> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-fbfa8bee.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> JupySQL est un outil de base de données multi-plateforme pour Jupyter.

# Utiliser JupySQL avec ClickHouse

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Dans ce guide, nous allons présenter une intégration avec ClickHouse.

Nous utiliserons JupySQL pour exécuter des requêtes sur ClickHouse.
Une fois les données chargées, nous les visualiserons à l’aide de graphiques SQL.

L’intégration entre JupySQL et ClickHouse est rendue possible par l’utilisation de la bibliothèque clickhouse\_sqlalchemy. Cette bibliothèque facilite la communication entre les deux systèmes et vous permet de vous connecter à ClickHouse et de spécifier le dialecte SQL. Une fois connecté, vous pouvez exécuter des requêtes SQL directement depuis l’UI native de ClickHouse, ou directement depuis le Jupyter notebook.

```python theme={null}
# Install required packages
%pip install --quiet jupysql clickhouse_sqlalchemy
```

Remarque : vous devrez peut-être redémarrer le noyau pour utiliser les paquets mis à jour.

```python theme={null}
import pandas as pd
from sklearn_evaluation import plot

# Import jupysql Jupyter extension to create SQL cells
%load_ext sql
%config SqlMagic.autocommit=False
```

**Vous devez vous assurer que votre instance ClickHouse est opérationnelle et accessible pour les prochaines étapes. Vous pouvez utiliser une version locale ou la version Cloud.**

**Remarque :** vous devrez ajuster la chaîne de connexion en fonction du type d’instance à laquelle vous essayez de vous connecter (URL, utilisateur, mot de passe). Dans l’exemple ci-dessous, nous avons utilisé une instance locale. Pour en savoir plus, consultez [ce guide](/fr/get-started/setup/install).

```python theme={null}
%sql clickhouse://default:@localhost:8123/default
```

```sql theme={null}
%%sql
CREATE TABLE trips
(
    `trip_id` UInt32,
    `vendor_id` Enum8('1' = 1, '2' = 2, '3' = 3, '4' = 4, 'CMT' = 5, 'VTS' = 6, 'DDS' = 7, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14, '' = 15),
    `pickup_date` Date,
    `pickup_datetime` DateTime,
    `dropoff_date` Date,
    `dropoff_datetime` DateTime,
    `store_and_fwd_flag` UInt8,
    `rate_code_id` UInt8,
    `pickup_longitude` Float64,
    `pickup_latitude` Float64,
    `dropoff_longitude` Float64,
    `dropoff_latitude` Float64,
    `passenger_count` UInt8,
    `trip_distance` Float64,
    `fare_amount` Float32,
    `extra` Float32,
    `mta_tax` Float32,
    `tip_amount` Float32,
    `tolls_amount` Float32,
    `ehail_fee` Float32,
    `improvement_surcharge` Float32,
    `total_amount` Float32,
    `payment_type` Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),
    `trip_type` UInt8,
    `pickup` FixedString(25),
    `dropoff` FixedString(25),
    `cab_type` Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),
    `pickup_nyct2010_gid` Int8,
    `pickup_ctlabel` Float32,
    `pickup_borocode` Int8,
    `pickup_ct2010` String,
    `pickup_boroct2010` String,
    `pickup_cdeligibil` String,
    `pickup_ntacode` FixedString(4),
    `pickup_ntaname` String,
    `pickup_puma` UInt16,
    `dropoff_nyct2010_gid` UInt8,
    `dropoff_ctlabel` Float32,
    `dropoff_borocode` UInt8,
    `dropoff_ct2010` String,
    `dropoff_boroct2010` String,
    `dropoff_cdeligibil` String,
    `dropoff_ntacode` FixedString(4),
    `dropoff_ntaname` String,
    `dropoff_puma` UInt16
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(pickup_date)
ORDER BY pickup_datetime;
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Terminé.

<table>
  <tr />
</table>

```sql theme={null}
%%sql
INSERT INTO trips
SELECT * FROM s3(
    'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{1..2}.gz',
    'TabSeparatedWithNames', "
    `trip_id` UInt32,
    `vendor_id` Enum8('1' = 1, '2' = 2, '3' = 3, '4' = 4, 'CMT' = 5, 'VTS' = 6, 'DDS' = 7, 'B02512' = 10, 'B02598' = 11, 'B02617' = 12, 'B02682' = 13, 'B02764' = 14, '' = 15),
    `pickup_date` Date,
    `pickup_datetime` DateTime,
    `dropoff_date` Date,
    `dropoff_datetime` DateTime,
    `store_and_fwd_flag` UInt8,
    `rate_code_id` UInt8,
    `pickup_longitude` Float64,
    `pickup_latitude` Float64,
    `dropoff_longitude` Float64,
    `dropoff_latitude` Float64,
    `passenger_count` UInt8,
    `trip_distance` Float64,
    `fare_amount` Float32,
    `extra` Float32,
    `mta_tax` Float32,
    `tip_amount` Float32,
    `tolls_amount` Float32,
    `ehail_fee` Float32,
    `improvement_surcharge` Float32,
    `total_amount` Float32,
    `payment_type` Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),
    `trip_type` UInt8,
    `pickup` FixedString(25),
    `dropoff` FixedString(25),
    `cab_type` Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),
    `pickup_nyct2010_gid` Int8,
    `pickup_ctlabel` Float32,
    `pickup_borocode` Int8,
    `pickup_ct2010` String,
    `pickup_boroct2010` String,
    `pickup_cdeligibil` String,
    `pickup_ntacode` FixedString(4),
    `pickup_ntaname` String,
    `pickup_puma` UInt16,
    `dropoff_nyct2010_gid` UInt8,
    `dropoff_ctlabel` Float32,
    `dropoff_borocode` UInt8,
    `dropoff_ct2010` String,
    `dropoff_boroct2010` String,
    `dropoff_cdeligibil` String,
    `dropoff_ntacode` FixedString(4),
    `dropoff_ntaname` String,
    `dropoff_puma` UInt16
") SETTINGS input_format_try_infer_datetimes = 0
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Terminé.

<table>
  <tr />
</table>

```python theme={null}
%sql SELECT count() FROM trips limit 5;
```

* clickhouse://default:\*\*\*@localhost:8123/default
  C'est fait.

<table>
  <tr>
    <th>count()</th>
  </tr>

  <tr>
    <td>1999657</td>
  </tr>
</table>

```python theme={null}
%sql SELECT DISTINCT(pickup_ntaname) FROM trips limit 5;
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Terminé.

<table>
  <tr>
    <th>pickup\_ntaname</th>
  </tr>

  <tr>
    <td>Morningside Heights</td>
  </tr>

  <tr>
    <td>Hudson Yards-Chelsea-Flatiron-Union Square</td>
  </tr>

  <tr>
    <td>Midtown-Midtown South</td>
  </tr>

  <tr>
    <td>SoHo-Tribeca-Civic Center-Little Italy</td>
  </tr>

  <tr>
    <td>Murray Hill-Kips Bay</td>
  </tr>
</table>

```python theme={null}
%sql SELECT round(avg(tip_amount), 2) FROM trips
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Terminé.

<table>
  <tr>
    <th>round(avg(tip\_amount), 2)</th>
  </tr>

  <tr>
    <td>1.68</td>
  </tr>
</table>

```sql theme={null}
%%sql
SELECT
    passenger_count,
    ceil(avg(total_amount),2) AS average_total_amount
FROM trips
GROUP BY passenger_count
```

* clickhouse://default:\*\*\*@localhost:8123/default
  C'est fait.

<table>
  <tr>
    <th>passenger\_count</th>
    <th>average\_total\_amount</th>
  </tr>

  <tr>
    <td>0</td>
    <td>22.69</td>
  </tr>

  <tr>
    <td>1</td>
    <td>15.97</td>
  </tr>

  <tr>
    <td>2</td>
    <td>17.15</td>
  </tr>

  <tr>
    <td>3</td>
    <td>16.76</td>
  </tr>

  <tr>
    <td>4</td>
    <td>17.33</td>
  </tr>

  <tr>
    <td>5</td>
    <td>16.35</td>
  </tr>

  <tr>
    <td>6</td>
    <td>16.04</td>
  </tr>

  <tr>
    <td>7</td>
    <td>59.8</td>
  </tr>

  <tr>
    <td>8</td>
    <td>36.41</td>
  </tr>

  <tr>
    <td>9</td>
    <td>9.81</td>
  </tr>
</table>

```sql theme={null}
%%sql
SELECT
    pickup_date,
    pickup_ntaname,
    SUM(1) AS number_of_trips
FROM trips
GROUP BY pickup_date, pickup_ntaname
ORDER BY pickup_date ASC
limit 5;
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Terminé.

<table>
  <tr>
    <th>pickup\_date</th>
    <th>pickup\_ntaname</th>
    <th>number\_of\_trips</th>
  </tr>

  <tr>
    <td>2015-07-01</td>
    <td>Bushwick North</td>
    <td>2</td>
  </tr>

  <tr>
    <td>2015-07-01</td>
    <td>Brighton Beach</td>
    <td>1</td>
  </tr>

  <tr>
    <td>2015-07-01</td>
    <td>Briarwood-Jamaica Hills</td>
    <td>3</td>
  </tr>

  <tr>
    <td>2015-07-01</td>
    <td>Williamsburg</td>
    <td>1</td>
  </tr>

  <tr>
    <td>2015-07-01</td>
    <td>Queensbridge-Ravenswood-Long Island City</td>
    <td>9</td>
  </tr>
</table>

```python theme={null}
# %sql DESCRIBE trips;
```

```python theme={null}
# %sql SELECT DISTINCT(trip_distance) FROM trips limit 50;
```

```sql theme={null}
%%sql --save short-trips --no-execute
SELECT *
FROM trips
WHERE trip_distance < 6.3
```

* clickhouse://default:\*\*\*@localhost:8123/default
  Exécution ignorée...

```python theme={null}
%sqlplot histogram --table short-trips --column trip_distance --bins 10 --with short-trips
```

```response theme={null}
<AxesSubplot: title={'center': "'trip_distance' from 'short-trips'"}, xlabel='trip_distance', ylabel='Count'>
```

<Image img="https://mintcdn.com/private-7c7dfe99-mintlify-fbfa8bee/amY-JDMREAaO7mx6/images/integrations/sql-clients/jupysql-plot-1.png?fit=max&auto=format&n=amY-JDMREAaO7mx6&q=85&s=f3924ba9bdb734674879987b5c9b20d0" size="md" alt="Histogramme montrant la répartition des distances de trajet en 10 classes à partir du jeu de données short-trips" border width="597" height="455" data-path="images/integrations/sql-clients/jupysql-plot-1.png" />

```python theme={null}
ax = %sqlplot histogram --table short-trips --column trip_distance --bins 50 --with short-trips
ax.grid()
ax.set_title("Trip distance from trips < 6.3")
_ = ax.set_xlabel("Trip distance")
```

<Image img="https://mintcdn.com/private-7c7dfe99-mintlify-fbfa8bee/amY-JDMREAaO7mx6/images/integrations/sql-clients/jupysql-plot-2.png?fit=max&auto=format&n=amY-JDMREAaO7mx6&q=85&s=72629de6610136783ecdb759d8077e4c" size="md" alt="Histogramme montrant la répartition des distances de trajet en 50 classes, avec grille, intitulé « Distance de trajet pour les trajets < 6,3 »" border width="597" height="455" data-path="images/integrations/sql-clients/jupysql-plot-2.png" />