In [37]:
%matplotlib inline
import pandas as pd
In [38]:
df = pd.read_csv("campus_shuttle.csv") #Isn't this easy? :D
df
Out[38]:
lat
long
0
42.086155
-75.965363
1
42.089149
-75.973244
2
42.086155
-75.965363
3
42.089054
-75.974480
4
42.086155
-75.965363
5
42.089054
-75.974480
6
42.085621
-75.965408
7
42.089054
-75.974480
8
42.085621
-75.965408
9
42.088333
-75.974632
10
42.085152
-75.964981
11
42.088333
-75.974632
12
42.085152
-75.964912
13
42.087086
-75.974480
14
42.085152
-75.964912
15
42.087086
-75.974480
16
42.085510
-75.964958
17
42.086395
-75.974365
18
42.085510
-75.964958
19
42.086395
-75.974365
20
42.086090
-75.965172
21
42.086052
-75.974297
22
42.086090
-75.965172
23
42.086052
-75.974297
24
42.086445
-75.965218
25
42.086052
-75.974297
26
42.086445
-75.965218
27
42.084621
-75.974014
28
42.086445
-75.965218
29
42.084621
-75.974014
...
...
...
155234
42.083786
-75.969101
155235
42.088047
-75.963303
155236
42.083786
-75.969101
155237
42.088047
-75.963303
155238
42.083988
-75.970047
155239
42.088047
-75.963303
155240
42.083988
-75.970047
155241
42.088440
-75.962906
155242
42.083984
-75.970695
155243
42.088440
-75.962906
155244
42.083984
-75.970695
155245
42.088428
-75.962891
155246
42.083984
-75.970695
155247
42.088428
-75.962891
155248
42.084560
-75.971603
155249
42.088428
-75.962891
155250
42.084560
-75.971603
155251
42.088428
-75.962891
155252
42.085335
-75.972664
155253
42.088428
-75.962891
155254
42.085335
-75.972664
155255
42.088428
-75.962891
155256
42.085335
-75.972664
155257
42.088459
-75.962898
155258
42.086483
-75.973511
155259
42.088459
-75.962898
155260
42.086483
-75.973511
155261
42.088642
-75.962852
155262
42.087521
-75.973824
155263
42.088642
-75.962852
155264 rows × 2 columns
In [40]:
df.plot.scatter(x="long", y="lat", s=1)
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x117344860>
In [ ]:
"""
If you're interested in more in depth analysis check out
https://github.com/jackfischer/occt-analysis
"""
Content source: HackBinghamton/club
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