Скачать или смотреть Calculate Geodesic Distance Between Points in Two DataFrames with Geopy

Learn how to effectively calculate geodesic distances between tuples of coordinates in two different DataFrames using Geopy and pandas.
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Calculating Geodesic Distances Between Points in Two DataFrames with Geopy

When working with geographical data in Python, one common task is to compute the distance between various points defined by their latitude and longitude. Whether you’re developing a location-based application or analyzing geographical trends, knowing how to calculate geodesic distances accurately is crucial. In this guide, we will explore how to use the geopy library in conjunction with pandas DataFrames for this purpose.

Understanding the Problem

In our example, we have two DataFrames, df1 and df2. Both contain a column named lat_lon, which holds tuples of latitude and longitude coordinates. Our goal is to:

Feed a location from df1 into a function.

Calculate the distance from that location to all points in df2.

Find and return the closest location in df2 to the point from df1.

We started with a function, but an error occurred when running it. The error message generated was:

[[See Video to Reveal this Text or Code Snippet]]

This indicates a misunderstanding of how geodesic expects input.

The Initial Code

Here is the initial code that caused the issue:

[[See Video to Reveal this Text or Code Snippet]]

The problem lies in how we are trying to calculate distances. The entire df2 DataFrame is being processed at once, rather than individual tuples.

The Solution

To rectify this, we need to modify our function to utilize a lambda function for iterating through df2['lat_lon']. This way, we can compute distances for each individual point. Here’s the revised function:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Revised Function

Lambda Function: The modified line uses apply with a lambda function, allowing for the calculation of distance for each tuple in df2['lat_lon'].

Distance Calculation: Instead of attempting to create a Series directly with distances, we create a new Series dists that stores computed distances.

Finding the Closest Location: The idxmin() function still identifies the index of the smallest distance, which we use to find the closest location.

Integrating with df1

Now that we have the get_distance function working correctly, we can seamlessly apply it to df1:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By leveraging geopy's capabilities alongside pandas, we can efficiently compute geodesic distances between geographic points stored in DataFrames. Remember to ensure that you're feeding the correct types of data into your distance functions.

With the necessary updates, you should now be able to calculate the closest locations across your DataFrames without running into errors. Keep experimenting and enhancing your data analysis skills in Python!

For any questions, feel free to leave a comment below!