https://github.com/lovasoa/kdsearch

Search k-dimensional datasets efficiently using KDTrees
https://github.com/lovasoa/kdsearch

kd-tree numpy pandas python3 scipy

Last synced: 3 months ago
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Search k-dimensional datasets efficiently using KDTrees

• Host: GitHub
• URL: https://github.com/lovasoa/kdsearch
• Owner: lovasoa
• License: gpl-3.0
• Created: 2017-03-01T21:20:13.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-03-03T14:03:27.000Z (almost 8 years ago)
• Last Synced: 2024-09-30T22:01:24.147Z (3 months ago)
• Topics: kd-tree, numpy, pandas, python3, scipy
• Language: Python
• Homepage:
• Size: 30.3 KB
• Stars: 4
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# KDSearch

Efficient K-dimensional queries in python using KDTrees with pandas and numpy.

[![Build Status](https://travis-ci.org/lovasoa/kdsearch.svg?branch=master)](https://travis-ci.org/lovasoa/kdsearch)

## Requirements

 - python 3.5+

 - numpy

 - pandas

## Installation

To install this package for the current user:

```

pip3 install --user kdsearch

```

## example

```python

import pandas

from kdsearch import KDTree

# We create a dataset with three points (1,3), (2,3) and (3,4)

df = pandas.DataFrame({"x": [1,2,3], "y":[3,3,4], "target": [0,1,1]})

tree = KDTree(df, ('x','y'), 'target')

# 

# Query all points with x between 0 and 10 and y between 0 and 3 (inclusive)

tree.query({"x":[0,10], "y":[0,3]})

# Statistics(sum=1, length=2)

tree.query({"x":[0,10], "y":[0,10]})

# Statistics(sum=2, length=3)

tree.query({"x":[5,10], "y":[5,10]})

# Statistics(sum=0, length=0)

```