5 Best Ways to Convert Python CSV to KML

💡 Problem Formulation: To leverage geographical data effectively, it’s important to be able to convert it between different formats. For instance, you might have coordinates and other information stored in a CSV file that you want to display on a map. The goal is converting this CSV data into a KML (Keyhole Markup Language) file, which can be used with mapping services like Google Earth. The input is a CSV with latitude, longitude, and perhaps additional data; the desired output is a KML file marking these locations.

Method 1: Using the simplekml Library

This method involves using the Python library simplekml to convert CSV data to KML. Simplekml is a powerful tool that allows for easy creation and parsing of KML files. It provides a simple and intutive interface for adding placemarks, paths, polygons, and more to your KML.

Here’s an example:

import simplekml
import csv

kml = simplekml.Kml()

with open('locations.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    for latitude, longitude, name in reader:
        kml.newpoint(name=name, coords=[(longitude, latitude)])

kml.save('locations.kml')

The code above reads a CSV file named ‘locations.csv’ and for each row, it creates a new point in the KML with the specified coordinates and name. The resulting KML file is called ‘locations.kml’.

Method 2: Using the pandas and simplekml Libraries with DataFrame

Combining pandas for CSV reading and simplekml for KML writing provides a powerful data analysis and KML creation workflow. Pandas provides high-level data structures that make manipulating and analyzing structured data quick, while simplekml allows for straightforward KML file creation.

Here’s an example:

import pandas as pd
import simplekml

df = pd.read_csv('locations.csv')
kml = simplekml.Kml()

for index, row in df.iterrows():
    kml.newpoint(name=row['name'], coords=[(row['longitude'], row['latitude'])])

kml.save('locations.kml')

The output is a KML file, ‘locations.kml’, with placemarks for each location in the CSV file, labeled with the name column from the CSV.

This snippet uses pandas to read the CSV into a DataFrame for easy data manipulation, and then iterates over each row to create corresponding KML points, which are then saved to ‘locations.kml’.

Method 3: Using ElementTree to Manually Build a KML File

If you prefer to work at a lower level or need more control over your KML structure, Python’s ElementTree XML API could be used. This involves manually building the KML structure by creating XML elements.

Here’s an example:

import xml.etree.ElementTree as ET
import csv

kml = ET.Element('kml', xmlns="http://www.opengis.net/kml/2.2")
document = ET.SubElement(kml, 'Document')
with open('locations.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    for latitude, longitude, name in reader:
        placemark = ET.SubElement(document, 'Placemark')
        ET.SubElement(placemark, 'name').text = name
        point = ET.SubElement(placemark, 'Point')
        ET.SubElement(point, 'coordinates').text = f"{longitude},{latitude}"

tree = ET.ElementTree(kml)
tree.write('locations.kml')

The output will be ‘locations.kml’, structured as an XML KML file with placemarks based on the CSV data.

This code manually constructs an XML KML structure with Python’s built-in ElementTree module, iterating through entries in the ‘locations.csv’ file to add placemark elements to the KML.

Method 4: Using csv2kml Third-Party Utility

There are third-party utilities specifically designed to convert CSV files to KML, such as csv2kml. This standalone tool can be executed from a command line or used as a Python library, providing a rapid and straightforward conversion process.

Here’s an example:

# Assuming that csv2kml is installed and available in your environment
# Run this command in your terminal
csv2kml locations.csv -o locations.kml

The output is similar to the previous methods: a ‘locations.kml’ file containing the geographic data structured as KML.

Instead of writing any Python code, this example demonstrates using a command-line utility, csv2kml, to convert a CSV file directly into a KML file, showcasing a practical solution for non-programmers or quick tasks.

Bonus One-Liner Method 5: Using Python’s csv and lxml Libraries for Quick Conversion

For a quick one-liner solution, you can mix Python’s standard CSV reading capabilities with the powerful lxml library’s XML building features for rapid KML file creation.

Here’s an example:

from lxml import etree as ET
import csv

with open('locations.csv', 'r') as csvfile, open('locations.kml', 'wb') as kmlfile:
    kml = ET.Element('kml', xmlns="http://www.opengis.net/kml/2.2")
    document = ET.SubElement(kml, 'Document')
    reader = csv.reader(csvfile)
    [ET.SubElement(ET.SubElement(document, 'Placemark'), 'Point').append(ET.Element('coordinates', text=f"{lon},{lat}")) for name, lat, lon in reader]
    kmlfile.write(ET.tostring(kml))

This will result in the ‘locations.kml’ file being created with the appropriate data from the CSV.

The provided code makes use of a list comprehension inside a file context manager to read the CSV and write the KML in one go, using lxml’s etree module to create and serialize the KML content.

Summary/Discussion

• Method 1: Using simplekml. Strengths include ease of use and readability. Weaknesses are that it can be slower with large datasets due to its high-level abstraction.
• Method 2: pandas with simplekml. Strengths include powerful data manipulation capabilities and readability. Weaknesses might be additional memory footprint due to the use of DataFrames.
• Method 3: Using ElementTree. Strengths are fine-grained control and no extra library dependencies. Weaknesses include more verbose code and complexity in managing XML namespaces.
• Method 4: Using csv2kml utility. Strength is the simplicity and lack of need for coding. Weaknesses could be less flexibility and dependence on an external utility.
• Bonus Method 5: csv and lxml one-liner. Strengths include brevity and efficiency. Weaknesses are reduced readability and potential difficulty in handling errors due to compactness.