Looping Through Two Separate Dataframes, Haversine Function, Store The Values
I have two dataFrames that I want to loop through, apply a Haversine function, and structure the results within a new array. I want to grab the lat, lng coordinates for the first r
Solution 1:
If you use loop with pandas and numpy, chances are high that you are doing it wrong. Learn and apply the vectorized functions that these libraries provide:
# Build an index that contain every pairing of Store - University
idx = pd.MultiIndex.from_product([da_store.index, da_univ.index], names=['Store', 'Univ'])
# Pull the coordinates of the store and the universities together# We don't need their name here
df = pd.DataFrame(index=idx) \
.join(da_store[['lat', 'lon']], on='Store') \
.join(da_univ[['LATITUDE', 'LONGITUDE']], on='Univ')
defhaversine_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
c = 2 * np.arcsin(np.sqrt(a))
km = 6367 * c
return km
df['Distance'] = haversine_np(*df[['lat', 'lon', 'LATITUDE', 'LONGITUDE']].values.T)
# The closest university to each store
min_distance = df.loc[df.groupby('Store')['Distance'].idxmin(), 'Distance']
# Pulling everything together
min_distance.to_frame().join(da_store, on='Store').join(da_univ, on='Univ') \
[['Restaurant Name', 'INSTNM', 'Distance']]
Result:
Restaurant Name INSTNM Distance
Store Univ
0 1 Weymouth Dual New England College of Business and Finance 15.651923
1 4 Somerset Dual Bay State College 68.921108
2 3 Westboro Mass Pike West Bancroft School of Massage Therapy 9.580468
3 2 Charlton Mass Pike East Assumption College 26.514269
4 2 Charlton Mass Pike West Assumption College 26.508821
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