tablegis 0.0.4

A powerful Python library for geospatial data processing, analysis and conversion - integrating pandas with GIS operations

tablegis

tablegis is a Python package for geospatial data processing and analysis, built on geopandas, pandas, shapely, and pyproj. It provides a series of utility functions to simplify common GIS operations.

Features

• Distance Calculation: Efficiently compute the nearest distance between DataFrames.
• Spatial Analysis: Create buffers (input in meters), Voronoi polygons, Delaunay triangulations, etc.
• Format Conversion: Easily convert between GeoDataFrame and formats like Shapefile, KML, etc.
• Coordinate Aggregation: Provides tools for aggregating coordinate points into grids.
• Geometric Operations: Includes merging polygons, calculating centroids, adding sectors, etc.

Installation

1、You can install tablegis from PyPI:

pip install tablegis

2、Or, install the latest version directly from the GitHub repository:

pip install git+https://github.com/Non-existent987/tablegis.git

3、After downloading the project, it is convenient to import from local files for modification.

import sys
import pandas as pd
# Find the file path of the tablegis you downloaded.
sys.path.insert(0, r'C:\Users\Administrator\Desktop\tablegis')
# Now it can be imported.
import tablegis as tg

Quick Start

Here is a simple example of how to use tablegis:

1. Find the nearest point (in df2) for each point in df1 and add its ID, longitude, latitude, and distance.

import pandas as pd
import tablegis as tg

# Create two example DataFrames
df1 = pd.DataFrame({
    'id': [1, 2, 3],
    'lon1': [116.404, 116.405, 116.406],
    'lat1': [39.915, 39.916, 39.917]
})

df2 = pd.DataFrame({
    'id': ['A', 'B', 'C', 'D'],
    'lon2': [116.403, 116.407, 116.404, 116.408],
    'lat2': [39.914, 39.918, 39.916, 39.919]
})

# Calculate the nearest 1 point
result = tg.min_distance_twotable(df1, df2, lon1='lon1', lat1='lat1', lon2='lon2', lat2='lat2', df2_id='id', n=1)
# Calculate the nearest 2 points
result2 = tg.min_distance_twotable(df1, df2, lon1='lon1', lat1='lat1', lon2='lon2', lat2='lat2', df2_id='id', n=2)

print("\nExample result (distance in meters):")
print(result)
print(result2)

Result Display:

Table df1: id lon1 lat1 A 114.0 30.0 B 114.1 30.1

Table df2: id lon2 lat2 p1 114.01 30.01 p2 114.05 30.05 p3 114.12 30.12

Nearest 1 point:

id	lon1	lat1	nearest1_id	nearest1_lon2	nearest1_lat2	nearest1_distance
A	114.0	30.0	p1	114.01	30.01	1470.515926
B	114.1	30.1	p3	114.12	30.12	2939.507557

Nearest 2 points:

id	lon1	lat1	nearest1_id	nearest1_lon2	nearest1_lat2	nearest1_distance	nearest2_id	nearest2_lon2	nearest2_lat2	nearest2_distance	mean_distance
A	114.0	30.0	p1	114.01	30.01	1470.515926	p2	114.05	30.05	7351.852775	4411.184351
B	114.1	30.1	p3	114.12	30.12	2939.507557	p2	114.05	30.05	7350.037700	5144.772629

2. Find the nearest point for each point within the same table and add its ID, longitude, latitude, and distance.

import pandas as pd
import tablegis as tg

# Create an example DataFrame
df2 = pd.DataFrame({
    'id': ['A', 'B', 'C', 'D'],
    'lon2': [116.403, 116.407, 116.404, 116.408],
    'lat2': [39.914, 39.918, 39.916, 39.919]
})

# Calculate the nearest 1 point
result = tg.min_distance_onetable(df2, 'lon2', 'lat2', idname='id', n=1)
# Calculate the nearest 2 points
result2 = tg.min_distance_onetable(df2, 'lon2', 'lat2', idname='id', n=2)

print("\nExample result (distance in meters):")
print(result)
print(result2)

Result Display:

Table df2:

id	lon2	lat2
p1	114.01	30.01
p2	114.05	30.05
p3	114.12	30.12

Nearest 1 point:

	id	lon2	lat2	nearest1_id	nearest1_lon2	nearest1_lat2	nearest1_distance
0	p1	114.01	30.01	p2	114.05	30.05	5881.336911
1	p2	114.05	30.05	p1	114.01	30.01	5881.336911
2	p3	114.12	30.12	p2	114.05	30.05	10289.545038

Nearest 2 points:

	id	lon2	lat2	nearest1_id	nearest1_lon2	nearest1_lat2	nearest1_distance	nearest2_id	nearest2_lon2	nearest2_lat2	nearest2_distance	mean_distance
0	p1	114.01	30.01	p2	114.05	30.05	5881.336911	p3	114.12	30.12	16170.880987	11026.108949
1	p2	114.05	30.05	p1	114.01	30.01	5881.336911	p3	114.12	30.12	10289.545038	8085.440974
2	p3	114.12	30.12	p2	114.05	30.05	10289.545038	p1	114.01	30.01	16170.880987	13230.213012

3、Replace the longitude and latitude columns in the table with the corresponding values in another coordinate system, and add a new column for longitude and latitude.

import pandas as pd
import tablegis as tg

# Create two sample DataFrames
df = pd.DataFrame({
    'id': ['A', 'B', 'C', 'D'],
    'lon': [116.403, 116.407, 116.404, 116.408],
    'lat': [39.914, 39.918, 39.916, 39.919]
})

# Convert the latitude and longitude in the 84 coordinate system to the latitude and longitude in the web_mercator system.
result = tg.to_lonlat(df,'lon','lat', from_crs="wgs84", to_crs="web_mercator")
print(result)

Result Display:
Added two columns of "web_mercator"：

id	lon	lat	web_mercator_lon	web_mercator_lat
A	116.403	39.914	12957922.69	4853452.853
B	116.407	39.918	12958367.96	4854033.408
C	116.404	39.916	12958034.01	4853743.126
D	116.408	39.919	12958479.28	4854178.552

4、Generate buffers of the specified range based on the longitude and latitude columns in the table and add the geometry.

import pandas as pd
import tablegis as tg

df = pd.DataFrame({
        'lon': [116.4074, 121.4737],
        'lat': [39.9042, 31.2304],
        'buffer_size': [500, 1000]
    })
# Set a 100-meter buffer zone
res_100 = tg.add_buffer(df,'lon','lat',100) 
# Set the buffer range according to the numbers in the "buffer_size" column.
res_buffer_size = tg.add_buffer(df,'lon','lat','buffer_size')
print(res_100)
print(res_buffer_size)

Result Display:

df table

	lon	lat	buffer_size
0	116.4074	39.9042	500
1	121.4737	31.2304	1000

Set a 100-meter buffer zone

	lon	lat	buffer_size	geometry
0	116.4074	39.9042	500	POLYGON ((116.40857 39.90421, 116.40856 39.904...
1	121.4737	31.2304	1000	POLYGON ((121.47475 31.23036, 121.47474 31.230...

Set the buffer range according to the numbers in the "buffer_size" column.

	lon	lat	buffer_size	geometry
0	116.4074	39.9042	500	POLYGON ((116.41325 39.90423, 116.41322 39.903...
1	121.4737	31.2304	1000	POLYGON ((121.48417 31.23003, 121.48408 31.229...

5、Convert the latitude and longitude columns in the table into point-shaped geometries and then transform them into a gdf.

import pandas as pd
import tablegis as tg

df = pd.DataFrame({
        'lon': [116.4074, 121.4737, 113.2644],
        'lat': [39.9042, 31.2304, 23.1291],
        'city': ['Beijing', 'Shanghai', 'Guangzhou']
    })
# Generate points based on latitude and longitude
result1 = tg.add_points(df)
print(result1)

Result Display:

lon	lat	city	geometry
116.4074	39.9042	Beijing	POINT (116.4074 39.9042)
121.4737	31.2304	Shanghai	POINT (121.4737 31.2304)
113.2644	23.1291	Guangzhou	POINT (113.2644 23.1291)

6、Concentrate the latitude and longitude data in the table using the method of expansion and recombination, and add the fused id as well as the range geom.

import pandas as pd
import tablegis as tg
# Prepare test data
test_data = pd.DataFrame({
    'lon': [116.40, 116.41, 116.50, 116.51],
    'lat': [39.90, 39.91, 39.95, 39.96],
    'name': ['A', 'B', 'C', 'D'],
    'value': [1, 2, 3, 4]
})

# Test 1: Return geometry
result_no_geom = tg.add_buffer_groupbyid(
    test_data, 
    lon='lon', 
    lat='lat',
    distance=1000,
    columns_name='group_id',
    id_label_prefix='id_',
    geom=True
)
# Test 2: Do not return geometry
result_geom = tg.add_buffer_groupbyid(
    test_data, 
    lon='lon', 
    lat='lat',
    distance=1000,
    columns_name='group_id',
    id_label_prefix='id_',
    geom=False
)

Result Display:

no geom

lon	lat	name	value	group_id
116.40	39.90	A	1	id_0
116.41	39.91	B	2	id_0
116.50	39.95	C	3	id_1
116.51	39.96	D	4	id_1

geom

lon	lat	name	value	group_id	geometry
116.40	39.90	A	1	id_0	POLYGON ((116.41149 39.8983, 116.41122 39.8974...)
116.41	39.91	B	2	id_0	POLYGON ((116.41149 39.8983, 116.41122 39.8974...)
116.50	39.95	C	3	id_1	POLYGON ((116.51149 39.94829, 116.51122 39.947...)
116.51	39.96	D	4	id_1	POLYGON ((116.51149 39.94829, 116.51122 39.947...)

Contributing

Contributions in all forms are welcome, including feature requests, bug reports, and code contributions.

License

This project is licensed under the MIT License.