python中pyecharts做旭日图的用法 python 画旭日图

文章目录

• 四、实例

• 4.1 带有 plotly.express 的旭日图

• 4.1.1 基础旭日图
• 4.1.2 带有 plotly.express 的矩形 DataFrame 的旭日形
• 4.1.3 改变path顺序，从而改变父子关系
• 4.1.4 在 px.sunburst 中具有连续颜色参数的矩形 DataFrame 的 Sunburst
• 4.1.5 在 px.sunburst 中具有离散颜色参数的矩形 DataFrame 的 Sunburst
• 4.1.6 对离散颜色使用显式映射
• 4.1.7 具有缺失值的矩形数据

四、实例

旭日图可视化从根到叶径向向外跨越的分层数据。与Icicle 图表和Treemaps类似，层次结构由labels( namesfor px.icicle) 和parents属性定义。根从中心开始，子代被添加到外环。

4.1 带有 plotly.express 的旭日图

Plotly Express是 Plotly 的易于使用的高级界面，它对各种类型的数据进行操作并生成易于样式化的图形。

使用px.sunburst，DataFrame 的每一行都表示为旭日形的一个扇区。

4.1.1 基础旭日图

import plotly.express as px
data = dict(
    character=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
    parent=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
    value=[10, 14, 12, 10, 2, 6, 6, 4, 4])

fig = px.sunburst(
    data,
    names='character',
    parents='parent',
    values='value',
)
fig.show()

4.1.2 带有 plotly.express 的矩形 DataFrame 的旭日形

分层数据通常存储为矩形数据框，不同的列对应于层次结构的不同级别。px.sunburst可以采用path与列列表对应的参数。请注意，path id如果给出，parent则不应提供。

import plotly.express as px
df = px.data.tips()
print(df)
'''
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
'''
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill')
fig.show()

4.1.3 改变path顺序，从而改变父子关系

import plotly.express as px
df = px.data.tips()
print(df)
'''
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
'''
fig = px.sunburst(df, path=['day', 'sex', 'time'], values='total_bill')
fig.show()

4.1.4 在 px.sunburst 中具有连续颜色参数的矩形 DataFrame 的 Sunburst

如果color传递了参数，则节点的颜色将计算为其子节点颜色值的平均值，并按其值加权。

import plotly.express as px
import numpy as np
df = px.data.gapminder().query("year == 2007")
print(df)
'''
                 country continent  year  ...     gdpPercap  iso_alpha  iso_num
11           Afghanistan      Asia  2007  ...    974.580338        AFG        4
23               Albania    Europe  2007  ...   5937.029526        ALB        8
35               Algeria    Africa  2007  ...   6223.367465        DZA       12
47                Angola    Africa  2007  ...   4797.231267        AGO       24
59             Argentina  Americas  2007  ...  12779.379640        ARG       32
...                  ...       ...   ...  ...           ...        ...      ...
1655             Vietnam      Asia  2007  ...   2441.576404        VNM      704
1667  West Bank and Gaza      Asia  2007  ...   3025.349798        PSE      275
1679         Yemen, Rep.      Asia  2007  ...   2280.769906        YEM      887
1691              Zambia    Africa  2007  ...   1271.211593        ZMB      894
1703            Zimbabwe    Africa  2007  ...    469.709298        ZWE      716

[142 rows x 8 columns]
'''
fig = px.sunburst(df, path=['continent', 'country'], values='pop',
                  color='lifeExp', hover_data=['iso_alpha'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))
fig.show()

4.1.5 在 px.sunburst 中具有离散颜色参数的矩形 DataFrame 的 Sunburst

当 的参数color对应于非数值数据时，使用离散颜色。如果一个扇区的color所有子项的列值相同，则使用相应的颜色，否则使用离散颜色序列的第一种颜色。

import plotly.express as px
df = px.data.tips()
print(df)
'''
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
'''
fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='day')
fig.show()

在下面的示例中，Saturday和Sunday扇区的颜色相同，Dinner因为只有周六和周日的晚餐条目。但是，对于女性 -> 星期五，有午餐和晚餐，因此使用“混合”颜色（此处为蓝色）。

import plotly.express as px
df = px.data.tips()
print(df)
'''
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
'''
fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='time')
fig.show()

4.1.6 对离散颜色使用显式映射

有关离散颜色的更多信息，请参阅专用页面。

import plotly.express as px
df = px.data.tips()
print(df)
'''
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
'''
fig = px.sunburst(df,
                  path=['sex', 'day', 'time'],
                  values='total_bill',
                  color='time',
                  color_discrete_map={'(?)':'black', 'Lunch':'gold', 'Dinner':'darkblue'})
fig.show()

4.1.7 具有缺失值的矩形数据

如果数据集不是完全矩形，则缺失值应提供为None. 请注意，None条目的父母必须是叶子，即None它不能有其他孩子（否则会引发ValueError）。

raise ValueError(
ValueError: ('None entries cannot have not-None children', regions    North
vendors     None
sectors    Other
Name: 4, dtype: object)

import plotly.express as px
import pandas as pd
vendors = ["A", "B", "C", "D", None, "E", "F", "G", "H", None]
sectors = ["Tech", "Tech", "Finance", "Finance", "Other",
           "Tech", "Tech", "Finance", "Finance", "Other"]
regions = ["North", "North", "North", "North", "North",
           "South", "South", "South", "South", "South"]
sales = [1, 3, 2, 4, 1, 2, 2, 1, 4, 1]
df = pd.DataFrame(
    dict(vendors=vendors, sectors=sectors, regions=regions, sales=sales)
)
print(df)
'''
  vendors  sectors regions  sales
0       A     Tech   North      1
1       B     Tech   North      3
2       C  Finance   North      2
3       D  Finance   North      4
4    None    Other   North      1
5       E     Tech   South      2
6       F     Tech   South      2
7       G  Finance   South      1
8       H  Finance   South      4
9    None    Other   South      1
'''
fig = px.sunburst(df, path=['regions', 'sectors', 'vendors'], values='sales')
fig.show()