python实战打卡---day12
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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') -
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') -
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') -
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') -
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') -
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') -
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() -
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')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'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]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() -
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')Bar(RectChart)是什么意思:RectChart是Bar的⼦类。
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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()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, )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"}) )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, )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。
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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()
```