使用 GeoPandas 在地图上绘制点组会产生空白图像
Using GeoPandas to plot groups of points on a map produces a blank image
我最近开始使用 GeoPandas 制作地图并发现它非常有用。我已经使用 Pandas 一段时间了，我发现迁移到 GeoPandas 相对轻松。但是，在使用 .dissolve() 函数对点进行分组后，我在地图上绘制点时遇到问题。
基本上，我选择了英国邮政编码数据以及从国家统计局邮政编码目录 (ONSPD) 下载的相关经度和纬度值。经度和纬度值基于 CRS WGS84。我可以转换为 CRS OSGB36 并毫无问题地在地图上绘制所有点。但是，如果我使用 .dissolve() 方法根据其他变量（例如 'group1' 和 'groups'）对点进行分组，我将无法再绘制这些点。
这是我到目前为止将所有点绘制在一起的代码：
import pandas as pd
import geopandas as gif
import matplotlib.pyplot as plot
import shapely
# Define a Pandas dataframe containing postcodes and
postcodeDF = pd.DataFrame({'pcd': ['RM175AG', 'NP181PH', 'LS8 1EN', 'HG1 1XQ', 'G11 6YB', 'TN218AB', 'GU138AL', 'CV344BD', 'YO126PH', 'SO172WT', 'PR2 8HN', 'TF1 2HD', 'M31 4FR', 'CH460UB', 'EX111LN', 'TS214DX', 'BN4 2LS', 'FY8 1XL', 'KA256BP', 'DA1 1QR'],
'ctry': ['E92000001', 'W92000004', 'E92000001', 'E92000001', 'S92000003', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'S92000003', 'E92000001'],
'long': [0.320423, -2.968257, -1.51314, -1.522386, -4.309171, 0.255878, -0.8502959999999999, -1.588886, -0.41805299999999995, -1.382926, -2.699804, -2.531778, -2.425061, -3.110276, -3.317868, -1.429442, -0.22178699999999998, -3.019257, -4.686457, 0.224912],
'lat': [51.491329, 51.628333000000005, 53.840114, 54.002427000000004, 55.870383, 50.966097999999995, 51.275081, 52.280791, 54.296222, 50.921857, 53.780733999999995, 52.692078, 53.415147, 53.394830000000006, 50.758146, 54.680499, 50.84042, 53.753078, 55.749168999999995, 51.441911],
'group1':['A']*10 + ['B']*10,
'group2':[True,False]*10})
# Set up geodataframe, initially with CRS = WGS84 (since that matches the long and lat co-ordinates)
crs = {'init':'epsg:4326'}
geometry = [shapely.geometry.Point(xy) for xy in zip(postcodeDF['long'], postcodeDF['lat'])]
postcodeGDF = gpd.GeoDataFrame(postcodeDF,
crs = crs,
geometry = geometry)
# Convert geometry to OSGB36
postcodeGDF = postcodeGDF.to_crs(epsg = 27700)
print(postcodeGDF)
地理数据框包含以下信息：
ctry group1 group2 lat long pcd \
0 E92000001 A True 51.491329 0.320423 RM175AG
1 W92000004 A False 51.628333 -2.968257 NP181PH
2 E92000001 A True 53.840114 -1.513140 LS8 1EN
3 E92000001 A False 54.002427 -1.522386 HG1 1XQ
4 S92000003 A True 55.870383 -4.309171 G11 6YB
5 E92000001 A False 50.966098 0.255878 TN218AB
6 E92000001 A True 51.275081 -0.850296 GU138AL
7 E92000001 A False 52.280791 -1.588886 CV344BD
8 E92000001 A True 54.296222 -0.418053 YO126PH
9 E92000001 A False 50.921857 -1.382926 SO172WT
10 E92000001 B True 53.780734 -2.699804 PR2 8HN
11 E92000001 B False 52.692078 -2.531778 TF1 2HD
12 E92000001 B True 53.415147 -2.425061 M31 4FR
13 E92000001 B False 53.394830 -3.110276 CH460UB
14 E92000001 B True 50.758146 -3.317868 EX111LN
15 E92000001 B False 54.680499 -1.429442 TS214DX
16 E92000001 B True 50.840420 -0.221787 BN4 2LS
17 E92000001 B False 53.753078 -3.019257 FY8 1XL
18 S92000003 B True 55.749169 -4.686457 KA256BP
19 E92000001 B False 51.441911 0.224912 DA1 1QR
geometry
0 POINT (561188.9840165515 179484.0452796911)
1 POINT (333075.0000681121 192612.9537310874)
2 POINT (432134.031689987 438316.9950631865)
3 POINT (431404.026064762 456371.9915770486)
4 POINT (255609.0244790429 666546.0027781442)
5 POINT (558502.0104336547 120942.0242662177)
6 POINT (480294.0287511199 153509.0281225364)
7 POINT (428143.9880141384 264818.0084207859)
8 POINT (503054.9928648224 490110.0245871434)
9 POINT (443470.0222579727 113781.050039533)
10 POINT (353983.9862037547 431827.9554603411)
11 POINT (364154.9903684837 310620.967712217)
12 POINT (371845.0195746602 391010.9943895991)
13 POINT (326267.022012488 389240.9700794323)
14 POINT (307141.054758316 96222.92213930591)
15 POINT (436886.0245473493 531865.0198253235)
16 POINT (525299.9996502266 106049.9600256799)
17 POINT (332890.0335889475 429005.9677596154)
18 POINT (231483.9972917534 653913.0284422053)
19 POINT (554726.0039838878 173783.0302315076)
可以用来绘制地图：
# Plot map
fig, ax = plt.subplots(1,
figsize = (4,5),
dpi = 72,
facecolor = 'lightblue')
ax.set_position([0,0,1,1]) # Puts axis to edge of figure
ax.set_axis_off() # Turns axis off so facecolour applies to axis area as well as bit around the outside
ax.get_xaxis().set_visible(False) # Turns the x axis off so that 'invisible' axis labels don't take up space
ax.get_yaxis().set_visible(False)
lims = plt.axis('equal')
# N.B. Code to plot shapefile has been deleted for clarity
postcodeGDF.plot(ax=ax)
plt.show()
地图（包括 shapefile 轮廓）如下所示：
但是，我想根据地理数据框中的其他变量（在本例中为变量 'group1' 和 'group2'）对邮政编码进行分组（并最终为每个组绘制不同的颜色和标记 –虽然我还没有那么远）。我使用 .dissolve() 方法对点进行了分组。
postcodesGroupby = postcodeGDF.dissolve(by = ['group1','group2'])
print(postcodesGroupby)
groupby 数据框如下所示：
geometry ctry
group1 group2
A False (POINT (333075.0000681121 192612.9537310874), ... W92000004
True (POINT (255609.0244790429 666546.0027781442), ... E92000001
B False (POINT (326267.022012488 389240.9700794323), P... E92000001
True (POINT (231483.9972917534 653913.0284422053), ... E92000001
lat long pcd
group1 group2
A False 51.628333 -2.968257 NP181PH
True 51.491329 0.320423 RM175AG
B False 52.692078 -2.531778 TF1 2HD
True 53.780734 -2.699804 PR2 8HN
但是，当我尝试使用以下方法绘制点时：
postcodesGroupby.plot(ax=ax)
...地图上没有显示任何点。
我怀疑我遗漏了一些明显的东西，但我盯着代码看了一会儿，再也见不到树木了。我将不胜感激地收到任何关于如何解决此问题的建议。
问题是 geopandas 目前还不支持绘制多点（并且 dissolve 方法将点分组为多点）。不幸的是，您得到的是空白图像而不是正确的错误消息 ..
但是，刚刚合并了一个 PR 以添加对绘制多点的支持：https://github.com/geopandas/geopandas/pull/683。
所以这将在下一个 geopandas 版本中起作用。
目前的解决方法是绘制各个点，但必须使用适当的分组颜色，以添加反映这些组的列：
# add a new column with an integer indicating the group number
postcodeGDF['group'] = postcodeGDF.groupby(['group1','group2']).ngroup()
postcodeGDF.plot(column='group', categorical=True, legend=True)
给出：
我最近开始使用 GeoPandas 制作地图并发现它非常有用。我已经使用 Pandas 一段时间了，我发现迁移到 GeoPandas 相对轻松。但是，在使用 .dissolve() 函数对点进行分组后，我在地图上绘制点时遇到问题。
基本上，我选择了英国邮政编码数据以及从国家统计局邮政编码目录 (ONSPD) 下载的相关经度和纬度值。经度和纬度值基于 CRS WGS84。我可以转换为 CRS OSGB36 并毫无问题地在地图上绘制所有点。但是，如果我使用 .dissolve() 方法根据其他变量（例如 'group1' 和 'groups'）对点进行分组，我将无法再绘制这些点。
这是我到目前为止将所有点绘制在一起的代码：
import pandas as pd
import geopandas as gif
import matplotlib.pyplot as plot
import shapely
# Define a Pandas dataframe containing postcodes and
postcodeDF = pd.DataFrame({'pcd': ['RM175AG', 'NP181PH', 'LS8 1EN', 'HG1 1XQ', 'G11 6YB', 'TN218AB', 'GU138AL', 'CV344BD', 'YO126PH', 'SO172WT', 'PR2 8HN', 'TF1 2HD', 'M31 4FR', 'CH460UB', 'EX111LN', 'TS214DX', 'BN4 2LS', 'FY8 1XL', 'KA256BP', 'DA1 1QR'],
'ctry': ['E92000001', 'W92000004', 'E92000001', 'E92000001', 'S92000003', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'E92000001', 'S92000003', 'E92000001'],
'long': [0.320423, -2.968257, -1.51314, -1.522386, -4.309171, 0.255878, -0.8502959999999999, -1.588886, -0.41805299999999995, -1.382926, -2.699804, -2.531778, -2.425061, -3.110276, -3.317868, -1.429442, -0.22178699999999998, -3.019257, -4.686457, 0.224912],
'lat': [51.491329, 51.628333000000005, 53.840114, 54.002427000000004, 55.870383, 50.966097999999995, 51.275081, 52.280791, 54.296222, 50.921857, 53.780733999999995, 52.692078, 53.415147, 53.394830000000006, 50.758146, 54.680499, 50.84042, 53.753078, 55.749168999999995, 51.441911],
'group1':['A']*10 + ['B']*10,
'group2':[True,False]*10})
# Set up geodataframe, initially with CRS = WGS84 (since that matches the long and lat co-ordinates)
crs = {'init':'epsg:4326'}
geometry = [shapely.geometry.Point(xy) for xy in zip(postcodeDF['long'], postcodeDF['lat'])]
postcodeGDF = gpd.GeoDataFrame(postcodeDF,
crs = crs,
geometry = geometry)
# Convert geometry to OSGB36
postcodeGDF = postcodeGDF.to_crs(epsg = 27700)
print(postcodeGDF)
地理数据框包含以下信息：
ctry group1 group2 lat long pcd \
0 E92000001 A True 51.491329 0.320423 RM175AG
1 W92000004 A False 51.628333 -2.968257 NP181PH
2 E92000001 A True 53.840114 -1.513140 LS8 1EN
3 E92000001 A False 54.002427 -1.522386 HG1 1XQ
4 S92000003 A True 55.870383 -4.309171 G11 6YB
5 E92000001 A False 50.966098 0.255878 TN218AB
6 E92000001 A True 51.275081 -0.850296 GU138AL
7 E92000001 A False 52.280791 -1.588886 CV344BD
8 E92000001 A True 54.296222 -0.418053 YO126PH
9 E92000001 A False 50.921857 -1.382926 SO172WT
10 E92000001 B True 53.780734 -2.699804 PR2 8HN
11 E92000001 B False 52.692078 -2.531778 TF1 2HD
12 E92000001 B True 53.415147 -2.425061 M31 4FR
13 E92000001 B False 53.394830 -3.110276 CH460UB
14 E92000001 B True 50.758146 -3.317868 EX111LN
15 E92000001 B False 54.680499 -1.429442 TS214DX
16 E92000001 B True 50.840420 -0.221787 BN4 2LS
17 E92000001 B False 53.753078 -3.019257 FY8 1XL
18 S92000003 B True 55.749169 -4.686457 KA256BP
19 E92000001 B False 51.441911 0.224912 DA1 1QR
geometry
0 POINT (561188.9840165515 179484.0452796911)
1 POINT (333075.0000681121 192612.9537310874)
2 POINT (432134.031689987 438316.9950631865)
3 POINT (431404.026064762 456371.9915770486)
4 POINT (255609.0244790429 666546.0027781442)
5 POINT (558502.0104336547 120942.0242662177)
6 POINT (480294.0287511199 153509.0281225364)
7 POINT (428143.9880141384 264818.0084207859)
8 POINT (503054.9928648224 490110.0245871434)
9 POINT (443470.0222579727 113781.050039533)
10 POINT (353983.9862037547 431827.9554603411)
11 POINT (364154.9903684837 310620.967712217)
12 POINT (371845.0195746602 391010.9943895991)
13 POINT (326267.022012488 389240.9700794323)
14 POINT (307141.054758316 96222.92213930591)
15 POINT (436886.0245473493 531865.0198253235)
16 POINT (525299.9996502266 106049.9600256799)
17 POINT (332890.0335889475 429005.9677596154)
18 POINT (231483.9972917534 653913.0284422053)
19 POINT (554726.0039838878 173783.0302315076)
可以用来绘制地图：
# Plot map
fig, ax = plt.subplots(1,
figsize = (4,5),
dpi = 72,
facecolor = 'lightblue')
ax.set_position([0,0,1,1]) # Puts axis to edge of figure
ax.set_axis_off() # Turns axis off so facecolour applies to axis area as well as bit around the outside
ax.get_xaxis().set_visible(False) # Turns the x axis off so that 'invisible' axis labels don't take up space
ax.get_yaxis().set_visible(False)
lims = plt.axis('equal')
# N.B. Code to plot shapefile has been deleted for clarity
postcodeGDF.plot(ax=ax)
plt.show()
地图（包括 shapefile 轮廓）如下所示：
但是，我想根据地理数据框中的其他变量（在本例中为变量 'group1' 和 'group2'）对邮政编码进行分组（并最终为每个组绘制不同的颜色和标记 –虽然我还没有那么远）。我使用 .dissolve() 方法对点进行了分组。
postcodesGroupby = postcodeGDF.dissolve(by = ['group1','group2'])
print(postcodesGroupby)
groupby 数据框如下所示：
geometry ctry
group1 group2
A False (POINT (333075.0000681121 192612.9537310874), ... W92000004
True (POINT (255609.0244790429 666546.0027781442), ... E92000001
B False (POINT (326267.022012488 389240.9700794323), P... E92000001
True (POINT (231483.9972917534 653913.0284422053), ... E92000001
lat long pcd
group1 group2
A False 51.628333 -2.968257 NP181PH
True 51.491329 0.320423 RM175AG
B False 52.692078 -2.531778 TF1 2HD
True 53.780734 -2.699804 PR2 8HN
但是，当我尝试使用以下方法绘制点时：
postcodesGroupby.plot(ax=ax)
...地图上没有显示任何点。
我怀疑我遗漏了一些明显的东西，但我盯着代码看了一会儿，再也见不到树木了。我将不胜感激地收到任何关于如何解决此问题的建议。
问题是 geopandas 目前还不支持绘制多点（并且 dissolve 方法将点分组为多点）。不幸的是，您得到的是空白图像而不是正确的错误消息 ..
但是，刚刚合并了一个 PR 以添加对绘制多点的支持：https://github.com/geopandas/geopandas/pull/683。 所以这将在下一个 geopandas 版本中起作用。
目前的解决方法是绘制各个点，但必须使用适当的分组颜色，以添加反映这些组的列：
# add a new column with an integer indicating the group number
postcodeGDF['group'] = postcodeGDF.groupby(['group1','group2']).ngroup()
postcodeGDF.plot(column='group', categorical=True, legend=True)
给出：