用Python分析过去10年地震分布~

import requests
import pandas as pd
# 读取数据
df = pd.read_excel(r'eqList.xlsx')

longitude_list = []
latitude_list = []
# 百度地图经纬度转换为高德地图经纬度
for i , location in enumerate(df[['经度(°)','纬度(°)']].values):
    location = str(location[0])+','+str(location[1])
    url = 'https://restapi.amap.com/v3/assistant/coordinate/convert?'
    
    parames = {
        'locations':location,
        'coordsys':'baidu',
        'key':'你的key',
        }
    
    r = eval(requests.get(url, params=parames).json()['locations'])
    # 经度
    longitude_list.append(r[0])
    # 纬度
    latitude_list.append(r[1])
    print(f'
{i+1}',end='')

df['经度(°)'] = longitude_list
df['纬度(°)'] = latitude_list

# 转换为时间格式
df['发震时刻'] = pd.to_datetime(df['发震时刻'])
# 获取年月
df['月份'] = df['发震时刻'].apply(lambda x: str(x)[:7])
# 获取小时
df['小时'] = df['发震时刻'].dt.hour

citys = []
provinces = []
for i , location in enumerate(df[['经度(°)','纬度(°)']].values):
    location = str(location[0])+','+str(location[1])
    url = 'https://restapi.amap.com/v3/geocode/regeo?'
    params = {
        'location':location,
        'key':'你的key',
        'extensions':'base',
        'batch':'false',
        'roadlevel':0,
        }
    
    r = requests.get(url, params=params)
    data = r.json()['regeocode']
    city = data['addressComponent']['city']
    province = data['addressComponent']['province']
    if len(city)==0:
        city = province
    citys.append(city)
    provinces.append(province)
    print(f'
{i+1}',end='')
df['城市'] = citys
df['省'] = provinces

# 中小地震（[2,4.6]）
df['年份'] = df['发震时刻'].dt.year
df_cn = df[df['省']!='[]']
df_xiao = df_cn.query('2<=`震级(M)`<=4.6')
df_xiao.groupby('年份')['发震时刻'].count().to_frame('次数').reset_index()

# 各省 破坏性地震
df_province = df_cn.query('`震级(M)`>=4.7').groupby('省')['发震时刻'].count().to_frame('次数').sort_values(by='次数',ascending=False).reset_index()

import folium.plugins as plugins
import folium

df = df[df['震级(M)']>=4.7]
ss1 = [[latitude,longitude] for latitude,longitude in df[['纬度(°)', '经度(°)']].values.tolist()]
m = folium.Map([39.904989, 116.405285],
              tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr='高德')
groups = folium.FeatureGroup('')
for l,label in zip(ss1,df['参考位置'].to_list()):
    groups.add_child(
        folium.CircleMarker(
            location=l,
            radius=1,
            color='red',
            fill=True,
            fill_color='red',
            fill_opacity=0.4,
            popup=folium.Popup(html=label,max_width=100),
        )
    )
m.add_child(groups)
m.add_child(folium.LatLngPopup()) 
m.save('地震分布.html')               

data_all = df[['纬度(°)', '经度(°)','震级(M)']].values.tolist()
m = folium.Map([39.904989, 116.405285],
               tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr='高德')
hm = plugins.HeatMap(data_all, radius=10, 
                     gradient={.1: 'green', .6: 'yellow', 1: 'red'},
                     )
hm.add_to(m)
hm.save('地震热力图.html')

data_move = []
date_list = df['月份'].sort_values().unique()
for month in date_list:
    data_move.append(df[df['月份'] == month][['纬度(°)', '经度(°)','震级(M)']].values.tolist())
m = folium.Map([39.904989, 116.405285],
               tiles='https://webrd01.is.autonavi.com/appmaptile?&x={x}&y={y}&z={z}&lang=zh_cn&size=1&scale=1&style=7',
               zoom_start=6,
               attr='高德')
time_index = df['月份'].sort_values().unique().tolist()
hm = plugins.HeatMapWithTime(data_move, 
                             index=time_index,
                             radius=10)

hm.add_to(m)
hm.save('地震动态热力图.html')