day10-pyecharts进阶

pyecharts官方文档:
https://pyecharts.org/#/zh-cn/intro
pyecharts官方文档

注意! 以下在jupyter notebook渲染,并非pycharm

一定要认真看官方文档!!!
一定要认真看官方文档!!!
一定要认真看官方文档!!!

pyecharts常用图表类型

1.柱状图

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker

c=(
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A",Faker.values())
    .add_yaxis("商家B",Faker.values())
    
    # 全局配置项
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"))
)

c.render_notebook()

import pandas as pd

s= pd.Series(data=[32,65,46,99,132,79,73],
             index=['哈士奇','萨摩耶','泰迪','金毛','牧羊犬','吉娃娃','柯基']
            )
s
"""
哈士奇     32
萨摩耶     65
泰迪      46
金毛      99
牧羊犬    132
吉娃娃     79
柯基      73
dtype: int64
"""

from pyecharts import options as opts
from pyecharts.charts import Bar

"""
默认情况下, pyecharts对pandas数据结构是不能识别的

需要使用 tolist() 将数据转化成列表
"""

c=(
    Bar()
    .add_xaxis(s.index.tolist())
    .add_yaxis("testA",s.values.tolist())
    
    # 全局配置项
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"))
)

c.render_notebook() 

2 横向柱状图

from pyecharts import options as opts
from pyecharts.charts import Bar


def bar_pic():
    c = (
        Bar()
        .add_xaxis(s.index.tolist())  # 添加X轴
        .add_yaxis("testA", s.values.tolist()).reversal_axis()  # 翻转XY轴

        # 全局配置项
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Bar-基本示例", subtitle="我是副标题"))

        # 系列配置项
        .set_series_opts(
            label_opts=opts.LabelOpts(position='right'))   # s.values的数据显示位置
        
    )
    return c


# c.render_notebook()

bar_pic().render_notebook()

3 折线图

import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker


x = ['2022-{:0>2d}'.format(s) for s in range(1, 13)]  # 增加
y1 = [5, 10, 26, 30, 35, 30, 20, 26, 40, 46, 40, 50]  # 增加
y2 = [8, 20, 24, 36, 40, 36, 40, 45, 50, 53, 48, 58]  # 增加

c = (
    Line() 
    .add_xaxis(x)
    .add_yaxis("商家A", y1, is_smooth=False)  # 折线
    .add_yaxis("商家B", y2, is_smooth=True)  # 平滑
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Line-smooth"),
        # 坐标轴配置项
        xaxis_opts=opts.AxisOpts(
            axislabel_opts=opts.LabelOpts(
                rotate=-45  #旋转
            )
        )
        
    )
)
c.render_notebook()

4 散点图

这里导入本地的一个Excel文件

df=pd.read_excel('./data/testdata.xlsx')

df

"""筛选数据"""
man_df = df[df['gender'] == 1][['height', 'weight']]

woman_df = df[df['gender'] == -1][['height', 'weight']]

man_df

man_df['height'].values
"""
array([180, 169, 181, 175, 172, 172, 173, 171, 170, 175, 172, 172, 172,
       175, 171, 177, 173, 173, 183, 180, 175, 170, 175, 178, 180, 167,
       172, 175, 180, 177, 173, 180, 173, 175, 170, 178, 171, 174, 170,
       169, 165, 167, 172, 170, 183, 172, 175, 168, 170, 155], dtype=int64)
"""

from pyecharts import options as opts
from pyecharts.charts import Scatter  # 散点图对象
from pyecharts.faker import Faker


c = (
    Scatter()
    .add_xaxis(man_df['height'])  # 对于x轴, 数值类型的数据直接传Series
    # .add_xaxis(list(range(0, 200)))
    .add_yaxis("男生", man_df['weight'].tolist(), label_opts=opts.LabelOpts(is_show=False))
    .add_yaxis("女生", woman_df['weight'].tolist(), label_opts=opts.LabelOpts(is_show=False))
    
    
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Scatter-显示分割线"),
        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
        yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),
    )
)
c.render_notebook()

5 饼图

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker

c = (
    Pie()
    .add("系列项1", [list(z) for z in zip(Faker.choose(), Faker.values())],
         # radius如果给的是一个字符串, 设置饼图的半径,  如果传列表, 第一项是内半径, 第二项是外半径
        radius=['50%', '70%'],
        )
    
    .add("系列项2", [list(z) for z in zip(Faker.choose(), Faker.values())],
         # radius如果给的是一个字符串, 设置饼图的半径,  如果传列表, 第一项是内半径, 第二项是外半径
        radius='40%',
        )
    
    
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Pie-基本示例"),
        legend_opts=opts.LegendOpts(
            type_='scroll',
            pos_left='20%',
            legend_icon='circle', #图例样式
#             orient='virtical',
            page_icon_size=30
        )
    )
    .set_series_opts(
        # 标签格式化器   {a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）
        label_opts=opts.LabelOpts(formatter="{a}--{b}: {c}-{d}%")
    )
)
c.render_notebook()

[list(z) for z in zip(Faker.choose(), Faker.values())]
"""
[['周一', 86],
 ['周二', 140],
 ['周三', 132],
 ['周四', 122],
 ['周五', 56],
 ['周六', 86],
 ['周日', 124]]
"""

6 词云图

本地的的json文件,以此生成一张词云图

from pyecharts import options as opts
from pyecharts.charts import WordCloud
import json
# 本地的文件
data = json.loads(open('./data/word_data.json', encoding='utf-8').read())

c = (
    WordCloud()
    .add(
        "",
        data,
        shape='cardioid',
        word_size_range=[20, 100],
        textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-自定义文字样式"))
)


c.render_notebook()

使用jieba统计词语出现的次数,按照某形状绘制词云图

# 在终端安装第三方中文分词库 
pip install jieba

import jieba

text_list = jieba.lcut(open('./data/contend.txt', mode='r', encoding='utf-8').read())
text_list

"""统计词语出现的次数"""
ciyu_dict = {
    
    }
for text in text_list:
    if text not in ciyu_dict.keys():
        ciyu_dict[text] = 1
    else:
        ciyu_dict[text] += 1

ciyu_dict

输出中有\n 这样无用的词语,也有无参考价值的此意

需要删除的词语

"""去除无用词语, 指的是没有情感取向的词语"""
not_use = open('./data/cn_stopwords.txt', mode='r', encoding='utf-8').readlines()
not_use = [txt.strip() for txt in not_use]

keys = list(ciyu_dict.keys())
for key in keys:
    if key in not_use:
        del ciyu_dict[key]

del ciyu_dict[' ']  # 删除空格
del ciyu_dict['\n']  # 删除\n
ciyu_dict

"""指定形状绘制词云图"""
c = (
    WordCloud()
    .add(
        "文章",
        data_pair=list(ciyu_dict.items()),
        word_size_range=[6, 66],  # 字体大小范围
        textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
        mask_image='./data/music.png'  # 指定图片形状
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-自定义文字样式"))
)


c.render_notebook()

7 Geo

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker

c = (
    Geo()
    .add_schema(maptype="china")  # 中国地图
    .add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
    .set_series_opts(label_opts=opts.LabelOpts())
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(is_piecewise=True),  # 是否分段
        title_opts=opts.TitleOpts(title="Geo-VisualMap（分段型）"),
    )
)
c.render_notebook()

8 Map

from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker

c = (
    Map()
    .add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_series_opts(label_opts=opts.LabelOpts())
    .set_global_opts(title_opts=opts.TitleOpts(title="Map-基本示例"))
)
c.render_notebook()

[list(z) for z in zip(Faker.provinces, Faker.values())]

"""
[['广东省', 61],
 ['北京市', 113],
 ['上海市', 73],
 ['江西省', 50],
 ['湖南省', 143],
 ['浙江省', 96],
 ['江苏省', 50]]
"""

9 分布方式

9.1 层叠多图

from pyecharts import options as opts
from pyecharts.charts import Bar, Line
from pyecharts.faker import Faker

v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
v3 = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]

# 绘制的柱状图
bar = (
    Bar()
    .add_xaxis(Faker.months)
    .add_yaxis("蒸发量", v1)
    .add_yaxis("降水量", v2)
    .extend_axis(
    	#设置y轴属性，设置了y轴的标签格式为"{value} °C"，并且y轴的刻度间隔为5。
        yaxis=opts.AxisOpts(
            axislabel_opts=opts.LabelOpts(formatter="{value} °C"), interval=5
        )
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Overlap-bar+line"),
        #设置y轴属性，设置了y轴的标签格式为"{value} ml"
        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} ml"))
    )
)

# 绘制折线图
# yaxis_index 值需要设置为1 , 因为层叠多图需要新增轴
line = Line().add_xaxis(Faker.months).add_yaxis("平均温度", v3, yaxis_index=1)  
bar.overlap(line)   # 在柱状图的基础上绘制折线图

bar.render_notebook()

9.2 并行多图

from pyecharts import options as opts
from pyecharts.charts import Line, Scatter, Grid  # Grid 并行多图对象
from pyecharts.faker import Faker

scatter = Scatter()
scatter.add_xaxis(Faker.choose())
scatter.add_yaxis("商家A", Faker.values())
scatter.add_yaxis("商家B", Faker.values())
scatter.set_global_opts(
    title_opts=opts.TitleOpts(title="Grid-Scatter"),
    legend_opts=opts.LegendOpts(pos_left="20%"),
)

line = Line()
line.add_xaxis(Faker.choose())
line.add_yaxis("商家A", Faker.values())
line.add_yaxis("商家B", Faker.values())
line.set_global_opts(
    title_opts=opts.TitleOpts(title="Grid-Line", pos_right="5%"),
    legend_opts=opts.LegendOpts(pos_right="20%"),
)
line2 = Line()
line2.add_xaxis(Faker.choose())
line2.add_yaxis("商家A", Faker.values())
line2.add_yaxis("商家B", Faker.values())
line2.set_global_opts(
    title_opts=opts.TitleOpts(title="Grid-Line", pos_right="5%"),
    legend_opts=opts.LegendOpts(pos_right="20%"),
)

grid = Grid()
grid.add(scatter, grid_opts=opts.GridOpts(pos_bottom='55%'))
grid.add(line, grid_opts=opts.GridOpts(pos_top='55%', pos_left='55%'))
grid.add(line2, grid_opts=opts.GridOpts(pos_top='55%', pos_right='55%'))

grid.render_notebook()

9.3 顺序多图

from pyecharts import options as opts
from pyecharts.charts import Bar, Geo, Page  # 导入顺序多图功能
from pyecharts.faker import Faker

bar = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(legend_opts=opts.LegendOpts(pos_left="20%"))
)

geo = (
    Geo()
    .add_schema(maptype="china")
    .add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(),
        title_opts=opts.TitleOpts(title="Grid-Geo-Bar"),
    )
)

page = Page()
page.add(bar)
page.add(geo)

page.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import Bar, Geo, Tab  # 导入选项卡多图功能
from pyecharts.faker import Faker

bar = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(legend_opts=opts.LegendOpts(pos_left="20%"))
)

geo = (
    Geo()
    .add_schema(maptype="china")
    .add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(),
        title_opts=opts.TitleOpts(title="Grid-Geo-Bar"),
    )
)

tab = Tab()
tab.add(bar, '柱状图')
tab.add(geo, '地图')

tab.render_notebook()

鼠标点击“柱状图”或者“地图”可以切换

9.4 时间线轮播图

from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline  # 时间线轮播功能
from pyecharts.faker import Faker

x = Faker.choose()  # 随机获取坐标轴数据

tl = Timeline()  # 创建一个时间线轮播组件

for i in range(2015, 2020):
    bar = Bar()
    bar.add_xaxis(x)
    y_a = Faker.values()
    y_b = Faker.values()
    bar.add_yaxis("商家A", y_a)
    bar.add_yaxis("商家B", y_b)
    bar.set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i)))
    tl.add(bar, "{}年".format(i))

tl.render_notebook()