semvec/pypi-packages huggingface dataset:
data-view 0.2.0
Automated view of dataset

Description
===========

Requirements
------------

* python >= 3.6
* numpy >= 1.16
* pandas >= 0.23
* matplotlib >= 3.02
* IPython >= 7.2.0

------------

Module, provides the function _view_, which displays general information on the data:
  - Result of method info()
  - Result of method describe()
  - for numeric / categorical variables - The number of nulls in the data (amount and percentage for each column)
  - grid of histograms for numeric variables
  - Top-5 of the most frequent categorical variables (for each)
  - Split count statistic by category with less 5 unique values
  - matrix correlation
  - dataframe of higher correlation pair (over 0.7)

Parameters (function view):
 * d - table with data
 * only_numeric - True / False, default: True. True - information output only by numerical variables, False - information output by numerical and categorical variables.
 * full_stats - True / False, default: False. False - output information on numerical variables without interquartile range, data boundaries without outliers, True - complete output with IQR, min and max by IQR and amount of outliers.
 * histograms - True / False, default: True. True - output with building histograms for numerical variables, False - without building histograms

Top-5 elements of categorical variables

 The postfix (_name / _count) is assigned to the name of the data column to create top-5 elements table:
  - __name_ - category name
  - __count_ - number of unique elements in this category. If there are less than 5 unique elements, then the values ​​in the _count field are filled -1

Count statistic of numbers variables split by some categorical variables
 - if number of unique elements in category variable less 5, then show count statistic

Correlation matrix
 - Correlation matrix with heatmap (pearson corr index)

Install
-------
	$ pip install data_view

Usage
-----

    $ python3

    from data_view import *

    d = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])

    view(d, only_numeric=True, histograms=False)
graphenemodeling 1.1.2
Code for modeling graphene

Graphene Modeling

[FULL DOCUMENTATION](https://graphenemodeling.readthedocs.io/en/latest/index.html)

This Python package **models the electronic and optical properties of graphene**. The intent is to reproduce results found in the literature so that researchers need not write their own code which wastes time and may contain bugs that alter results. To boost confidence in the code in this repository, every piece of code should be accompanied by a figure that reproduces a result from a published paper.

View the [full documentation](https://graphenemodeling.readthedocs.io/en/latest/index.html).

**Install** the latest version with

```
> pip install graphenemodeling
```

Alternatively, if you are comfortable building from source, first select the latest stable version under `Branch: master -> Tags -> v#.#.#` and download or clone the repository.

**Test** your installation with the command

```
> python -m graphenemodeling
```

You should see the greeting

```
	Welcome to graphenemodeling version #.#.#!

    For a quick overview of this package's capabilities, run
    > python -m graphenemodeling overview

    For issues, contact Gregory Holdman at gholdman@protonmail.com
    or submit an issue or pull request on github.

```