Clip_MuLTY is written in python and use for clipped multiple layers using single shapefile.

Before running this code in your system you have to install geopandas library in your system. To install the geopandas library click_Here

Now we will breakdown and see how to execute the code:

import geopandas
import os

These are import statements that import the geopandas and os modules. The geopandas module is used for working with geospatial data, and the os module provides a way to interact with the operating system.

input_folder = r"E:/Python_Tutorial/Shapefiles"
output_folder = r"E:/Python_Tutorial/output"

These lines set the input and output folder paths for the shapefiles that will be read and created, respectively. You can change the path as per your desired location.

clip_shapefile = r"E:/Python_Tutorial/clipped_shapefiles/clipped.shp"

In this line you mentioned your shapefile which is used for clipping all the shapefiles stored in folder.

clipper = gpd.read_file(clip_shapefile)

This line reads the shapefile used for clipping and stores it in the variable clipper using the gpd.read_file() method.

for filename in os.listdir(input_folder):
   if filename.endswith('.shp'):
       # load the shapefile
       shp = gpd.read_file(os.path.join(input_folder, filename))

These lines start a loop that will iterate over all the shapefiles in the input_folder. For each file, if the file extension is .shp, the file is read using gpd.read_file() and stored in the variable shp.

       clipped = gpd.clip(shp, clipper)

This line clips the shp shapefile using the clipper shapefile using the gpd.clip() method. The clipped shapefile is stored in the variable clipped.

       output_path = os.path.join(output_folder, filename)

This line sets the output path for the clipped shapefile using os.path.join() to concatenate the output_folder and the input filename.

       clipped.to_file(output_path)

This line saves the clipped shapefile to the output path using the to_file() method.

So now after explaining all the code line by line, here is the full code:

import geopandas as gpd
import os
# set input and output paths
input_folder = r"E:/Python_Tutorial/Shapefiles"
output_folder = r"E:/Python_Tutorial/output"
# set the shapefile used for clipping
clip_shapefile = r"E:/Python_Tutorial/clipped_shapefiles/clipped.shp"

# load the clipping shapefile
clipper = gpd.read_file(clip_shapefile)
# loop through all shapefiles in the input folder
for filename in os.listdir(input_folder):
    if filename.endswith('.shp'):
        # load the shapefile
        shp = gpd.read_file(os.path.join(input_folder, filename))
        
        # clip the shapefile using the clipping shapefile
        clipped = gpd.clip(shp, clipper)
        
        # set the output path
        output_path = os.path.join(output_folder, filename)
        
        # save the clipped shapefile to the output path
        clipped.to_file(output_path)

Clipping multiple shapefiles with a single shapefile at a time can have several benefits, including:

1. Efficiency: Instead of manually clipping each shapefile using the same clipper shapefile, automating the process can save time and effort.

2. Consistency: By using the same clipper shapefile for all the input shapefiles, the resulting clipped shapefiles will have consistent boundaries and can be used together in analyses or mapping.

3. Accuracy: Clipping multiple shapefiles with a single clipper shapefile can reduce the potential for errors and inconsistencies that can arise when manually clipping multiple shapefiles with different clipper shapefiles.

4. Reproducibility: By using a script to clip multiple shapefiles with a single clipper shapefile, the process can be easily reproduced and shared with others, ensuring consistency and accuracy across different users or projects.

Overall, clipping multiple shapefiles with a single shapefile at a time can improve efficiency, consistency, accuracy, and reproducibility in geospatial data processing and analysis.

• Email: [email protected].

• LinkedIn: Koushik Ghosh

• ResearchGate: Koushik Ghosh