python实战打卡---day12_python union[str, none]

作者：盐析白兔 | 2024-03-01 07:20:40

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python union[str, none]

pyecharts水球图
⽔球图的取值 [0.67, 0.30, 0.15]表⽰下图中的三个波浪线，⼀般代表三个百分⽐:

from pyecharts import options as opts
from pyecharts.charts import Liquid, Page
from pyecharts.globals import SymbolType
def liquid() -> Liquid:
    c = (
        Liquid()
        .add("lq", [0.67, 0.30, 0.15])
        .set_global_opts(title_opts=opts.TitleOpts(title="Liquid"))
    )
    return c
liquid().render('C:/jupyter_notebook/venv_env/myhtml/liquid.html')
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pyecharts饼图

from pyecharts import options as opts
from pyecharts.charts import Pie
from random import randint
def pie_base() -> Pie:
    c = (
        Pie()
        .add("", [list(z) for z in zip(['宝马', '法拉利', '奔驰', '奥迪', '⼤众',
        '丰⽥', '特斯拉'],
        [randint(1, 20) for _ in range(7)])])
        .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本⽰例"))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    )
    return c
pie_base().render('C:/jupyter_notebook/venv_env/myhtml/pie_pyecharts.html')
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pyecharts极坐标图

极坐标表⽰为 (夹⾓,半径) ，如(6,94)表⽰夹⾓为6，半径94的点：

import random
from pyecharts import options as opts
from pyecharts.charts import Page, Polar
def polar_scatter0() -> Polar:
    data = [(alpha, random.randint(1, 100)) for alpha in range(101)] # r =random.randint(1, 100)
    print(data)
    c = (
        Polar()
        .add("", data, type_="bar", label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(title_opts=opts.TitleOpts(title="Polar"))
    )
    return c
polar_scatter0().render('C:/jupyter_notebook/venv_env/myhtml/polar.html')
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pyecharts词云图

(“C”,65)表⽰在本次统计中C语⾔出现65次：

from pyecharts import options as opts
from pyecharts.charts import Page, WordCloud
from pyecharts.globals import SymbolType
words = [
    ("Python", 100),
    ("C++", 80),
    ("Java", 95),
    ("R", 50),
    ("JavaScript", 79),
    ("C", 65)
]
def wordcloud() -> WordCloud:
    c = (
        WordCloud()
        # word_size_range: 单词字体⼤⼩范围
        .add("", words, word_size_range=[20, 100], shape='cardioid')
        .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud"))
    )
    return c
wordcloud().render('C:/jupyter_notebook/venv_env/myhtml/wordcloud.html')
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pyecharts系列柱状图

from pyecharts import options as opts
from pyecharts.charts import Bar
from random import randint
def bar_series() -> Bar:
    c = (
        Bar()
        .add_xaxis(['宝马', '法拉利', '奔驰', '奥迪', '⼤众', '丰⽥', '特斯拉'])
        .add_yaxis("销量", [randint(1, 20) for _ in range(7)])
        .add_yaxis("产量", [randint(1, 20) for _ in range(7)])
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar的主标题",
        subtitle="Bar的副标题"))
    )
    return c
bar_series().render('C:/jupyter_notebook/venv_env/myhtml/bar_series.html')
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pyecharts热力图

热⼒图描述的实际是三维关系，x轴表⽰车型，y轴表⽰国家，每个⾊块的颜⾊值代表销量，颜⾊刻度尺显⽰在左下⾓，颜⾊越红表⽰销量越⼤。

import random
from pyecharts import options as opts
from pyecharts.charts import HeatMap
def heatmap_car() -> HeatMap:
    x = ['宝马', '法拉利', '奔驰', '奥迪', '⼤众', '丰⽥', '特斯拉']
    y = ['中国','⽇本','南⾮','澳⼤利亚','阿根廷','阿尔及利亚','法国','意⼤利','加拿⼤']
    value = [[i, j, random.randint(0, 100)]
                for i in range(len(x)) for j in range(len(y))]
    c = (
        HeatMap()
        .add_xaxis(x)
        .add_yaxis("销量", y, value)
        .set_global_opts(
        title_opts=opts.TitleOpts(title="HeatMap"),
        visualmap_opts=opts.VisualMapOpts(),
        )
    )
    return c
heatmap_car().render('C:/jupyter_notebook/venv_env/myhtml/heatmap_pyecharts.html')
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matplotlib绘制动画

matplotlib是python中最经典的绘图包，⾥⾯ animation模块能绘制动画。

# 导入模块
from matplotlib import pyplot as plt
from matplotlib import animation
from random import randint, random
# ⽣成数据， frames_count是帧的个数， data_count每个帧的柱⼦个数
class Data:
    data_count = 32
    frames_count = 2
    def __init__(self, value):
        self.value = value
        self.color = (0.5, random(), random()) #rgb
    # 造数据
    @classmethod
    def create(cls):
        return [[Data(randint(1, cls.data_count)) for _ in range(cls.data_count)]
            for frame_i in range(cls.frames_count)]
    # 绘制动画： animation.FuncAnimation函数的回调函数的参数 fi表⽰第⼏帧，注意要调⽤axs.cla()清除上⼀帧。
def draw_chart():
    fig = plt.figure(1, figsize=(16, 9))
    axs = fig.add_subplot(111)
    axs.set_xticks([])
    axs.set_yticks([])
    # ⽣成数据
    frames = Data.create()
    def animate(fi):
        axs.cla() # clear last frame
        axs.set_xticks([])
        axs.set_yticks([])
        return axs.bar(list(range(Data.data_count)), # X
        [d.value for d in frames[fi]], # Y
        1, # width
        color=[d.color for d in frames[fi]] # color
        )
    # 动画展⽰
    anim = animation.FuncAnimation(fig, animate, frames=len(frames))
    plt.show()
draw_chart()
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pyecharts绘图属性设置方法

pyecharts中，y轴如何显⽰在右侧:
```
# 这是pyecharts中⼀般的绘图步骤：
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
def bar_base() -> Bar:
    c = (
    Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本⽰例",
        subtitle="我是副标题"))
    )
    return c
bar_base().render('C:/jupyter_notebook/venv_env/myhtml/bar.html')
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```
那么，如何设置y轴显⽰在右侧，添加⼀⾏代码：

.set_global_opts(yaxis_opts=opts.AxisOpts(position=‘right’))
```
# 这是pyecharts中⼀般的绘图步骤：
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
def bar_base() -> Bar:
    c = (
    Bar()
        .add_xaxis(Faker.choose())
        .add_yaxis("商家A", Faker.values())
        .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本⽰例",
        subtitle="我是副标题"))
        .set_global_opts(yaxis_opts=opts.AxisOpts(position='right'))
    )
    return c
bar_base().render('C:/jupyter_notebook/venv_env/myhtml/bar11.html'
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```
如何锁定这个属性，⾸先应该在set_global_opts函数的参数中找，它⼀共有以下 11个设置参数，它们位于模块 charts.py:

title_opts: types.Title = opts.TitleOpts(),
legend_opts: types.Legend = opts.LegendOpts(),
tooltip_opts: types.Tooltip = None,
toolbox_opts: types.Toolbox = None,
brush_opts: types.Brush = None,
xaxis_opts: types.Axis = None,
yaxis_opts: types.Axis = None,
visualmap_opts: types.VisualMap = None,
datazoom_opts: types.DataZoom = None,
graphic_opts: types.Graphic = None,
axispointer_opts: types.AxisPointer = None

因为是设置y轴显⽰在右侧，⾃然想到设置参数 yaxis_opts，因为其类型为 types.Axis，所以再进⼊ types.py，同时定位到 Axis：
```
Axis = Union[opts.AxisOpts, dict, None]
1
```
Union是pyecharts中可容纳多个类型的并集列表，也就是Axis可能为 opts.AxisOpt, dict, 或 None 三种类型。查看第⼀个 opts.AxisOpt类，它共定义以下 25个参数：

type_: Optional[str] = None,
name: Optional[str] = None,
is_show: bool = True,
is_scale: bool = False,
is_inverse: bool = False,
name_location: str = “end”,
name_gap: Numeric = 15,
name_rotate: Optional[Numeric] = None,
interval: Optional[Numeric] = None,
grid_index: Numeric = 0, position: Optional[str] =
None,
offset: Numeric = 0,
split_number: Numeric = 5,
boundary_gap: Union[str, bool, None] = None,
min_: Union[Numeric, str, None] = None,
max_: Union[Numeric, str, None] = None,
min_interval: Numeric = 0, max_interval: Optional[Numeric]
= None,
axisline_opts: Union[AxisLineOpts, dict, None] = None,
axistick_opts: Union[AxisTickOpts, dict, None] = None,
axislabel_opts: Union[LabelOpts, dict, None] = None,
axispointer_opts: Union[AxisPointerOpts, dict, None] = None,
name_textstyle_opts: Union[TextStyleOpts, dict, None] = None,
splitarea_opts: Union[SplitAreaOpts, dict, None] = None,
splitline_opts: Union[SplitLineOpts, dict] = SplitLineOpts(),

pyecharts绘图属性设置方法(下)

def bar_border_radius():
    c = (
        Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(animation_delay=500, animation_easing="cubicOut"),theme=ThemeType.MACARONS))
        .add_xaxis( ["草莓", "芒果", "葡萄", "雪梨", "西⽠", "柠檬", "车厘⼦"])
        .add_yaxis("A",
            Faker.values(),category_gap="50%",markpoint_opts=opts.MarkPointOpts(),is_selected=True)
        .set_series_opts(itemstyle_opts={
    "normal": {
    "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0,
    1, [{
    offset: 0,
    color: 'rgba(0, 244, 255, 1)'
    }, {
    offset: 1,
    color: 'rgba(0, 77, 167, 1)'
    }], false)"""),
    "barBorderRadius": [6, 6, 6, 6],
    "shadowColor": 'rgb(0, 160, 221)',
    }}, markpoint_opts=opts.MarkPointOpts(
    data=[
    opts.MarkPointItem(type_="max", name="最⼤值"),
    opts.MarkPointItem(type_="min", name="最⼩值"),
    ]
    ),markline_opts=opts.MarkLineOpts(
    data=[
    opts.MarkLineItem(type_="min", name="最⼩值"),
    opts.MarkLineItem(type_="max", name="最⼤值")
    ]
    ))
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-参数使⽤例⼦"),
    toolbox_opts=opts.ToolboxOpts(),yaxis_opts=opts.AxisOpts(position="right",name="Y轴"),datazoom_opts=opts.DataZoomOpts(),)
    )
    return c
bar_border_radius().render()
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pyecharts原来可以这样快速⼊门(上)

pyecharts官档介绍-五个特性:

1)简洁的 API 设计，使⽤如丝滑般流畅，⽀持链式调⽤;

2)囊括了 30+ 种常见图表，应有尽有;

3)⽀持主流 Notebook 环境，Jupyter Notebook 和 JupyterLab;

4)可轻松集成⾄ Flask，Django 等主流 Web 框架;

5)⾼度灵活的配置项，可轻松搭配出精美的图表
```
from pyecharts.charts import Bar
bar = Bar()
bar.add_xaxis(["衬衫", "⽺⽑衫", "雪纺衫", "裤⼦", "⾼跟鞋", "袜⼦"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
# render 会⽣成本地 HTML ⽂件，默认会在当前⽬录⽣成 render.html ⽂件
# 也可以传⼊路径参数，如 bar.render("mycharts.html")
bar.render('C:/jupyter_notebook/venv_env/myhtml/bar21.html')
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```
Bar(RectChart)是什么意思:RectChart是Bar的⼦类。
pyecharts原来可以这样快速⼊门(中)

⼀切皆options。pyecharts⽤起来很爽的另⼀个重要原因，参数配置项封装的⾮常nice，通过定义⼀些列基础的配置组件，⽐如 global_options.py模块中定义的配置对象有以下 27个

AngleAxisItem,
AngleAxisOpts,
AnimationOpts,
Axis3DOpts,
AxisLineOpts,
AxisOpts,
AxisPointerOpts,
AxisTickOpts,
BrushOpts, CalendarOpts,
DataZoomOpts,
Grid3DOpts, GridOpts,
InitOpts, LegendOpts,
ParallelAxisOpts,
ParallelOpts,
PolarOpts,
RadarIndicatorItem,
RadiusAxisItem,
RadiusAxisOpts,
SingleAxisOpts,
TitleOpts,
ToolBoxFeatureOpts,
ToolboxOpts, TooltipOpts,
VisualMapOpts,

pyecharts原来可以这样快速⼊门(下)

第二个例子

了解上⾯的配置对象后，再看官档给出的第⼆个例⼦，与第⼀个例⼦相⽐，增加了⼀⾏代码： set_global_opts函数。

from pyecharts.charts import Bar
from pyecharts import options as opts
# V1 版本开始⽀持链式调⽤
# 你所看到的格式其实是 `black` 格式化以后的效果
# 可以执⾏ `pip install black` 下载使⽤
bar = (
Bar()
    .add_xaxis(["衬衫", "⽺⽑衫", "雪纺衫", "裤⼦", "⾼跟鞋", "袜⼦"])
    .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
    .set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"))
bar.render()
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set_global_opts函数在pyecharts中被⾼频使⽤，它定义在底层基础模块 Chart.py中，它是前⾯说到的 RectChart的⼦类， Bar类的孙⼦类。浏览下函数的参数：

def set_global_opts(
    self,
    title_opts: types.Title = opts.TitleOpts(),
    legend_opts: types.Legend = opts.LegendOpts(),
    tooltip_opts: types.Tooltip = None,
    toolbox_opts: types.Toolbox = None,
    brush_opts: types.Brush = None,
    xaxis_opts: types.Axis = None,
    yaxis_opts: types.Axis = None,
    visualmap_opts: types.VisualMap = None,
    datazoom_opts: types.DataZoom = None,
    graphic_opts: types.Graphic = None,
    axispointer_opts: types.AxisPointer = None,
    )
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以第⼆个参数 title_opts为例，说明 pyecharts中参数赋值的风格。⾸先， title_opts是默认参数，默认值为 opts.TitleOpts()，这个对象在上⼀节中，我们提到过，是 global_options.py模块中定义的 27个配置对象种的⼀个。其次，pyecharts中为了增强代码可读性，参数的类型都显⽰的给出。此处它的类型为： types.Title. 这是什么类型？它的类型不是 TitleOpts吗？不急，看看Title这个类型的定义：

Title = Union[opts.TitleOpts, dict]

原来 Title可能是 opts.TitleOpts, 也可能是python原⽣的 dict. 通过 Union实现的就是这种类型效果。所以这就解释了官档中为什么说也可以使⽤字典配置参数的问题，如下官档：

# 或者直接使⽤字典参数
# .set_global_opts(title_opts={"text": "主标题", "subtext": "副标题"})
)
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最后，真正的关于图表的标题相关的属性都被封装到TitleOpts类中，⽐如 title, subtitle属性，查看源码，TitleOpts对象还有更多属性：

class TitleOpts(BasicOpts):
    def __init__(
        self,
        title: Optional[str] = None,
        title_link: Optional[str] = None,
        title_target: Optional[str] = None,
        subtitle: Optional[str] = None,
        subtitle_link: Optional[str] = None,
        subtitle_target: Optional[str] = None,
        pos_left: Optional[str] = None,
        pos_right: Optional[str] = None,
        pos_top: Optional[str] = None,
        pos_bottom: Optional[str] = None,
        padding: Union[Sequence, Numeric] = 5,
        item_gap: Numeric = 10,
        title_textstyle_opts: Union[TextStyleOpts, dict, None] = None,
        subtitle_textstyle_opts: Union[TextStyleOpts, dict, None] = None,
    )
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OK. 到此跟随⼊门的官方文档，结合两个例⼦实现的背后源码，探讨了：

1)与包结构组织相关的 init.py；

2)类的继承关系:Bar->RectChart->Chart；

3)链式调⽤；

4)重要的参数配置包 options，以TitleOpts类为例， set_global_opts将它装载到Bar类中实现属性⾃定义。

1分钟学会画 pairplot 图

seaborn 绘图库，基于 matplotlib 开发，提供更⾼层绘图接⼜。 学习使⽤ seaborn 绘制 pairplot 图 pairplot 图能直观的反映出两两特征间的关系，帮助我们对数据集建⽴初步印象，更好的完成分类和 聚类任务。 使⽤ skearn 导⼊经典的 Iris 数据集，共有 150 条记录，4 个特征，target 有三种不同值。

使⽤ seaborn 绘制 sepal_length, petal_length 两个特征间的关系矩阵：

```python
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree
import pandas as pd
iris = load_iris()
df=pd.DataFrame(iris.data, columns=iris.feature_names)
sns.set(style="ticks")
df02 = df.iloc[:,:] # 选择⼀对特征
print(df02)
sns.pairplot(df02)
plt.show()
```

设置颜⾊多显：

```python
import matplotlib.pylab as plt
import seaborn as sns
 
df = sns.load_dataset('iris')
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
 
sns.pairplot(df, kind="scatter", hue="species", markers=["o", "s", "D"], palette="Set2")
plt.show()
```

绘制所有特征散点矩阵：

```python
import matplotlib.pylab as plt
import seaborn as sns
 
df = sns.load_dataset('iris')
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
 
sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))
plt.show()
```
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第五部分结束！！！！！！！

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