How to Use Geolocation Data and Temperature Values with the Meteostat Library in Python

Working with Geolocation Data and Temperature in Python

As a data scientist or analyst, working with geospatial data can be a fascinating and challenging task. In this article, we’ll explore how to use the Meteostat library in Python to retrieve temperature values for a given location and time. We’ll also delve into using Pandas dataframes to store and manipulate geolocation data.

Introduction

The Meteostat library provides a convenient way to access weather data from various sources, including the European Centre for Medium-Range Weather Forecasts (ECMWF). The library’s Point class allows us to specify a location by latitude, longitude, and altitude. We can then use this location to fetch hourly climate data using the Hourly class.

However, working with geolocation data and temperature values in Python requires some understanding of how to manipulate Pandas dataframes, work with datetime objects, and interact with the Meteostat library. In this article, we’ll cover these topics and provide examples of how to use the Meteostat library to retrieve temperature values for a given location and time.

Working with Geolocation Data

Geolocation data is typically stored in a Pandas dataframe, which is a 2-dimensional labeled data structure with columns of potentially different types. In this example, we have a dataframe geo_time containing latitude, longitude, altitude, and start date values:

import pandas as pd

df = pd.DataFrame(
    {
        "latitude": [48.2393, 35.5426, 49.2466],
        "longitude": [11.5713, 139.5975, -123.2214],
        "altitude": [520, 5, 5],
        "start": ["2020-03-12 13:00:00", "2020-07-31 18:00:00", "2020-06-23 11:00:00"],
    }
)

Converting Start Date to Datetime

To work with datetime objects, we need to convert the start date values in the dataframe to a suitable format. We can use the pd.to_datetime function to achieve this:

df["start"] = pd.to_datetime(df["start"], format="%Y-%m-%d %H:%M:%S")

Fetching Temperature Values

Now that we have converted the start date values, we can use the Meteostat library to fetch temperature values for each location in our dataframe. We’ll create a new column temp and use the apply method to iterate over each row in the dataframe:

df["temp"] = df.apply(
    lambda x: Hourly(
        Point(x["latitude"], x["longitude"], x["altitude"]),
        x["start"],
        x["start"],
    )
    .fetch()["temp"]
    .values[0],
    axis=1,
)

Interpreting the Results

The resulting dataframe df now contains temperature values for each location and time. We can print the dataframe to verify the results:

print(df)
# Output
   latitude  longitude  altitude               start  temp
0   48.2393    11.5713       520 2020-03-12 13:00:00  16.8
1   35.5426   139.5975         5 2020-07-31 18:00:00  24.3
2   49.2466  -123.2214         5 2020-06-23 11:00:00  14.9

Conclusion

In this article, we’ve demonstrated how to use the Meteostat library in Python to retrieve temperature values for a given location and time. We’ve also covered how to work with Pandas dataframes, convert start date values to datetime objects, and iterate over each row in the dataframe using the apply method.

By following these steps and examples, you should be able to manipulate geolocation data and temperature values in Python using the Meteostat library and Pandas.

Last modified on 2023-11-18