How Can I Calculate Distances Between Latitude and Longitude Coordinates Quickly in Python?

Fast Haversine Approximation: A Python/Pandas Solution

Introduction

Calculating distances between latitude and longitude coordinates is a common task in geospatial data analysis. However, using the traditional Haversine formula for millions of rows can be computationally expensive. This article presents a fast alternative using NumPy vectorization for faster execution times.

Vectorized NumPy Solution

The NumPy vectorization approach utilizes NumPy functions that operate on arrays of data, avoiding the slowness of looping in Python. Below is the vectorized version of the Haversine function:

<code class="python">import numpy as np

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

    c = 2 * np.arcsin(np.sqrt(a))
    km = 6378.137 * c
    return km</code>

Usage

To use the vectorized solution, the input latitude and longitude values should be NumPy arrays. For example, to calculate distances for a Pandas DataFrame, you can extract the necessary columns and use the haversine_np function as follows:

<code class="python">df = pd.DataFrame(...your_dataframe...)
lon1, lat1, lon2, lat2 = df['lon1'], df['lat1'], df['lon2'], df['lat2']
df['distance'] = haversine_np(lon1, lat1, lon2, lat2)</code>

Benefits

The NumPy vectorization approach significantly improves performance, enabling the calculation of millions of distances instantaneously. This is achieved by avoiding the slowness of looping and utilizing NumPy's efficient array operations.

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