黑马程序员python教程，8天python从入门到精通，学python看这套就

第105节，你们的地图也是这样子的吗？

from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts

# 数据对象创建
map = Map()
# 数据准备
data =[
    ("北京市",99),
    ("上海市",199),
    ("广动省",399),
    ("江苏省",699),
    ("山东省",999)
]
# 数据添加
map.add("地图",data,"china",)

# 设置全局的数据
map.set_global_opts(
    visualmap_opts=VisualMapOpts(
        is_show=True,
        is_piecewise=True,
        # 数据范围校准
        pieces=[
            {"min":1 ,"max": 9,"label":"1-9人", "color":"#CCFFFF"},
            {"min":10 ,"max": 99,"label":"10-99人", "color":"#FFFF99"},
            {"min":100 ,"max":499 ,"label":"100-499人", "color":"#FF9966"},
            {"min":500 ,"max": 999,"label":"500-999人", "color":"#FF6666"},
            {"min":1000 ,"max": 9999,"label":"1000-9999人", "color":"#CC3333"},
            {"min":10000,"label":"10000以上", "color":"#990033"}
        ])
)
# 数据生成
map.render()
#

P106 数据补充，最笨的方法

import json
from pyecharts.charts import Map
from pyecharts.options import LabelOpts
from pyecharts.options import *
from pyecharts import options as opts


fc = open("D:/疫情.txt",'r',encoding='UTF-8')

fc_data = fc.read()

fc_json = json.loads(fc_data)

fc_province_list = fc_json["areaTree"][0]["children"]

# 接收需要的数据
data_list = []

# 循环获取需要的数据
for province_list in fc_province_list :
    province_name = province_list["name"]
    if province_name == "新疆":
        province_name = province_name+"维吾尔自治区"
    elif  province_name == "西藏":
        province_name = province_name + "自治区"
    elif province_name == "广西":
        province_name = province_name + "壮族自治区"
    elif province_name == "重庆" or province_name == "北京" or province_name == "天津":
        province_name = province_name + "市"
    elif province_name == "内蒙古" :
        province_name = province_name + "自治区"
    elif province_name == "宁夏":
        province_name = province_name + "回族自治区"
    elif province_name == "香港" or province_name == "澳门":
        province_name = province_name + "特别行政区"
    else:
        province_name = province_name + "省"

    province_confirm = province_list["total"]["confirm"]
    data_list.append((province_name,province_confirm))

map = Map()

map.add("个省份确诊人数",data_list,"china")

#全局选项
map.set_global_opts(
    title_opts=TitleOpts("全国疫情地图"),
    visualmap_opts=VisualMapOpts(
        is_show=True,
        is_piecewise=True,
        pieces=[
            {"min": 1, "max": 9, "label": "1-9人", "color": "#CCFFFF"},
            {"min": 10, "max": 99, "label": "10-99人", "color": "#FFFF99"},
            {"min": 100, "max": 499, "label": "100-499人", "color": "#FF9966"},
            {"min": 500, "max": 999, "label": "500-999人", "color": "#FF6666"},
            {"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#CC3333"},
            {"min": 10000, "label": "10000以上", "color": "#990033"}
        ]

    )
)

map.render()

fc.close()


from pyecharts.charts import Bar
from pyecharts.options import *
from pyecharts.charts import Timeline
from pyecharts.globals import ThemeType


# 文件读取
f = open("D:/1960-2019全球GDP数据.csv",'r',encoding="GB2312")
# 读取所有行,并接收
data_lines =  f.readlines()
# 关闭文件
f.close()
# 删除第一条数据
data_lines.pop(0)

# 数据获取
data_dict = {}

# 获取每行的数据
for lines in data_lines:
    year = int(lines.split(",")[0])
    country = lines.split(",")[1]
    gdp = float(lines.split(",")[2])

    try:
        data_dict[year].append([country,gdp])
    except KeyError:
        data_dict[year] = []
        data_dict[year].append([country,gdp])

# print(data_dict)

# 排序年份
sort_list_year = sorted(data_dict.keys())
# 创建时间线对象
timeLine = Timeline({"theme":ThemeType.LIGHT})

# 循环获取国家数据
for year in sort_list_year:
    # 取出前8的国家
    data_dict[year].sort(key=lambda element:element[1],reverse=True)
    year_date = data_dict[year][0:8]
    # 构建柱状图
    x_data =[]
    y_data =[]
    for country_gdp in year_date:
        x_data.append(country_gdp[0])
        y_data.append(country_gdp[1]/100000000)
    # 构建柱状图
    bar = Bar()

    # 数据反转
    x_data.reverse()
    y_data.reverse()
    bar.add_xaxis(x_data)
    bar.add_yaxis("GDP(亿)",y_data,label_opts=LabelOpts(position="right"))

    # 反转 x,y
    bar.reversal_axis()

    # 设置每一年的标题
    bar.set_global_opts(
        title_opts=TitleOpts(title=f"{year}年全球前8GDP数据")
    )
    timeLine.add(bar,str(year))

timeLine.add_schema(
    play_interval=500,
    is_timeline_show=True,
    is_auto_play=True,
    is_loop_play=False
)
timeLine.render("1960-2014年全球GDP数据.html")