dataviper

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Data Profiling and Basic Quality Assessment tool, for the very beginning phase of your project.

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- [dataviper](#dataviper)
- [Example](#example)
- [Why?](#why)
- [Data Sources](#data-sources)
- [APIs](#apis)
  - [`profile`](#profile)
  - [`pivot`](#pivot)
  - [`joinability`](#joinability)
- [Issues and TODOs](#issues-and-todos)

---

# dataviper

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[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://raw.githubusercontent.com/otiai10/dataviper/main/dockerfiles/setup.Python3.6.Dockerfile)

`dataviper` is a SQL-based tool to get the basic data preparation done in easy way, with doing

- Create "Data Profile" report of a table
- One-hot encode for "Categorical Columns" and create a "one-hot" table
- Check "Joinability" between 2 tables
- // TODO: and more

# Example

```sh
pip install dataviper
```

Your `main.py` will look like this

```python
from dataviper import Client
from dataviper.source import MySQL

client = Client(source=MySQL({
    'host': 'localhost',
    'user': 'root',
    'password': 'xxxxxx',
    'database': 'your_database'
}))

with client.connect():
    profile = client.profile('Your_Table')
    profile.to_excel()
```

```sh
python3 main.py
# Then you will get 'profile_Your_Table.xlsx' 🤗
```

![image](https://user-images.githubusercontent.com/931554/79642622-00b70900-819f-11ea-89ff-524c9c93ceea.png)

# Why?

It's known that "Data Profiling" needs to be done with scanning all the rows in a table. If you try to do this naively by `pandas` or any libraries which internally use `pandas`, it's not avoidable to use bunch of memory of your local machine and freeze your work.

`dataviper` is a SQL-based Data Profiling tool, which simply and dynamically generates SQLs and lets the database machine do the annoying calculation.

With `dataviper`, you don't have to have massive local computer. All you need are a stable network and reachable SQL db.

# Data Sources

You can choose your data source from

- [ ] SQL Server
    - [x] `profile`
    - [x] `pivot`
    - [x] `joinability`
    - [ ] `histogram`
- [ ] MySQL
    - [x] `profile`
    - [ ] `pivot`
    - [ ] `joinability`
    - [ ] `histogram`
- [ ] PostgreSQL
- [ ] CSV
    - [x] `profile`
    - [ ] `pivot`
    - [ ] `joinability`
    - [ ] `histogram`
- [ ] Excel

# APIs

## `profile`

Create "Data Profile" excel file of a specified table.

When you have `Sales` table like this

| id | region | sales_type | price | rep_id |
|:---:|:--------:|:----------:|:---------:|:-------:|
| 1 | jp | phone | 240 | 115723 |
| 2 | us | web | 90 |    125901 |
| 3 | jp | web | 560 |     8003 |
| 4 | us | shop | 920 |  182234 |
| 5 | jp | NULL | 90 |    92231 |
| 6 | us | shop | 180 |  100425 |
| 7 | us | shop | 70 |    52934 |

```python
with client.connect() as conn:
    table_name = 'Sales'
    profile = client.profile(table_name, example_count=3)
    profile.to_excel()
```

then you will get `profile_Sales.xlsx` file with

| column_name | data_type | null_count | null_% | unique_count | unique_% | min | max | avg | std | example_top_3 | example_last_3 |
|:-----------:|:----------:|:----------:|:------:|:------------:|:-------:|:---:|:---:|:----:|:---:|:------------:|:--------------:|
| id         | bigint  | 0 | 0     | 7 | 100.00 | 1 | 7 | 4.0 | 2.0 | [1,2,3]         | [5,6,7]          |
| region     | varchar | 0 | 0     | 2 | 28.57  |   |   |     |     | [jp,us,jp]      | [jp,us,us]       |
| sales_type | varchar | 1 | 14.28 | 3 | 42.85  |   |   |     |     | [phone,web,web] | [None,shop,shop] |
| price      | int     | 0 | 0     | 6 | 85.71  | 70 | 920 | 307.1428 | 295.379 | [240,90,560] | [90,180,70] |
| rep_id     | int     | 0 | 0     | 7 | 100.00 | 8003 |182234 | 96778.7142 | 51195.79065 | [115723,125901,8003] | [92231,100425,52934] |

## `pivot`

Spread categorical columns to N binary columns.

When you have `Sales` table like above, do

```python
with client.connect() as conn:
    table_name = 'Sales'
    key = 'id'
    categorical_columns = ['region', 'sales_type']
    profile = client.get_schema(table_name)
    client.pivot(profile, key, categorical_columns)
```

then you will get `Sales_PIVOT_YYYYmmddHHMM` table with

| id | region_jp | region_us | sales_type_phone | sales_type_web | sales_type_shop |
|:--:|:---------:|:---------:|:----------------:|:--------------:|:---------------:|
| 1  |  1        | 0         | 1                | 0              | 0               |
| 2  |  0        | 1         | 0                | 1              | 0               |
| 3  |  1        | 0         | 0                | 1              | 0               |
| 4  |  0        | 1         | 0                | 0              | 1               |
| 5  |  1        | 0         | 0                | 0              | 0               |
| 6  |  0        | 1         | 0                | 0              | 1               |
| 7  |  0        | 1         | 0                | 0              | 1               |

## `joinability`

Count how much 2 tables can be joined.

When you have `Sales` table like above, and `Reps` table like this

| id     | name    | tenure |
|:------:|:-------:|:------:|
| 8003   | Hiromu  | 9      |
| 8972   | Ochiai  | 6      |
| 52934  | Taro    | 1      |
| 92231  | otiai10 | 2      |
| 100425 | Hanako  | 7      |
| 125901 | Chika   | 3      |
| 182234 | Mary    | 5      |
| 199621 | Jack    | 1      |

```python
with client.connect() as conn:
    report = client.joinability(on={'Sales': 'rep_id', 'Reps': 'id'})
    report.to_excel()
```

then you will get `join_Sales_Reps.xlsx` file with

| table | key      | total | match | match_% | drop | drop_% |
|:------:|:-------:|:------:|:-----:|:------:|:-----:|:------:|
| Sales | [rep_id] | 7 | 6 | 85.714 | 1 | 14.285 |
| Reps  | [id]     | 8 | 6 | 75.00 | 2 | 25.00 |

# Issues and TODOs

- https://github.com/otiai10/dataviper/issues

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