如何用Python画出好看的地图
大数据DT
共 4798字，需浏览 10分钟
·
2020-11-20 22:23
导读：最近正好在学空间数据处理，本文就探讨一下用Python如何画出好看的地图。
folium Plotly GeoPandas+Matplotlib
01 folium
1import folium
2import pandas as pd
3#输入上海经纬度，尺度
4latitude = 31.2
5longitude = 121.5
6sh_map = folium.Map(location=[latitude, longitude], zoom_start=10)
7sh_map
1sh_map = folium.Map(location=[latitude, longitude], zoom_start=10,tiles='Stamen Toner')
2sh_map
1# 创建特征组
2hotpots = folium.map.FeatureGroup()
3# Loop through the 200 crimes and add each to the incidents feature group
4for lat, lng, in zip(data.lat, data.lon):
5 hotpots.add_child(
6 folium.CircleMarker(
7 [lat, lng],
8 radius=7, # define how big you want the circle markers to be
9 color='yellow',
10 fill=True,
11 fill_color='red',
12 fill_opacity=0.4
13 )
14 )
15
16# 把火锅特征组放到上海地图上
17sh_map =folium.Map(location=[latitude, longitude], zoom_start=10)
18sh_map.add_child(hotpots)
1latitudes = list(datas.lat)
2longitudes = list(datas.lon)
3labels = list(datas.name)
4for lat, lng, label in zip(latitudes, longitudes, labels):
5 folium.Marker([lat, lng], popup=label).add_to(sh_map)
6sh_map
1hots=data.groupby('adname',as_index=False).agg({'name':"count"})
2hots.columns=['district','num']
3hots
1#输入上海经纬度，尺度
2latitude = 31.2
3longitude = 121.5
4map = folium.Map(location=[latitude, longitude], zoom_start=12)
5folium.Choropleth(
6 geo_data=geo_data,
7 data=hots,
8 columns=['district','num'],
9 key_on='feature.properties.name',
10 #fill_color='red',
11 fill_color='YlOrRd',
12 fill_opacity=0.7,
13 line_opacity=0.2,
14 highlight=True,
15 legend_name='Hotpot Counts in Shanghai'
16).add_to(map)
17map
02 Plotly
1import plotly.express as px
2import plotly.graph_objs as go
3latitude = 31.2
4longitude = 121.5
5fig = px.choropleth_mapbox(
6 data_frame=hots,
7 geojson=geo_data,
8 color='num',
9 locations="district",
10 featureidkey="properties.name",
11 mapbox_style="carto-positron",
12 color_continuous_scale='viridis',
13 center={"lat": latitude, "lon": longitude},
14 zoom=7.5,
15)
16fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
17fig.show()
03 GeoPandas+Matplotlib
1import matplotlib.pyplot as plt
2import matplotlib as mpl
3# 中文和负号的正常显示
4mpl.rcParams['font.sans-serif'] = ['Times New Roman']
5mpl.rcParams['font.sans-serif'] = [u'SimHei']
6mpl.rcParams['axes.unicode_minus'] = False
7# 裁剪上海shape
8fig, ax = plt.subplots(figsize=(20, 10))
9ax=geo_data.plot(ax=ax,facecolor='grey',edgecolor='white',linestyle='--',alpha=0.8)
10ax.axis('off') # 移除坐标轴
1need_data=pd.merge(geo_data,hots[['district','num']],left_on='name',
2 right_on='district')
3need_data
1need_data = gpd.GeoDataFrame(need_data)
2need_data.geom_type
1fig, ax = plt.subplots(figsize=(20, 10))
2need_data.plot('num', cmap='OrRd',ax=ax)
3ax.axis('off') # 移除坐标轴
4cmap = mpl.cm.get_cmap('OrRd')
5norm = mpl.colors.Normalize(min(need_data['num']), max(need_data['num']))
6fcb = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),ax=ax)
7ax=geo_data.plot(ax=ax,facecolor='grey',edgecolor='white',linestyle='--',alpha=0.2)
8ax.set_title('Hotpot Count in Shanghai', fontsize=20)
1fig, ax = plt.subplots(figsize=(20, 10))
2need_data.plot('num', cmap='OrRd',ax=ax)
3for idx, _ in enumerate(need_data.geometry.representative_point()):
4 # 提取行政区名称
5 region = need_data.loc[idx, 'name']
6 ax.text(_.x, _.y, region, ha="center", va="center", size=8)
7ax.axis('off') # 移除坐标轴
8cmap = mpl.cm.get_cmap('OrRd')
9norm = mpl.colors.Normalize(min(need_data['num']), max(need_data['num']))
10fcb = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),ax=ax)
11ax=geo_data.plot(ax=ax,facecolor='grey',edgecolor='white',linestyle='--',alpha=0.2)
12ax.set_title('Hotpot Count in Shanghai', fontsize=20)
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