无法在 matplotlib 中以度数绘制坐标
Trouble plotting coordinates in degrees in matplotlib
因此，我目前正在处理导入到 python 中的 GTFS 数据。我有这个 table bronx_frequencyTable，其中包含 shape_pt_lon 和 shape_pt_lat 中的纵向坐标。但是，当我 运行 以下代码时：
for index, row in bronx_frequencyTable.iterrows():
map.plot(row['shape_pt_lon'], row['shape_pt_lat'], latlon=True, linestyle='solid')
plt.show()
除了我已经绘制的海岸线之外，实际上什么也没有出现。有人知道我如何根据我的数据绘制线条吗？
数据：
shape_id shape_pt_lat shape_pt_lon shape_pt_sequence num_trips
0 BX010026 40.809663 -73.928240 10001 143
1 BX010026 40.809620 -73.928135 10002 143
2 BX010026 40.810075 -73.927781 10003 143
3 BX010026 40.810130 -73.927698 10004 143
4 BX010026 40.810860 -73.927191 10005 143
5 BX010026 40.810970 -73.927140 10006 143
6 BX010026 40.811036 -73.927223 10007 143
7 BX010026 40.811104 -73.927310 10008 143
8 BX010026 40.811725 -73.928126 10009 143
9 BX010026 40.812036 -73.928541 10010 143
10 BX010026 40.812338 -73.928949 10011 143
11 BX010026 40.812621 -73.929328 10012 143
12 BX010026 40.812722 -73.929458 10013 143
13 BX010026 40.812733 -73.929476 10014 143
14 BX010026 40.812816 -73.929515 10015 143
15 BX010026 40.812840 -73.929544 10016 143
16 BX010026 40.812912 -73.929631 10017 143
17 BX010026 40.813090 -73.929844 10018 143
18 BX010026 40.813521 -73.929548 10019 143
19 BX010026 40.813521 -73.929548 20001 143
20 BX010026 40.814235 -73.929059 20002 143
21 BX010026 40.814418 -73.928990 20003 143
22 BX010026 40.814863 -73.928787 20004 143
23 BX010026 40.816515 -73.928149 20005 143
24 BX010026 40.816820 -73.928027 20006 143
25 BX010026 40.816820 -73.928027 30001 143
26 BX010026 40.817198 -73.927874 30002 143
27 BX010026 40.817568 -73.927700 30003 143
28 BX010026 40.817632 -73.927668 30004 143
29 BX010026 40.818540 -73.927240 30005 143
... ... ... ... ... ...
41788 SBS410037 40.870280 -73.878387 90011 365
41789 SBS410037 40.870338 -73.878271 90012 365
41790 SBS410037 40.870680 -73.877612 90013 365
41791 SBS410037 40.871047 -73.876928 90014 365
41792 SBS410037 40.871118 -73.876827 90015 365
41793 SBS410037 40.871217 -73.876700 90016 365
41794 SBS410037 40.871402 -73.876513 90017 365
41795 SBS410037 40.871402 -73.876513 100001 365
41796 SBS410037 40.872958 -73.874939 100002 365
41797 SBS410037 40.873167 -73.874765 100003 365
41798 SBS410037 40.873383 -73.874570 100004 365
41799 SBS410037 40.875086 -73.873225 100005 365
41800 SBS410037 40.876954 -73.871996 100006 365
41801 SBS410037 40.877075 -73.871938 100007 365
41802 SBS410037 40.878013 -73.871755 100008 365
41803 SBS410037 40.878392 -73.871661 100009 365
41804 SBS410037 40.878392 -73.871661 110001 365
41805 SBS410037 40.878644 -73.871598 110002 365
41806 SBS410037 40.878616 -73.871378 110003 365
41807 SBS410037 40.878396 -73.870496 110004 365
41808 SBS410037 40.878352 -73.870330 110005 365
41809 SBS410037 40.878228 -73.869842 110006 365
41810 SBS410037 40.878206 -73.869762 110007 365
41811 SBS410037 40.877798 -73.868049 110008 365
41812 SBS410037 40.877632 -73.867377 110009 365
41813 SBS410037 40.877505 -73.866849 110010 365
41814 SBS410037 40.877445 -73.866643 110011 365
41815 SBS410037 40.877373 -73.866415 110012 365
41816 SBS410037 40.877326 -73.866271 110013 365
41817 SBS410037 40.877769 -73.866017 110014 365
完整代码：
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import pandas as pd
#Calculate base frequency.
base_max_routes = 4
base_directions = 2
base_minhour = 7
base_maxhour = 19
base_bph = 6
base_frequency = base_max_routes * base_directions * (base_maxhour - base_minhour) * base_bph
# Read the shape files from the bus data.
bronx_shapes = pd.read_csv('../Bronx Data/shapes.txt')
bronx_trips = pd.read_csv('../Bronx Data/trips.txt')
bronx_numTrips = bronx_trips.groupby('shape_id').size()
bronx_numTrips.name = 'num_trips'
bronx_frequencyTable = bronx_shapes.join(bronx_numTrips, on=['shape_id'], how='inner')
print(bronx_frequencyTable)
# Create a map of New York City centered on Manhattan.
map = Basemap(resolution="h", projection="stere", width=50000, height=50000, lon_0=-73.935242, lat_0=40.730610)
map.drawcoastlines()
# Map the bus routes.
for index, row in bronx_frequencyTable.iterrows():
map.plot(row['shape_pt_lon'], row['shape_pt_lat'], latlon=True, linestyle='solid')
plt.show()
您正在逐点绘制。相反，您应该直接绘制列：
map.plot(bronx_frequencyTable['shape_pt_lon'], bronx_frequencyTable['shape_pt_lat'], latlon=True, linestyle='solid')
因此，我目前正在处理导入到 python 中的 GTFS 数据。我有这个 table bronx_frequencyTable，其中包含 shape_pt_lon 和 shape_pt_lat 中的纵向坐标。但是，当我 运行 以下代码时：
for index, row in bronx_frequencyTable.iterrows():
map.plot(row['shape_pt_lon'], row['shape_pt_lat'], latlon=True, linestyle='solid')
plt.show()
除了我已经绘制的海岸线之外，实际上什么也没有出现。有人知道我如何根据我的数据绘制线条吗？
数据：
shape_id shape_pt_lat shape_pt_lon shape_pt_sequence num_trips
0 BX010026 40.809663 -73.928240 10001 143
1 BX010026 40.809620 -73.928135 10002 143
2 BX010026 40.810075 -73.927781 10003 143
3 BX010026 40.810130 -73.927698 10004 143
4 BX010026 40.810860 -73.927191 10005 143
5 BX010026 40.810970 -73.927140 10006 143
6 BX010026 40.811036 -73.927223 10007 143
7 BX010026 40.811104 -73.927310 10008 143
8 BX010026 40.811725 -73.928126 10009 143
9 BX010026 40.812036 -73.928541 10010 143
10 BX010026 40.812338 -73.928949 10011 143
11 BX010026 40.812621 -73.929328 10012 143
12 BX010026 40.812722 -73.929458 10013 143
13 BX010026 40.812733 -73.929476 10014 143
14 BX010026 40.812816 -73.929515 10015 143
15 BX010026 40.812840 -73.929544 10016 143
16 BX010026 40.812912 -73.929631 10017 143
17 BX010026 40.813090 -73.929844 10018 143
18 BX010026 40.813521 -73.929548 10019 143
19 BX010026 40.813521 -73.929548 20001 143
20 BX010026 40.814235 -73.929059 20002 143
21 BX010026 40.814418 -73.928990 20003 143
22 BX010026 40.814863 -73.928787 20004 143
23 BX010026 40.816515 -73.928149 20005 143
24 BX010026 40.816820 -73.928027 20006 143
25 BX010026 40.816820 -73.928027 30001 143
26 BX010026 40.817198 -73.927874 30002 143
27 BX010026 40.817568 -73.927700 30003 143
28 BX010026 40.817632 -73.927668 30004 143
29 BX010026 40.818540 -73.927240 30005 143
... ... ... ... ... ...
41788 SBS410037 40.870280 -73.878387 90011 365
41789 SBS410037 40.870338 -73.878271 90012 365
41790 SBS410037 40.870680 -73.877612 90013 365
41791 SBS410037 40.871047 -73.876928 90014 365
41792 SBS410037 40.871118 -73.876827 90015 365
41793 SBS410037 40.871217 -73.876700 90016 365
41794 SBS410037 40.871402 -73.876513 90017 365
41795 SBS410037 40.871402 -73.876513 100001 365
41796 SBS410037 40.872958 -73.874939 100002 365
41797 SBS410037 40.873167 -73.874765 100003 365
41798 SBS410037 40.873383 -73.874570 100004 365
41799 SBS410037 40.875086 -73.873225 100005 365
41800 SBS410037 40.876954 -73.871996 100006 365
41801 SBS410037 40.877075 -73.871938 100007 365
41802 SBS410037 40.878013 -73.871755 100008 365
41803 SBS410037 40.878392 -73.871661 100009 365
41804 SBS410037 40.878392 -73.871661 110001 365
41805 SBS410037 40.878644 -73.871598 110002 365
41806 SBS410037 40.878616 -73.871378 110003 365
41807 SBS410037 40.878396 -73.870496 110004 365
41808 SBS410037 40.878352 -73.870330 110005 365
41809 SBS410037 40.878228 -73.869842 110006 365
41810 SBS410037 40.878206 -73.869762 110007 365
41811 SBS410037 40.877798 -73.868049 110008 365
41812 SBS410037 40.877632 -73.867377 110009 365
41813 SBS410037 40.877505 -73.866849 110010 365
41814 SBS410037 40.877445 -73.866643 110011 365
41815 SBS410037 40.877373 -73.866415 110012 365
41816 SBS410037 40.877326 -73.866271 110013 365
41817 SBS410037 40.877769 -73.866017 110014 365
完整代码：
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import pandas as pd
#Calculate base frequency.
base_max_routes = 4
base_directions = 2
base_minhour = 7
base_maxhour = 19
base_bph = 6
base_frequency = base_max_routes * base_directions * (base_maxhour - base_minhour) * base_bph
# Read the shape files from the bus data.
bronx_shapes = pd.read_csv('../Bronx Data/shapes.txt')
bronx_trips = pd.read_csv('../Bronx Data/trips.txt')
bronx_numTrips = bronx_trips.groupby('shape_id').size()
bronx_numTrips.name = 'num_trips'
bronx_frequencyTable = bronx_shapes.join(bronx_numTrips, on=['shape_id'], how='inner')
print(bronx_frequencyTable)
# Create a map of New York City centered on Manhattan.
map = Basemap(resolution="h", projection="stere", width=50000, height=50000, lon_0=-73.935242, lat_0=40.730610)
map.drawcoastlines()
# Map the bus routes.
for index, row in bronx_frequencyTable.iterrows():
map.plot(row['shape_pt_lon'], row['shape_pt_lat'], latlon=True, linestyle='solid')
plt.show()
您正在逐点绘制。相反，您应该直接绘制列：
map.plot(bronx_frequencyTable['shape_pt_lon'], bronx_frequencyTable['shape_pt_lat'], latlon=True, linestyle='solid')