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python——Echarts现交互式动态可视化

🕗 发布于 2024-10-09 16:24 echarts  前端  javascript  

数据展示

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155.704128.2218116.472495.093379.0724

在 Python 中，可以使用 Echarts 来实现交互式动态可视化。Echarts 是一个强大的可视化库，能够创建各种精美的图表和可视化效果。

以下是实现步骤：

一、安装必要的库

首先，确保你已经安装了 Python 和以下库：

  • pyecharts：用于在 Python 中生成 Echarts 图表。

可以使用以下命令安装 pyecharts：

pip install pyecharts

二、创建图表

  1. 导入所需的模块：
from pyecharts.charts import Bar
from pyecharts import options as opts
  1. 创建一个柱状图示例：
bar = Bar()
bar.add_xaxis(["A", "B", "C", "D", "E"])
bar.add_yaxis("Series 1", [10, 20, 30, 40, 50])
bar.set_global_opts(title_opts=opts.TitleOpts(title="Sample Bar Chart"))

三、渲染图表

  1. 可以将图表渲染为 HTML 文件：
bar.render("bar_chart.html")
  1. 也可以在 Jupyter Notebook 中直接显示图表：
bar.render_notebook()


import pyecharts.options as opts
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Timeline, Grid, Bar, Map, Pie
import pandas as pd
data = pd.read_excel('全国各省财政收入.xlsx',index_col=0)

years=list(data.keys()) #获取列名
citys=list(data.index)    #获取索引行名
citys=[city.replace('省','').replace('市','').replace('自治区','') for city in citys]
datas=[]
for time in years:
    dict_year={}
    dict_year['time']=time
    data_list=[[i,j] for i,j in zip(citys,list(data[time]))]
    dict_year['data']=sorted(data_list, key=(lambda x: x[1]),reverse=True)
    datas.append(dict_year)
def get_year_chart(year: int):
    map_data = [datasDict["data"] for datasDict in datas if datasDict["time"]==year][0][:10]
    print(map_data)
    min_data, max_data = (
            min([d[1] for d in map_data]),
            max([d[1] for d in map_data]),
        )
    map_chart = (
            Map(init_opts=opts.InitOpts(theme=ThemeType.DARK))## 实例化一个Map对象
            .add(
                series_name="",
                data_pair=map_data,
                label_opts=opts.LabelOpts(is_show=True),
                is_map_symbol_show=False,
                maptype="china"
            )#创建地图
            #Map图表中add基本参数为：add(‘图例名称’,date,maptype)，maptype选项分为：全球：world；中国：china；
            #set_series_opts(label_opts=opts.LabelOpts(is_show=False))作用为不显示地图中省份名称
            #设置全局配置项
            .set_global_opts(
                title_opts=opts.TitleOpts(
                    title="{}年以来中国各省GDP排名情况".format(year),
                    subtitle="GDP单位:亿元",
                    pos_left="center",
                    pos_top="top",
                    title_textstyle_opts=opts.TextStyleOpts(
                        font_size=25, color="rgba(123,104,238, 0.9)"
                    ),
                ),
                visualmap_opts=opts.VisualMapOpts(
                    is_calculable=True,
                    dimension=0,
                    pos_left="10",
                    pos_top="center",#
                    range_text=["High", "Low"],
                    range_color=["lightskyblue", "yellow", "orangered"],#visualMap 组件过渡颜色 (可以自定义)(颜色从淡到深)
                    textstyle_opts=opts.TextStyleOpts(color="#ddd"),#文字样式配置项
                    min_=min_data,
                    max_=max_data,
                ),#视觉映射配置项，设置最大值后不同金额会显示不同颜色
            )
        )
    bar_x_data = [x[0] for x in map_data]
    bar_y_data = [x[1] for x in map_data]
    bar = (
            Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
            .add_xaxis(xaxis_data=bar_x_data)
            .add_yaxis(
                series_name="",
                y_axis=bar_y_data,
                label_opts=opts.LabelOpts(
                    is_show=True, position="right", formatter="{b}: {c}"
                ),
            )
            .reversal_axis()#横向柱状图展示
            .set_global_opts(
                xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),#对X轴和Y轴标上名字
                yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),#柱状图X轴和Y轴上面的个性化标记,旋转坐标轴: 解决坐标轴名字过长的问题
                tooltip_opts=opts.TooltipOpts(is_show=True),#要设置柱形图上面的提示框，这个时候需要用到TooltipOpts。
                visualmap_opts=opts.VisualMapOpts(
                    is_calculable=True,#是否显示拖拽用的手柄
                    dimension=0,
                    pos_left="10",
                    pos_top="center",
                    range_text=["High", "Low"],
                    range_color=["lightskyblue", "yellow", "orangered"],
                    textstyle_opts=opts.TextStyleOpts(color="#ddd"),
                    min_=min_data,
                    max_=max_data,
                ),
            )
        )
    pie_data = [[x[0], x[1]] for x in map_data]
    percent_sum = sum([x[1] for x in map_data])
    print(percent_sum)
    rest_value = 0
    for d in map_data:
        rest_percent = 100.0
        rest_percent = rest_percent - percent_sum
        rest_value = d[1] * (rest_percent / d[1])
    pie = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK))
        .add(
            series_name="",
            data_pair=pie_data,
            radius=["12%", "20%"],# #百分比根据实际情况进行修改（空心和实心）,饼图的半径，数组的第一项是内半径，第二项是外半径
            center=["80%", "90%"],#饼图的中心（圆心）坐标，数组的第一项是横坐标，第二项是纵坐标
            itemstyle_opts=opts.ItemStyleOpts(
                border_width=1, border_color="rgba(0,0,0,0.3)"#RGBA，比如'rgba(128, 128, 128, 0.5),0.5参数通道表示不透明度
            ),## 图元样式配置项
        )
        .set_global_opts(
            tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
            legend_opts=opts.LegendOpts(is_show=False),
        )
    )
    grid_chart = (
        Grid()#Grid 类：并行显示多张图
        .add(
            bar,
            grid_opts=opts.GridOpts(
                pos_left="10", pos_right="45%", pos_top="70%", pos_bottom="5"
            ),
        )
        .add(pie, grid_opts=opts.GridOpts())
        .add(map_chart, grid_opts=opts.GridOpts())
    )

    return grid_chart
# 生成时间轴实现轮播
time_list = list(range(2000,2020))
#timeline本身是时间轴，Timeline 类：提供时间线轮播多张图
timeline = Timeline(
    init_opts=opts.InitOpts(width="900px", height="400px", theme=ThemeType.DARK)
)
for time in time_list:
    g = get_year_chart(year=time)
    timeline.add(g, time_point=str(time))
timeline.add_schema(
    orient="vertical",
    is_auto_play=True,#是否自动播放。
    is_inverse=True,##是否反向放置 timeline，反向则首位颠倒过来
    play_interval=500,#表示播放的速度（跳动的间隔）
    pos_left="null",
    pos_right="5",
    pos_top="20",
    pos_bottom="20",
    width="50",
    label_opts=opts.LabelOpts(is_show=True, color="#fff"),
)
timeline.render(path='test_bar.html')

import pyecharts.options as opts
from pyecharts.charts import Timeline, Map
import pandas as pd

# 读取 Excel 文件中的数据（假设你的 Excel 文件中包含省份和对应年份的 GDP 数据）
data = pd.read_excel('全国各省财政收入.xlsx', index_col=0)
years = list(data.keys())
citys = list(data.index)
citys = [city.replace('省', '').replace('市', '').replace('自治区', '') for city in citys]

# 重新组织数据
total_data = {}
for year in years:
    temp = list(data[year])
    total_data[year] = []
    for i in range(len(temp)):
        total_data[year].append({"name": citys[i], "value": temp[i]})


# 获取指定年份的中国地图图表
def get_map_chart(year: int) -> Map:
    map_chart = (
        Map()
      .add("GDP", total_data[year], "china")
      .set_global_opts(
            title_opts=opts.TitleOpts(title=f"{year}年各省 GDP 分布"),
            visualmap_opts=opts.VisualMapOpts(max_=max([item["value"] for item in total_data[year]]),
                                              min_=min([item["value"] for item in total_data[year]]))
        )
    )
    return map_chart


# 生成时间轴的图
timeline = Timeline()

for y in years:
    timeline.add(get_map_chart(year=y), time_point=str(y))

# 设置时间轴自动播放等属性
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("gdp_china_map_timeline.html")

 

import pyecharts.options as opts
from pyecharts.charts import Timeline, Bar, Pie
import pandas as pd


# 读取 Excel 文件中的数据
data = pd.read_excel('全国各省财政收入.xlsx', index_col=0)
years = list(data.keys())
citys = list(data.index)
citys = [city.replace('省', '').replace('市', '').replace('自治区', '') for city in citys]

# 重新组织数据
total_data = {}
for year in years:
    temp = list(data[year])
    total_data[year] = []
    for i in range(len(temp)):
        total_data[year].append({"name": citys[i], "value": temp[i]})

    max_data = max(temp)
    sum_data = sum(temp)
    total_data[str(year) + "max"] = int(max_data / 100) * 100
    total_data[str(year) + "sum"] = sum_data


# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Bar:
    bar = (
        Bar()
       .add_xaxis(xaxis_data=[item["name"] for item in total_data[year]])
       .add_yaxis(
            series_name="财政收入",
            y_axis=[item["value"] for item in total_data[year]],
            label_opts=opts.LabelOpts(is_show=False),
        )
       .set_global_opts(
            title_opts=opts.TitleOpts(
                title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"
            ),
            tooltip_opts=opts.TooltipOpts(
                is_show=True, trigger="axis", axis_pointer_type="shadow"
            ),
            legend_opts=opts.LegendOpts(
                selected_map={
                    "财政收入": False,
                }
            ),
        )
    )
    return bar


# 生成时间轴的图
timeline = Timeline()

for y in years:
    timeline.add(get_year_overlap_chart(year=y), time_point=str(y))

# 设置时间轴自动播放等属性
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("finance_indices_2003.html")

 

 

import pyecharts.options as opts
from pyecharts.charts import Pie
import pandas as pd
import pandas as pd
from pyecharts.charts import Bar, Scatter
from pyecharts import options as opts

# 读取 Excel 文件中的数据
data = pd.read_excel('全国各省财政收入.xlsx', index_col=0)
years = list(data.keys())
citys = list(data.index)
citys = [city.replace('省', '').replace('市', '').replace('自治区', '') for city in citys]

# 重新组织数据
total_data = {}
for year in years:
    temp = list(data[year])
    total_data[year] = []
    for i in range(len(temp)):
        total_data[year].append({"name": citys[i], "value": temp[i]})

# 选择某一年份（这里以第一年为例）来绘制饼图
year = years[0]
data_for_pie = total_data[year]

# 获取饼图
def get_pie_chart() -> Pie:
    pie = (
        Pie()
      .add(
            "",
            [(item["name"], item["value"]) for item in data_for_pie],
            radius=["30%", "75%"],
        )
      .set_global_opts(
            title_opts=opts.TitleOpts(title=f"{year}年全国各省财政收入饼图"),
            tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),
        )
      .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    )
    return pie

get_pie_chart().render("pie_chart.html")

 


原文地址：https://blog.csdn.net/weixin_66547608/article/details/142750464

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