5 Best Ways to Convert CSV to GPX in Python : Emily Rosemary Collins

5 Best Ways to Convert CSV to GPX in Python
by: Emily Rosemary Collins
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💡 Problem Formulation: Converting data from a CSV file to GPX format is a common requirement for professionals working with GPS and location data. For instance, you might need to convert a list of latitude and longitude coordinates from a CSV file to a GPX file to use with GPS software or services. This article outlines methods to achieve this conversion using Python.

Method 1: Using pandas and gpxpy Libraries

Combining the pandas library for CSV data manipulation and the gpxpy library for creating GPX files, this method offers a robust solution for converting between file formats. It provides a high level of customization and error-handling capabilities.

Here’s an example:

import pandas as pd
import gpxpy
import gpxpy.gpx

# Read CSV file
data = pd.read_csv('locations.csv')

# Create a new GPX object
gpx = gpxpy.gpx.GPX()

# Create waypoints
for index, row in data.iterrows():
    waypoint = gpxpy.gpx.GPXWaypoint(latitude=row['latitude'], longitude=row['longitude'])

# Save to a GPX file
with open('output.gpx', 'w') as f:

Output GPX file: output.gpx with waypoints from the CSV data.

This code snippet reads a CSV file into a pandas DataFrame, iterates over its rows to create waypoints, adds them to a GPX object, and finally writes the GPX file to disk. It’s concise and leverages the power of existing libraries for data handling and format conversion.

Method 2: Using csv and lxml Libraries

For those who prefer lower-level control over the GPX file construction, the csv and lxml.etree libraries provide a means to manually build the GPX structure. This method requires a more in-depth understanding of the GPX XML schema.

Here’s an example:

import csv
from lxml import etree as ET

# Create the root GPX element
gpx = ET.Element('gpx', version="1.1", creator="csv_to_gpx")

# Read CSV file and create GPX waypoints
with open('locations.csv', 'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        wpt_element = ET.SubElement(gpx, 'wpt', lat=row['latitude'], lon=row['longitude'])
        ET.SubElement(wpt_element, 'name').text = row['name']

# Write to a GPX file
tree = ET.ElementTree(gpx)
tree.write('output.gpx', pretty_print=True, xml_declaration=True, encoding='UTF-8')

Output GPX file: output.gpx with waypoints and names from the CSV data.

This snippet manually creates a GPX file from a CSV using the csv module to read input and the lxml library to build the GPX XML. The result is a customized GPX file written precisely to the user’s specifications.

Method 3: Using Simple Template Substitution

If you don’t need extensive GPX features and your CSV file format is always the same, a simple string template substitution using Python’s string.Template can be surprisingly efficient.

Here’s an example:

from string import Template

# Template for a GPX waypoint
wpt_template = Template('<wpt lat="$latitude" lon="$longitude"><name>$name</name></wpt>')

# Read CSV file and substitute values into the template
gpx_content = '<gpx creator="csv_to_gpx">\n'
with open('locations.csv', 'r') as csvfile:
    next(csvfile)  # Skip header line
    for line in csvfile:
        latitude, longitude, name = line.strip().split(',')
        gpx_content += wpt_template.substitute(latitude=latitude, longitude=longitude, name=name) + '\n'

gpx_content += '</gpx>'

# Write to a GPX file
with open('output.gpx', 'w') as f:

Output GPX content: Plain text representation of a GPX file containing waypoints.

This method skips CSV and GPX parsing libraries entirely and uses pure Python templating to generate a GPX format. It’s useful for simple, one-off tasks with predictable CSV structures but lacks the robustness and flexibility of a full parser.

Method 4: Command-Line Tools via Python

Packages like gpx_csv_converter provide command-line tools that can be invoked from Python using the subprocess module. This is helpful when you prefer to use a tried-and-tested standalone utility.

Here’s an example:

import subprocess

# Assuming 'gpx_csv_converter' is installed and added to the PATH
subprocess.run(['gpx_csv_converter', 'locations.csv', 'output.gpx'])

No output in Python; check the output.gpx file created in the working directory.

This snippet leverages the external ‘gpx_csv_converter’ tool to perform the conversion outside of the Python environment. It’s an excellent approach when such reliable tools are available and can be easily integrated into Python scripts.

Bonus One-Liner Method 5: pandas and GeoPandas

For those already using the geospatial data in pandas, the geopandas extension offers an even simpler one-liner conversion to save a DataFrame directly to a GPX file.

Here’s an example:

import pandas as pd
import geopandas

# Read CSV file as a GeoDataFrame
gdf = geopandas.GeoDataFrame(pd.read_csv('locations.csv'))

# Save it directly as a GPX file
gdf.to_file('output.gpx', driver='GPX')

Output GPX file: output.gpx, generated from the GeoDataFrame.

GeoPandas abstracts away the details of the file format conversion, offering a direct method for GeoDataFrame users to export their geospatial data as GPX. This method is simple, clean, and effective but requires that you’re working within the GeoPandas environment.


  • Method 1: pandas and gpxpy. Highly customizable and Pythonic. May have a learning curve for newcomers.
  • Method 2: csv and lxml. Offers granular control of the GPX XML schema. Requires more code and an understanding of XML.
  • Method 3: Simple Template Substitution. Quick for simple structures and small datasets. Not robust or flexible for varying schemas.
  • Method 4: Command-Line Tools via Python. Utilizes proven external tools and simplifies integration. External dependencies and less control over the process.
  • Method 5: pandas and GeoPandas. The simplest method for those in the GeoPandas ecosystem. Limited to users of GeoPandas.

March 02, 2024 at 03:41AM
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