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# Connecting to a Database
The data you wish to visualize may be stored in a database. Let's use SQLAlchemy to quickly extract database content into pandas Dataframe format so we can use it in gradio.
First install `pip install sqlalchemy` and then let's see some examples.
## SQLite
```python
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///your_database.db')
with gr.Blocks() as demo:
gr.LinePlot(pd.read_sql_query("SELECT time, price from flight_info;", engine), x="time", y="price")
```
Let's see a a more interactive plot involving filters that modify your SQL query:
```python
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///your_database.db')
with gr.Blocks() as demo:
origin = gr.Dropdown(["DFW", "DAL", "HOU"], value="DFW", label="Origin")
gr.LinePlot(lambda origin: pd.read_sql_query(f"SELECT time, price from flight_info WHERE origin = {origin};", engine), inputs=origin, x="time", y="price")
```
## Postgres, mySQL, and other databases
If you're using a different database format, all you have to do is swap out the engine, e.g.
```python
engine = create_engine('postgresql://username:password@host:port/database_name')
```
```python
engine = create_engine('mysql://username:password@host:port/database_name')
```
```python
engine = create_engine('oracle://username:password@host:port/database_name')
``` |