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I have dataset contains Latitude and Longitude. Now i want to determine distance between these 2 points. Could you help me in solving this? In my dataset i have only 2 variables Longitude = -73.953918 Latitude = 40.778873

ramreddy bolla
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  • Does this answer your question? [Calculating distance between two points using latitude longitude and altitude (elevation)](https://stackoverflow.com/questions/33111529/calculating-distance-between-two-points-using-latitude-longitude-and-altitude-e) – Grzegorz Skibinski Jan 01 '21 at 11:35
  • Thanks for prompt reply. In my dataset i have only 2 variables like Longitude = -73.953918 Latitude = 40.778873. – ramreddy bolla Jan 01 '21 at 11:37
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    Please read: https://en.m.wikipedia.org/wiki/Longitude and https://en.m.wikipedia.org/wiki/Latitude - to answer your question if you want to calculate distance between longitude and latitude it's always 0 (because these 2 values refer to 2 intersecting lines) – Grzegorz Skibinski Jan 01 '21 at 11:49
  • can you provide a sample of your data set and create expected output as example ? so we can help you? – adir abargil Jan 01 '21 at 12:53
  • sklearn has got [haversine distance metric](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html) implemented. it gives you angular distance, which you can multiply by the radius of earth and get the result. (according to the docs, upper bound error 1% exists due to small eccentricity) – Alireza Jan 01 '21 at 14:08

1 Answers1

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if you have pandas dataframe then convert each column to a np array, and this function can work for either scalars or numpy array, just be consistent with lat and lng :

def haversine_np(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points
    on the earth (specified in decimal degrees)
    All args must be of equal length.
    """
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = np.sin(dlat / 2.0) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2.0) ** 2
    kms= (2 * 6367) * np.arcsin(np.sqrt(a))
    return kms

example:

np.random.seed(42)
df = pd.DataFrame(dict(lat=np.random.uniform(32,33,(100,)),lng=np.random.uniform(33,34,(100,))))
point  = dict(lat=32.5,lng=33.5)
df['distance_km'] = haversine_np(df['lng'].values,df['lat'].values,point['lng'],point['lat'])
df
>>>
    lat         lng         distance_km
0   32.374540   33.031429   46.104413
1   32.950714   33.636410   51.683653
2   32.731994   33.314356   31.089653
3   32.598658   33.508571   10.992790
4   32.156019   33.907566   54.090586
... ... ... ...
95  32.493796   33.349210   14.149670
96  32.522733   33.725956   21.324490
97  32.427541   33.897110   38.093632
98  32.025419   33.887086   64.065001
99  32.107891   33.779876   50.888535
adir abargil
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