How to Build an ETL App for OpenWeatherMap Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build OpenWeatherMap-connected applications and pipelines for extracting, transforming, and loading OpenWeatherMap data. This article shows how to connect to OpenWeatherMap with the CData Python Connector and use petl and pandas to extract, transform, and load OpenWeatherMap data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live OpenWeatherMap data in Python. When you issue complex SQL queries from OpenWeatherMap, the driver pushes supported SQL operations, like filters and aggregations, directly to OpenWeatherMap and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to OpenWeatherMap Data

Connecting to OpenWeatherMap data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Using API Key Authentication

To obtain an API key, sign up for a free account at https://openweathermap.org/api and navigate to the API keys section of your dashboard. Copy your API key for use in the connection configuration.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your OpenWeatherMap API key.

After installing the CData OpenWeatherMap Connector, follow the procedure below to install the other required modules and start accessing OpenWeatherMap through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for OpenWeatherMap Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData OpenWeatherMap Connector to create a connection for working with OpenWeatherMap data.

cnxn = mod.connect("Profile=C:\path\to\OpenWeatherMap.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_openweathermap_api_key";")

Create a SQL Statement to Query OpenWeatherMap

Use SQL to create a statement for querying OpenWeatherMap. In this article, we read data from the AccumulatedPrecipitation entity.

sql = "SELECT ,  FROM AccumulatedPrecipitation WHERE Latitude = '40.7128'"

Extract, Transform, and Load the OpenWeatherMap Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the OpenWeatherMap data. In this example, we extract OpenWeatherMap data, sort the data by the column, and load the data into a CSV file.

Loading OpenWeatherMap Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'accumulatedprecipitation_data.csv')

With the CData API Driver for Python, you can work with OpenWeatherMap data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to OpenWeatherMap data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\path\to\OpenWeatherMap.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_openweathermap_api_key";")

sql = "SELECT ,  FROM AccumulatedPrecipitation WHERE Latitude = '40.7128'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'accumulatedprecipitation_data.csv')