semvec/pypi-packages huggingface dataset:
gpmap 0.7.0
A Python API for managing genotype-phenotype map data
License: MIT
Requires Python: >=3.3.0

# GPMap

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*A Python API for managing genotype-phenotype map data*

GPMap defines a flexible object for managing genotype-phenotype (GP) map data. At it's core,
it stores all data in Pandas DataFrames and thus, interacts seamlessly with the
PyData egosystem.

To visualize genotype-phenotype objects created by GPMap, checkout [GPGraph](https://github.com/Zsailer/gpgraph).

<img src="docs/_img/gpm.png"> <img src="docs/_img/dataframe.png" width="400">

## Basic example

Import the package's base object.
```python
from gpmap import GenotypePhenotypeMap
```

Pass your data to the object.
```python

# Data
wildtype = "AAA"
genotypes = ["AAA", "AAT", "ATA", "TAA", "ATT", "TAT", "TTA", "TTT"]
phenotypes = [0.1, 0.2, 0.2, 0.6, 0.4, 0.6, 1.0, 1.1]
stdeviations = [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]

# Initialize the object
gpm = GenotypePhenotypeMap(wildtype,
                           genotypes,
                           phenotypes,
                           stdeviations=stdeviations)

# Check out the data.
gpm.data
```
<img src="docs/_img/dataframe.png" width="350">


Or load a dataset from disk.
```python
gpm = GenotypePhenotypeMap.read_json("data.json")
```

## Installation

**Users**
This simplest way to install this package is using pip:
```
pip install gpmap
```

**Developers**
The recommended way to install this package for development is using `pipenv`.

1. Clone this repository:
```
git clone https://github.com/harmslab/gpmap
```
2. Install the package using pipenv.
```
cd gpmap
pipenv install --dev -e .
```
3. Run tests using pytest
```
pytest
```

## Dependencies

The following modules are required. Also, the examples/tutorials are written in Jupyter notebooks and require IPython to be installed.

* [Numpy](http://www.numpy.org/)
* [Pandas](https://pandas.pydata.org/)
legaltechtools 1.0.2
Legal Technology scraping and data analysis library
License: MIT
Keywords: LegalTech,LegalTechTools

# legaltechtools

![Platform](https://img.shields.io/badge/platform-python-green.svg?style=flat&logo=appveyor)

*An open source library that can be used to scrape and perform data analysis on legal tech companies*


## Contents

1. [About](#about)
2. [Installation](#installation)
3. [Authors](#authors)

## About

The library consists of two different classes, one intended for scraping and one intended for data analysis.

#### Scraping

The scraping class can be used obtain information on legal tech companies including  company urls, social media urls, and the subsets of legal-tech that a particular company falls into.
Additionally, existing information can be verified by calling `ScrapingTools.verify_url()`

#### Data Analysis

The data analysis class can be used to uncover insights about data that has already been collected. Some of the metrics that can be found include determining the distribution of companies by country, the average success of a legal-tech company in a particular region, countries where new legal-tech companies are being founded, and the number of legal-tech companies founded per year. 

## Installation

The library can easily be installed via pip:

`pip install legaltechtools`

## Authors

This library was written by Neel Kandlikar, Neeraj Rattehalli, and David Fletcher under the mentorship of Dr. Pieter Gunst.