5 Best Ways to Convert CSV to KMZ with Python – Be on the Right Side of Change

💡 Problem Formulation: In the world of digital mapping and geographic datasets, users frequently need to convert tabular data in CSV format into the geospatial KMZ format used by applications like Google Earth. For instance, someone might have a CSV file containing a list of locations with their corresponding latitude and longitude values and want to convert this data into a KMZ file to create a visual map representation. This article explores how to automate this conversion using Python.

Method 1: Using Simplekml and Pandas Libraries

Simplekml is a Python library for generating KML (or KMZ) files. In combination with Pandas for CSV handling, we can create a script that reads a CSV file, iterates through the rows, and generates placemarks in a KML file, which is then compressed to KMZ format. This method is straightforward and customizable.

Here’s an example:

import pandas as pd
import simplekml

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

# Create a KML object
kml = simplekml.Kml()

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

# Save as KMZ
kml.savekmz('locations.kmz')

The output of this code snippet is a KMZ file named ‘locations.kmz’ with placemarks for each location in the CSV file.

This Python snippet uses the Pandas library to read the CSV file ‘locations.csv’, iterates through each row, and uses Simplekml to create a KML object with placemarks at the specified coordinates. Finally, it saves the data as a KMZ file, ready for use in geospatial applications.

Method 2: Using csv and pykml Libraries

This method combines Python’s native csv module with pykml to parse and construct the KML file. It allows for fine-grained control of the KML structure and attributes. This is suitable for users who require more advanced KML customizations.

Here’s an example:

import csv
from pykml.factory import KML_ElementMaker as KML

# Create the root KML element
doc = KML.kml(KML.Document())

with open('locations.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # Skip the header row
    for row in reader:
        doc.Document.append(
          KML.Placemark(
            KML.name(row[0]),
            KML.Point(KML.coordinates(f"{row[2]},{row[1]}"))
          )
        )

# Output KMZ
with open('locations.kmz', 'w') as kmzfile:
    kmzfile.write(doc.tostring(prettyprint=True))

The output of this code snippet is a ‘locations.kmz’ file with detailed placemarks.

The code reads a CSV file and constructs a KML document using the pykml library. For each row in the CSV, a placemark is created with a name and a point. The resulting KML is converted to a text string and saved as ‘locations.kmz’.

Method 3: Using Geopandas and Fiona

Geopandas is an excellent library for handling geospatial data. Using Geopandas in conjunction with Fiona to handle file conversion provides a robust method for converting CSV data into KMZ files.

Here’s an example:

import geopandas as gpd
from shapely.geometry import Point

# Read CSV data into a GeoDataFrame
df = gpd.read_file('locations.csv')

# Convert DataFrame to a GeoDataFrame
geometry = [Point(xy) for xy in zip(df.longitude, df.latitude)]
gdf = gpd.GeoDataFrame(df, crs='EPSG:4326', geometry=geometry)

# Save to a file in KMZ format
gdf.to_file('locations.kmz', driver='KML')

The output is a KMZ file ‘locations.kmz’ with geospatial data from the CSV.

This snippet transforms the CSV data into a geospatial DataFrame with Geopandas, identifies the points’ geometry and specifies the coordinate reference system (CRS). It then saves the GeoDataFrame as KMZ using the ‘KML’ driver from Fiona.

Method 4: Using the csv, zipfile, and lxml.etree Libraries

This more hands-on method involves creating the KML file from the CSV data using lxml for XML handling and zipfile for creating the KMZ archive. It gives users the most flexibility for defining the KML structure.

Here’s an example:

import csv
import zipfile
from lxml import etree as ET

kml_root = ET.Element('kml', xmlns='http://www.opengis.net/kml/2.2')
document = ET.SubElement(kml_root, 'Document')

with open('locations.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # Skip the header row
    for name, latitude, longitude 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}"

# Create KMZ
kml_tree = ET.ElementTree(kml_root)
kml_tree.write('doc.kml')

with zipfile.ZipFile('locations.kmz', 'w') as zip_file:
    zip_file.write('doc.kml')

The output is an archived KMZ file ‘locations.kmz’ containing the created ‘doc.kml’ file.

In this method, the CSV file is read and each row is used to construct elements in an XML tree representing KML data. This XML is then saved to a ‘doc.kml’, which is compressed into a KMZ file using Python’s zipfile module.

Bonus One-Liner Method 5: Using Command Line Tools

For those who want to avoid writing a full script, command line tools like GDAL’s ogr2ogr can perform the conversion with a single command. This method is fast and leverages a powerful library, but offers less programming control.

Here’s an example:

ogr2ogr -f "KML" locations.kmz locations.csv -oo X_POSSIBLE_NAMES=lon* -oo Y_POSSIBLE_NAMES=lat* -oo KEEP_GEOM_COLUMNS=NO

The output of this line is a KMZ file ‘locations.kmz’ directly converted from ‘locations.csv’.

This one-liner uses the ogr2ogr utility from the GDAL library to convert a CSV file into a KMZ file, identifying longitude and latitude columns with pattern matching.

Summary/Discussion

Method 1: Simplekml and Pandas. Easy for those familiar with Python data manipulation. Limited to simple KML features.
Method 2: csv and pykml Libraries. Offers detailed KML customizations. Can become cumbersome for complex KML structures.
Method 3: Geopandas and Fiona. Robust for geospatial data manipulation. Requires additional libraries and handling of geospatial data.
Method 4: csv, zipfile, and lxml.etree Libraries. Most flexible for XML customization. It might be overkill for simple conversions.
Method 5: Command Line Tools. Quick and powerful for users comfortable with command line operations. Less control over the conversion process.