SQLite_databases_for_learning_data_science

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SQLite_databases_for_learning_data_science

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The Iris, Titanic, and other common example datasets in SQLite format for practicing queries, exploratory analysis, machine learning, etc.

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# SQLite Databases for Learning Data Science

The Iris, Titanic, and other [seaborn example datasets](https://github.com/mwaskom/seaborn-data) in `.db` format for practicing SQL queries, exploratory analysis, machine learning, etc.

Each database starts with an `Observation` table, which contains only boolean and numeric types. Text data are split off into related tables.

## Contents
- [Example: Querying the Iris dataset with Python](#example)
- [Schemata](#schemata)
    - [x] [`anscombe.db`](#anscombe)
    - [x] [`attention.db`](#attention)
    - [ ] `brain_networks.db`
    - [x] [`car_crashes.db`](#car-crashes)
    - [x] [`diamonds.db`](#diamonds)
    - [x] [`dots.db`](#dots)
    - [x] [`exercise.db`](#exercise)
    - [x] [`flights.db`](#flights)
    - [x] [`fmri.db`](#fmri)
    - [x] [`gammas.db`](#gammas)
    - [x] [`iris.db`](#iris)
    - [x] [`mpg.db`](#mpg)
    - [x] [`planets.db`](#planets)
    - [x] [`tips.db`](#tips)
    - [x] [`titanic.db`](#titanic)
- [Credits](#credits)

## Example
Querying the [Iris dataset](#iris) with Python:
```python
import sqlite3
import pandas as pd

db = sqlite3.connect('iris.db')
sql = """
    SELECT 
        O.petal_length, 
        O.petal_width, 
        O.sepal_length, 
        O.sepal_width, 
        S.species
    FROM Observation AS O
    JOIN Species AS S ON S.species_id = O.species_id
"""
df = pd.read_sql_query(sql, db)
df.head()
```
Result:
| | petal_length | petal_width | sepal_length | sepal_width | species |
| :-- | :-- | :-- | :-- | :-- | :-- |
| **0** | 1.4 | 0.2 | 5.1 | 3.5 | setosa |
| **1** | 1.4 | 0.2 | 4.9 | 3.0 | setosa |
| **2** | 1.3 | 0.2 | 4.7 | 3.2 | setosa |
| **3** | 1.5 | 0.2 | 4.6 | 3.1 | setosa |
| **4** | 1.4 | 0.2 | 5.0 | 3.6 | setosa |

## Schemata
### Anscombe

### Attention

### Car Crashes

### Diamonds

### Dots

### Exercise

### Flights

### FMRI

### Gammas

### Iris

### MPG

### Planets

### Tips

### Titanic

## Credits

These databases were created by converting the .csv files in the [seaborn data repository](https://github.com/mwaskom/seaborn-data) to SQLite using Pandas and SQLAlchemy. The datasets themselves are all either open source or in the public domain.

Conversion and documentation by David James Knight.

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