Metadata-Version: 2.1
Name: relatable
Version: 0.2.0
Summary: Document-oriented to relational conversion
Author-email: "Alexander M. Giordano" <am.giordano.carmena@gmail.com>
License: Copyright (c) 2018 The Python Packaging Authority
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/am-giordano/relatable
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# relatable

relatable is a Python package for converting a collection of documents, 
such as a MongoDB collection, into an interrelated set of tables, such as a 
schema in a relational database.

## Installation

```
pip3 install relatable
```

## Example of use

Consider the following list of dictionaries:

```
docs = [
  {
    "name": "Alice",
    "age": 34,
    "experience": [
      {
        "company": "Google",
        "role": "Software Engineer",
        "from": 2020,
        "to": 2022,
        "responsibilities": [
          "Google stuff",
          "Mark TensorFlow issues as \"Won't Do\""
        ],
        "technologies": [
          "C++",
          "LolCode"
        ]
      },
      {
        "company": "Facebook",
        "role": "Senior Data Scientist",
        "from": 2017,
        "to": 2020,
        "responsibilities": [
          "Censor media",
          "Learn the foundations of ML",
          "Do Kaggle competitions"
        ],
        "technologies": [
          "Python",
          "Excel"
        ]
      }
    ]
  },
  {
    "name": "Bob",
    "age": 27,
    "experience": [
      {
        "company": "OpenAI",
        "role": "NLP Engineer",
        "from": 2019,
        "to": 2022,
        "responsibilities": [
          "Assert that GPT-2 is racist",
          "Assert that GPT-3 is racist",
          "Develop a prototype of a premium non-racist language model"
        ],
        "technologies": [
          "Triton",
          "LaTeX"
        ]
      }
    ]
  }
]
```

To generate a relational schema for the above data, let's instantiate a `RelationalSchema` with `docs` as input:

```
from relatable import RelationalSchema

rs = RelationalSchema(docs)
```

Once the RelationalSchema is instantiated, we can check its metadata. This metadata is a list of flat dictionaries, so 
we can make use of Pandas to load it into a DataFrame:

```
import pandas as pd

pd.DataFrame(rs.generate_metadata())
```

|     | table | field                       | type    | nullable | unique |
|----:|:------|:----------------------------|:--------|:---------|:-------|
|   0 | t0    | t0_id                       | Integer | False    | True   |
|   1 | t0    | name                        | String  | False    | True   |
|   2 | t0    | age                         | Integer | False    | True   |
|   3 | t1    | t1_id                       | Integer | False    | True   |
|   4 | t1    | t0_id                       | Integer | False    | False  |
|   5 | t1    | experience.company          | String  | False    | True   |
|   6 | t1    | experience.role             | String  | False    | True   |
|   7 | t1    | experience.from             | Integer | False    | True   |
|   8 | t1    | experience.to               | Integer | False    | False  |
|   9 | t2    | t2_id                       | Integer | False    | True   |
|  10 | t2    | t1_id                       | Integer | False    | False  |
|  11 | t2    | t0_id                       | Integer | False    | False  |
|  12 | t2    | experience.technologies     | String  | False    | True   |
|  13 | t3    | t3_id                       | Integer | False    | True   |
|  14 | t3    | t1_id                       | Integer | False    | False  |
|  15 | t3    | t0_id                       | Integer | False    | False  |
|  16 | t3    | experience.responsibilities | String  | False    | True   | 

We can see that `RelationalSchema` has inferred a relational schema containing four tables with primary keys and 
foreign keys.

It would be nice to rename these tables with a more descriptive name, and also rename some columns. We can do so with 
the `rename` and `rename_column` methods:

```
rs.rename_table("t0", "person")
rs.rename_table("t1", "job")
rs.rename_table("t2", "technology")
rs.rename_table("t3", "responsibility")

for name in ["company", "role", "from", "to"]:
    rs.rename_column("job", f"experience.{name}", name)

rs.rename_column("technology", "experience.technologies", "technology")
rs.rename_column("responsibility", "experience.responsibilities", "responsibility")

pd.DataFrame(rs.generate_metadata())
```

|     | table          | field             | type    | nullable | unique |
|----:|:---------------|:------------------|:--------|:---------|:-------|
|   0 | person         | person_id         | Integer | False    | True   |
|   1 | person         | name              | String  | False    | True   |
|   2 | person         | age               | Integer | False    | True   |
|   3 | job            | job_id            | Integer | False    | True   |
|   4 | job            | person_id         | Integer | False    | False  |
|   5 | job            | company           | String  | False    | True   |
|   6 | job            | role              | String  | False    | True   |
|   7 | job            | from              | Integer | False    | True   |
|   8 | job            | to                | Integer | False    | False  |
|   9 | technology     | technology_id     | Integer | False    | True   |
|  10 | technology     | job_id            | Integer | False    | False  |
|  11 | technology     | person_id         | Integer | False    | False  |
|  12 | technology     | technology        | String  | False    | True   |
|  13 | responsibility | responsibility_id | Integer | False    | True   |
|  14 | responsibility | job_id            | Integer | False    | False  |
|  15 | responsibility | person_id         | Integer | False    | False  |
|  16 | responsibility | responsibility    | String  | False    | True   | 

Finally, let's look at each of the tables:

```
dfs = [pd.DataFrame(t.data).set_index(t.primary_key) for t in rs.tables]
```

Table person:

| person_id | name  | age |
|----------:|:------|----:|
|         0 | Alice |  34 |
|         1 | Bob   |  27 | 

Table job:

| job_id | person_id | company  | role                  | from |   to |
|-------:|----------:|:---------|:----------------------|-----:|-----:|
|      0 |         0 | Google   | Software Engineer     | 2020 | 2022 |
|      1 |         0 | Facebook | Senior Data Scientist | 2017 | 2020 |
|      2 |         1 | OpenAI   | NLP Engineer          | 2019 | 2022 | 

Table technology:

| technology_id | job_id | person_id | technology |
|--------------:|-------:|----------:|:-----------|
|             0 |      0 |         0 | C++        |
|             1 |      0 |         0 | LolCode    |
|             2 |      1 |         0 | Python     |
|             3 |      1 |         0 | Excel      |
|             4 |      2 |         1 | Triton     |
|             5 |      2 |         1 | LaTeX      | 

Table responsibility:

| responsibility_id | job_id | person_id | responsibility                                             |
|------------------:|-------:|----------:|:-----------------------------------------------------------|
|                 0 |      0 |         0 | Google stuff                                               |
|                 1 |      0 |         0 | Mark TensorFlow issues as "Won't Do"                       |
|                 2 |      1 |         0 | Censor media                                               |
|                 3 |      1 |         0 | Learn the foundations of ML                                |
|                 4 |      1 |         0 | Do Kaggle competitions                                     |
|                 5 |      2 |         1 | Assert that GPT-2 is racist                                |
|                 6 |      2 |         1 | Assert that GPT-3 is racist                                |
|                 7 |      2 |         1 | Develop a prototype of a premium non-racist language model | 
