CODEWITHSUNDEEP
pythonpandashaversine
Var0115
Var0115

Reputation: 21

calculate distance between two geocodes in a dataframe

Need to get a distances for lat long pairs using haversine distance for this data frame ( named df). The requirement is to get the distance added in a new column in the same data frame (df).

Name geo1 geo2
ABC (52.2296756,21.0122287) (51.3490756,23.0922287)
XYZ (52.3490756,23.0922287) (51.2296756,21.0122287)

Upvotes: 0

Views: 311

Answers (2)

Var0115
Var0115

Reputation: 21

This worked as well

#splitting lat longs
split_data = df.geo1.strip(')').str.strip('(').str.split(',')
df['geo1_lat'] = split_data.apply(lambda x: x[0])
df['geo1_long'] = split_data.apply(lambda x: x[1])

split_data = df.geo2.strip(')').str.strip('(').str.split(',')
df['geo2_lat'] = split_data.apply(lambda x: x[0])
df['geo2_long'] = split_data.apply(lambda x: x[1])

def haversine_distance(lat1, lon1, lat2, lon2):
   r = 6371
   phi1 = np.radians(lat1)
   phi2 = np.radians(lat2)
   delta_phi = np.radians(lat2 - lat1)
   delta_lambda = np.radians(lon2 - lon1)
   a = np.sin(delta_phi / 2)**2 + np.cos(phi1) * np.cos(phi2) *   np.sin(delta_lambda / 2)**2
   res = r * (2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)))
   return np.round(res*1000, 2)

df['distance'] = df[['geo1_lat','geo1_long','geo2_lat','geo2_long']].apply(lambda x: haversine(x[1], x[0], x[3], x[2]), axis=1)

Upvotes: 0

Corralien
Corralien

Reputation: 120391

If you refer to this Python's implementation of haversine distance:

df["distance"] = df[["geo1", "geo2"]].apply(lambda x: haversine(*x.geo1, *x.geo2), axis="columns")

>>> df
  Name                      geo1                      geo2    distance
0  ABC  (52.2296756, 21.0122287)  (51.3490756, 23.0922287)  248.451222
1  XYZ  (52.3490756, 23.0922287)  (51.2296756, 21.0122287)  258.456800

Upvotes: 1

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