px.scatter_polar(df, r = 'day',
                        theta = 'total_bill', 
                        color='time')
plot.show()

输出：

示例 3：使用符号参数

Python3

import plotly.express as px
  
df = px.data.tips()
  
plot = px.scatter_polar(df, r = 'day', 
                        theta = 'total_bill',
                        color = 'time',
                        symbol = 'tip')
plot.show()

输出：