# DataTypeSystem
This Python package provides a type system for different data structures that are
coercible to full arrays. It is Python translation of the code of the Raku package
["Data::TypeSystem"](https://raku.land/zef:antononcube/Data::TypeSystem), [AAp1].
------
## Installation
### Install from GitHub
```shell
pip install -e git+https://github.com/antononcube/Python-packages.git#egg=DataTypeSystem-antononcube\&subdirectory=DataTypeSystem
```
### From PyPi
```shell
pip install DataTypeSystem
```
------
## Usage examples
The type system conventions follow those of Mathematica's
[`Dataset`](https://reference.wolfram.com/language/ref/Dataset.html)
-- see the presentation
["Dataset improvements"](https://www.wolfram.com/broadcast/video.php?c=488&p=4&disp=list&v=3264).
Here we get the Titanic dataset, change the "passengerAge" column values to be numeric,
and show dataset's dimensions:
```python
import pandas
dfTitanic = pandas.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv')
dfTitanic = dfTitanic[["sex", "age", "pclass", "survived"]]
dfTitanic = dfTitanic.rename(columns ={"pclass": "class"})
dfTitanic.shape
```
(891, 4)
Here is a sample of dataset's records:
```python
from DataTypeSystem import *
dfTitanic.sample(3)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sex</th>
<th>age</th>
<th>class</th>
<th>survived</th>
</tr>
</thead>
<tbody>
<tr>
<th>555</th>
<td>male</td>
<td>62.0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>278</th>
<td>male</td>
<td>7.0</td>
<td>3</td>
<td>0</td>
</tr>
<tr>
<th>266</th>
<td>male</td>
<td>16.0</td>
<td>3</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>
Here is the type of a single record:
```python
deduce_type(dfTitanic.iloc[12].to_dict())
```
Struct([age, class, sex, survived], [float, int, str, int])
Here is the type of single record's values:
```python
deduce_type(dfTitanic.iloc[12].to_dict().values())
```
Tuple([Atom(<class 'str'>), Atom(<class 'float'>), Atom(<class 'int'>), Atom(<class 'int'>)])
Here is the type of the whole dataset:
```python
deduce_type(dfTitanic.to_dict())
```
Assoc(Atom(<class 'str'>), Assoc(Atom(<class 'int'>), Atom(<class 'str'>), 891), 4)
Here is the type of "values only" records:
```python
valArr = dfTitanic.transpose().to_dict().values()
deduce_type(valArr)
```
Vector(Struct([age, class, sex, survived], [float, int, str, int]), 891)
-------
## References
[AAp1] Anton Antonov,
[Data::TypeSystem Raku package](https://github.com/antononcube/Raku-Data-TypeSystem),
(2023),
[GitHub/antononcube](https://github.com/antononcube/).
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"description": "# DataTypeSystem\n\nThis Python package provides a type system for different data structures that are \ncoercible to full arrays. It is Python translation of the code of the Raku package\n[\"Data::TypeSystem\"](https://raku.land/zef:antononcube/Data::TypeSystem), [AAp1].\n\n------\n\n## Installation\n\n### Install from GitHub\n\n```shell\npip install -e git+https://github.com/antononcube/Python-packages.git#egg=DataTypeSystem-antononcube\\&subdirectory=DataTypeSystem\n```\n\n### From PyPi\n\n```shell\npip install DataTypeSystem\n```\n\n------\n\n## Usage examples\n\nThe type system conventions follow those of Mathematica's \n[`Dataset`](https://reference.wolfram.com/language/ref/Dataset.html) \n-- see the presentation \n[\"Dataset improvements\"](https://www.wolfram.com/broadcast/video.php?c=488&p=4&disp=list&v=3264).\n\nHere we get the Titanic dataset, change the \"passengerAge\" column values to be numeric, \nand show dataset's dimensions:\n\n\n```python\nimport pandas\n\ndfTitanic = pandas.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv')\ndfTitanic = dfTitanic[[\"sex\", \"age\", \"pclass\", \"survived\"]]\ndfTitanic = dfTitanic.rename(columns ={\"pclass\": \"class\"})\ndfTitanic.shape\n```\n\n\n\n\n (891, 4)\n\n\n\nHere is a sample of dataset's records:\n\n\n```python\nfrom DataTypeSystem import *\n\ndfTitanic.sample(3)\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sex</th>\n <th>age</th>\n <th>class</th>\n <th>survived</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>555</th>\n <td>male</td>\n <td>62.0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>278</th>\n <td>male</td>\n <td>7.0</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>266</th>\n <td>male</td>\n <td>16.0</td>\n <td>3</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nHere is the type of a single record:\n\n\n```python\ndeduce_type(dfTitanic.iloc[12].to_dict())\n```\n\n\n\n\n Struct([age, class, sex, survived], [float, int, str, int])\n\n\n\nHere is the type of single record's values:\n\n\n```python\ndeduce_type(dfTitanic.iloc[12].to_dict().values())\n```\n\n\n\n\n Tuple([Atom(<class 'str'>), Atom(<class 'float'>), Atom(<class 'int'>), Atom(<class 'int'>)])\n\n\n\nHere is the type of the whole dataset:\n\n\n```python\ndeduce_type(dfTitanic.to_dict())\n```\n\n\n\n\n Assoc(Atom(<class 'str'>), Assoc(Atom(<class 'int'>), Atom(<class 'str'>), 891), 4)\n\n\n\nHere is the type of \"values only\" records:\n\n\n```python\nvalArr = dfTitanic.transpose().to_dict().values()\ndeduce_type(valArr)\n```\n\n\n\n\n Vector(Struct([age, class, sex, survived], [float, int, str, int]), 891)\n\n\n\n-------\n\n## References\n\n[AAp1] Anton Antonov,\n[Data::TypeSystem Raku package](https://github.com/antononcube/Raku-Data-TypeSystem),\n(2023),\n[GitHub/antononcube](https://github.com/antononcube/).\n",
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