https://github.com/caiocarneloz/pdtug
Unweighted graph from Pandas numerical Dataframe
https://github.com/caiocarneloz/pdtug
graph machine-learning pandas-dataframe python random-walk
Last synced: about 2 months ago
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Unweighted graph from Pandas numerical Dataframe
- Host: GitHub
- URL: https://github.com/caiocarneloz/pdtug
- Owner: caiocarneloz
- Created: 2019-05-04T19:04:47.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-09-27T12:50:02.000Z (almost 7 years ago)
- Last Synced: 2025-11-21T16:03:42.405Z (8 months ago)
- Topics: graph, machine-learning, pandas-dataframe, python, random-walk
- Language: Python
- Size: 11.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# pdtug
Unweighted graphs from pandas numerical dataframes
## Getting Started
#### Dependencies
You need Python 3.7 or later to use **pdtug**. You can find it at [python.org](https://www.python.org/).
You also need pandas and numpy packages, which is available from [PyPI](https://pypi.org). If you have pip, just run:
```
pip install pandas
pip install numpy
```
#### Installation
Clone this repo to your local machine using:
```
git clone https://github.com/caiocarneloz/pdtug.git
```
## Features
- Get a unweighted graph from a pandas dataframe
- ~~Choose from multiple distance functions~~(soon)
- ~~Choose between k-nearest neighbours and distance threshold~~(soon)
## Usage
The **pdtug** function takes as argument a dataframe containing numerical data and an integer _k_ value which represents the number of nearest neighbours to be considered on the edge creation. As example, the image below shows the relationship between nodes with _k_ = 2:
As output, the function returns a python dictionary with the format of an adjacency list:
```
{0: [8, 3, 4, 7],
1: [8, 6, 7],
2: [8, 9, 5, 6],
3: [0, 8, 4],
4: [8, 0, 3],
5: [8, 9, 2],
6: [1, 2, 7],
7: [0, 1, 6],
8: [0, 1, 2, 3, 4, 5, 9],
9: [8, 2, 5]}
```
Each value corresponds to the dataframe row index.