Recalculate between consecutive rows of pandas dataframe
I would like to calculate the distance along the path of the GPS coordinates that are stored in two columns in a data frame.
import pandas as pd
df = pd.DataFrame({ 'lat' : [1, 2.5, 3, 1.2],
'lng' : [1, 1, 2.1, 1],
'label': ['foo', 'bar', 'zip', 'foo']})
print df
Output
label lat lng
0 foo 1.0 1.0
1 bar 2.5 1.0
2 zip 3.0 2.1
3 foo 1.2 1.0
GPS coordinates are stored in radians. Thus, the distance between the first and second lines of the data block can be calculated as follows:
import math as m
r1 = 0
r2 = 1
distance =m.acos(m.sin(df.lat[r1]) * m.sin(df.lat[r2]) +
m.cos(df.lat[r1]) * m.cos(df.lat[r2]) * m.cos(df.lng[r2]-df.lng[r1]))*6371
I would like to repeat this calculation between each pair of consecutive lines and then add each short distance to the longer final distance for the full path.
I could put this in a loop for n-1 lines in the dataframe, but is there a more pythonic way to do this?
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1 answer
Vectorized function Haversine
:
def haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):
"""
slightly modified version: of http://stackoverflow.com/a/29546836/2901002
Calculate the great circle distance between two points
on the earth (specified in decimal degrees or in radians)
All (lat, lon) coordinates must have numeric dtypes and be of equal length.
"""
if to_radians:
lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])
a = np.sin((lat2-lat1)/2.0)**2 + \
np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2
return earth_radius * 2 * np.arcsin(np.sqrt(a))
Decision:
df['dist'] = haversine(df['lat'], df['lng'],
df['lat'].shift(), df['lng'].shift(),
to_radians=False)
Result:
In [65]: df
Out[65]:
label lat lng dist
0 foo 1.0 1.0 NaN
1 bar 2.5 1.0 9556.500000
2 zip 3.0 2.1 7074.983158
3 foo 1.2 1.0 10206.286067
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