Скачать или смотреть Solving the Challenge of Merging Two Geodataframes with Geometries in GeoPandas

A practical guide on how to merge two `GeoDataFrames` in Python using `GeoPandas` when one contains only geometries.
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Merging Geodataframes with Geometry Columns in GeoPandas

GeoPandas is a powerful tool for working with geospatial data in Python, but merging dataframes can sometimes be tricky, especially when one of the dataframes is composed solely of geometry data. In this post, we will explore how to effectively combine two geodataframes — one containing statistical data and the other holding geometric information — into a single comprehensive dataframe.

The Challenge

Imagine you have two geodataframes to work with:

Dataframe 1 (df1) is composed of various statistical measures across 1700 rows, detailing metrics like mean, standard deviation, max, min, day, and date.

Dataframe 2 (df2) consists of 1700 rows, but it solely contains a geometry column with geometric shapes (like polygons).

Your goal is to merge these two dataframes so that the resulting dataframe, df3, not only retains all the columns from df1 but also appends the geometry from df2 as the last column, preserving its geographic properties.

Example of Dataframes

Here's a glimpse at what the two dataframes look like:

Dataframe 1 (df1):

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

Dataframe 2 (df2):

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

Expected Outcome

The desired merged dataframe (df3) should look like this:

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

The Solution

To achieve this merging effectively, we can use the pd.concat() function from the pandas library. Below is a step-by-step explanation of the process to create the combined geodataframe.

Step 1: Import Libraries

First, ensure you have imported the necessary libraries:

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

Step 2: Combine the Dataframes

You can concatenate the two dataframes along the columns (axis=1) and specify the coordinate reference system (CRS) to retain the geometry properties. The following line of code accomplishes this:

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

Step 3: View the Combined Dataframe

Once you execute the above code, you can check the output using just:

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

You should see a properly merged dataframe, with all columns from df1 and the geometry column from df2 neatly appended at the end.

Conclusion

Merging two geodataframes in GeoPandas might present challenges at first, especially when dealing with differing structures. However, using pd.concat() offers a straightforward solution for combining a dataframe with statistical data and another containing geospatial geometries. By following the steps outlined above, you can easily integrate your geodata, enhancing your analysis capabilities.

Happy coding, and may your geospatial analyses be smooth!