Geocoding with Placekey: A Step-by-Step Guide, Using Python

Introduction

Geocoding, the process of converting addresses into geographic coordinates, is essential for many data-driven projects. Handling large datasets or inconsistent data formats can be tough. With Placekey, you can automate geocoding using a Python script. If you're working with large datasets, check out the companion notebook linked here.

Understanding the Placekey Geocoding Process

Placekey is a simple API for open entity matching for addresses and points-of-interest. This helps with deduping, matching, syncing, and merging physical places. Beyond an ID, the API also has the ability to return geocodes. The script below will show you how to use the API to generate Placekeys and geocodes.

Guide to Using the Placekey Geocoder with Python

1. Initial Setup

To start, the script sets up the environment by importing necessary libraries and initializing the templates we’ll use in our requests.

import requests
import pandas as pd
from time import sleep
import json

# Replace with your actual API key
API_KEY = 'YOUR_API_KEY'

# Define the endpoint URL and headers
ENDPOINT_URL = 'https://api.placekey.io/v1/placekey'
HEADERS = {
    'apikey': API_KEY,
    'Content-Type': 'application/json',
}
FIELDS = {"fields": ["geocode"]}

2. Reading Data from a CSV File

The script reads your address data from a CSV file and prepares it for geocoding by selecting the necessary columns.

# Read the CSV file into a DataFrame
csv_path = 'YOUR_CSV_PATH'
df = pd.read_csv(csv_path).head(250)  # Limiting to 250 rows for this example
print(df.columns)

# Select relevant columns
df = df[["address", "city", "state", "zipcode", "iso_country_code"]]

3. Handling API Requests

We then iterate over each address, sending it to the Placekey API and pausing briefly between requests to respect API rate limits. The results are stored for later use.

def get_placekeys(df):
    results = []
    for index, row in df.iterrows():
        address_dict = {
            "street_address": row["address"],
            "city": row["city"],
            "region": row["state"],
            "postal_code": row["zipcode"],
            "iso_country_code": row["iso_country_code"],
        }
        data = {"query": address_dict}
        response = requests.post(ENDPOINT_URL, headers=HEADERS, json=data)
        if response.status_code == 200:
            placekey = response.json().get("placekey")
            results.append(placekey)
        else:
            print(f"Error for index {index}: {response.text}")
        sleep(0.1)  # Sleep to avoid hitting rate limits
    return results

4. Adding Placekeys to Your Data

Finally, the Placekeys are added to your dataset, and the updated data is saved to a new CSV file for further use.

# Get Placekeys for the addresses
df['placekey'] = get_placekeys(df)

# Save the DataFrame with Placekeys to a new CSV
df.to_csv('geocoded_addresses.csv', index=False)
print("Geocoding complete. Results saved to 'geocoded_addresses.csv'.")

Conclusion

With Placekey, geocoding is easy and straightforward. The process is automated, saving you time and ensuring accurate results. Whether you're working with a few addresses or a vast database, Placekey's geocoding API simplifies your workflow and enhances the value of your location data.

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Placekey Tutorials