[1058] Integrate points within the same polygons as the centroid (Points, Cadastre Polygons)

The function: 

def integration_points_within_polyogns(points_gdf, polygons_gdf):
    """_summary_
 
    Args:
        points_gdf (geodataframe): Point based geodataframe
        polygons_gdf (geodataframe): Polygon based geodataframe
 
    Returns:
        joined_gdf (geodataframe): Matched records and replace the latlon information from the polygons
        unjoined_gdf (geodataframe): Unmatched records
    """
    # Spatial join to find points within polygons
    joined_gdf = gpd.sjoin(points_gdf, polygons_gdf, how="inner", predicate="intersects")
 
    # Calculate the centroid (representative point) of the polygon
    polygons_gdf['centroid'] = polygons_gdf["geometry"].apply(lambda x: x.centroid if x.contains(x.centroid) else x.representative_point())
 
    # Update points to the centroid (representative point) of the polygon
    # .values: only update the corresponding values not based on the index
    joined_gdf['geometry'] = polygons_gdf.loc[joined_gdf.index_right, 'centroid'].values
    joined_gdf = joined_gdf.drop("index_right", axis=1)
 
    # calculate the difference of two RangeIndex data
    unjoined_gdf = points_gdf.reindex(points_gdf.index.difference(joined_gdf.index)) 
 
    return joined_gdf.copy(), unjoined_gdf.copy()

---

To integrate points within a specific polygon and set the centroid of the polygon as the new location for those points, you can use the geopandas library in Python. Here’s a step-by-step guide:

1. Import necessary libraries:

   import geopandas as gpd
   from shapely.geometry import Point, Polygon

2. Create sample GeoDataFrames:

   # Sample point-based GeoDataFrame
   points_data = {'geometry': [Point(1, 1), Point(2, 2), Point(3, 3)]}
   points_gdf = gpd.GeoDataFrame(points_data, crs="EPSG:4326")
    
   # Sample polygon-based GeoDataFrame
   polygons_data = {'geometry': [Polygon([(0, 0), (4, 0), (4, 4), (0, 4)])]}
   polygons_gdf = gpd.GeoDataFrame(polygons_data, crs="EPSG:4326")

3. Find points within polygons:

   # Spatial join to find points within polygons
   joined_gdf = gpd.sjoin(points_gdf, polygons_gdf, how="inner", op="within")

4. Calculate the centroid (representative point) of the polygon:

   # Calculate the centroid (representative point) of the polygon
   # polygons_gdf['centroid'] = polygons_gdf.centroid
   polygons_gdf['centroid'] = polygons_gdf["geometry"].apply(lambda x: x.centroid if x.contains(x.centroid) else x.representative_point())

5. Update points to the centroid of the polygon:

   # Update points to the centroid of the polygon
   joined_gdf['geometry'] = polygons_gdf.loc[joined_gdf.index_right, 'centroid'].values

6. Result:

   print(joined_gdf)

Here’s the complete code together:

import geopandas as gpd
from shapely.geometry import Point, Polygon
 
# Sample point-based GeoDataFrame
points_data = {'geometry': [Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4), Point(5, 5), Point(6, 6), Point(14, 10), Point(13, 13)]}
points_gdf = gpd.GeoDataFrame(points_data)
 
# Sample polygon-based GeoDataFrame
polygons_data = {'geometry': [Polygon([(1, 0), (3, 0), (3, 3), (0, 3), (0, 1), (1, 1.5), (1, 2), (2, 2), (2, 1), (1.5, 1)]), Polygon([(10, 10), (14, 10), (14, 14), (10, 14)])]}
polygons_gdf = gpd.GeoDataFrame(polygons_data)
 
# Spatial join to find points within polygons
joined_gdf = gpd.sjoin(points_gdf, polygons_gdf, how="inner", predicate="intersects")
 
# Calculate the centroid (representative point) of the polygon
polygons_gdf['centroid'] = polygons_gdf["geometry"].apply(lambda x: x.centroid if x.contains(x.centroid) else x.representative_point())
 
# Update points to the centroid (representative point) of the polygon
# .values: only update the corresponding values not based on the index
joined_gdf['geometry'] = polygons_gdf.loc[joined_gdf.index_right, 'centroid'].values
 
# calculate the difference of two RangeIndex data
unjoined_gdf = points_gdf.reindex(points_gdf.index.difference(joined_gdf.index)) 
 
print(joined_gdf)
print(unjoined_gdf)

This script will update the points within the polygon to the centroid of that polygon. If you have multiple polygons and points, you might need to adjust the logic to handle each polygon separately.

Feel free to ask if you have any more questions or need further assistance!

---

To get the remainder of a RangeIndex “A” after removing the elements of another RangeIndex “B” that it contains, you can use the difference method in pandas. Here’s how you can do it:

1. Import pandas:

   import pandas as pd

2. Create the RangeIndex objects:

   A = pd.RangeIndex(start=0, stop=10, step=1)
   B = pd.RangeIndex(start=3, stop=6, step=1)

3. Get the remainder of A after removing B:

   remainder = A.difference(B)

4. Print the result:

   print(remainder)

Here’s the complete code together:

import pandas as pd
 
# Create RangeIndex objects
A = pd.RangeIndex(start=0, stop=10, step=1)
B = pd.RangeIndex(start=3, stop=6, step=1)
 
# Get the remainder of A after removing B
remainder = A.difference(B)
 
print(remainder)

This will output:

RangeIndex(start=0, stop=3, step=1)
RangeIndex(start=6, stop=10, step=1)

This means that the elements 3, 4, and 5 have been removed from A, leaving the remainder as RangeIndex(start=0, stop=3, step=1) and RangeIndex(start=6, stop=10, step=1).

Feel free to ask if you have any more questions or need further assistance!

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To use a RangeIndex for a DataFrame in pandas, you can either create a DataFrame with a RangeIndex by default or explicitly set a RangeIndex for an existing DataFrame. Here are a few methods to achieve this:

Method 1: Default RangeIndex

When you create a DataFrame without specifying an index, pandas automatically uses a RangeIndex starting from 0 with steps of 1.

import pandas as pd
 
# Sample data for DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
 
# Create the DataFrame without specifying an index
df = pd.DataFrame(data)
 
# The default index of the DataFrame is a RangeIndex
print(df.index)

Method 2: Using pd.RangeIndex()

You can explicitly create a RangeIndex and set it as the index of a DataFrame.

import pandas as pd
 
# Sample data for DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
 
# Create the DataFrame
df = pd.DataFrame(data)
 
# Create a RangeIndex
range_index = pd.RangeIndex(start=0, stop=len(df), step=1)
 
# Set the RangeIndex as the index of the DataFrame
df.index = range_index
 
print(df.index)

Method 3: Using reset_index()

If you have a DataFrame with an existing index and you want to reset it to a RangeIndex, you can use the reset_index() method.

import pandas as pd
 
# DataFrame with an existing index
df = pd.DataFrame({'col1': [10, 20], 'col2': [30, 40]}, index=['a', 'b'])
 
# Reset the DataFrame to a RangeIndex
df_reset = df.reset_index(drop=True)
 
print(df_reset.index)

Method 4: Using reindex()

You can also use the reindex() method to set a RangeIndex.

import pandas as pd
 
# Sample data for DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
 
# Create the DataFrame
df = pd.DataFrame(data)
 
# Create a RangeIndex
range_index = pd.RangeIndex(start=0, stop=len(df), step=1)
 
# Reindex the DataFrame
df_reindexed = df.reindex(range_index)
 
print(df_reindexed.index)

These methods will help you work with RangeIndex in your DataFrame, providing flexibility depending on your specific needs. If you have any more questions or need further assistance, feel free to ask!