Here's the modified version of the Python module that uses the `geopy.distance` library instead of the Haversine formula to calculate the distance between successive rows:
```python
import pandas as pd
from geopy.distance import geodesic
def calculate_distances(df):
distances = [0] # First entry has no previous point, so distance is 0
for i in range(1, len(df)):
# Get coordinates for the current and previous points
coord1 = (df.loc[i-1, 'latitude'], df.loc[i-1, 'longitude'])
coord2 = (df.loc[i, 'latitude'], df.loc[i, 'longitude'])
# Calculate the geodesic distance in kilometers
distance_km = geodesic(coord1, coord2).kilometers
distances.append(distance_km)
return distances
def add_distance_to_csv(input_csv, output_csv):
# Read the CSV file
df = pd.read_csv(input_csv)
# Calculate distances and add as a new column
df['distance_km'] = calculate_distances(df)
# Save the updated DataFrame to a new CSV file
df.to_csv(output_csv, index=False)
# Example usage:
# add_distance_to_csv('input_data.csv', 'output_data_with_distances.csv')
```
### Explanation of Changes:
1. **Using `geopy.distance.geodesic`**: This library simplifies the distance calculation. It uses the WGS-84 ellipsoid model of the Earth to calculate the geodesic distance between two points.
- The `geodesic` function takes two tuples of coordinates (latitude, longitude) and returns the distance between them. The `.kilometers` attribute returns the result in kilometers.
2. **`calculate_distances` Function**:
- For each row in the DataFrame, it retrieves the coordinates of the current and previous rows.
- It then calculates the geodesic distance between them using `geodesic`.
### How to Install `geopy`:
If you haven't already installed `geopy`, you can do so using pip:
```bash
pip install geopy
```
### Example:
For a CSV file with the following content:
```csv
latitude,longitude
34.052235,-118.243683
34.052500,-118.244000
34.052800,-118.244300
```
The output will include an additional `distance_km` column, for example:
```csv
latitude,longitude,distance_km
34.052235,-118.243683,0
34.052500,-118.244000,0.034
34.052800,-118.244300,0.037
```