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这期内容当中小编将会给大家带来有关Python中怎么绘制矢量数据，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
学习目标：
为多个矢量数据集绘制地图，并根据属性进行配色
自定义地图图例
在本节中，将学习如何自定义地图符号和用于在Python中表示矢量数据的颜色和符号，使用geopandas和matplotlib进行地图绘制
首先导入需要使用到的包:
import os import matplotlib.pyplot as plt import numpy as np from shapely.geometry import box import geopandas as gpd import earthpy as et
# 下载数据
# data = et.data.get_data('spatial-vector-lidar')
os.chdir(os.path.join(et.io.HOME, 'learning','python_data_plot'))# 导入数据 sjer_roads_path="data/california/madera-county-roads/tl_2013_06039_roads.shp" sjer_roads = gpd.read_file(sjer_roads_path) print(type(sjer_roads['RTTYP'])) print(sjer_roads['RTTYP'].unique())
<class 'pandas.core.series.Series'> ['M' None 'S' 'C']
可以看出道路类型中有一些缺失的值，由于需要绘制所有的道路类型，甚至那些设置为None的道路类型，下面将RTTYP属性None为Unknown
sjer_roads['RTTYP'].replace(np.nan,"Unknown",inplace=True) # sjer_roads.loc[sjer_roads['RTTYP'].isnull(), 'RTTYP'] = 'Unknown' print(sjer_roads['RTTYP'].unique())
['M' 'Unknown' 'S' 'C']
如果使用geopandas.Plot()绘制数据，当设置了column =参数后，则geopandas将为线条自动选择颜色，可以使用legend = True参数添加图例
fig, ax = plt.subplots(figsize=(14,6)) sjer_roads.plot(column='RTTYP', categorical=True, legend=True, ax=ax ) # 调整图例位置 leg = ax.get_legend() leg.set_bbox_to_anchor((1.15,0.5)) # 隐藏边框 ax.set_axis_off() plt.show()
为了按属性值绘制一个矢量图层，这样每条道路图层就会根据它各自的属性值来着色，所以图例也代表了同样的符号，需要三个步骤：
创建一个将特定颜色与特定属性值关联的字典
循环遍历并将该颜色应用于每个属性值
最后，在绘图中添加一个label参数，以便调用ax.legend()生成最终的图例
下面，先创建一个字典来定义您希望使用哪种颜色绘制每种道路类型：
# Create a dictionary where you assign each attribute value to a particular color
roadPalette = {'M': 'blue',
'S': 'green',
'C': 'purple',
'Unknown': 'grey'}
roadPalette{'M': 'blue', 'S': 'green', 'C': 'purple', 'Unknown': 'grey'}接下来，循环遍历每个属性值，并使用字典中指定的颜色用该属性值绘制线条
fig, ax = plt.subplots(figsize=(10,10))
# 根据道路类型分组进行绘制
for ctype,data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
data.plot(color=color,
ax=ax,
label=ctype
)
ax.legend(bbox_to_anchor=(1.0, .5), prop={'size': 12})
ax.set(title='Madera County Roads')
ax.set_axis_off()
plt.show()
可以通过linewidth =属性对线条宽度进行设置，
fig, ax = plt.subplots(figsize=(10, 10))
# Loop through each group (unique attribute value) in the roads layer and assign it a color
for ctype, data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
data.plot(color=color,
ax=ax,
label=ctype,
linewidth=4) # Make all lines thicker
# Add title and legend to plot
ax.legend()
ax.set(title='Madera County Roads')
ax.set_axis_off()
plt.show()
与着色相同，先创建线条宽度与类型的映射关系，然后分组进行循环绘制
# Create dictionary to map each attribute value to a line width
lineWidths = {'M': 1, 'S': 1, 'C': 4, 'Unknown': .5}
# Plot data adjusting the linewidth attribute
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_axis_off()
for ctype, data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
data.plot(color=color,
ax=ax,
label=ctype,
# Assign each group to a line width using the dictionary created above
linewidth=lineWidths[ctype])
ax.legend()
ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')
plt.show()
在上面的实验中，使用label=True显示图例，ax.legend()的loc=参数可以对图例位置进行调整，ax.legend()的常用参数有：
loc=(how-far-right,how-far-above)
fontsize=，设置图例字体大小
frameon=，是否显示图例边框
lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}
fig, ax = plt.subplots(figsize=(10, 10))
# Loop through each attribute value and assign each
# with the correct color & width specified in the dictionary
for ctype, data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
label = ctype
data.plot(color=color,
ax=ax,
linewidth=lineWidths[ctype],
label=label)
ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')
# Place legend in the lower right hand corner of the plot
ax.legend(loc='lower right',
fontsize=15,
frameon=True)
ax.set_axis_off()
plt.show()观察当将图例frameon属性设置为False并调整线宽时会发生什么情况，注意loc = ()参数被赋予一个元组，它定义了图例相对于绘图区域的x和y的位置
lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}
fig, ax = plt.subplots(figsize=(10, 10))
for ctype, data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
label = ctype
data.plot(color=color,
ax=ax,
linewidth=lineWidths[ctype],
label=label)
ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')
ax.legend(loc=(1, 0.5),
fontsize=15,
frameon=False,
title="LEGEND")
ax.set_axis_off()
plt.show()
同时对线宽和颜色进行调整
roadPalette = {'M': 'grey', 'S': "blue",
'C': "magenta", 'Unknown': "lightgrey"}
lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}
fig, ax = plt.subplots(figsize=(10, 10))
for ctype, data in sjer_roads.groupby('RTTYP'):
color = roadPalette[ctype]
label = ctype
data.plot(color=color,
ax=ax,
linewidth=lineWidths[ctype],
label=label)
ax.set(title='Madera County Roads \n Pretty Colors')
ax.legend(loc='lower right',
fontsize=20,
frameon=False)
ax.set_axis_off()
plt.show()接下来，向地图添加另一个图层，看看如何创建一个更复杂的地图，添加SJER_plot_centroids shapefile，并同时表示两个图层的图例
该点图层包含三种类型：grass,soil,trees
# 导入点图层 sjer_plots_path ="data/california/neon-sjer-site/vector_data/SJER_plot_centroids.shp" sjer_plots = gpd.read_file(sjer_plots_path) sjer_plots.head(5)
<div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}</style> <table border="1" class="dataframe"> <thead> <tr > <th></th> <th>Plot_ID</th> <th>Point</th> <th>northing</th> <th>easting</th> <th>plot_type</th> <th>geometry</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>SJER1068</td> <td>center</td> <td>4111567.818</td> <td>255852.376</td> <td>trees</td> <td>POINT (255852.376 4111567.818)</td> </tr> <tr> <th>1</th> <td>SJER112</td> <td>center</td> <td>4111298.971</td> <td>257406.967</td> <td>trees</td> <td>POINT (257406.967 4111298.971)</td> </tr> <tr> <th>2</th> <td>SJER116</td> <td>center</td> <td>4110819.876</td> <td>256838.760</td> <td>grass</td> <td>POINT (256838.760 4110819.876)</td> </tr> <tr> <th>3</th> <td>SJER117</td> <td>center</td> <td>4108752.026</td> <td>256176.947</td> <td>trees</td> <td>POINT (256176.947 4108752.026)</td> </tr> <tr> <th>4</th> <td>SJER120</td> <td>center</td> <td>4110476.079</td> <td>255968.372</td> <td>grass</td> <td>POINT (255968.372 4110476.079)</td> </tr> </tbody> </table> </div>
就像上面所做的一样，创建一个字典来指定与每个图形类型相关联的颜色
pointsPalette = {'trees': 'chartreuse',
'grass': 'darkgreen', 'soil': 'burlywood'}
lineWidths = {'M': .5, 'S': 2, 'C': 2, 'Unknown': .5}
fig, ax = plt.subplots(figsize=(10, 10))
for ctype, data in sjer_plots.groupby('plot_type'):
color = pointsPalette[ctype]
label = ctype
data.plot(color=color,
ax=ax,
label=label,
markersize=100)
ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')
ax.legend(fontsize=20,
frameon=True,
loc=(1, .1),
title="LEGEND")
ax.set_axis_off()
plt.show()
接下来，在道路图层上叠加绘制点数据，然后创建一个包含线和点的自定义图例
注意: 在这个例子中，两个图层的投影信息必须匹配
# Reproject the data # 数据投影 sjer_roads_utm = sjer_roads.to_crs(sjer_plots.crs)
fig, ax = plt.subplots(figsize=(10, 10))
# 点图层绘制
for ctype, data in sjer_plots.groupby('plot_type'):
color = pointsPalette[ctype]
label = ctype # label参数对于图例的生成很重要
data.plot(color=color,
ax=ax,
label=label,
markersize=100)
# 道路图层绘制
for ctype, data in sjer_roads_utm.groupby('RTTYP'):
color = roadPalette[ctype]
label = ctype
data.plot(color=color,
ax=ax,
linewidth=lineWidths[ctype],
label=label)
ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')
ax.legend(fontsize=15,
frameon=False,
loc=('lower right'),
title="LEGEND")
ax.set_axis_off()
plt.show()上述就是小编为大家分享的Python中怎么绘制矢量数据了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注亿速云行业资讯频道。
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