I am a newbie in pandas datamining. I have GPS dataset that consists of timestamp, longitude and latitude values. My dataset looks like this.
In [3]:
import pandas as pd
import numpy as np
df = pd.read_csv('D:GPS.csv', index_col=None)
df
Out[3]:
time mLongitude mLongitude
0 2014-06-30 00:00:00 94.500000 126.998428
1 2014-06-30 00:00:00 94.500000 126.998428
2 2014-06-30 00:00:00 94.500000 126.998428
3 2014-06-30 00:00:00 94.500000 126.998428
4 2014-06-30 00:00:00 94.500000 126.998428
5 2014-06-30 00:00:00 94.500000 126.998428
6 2014-06-30 00:00:00 94.500000 126.998428
7 2014-06-30 00:00:00 94.500000 126.998428
8 2014-06-30 00:00:00 94.500000 126.998428
9 2014-06-30 00:00:00 94.500000 126.998428
10 2014-06-30 00:00:00 94.500000 126.998428
11 2014-06-30 00:00:00 94.500000 126.998428
12 2014-06-30 00:00:00 94.500000 126.998428
13 2014-06-30 00:00:00 94.500000 126.998428
14 2014-06-30 00:00:00 94.500000 126.998428
15 2014-06-30 00:00:00 94.500000 126.998428
... ... ... ...
9467 2014-08-02 00:00:00 44.299999 126.902259
9468 2014-08-02 00:00:00 44.299999 126.902259
9469 2014-08-02 00:00:00 44.299999 126.902259
9470 2014-08-02 00:00:00 44.299999 126.902259
9471 2014-08-02 00:00:00 44.299999 126.902259
9472 2014-08-02 00:00:00 44.299999 126.902259
In here, I want to calculate traveling distance for each day. And then the example of the output would be like this:
time distance (meter)
2014-06-30 1000
2014-07-01 500
.... ...
2014-08-02 1500
Thw following is adapted from my answer:
In [133]:
import math
df['distance'] = 6367 * 2 * np.arcsin(np.sqrt(np.sin(np.radians(df['mLatitude']) - math.radians(37.2175900)/2)**2 + math.cos(math.radians(37.2175900)) * np.cos(np.radians(df['mLatitude']) * np.sin(np.radians(df['mLongitude']) - math.radians(-56.7213600)/2)**2)))
df
Out[133]:
time mLongitude mLatitude distance
index
0 2014-06-30 94.500000 126.998428 16032.604625
1 2014-06-30 94.500000 126.998428 16032.604625
2 2014-06-30 94.500000 126.998428 16032.604625
3 2014-06-30 94.500000 126.998428 16032.604625
4 2014-06-30 94.500000 126.998428 16032.604625
5 2014-06-30 94.500000 126.998428 16032.604625
6 2014-06-30 94.500000 126.998428 16032.604625
7 2014-06-30 94.500000 126.998428 16032.604625
8 2014-06-30 94.500000 126.998428 16032.604625
9 2014-06-30 94.500000 126.998428 16032.604625
10 2014-06-30 94.500000 126.998428 16032.604625
11 2014-06-30 94.500000 126.998428 16032.604625
12 2014-06-30 94.500000 126.998428 16032.604625
13 2014-06-30 94.500000 126.998428 16032.604625
14 2014-06-30 94.500000 126.998428 16032.604625
15 2014-06-30 94.500000 126.998428 16032.604625
9467 2014-08-02 44.299999 126.902259 10728.740464
9468 2014-08-02 44.299999 126.902259 10728.740464
9469 2014-08-02 44.299999 126.902259 10728.740464
9470 2014-08-02 44.299999 126.902259 10728.740464
9471 2014-08-02 44.299999 126.902259 10728.740464
9472 2014-08-02 44.299999 126.902259 10728.740464
In [137]:
df.set_index('time').resample('D', how='mean')
Out[137]:
mLongitude mLatitude distance
time
2014-06-30 94.500000 126.998428 16032.604625
2014-08-02 44.299999 126.902259 10728.740464
It's unclear if your time is alread a datetime or not but if not you can convert it: df['time'] = pd.to_datetime(df['time']), I also relabelled the columns as you had 2 mLongitude, I'm assuming the 2nd one should be latitude
Collected from the Internet
Please contact [email protected] to delete if infringement.
Comments