import pandas as pd

# 讀取CSV
df = pd.read_csv('cities_data.csv')

# 處理缺失值
df = df.dropna(subset=['latitude', 'longitude'])  # 刪除缺失經(jīng)緯度的記錄
df['population'] = df['population'].fillna(df['population'].median())  # 中位數(shù)填充人口缺失

# 統(tǒng)一格式
df['category'] = df['category'].str.upper()  # 類別統(tǒng)一大寫

from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="geoapiExercises")

def get_coordinates(city_name):
    location = geolocator.geocode(city_name)
    if location:
        return location.latitude, location.longitude
    return None, None

# 示例：為"成都"獲取坐標(biāo)
lat, lon = get_coordinates("成都")

import plotly.express as px

# 基礎(chǔ)散點地圖
fig = px.scatter_geo(df, 
                     locations="city",  # 使用城市名列
                     size="population",  # 點大小映射人口
                     color="gdp",       # 顏色映射GDP
                     projection="natural earth",  # 地圖投影類型
                     title="中國主要城市人口與GDP分布")

fig.show()

# 假設(shè)有省份級數(shù)據(jù)
province_df = pd.read_csv('provinces_data.csv')

fig = px.choropleth(province_df,
                    locations="province",  # 省份列
                    locationmode="china",   # 指定為中國地圖
                    color="growth_rate",   # 顏色映射增長率
                    color_continuous_scale="RdYlGn",  # 紅黃綠色標(biāo)
                    title="各省經(jīng)濟(jì)增長率熱力圖")

fig.show()

# 假設(shè)有航線數(shù)據(jù)
flight_df = pd.DataFrame({
    'origin': ['北京', '上海', '廣州'],
    'destination': ['上海', '廣州', '北京'],
    'flights': [120, 95, 80]
})

fig = px.line_geo(flight_df,
                  locations="origin",
                  color="flights",
                  color_continuous_scale=px.colors.sequential.Plasma,
                  projection="orthographic",  # 球體投影
                  title="主要城市航線熱度圖")

fig.update_geos(fitbounds="locations")  # 自動調(diào)整視角
fig.show()

fig.update_geos(
    scope="asia",  # 只顯示亞洲
    visible=[],    # 隱藏所有國家邊界
    landcolor="rgb(217, 217, 217)",  # 陸地顏色
    oceancolor="rgb(186, 224, 238)",  # 海洋顏色
    showcoastlines=True  # 顯示海岸線
)

fig.update_traces(
    text=[f"{row['city']}<br>人口: {row['population']}萬<br>GDP: {row['gdp']}億" 
          for _, row in df.iterrows()],
    hoverinfo="text"  # 自定義懸停信息
)

# 先創(chuàng)建底圖
base_fig = px.scatter_geo(df, locations="city", size="population", color_discrete_sequence=["gray"])

# 添加新圖層
base_fig.add_trace(
    go.Scattergeo(
        locations=df["city"],
        locationmode="country names",
        mode="markers+text",
        marker=dict(size=df["gdp"]/1000, color="red"),
        text=df["city"],
        textposition="top center",
        showlegend=False
    )
)

# 讀取數(shù)據(jù)
air_df = pd.read_csv('air_quality.csv')

# 清洗數(shù)據(jù)
air_df = air_df.dropna(subset=['latitude', 'longitude'])
air_df['pm25'] = pd.to_numeric(air_df['pm25'], errors='coerce')
air_df = air_df.dropna(subset=['pm25'])

# 添加分類字段
def categorize_pm(value):
    if value < 35:
        return "優(yōu)"
    elif value < 75:
        return "良"
    elif value < 115:
        return "輕度污染"
    elif value < 150:
        return "中度污染"
    else:
        return "重度污染"

air_df['category'] = air_df['pm25'].apply(categorize_pm)

fig = px.scatter_geo(air_df,
                     lat="latitude",
                     lon="longitude",
                     color="category",
                     size="pm25",
                     color_discrete_map={
                         "優(yōu)": "green",
                         "良": "yellow",
                         "輕度污染": "orange",
                         "中度污染": "red",
                         "重度污染": "purple"
                     },
                     size_max=30,
                     title="全國城市PM2.5實時分布圖",
                     scope="china")

# 添加自定義懸停信息
fig.update_traces(
    hovertemplate="<b>%{customdata[0]}</b><br>PM2.5: %{customdata[1]}μg/m3<br>等級: %{customdata[2]}"
)
fig.for_each_trace(lambda t: t.update(customdata=air_df[['city', 'pm25', 'category']].values))

fig.show()

fig.update_geos(center=dict(lon=116.4, lat=39.9))  # 定位到北京

# 預(yù)定義色標(biāo)列表
px.colors.sequential.Blues  # 藍(lán)漸變色
px.colors.diverging.RdYlGn  # 紅黃綠色標(biāo)
px.colors.qualitative.Pastel  # 柔和分類色

fig.write_image("map.png", scale=2, width=1600, height=900)
# 需要安裝kaleido庫：pip install kaleido

fig.update_layout(
    updatemenus=[
        dict(
            type="buttons",
            direction="right",
            buttons=list([
                dict(args=[{"color": "category"}], label="按等級分類"),
                dict(args=[{"color": "pm25"}], label="按數(shù)值漸變")
            ])
        )
    ]
)