Pandas Dataframe: join items in range based on their geo coordinates (longitude and latitude)

Question

I got a dataframe that contains places with their latitude and longitude. Imagine for example cities.

df = pd.DataFrame([{\'city\':\"Berlin\", \'lat\':52.524         


        

Answer 1

UPDATE: I would suggest first to buiuld a distance DataFrame:

from scipy.spatial.distance import squareform, pdist
from itertools import combinations

# see definition of "haversine_np()" below     
x = pd.DataFrame({'dist':pdist(df[['lat','lng']], haversine_np)},
                 index=pd.MultiIndex.from_tuples(tuple(combinations(df['city'], 2))))

efficiently produces pairwise distance DF (without duplicates):

In [106]: x
Out[106]:
                       dist
Berlin  Potsdam   27.198616
        Hamburg  255.063541
Potsdam Hamburg  242.311890

---

Old answer:

Here is a bit optimized version, which uses scipy.spatial.distance.pdist method:

from scipy.spatial.distance import squareform, pdist

# slightly modified version: of http://stackoverflow.com/a/29546836/2901002
def haversine_np(p1, p2):
    """
    Calculate the great circle distance between two points
    on the earth (specified in decimal degrees)

    All args must be of equal length.    

    """
    lat1, lon1, lat2, lon2 = np.radians([p1[0], p1[1],
                                         p2[0], p2[1]])
    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

    c = 2 * np.arcsin(np.sqrt(a))
    km = 6367 * c
    return km

x = pd.DataFrame(squareform(pdist(df[['lat','lng']], haversine_np)),
                 columns=df.city.unique(),
                 index=df.city.unique())

this gives us:

In [78]: x
Out[78]:
             Berlin     Potsdam     Hamburg
Berlin     0.000000   27.198616  255.063541
Potsdam   27.198616    0.000000  242.311890
Hamburg  255.063541  242.311890    0.000000

let's count number of cities where the distance is greater than 30:

In [81]: x.groupby(level=0, as_index=False) \
    ...:  .apply(lambda c: c[c>30].notnull().sum(1)) \
    ...:  .reset_index(level=0, drop=True)
Out[81]:
Berlin     1
Hamburg    2
Potsdam    1
dtype: int64