---  
slug: /ja/integrations/jupysql  
sidebar_label: Jupyter notebooks  
description: JupysqlはJupyter向けのマルチプラットフォームデータベースツールです。  
---

# ClickHouseでJupySQLを使用する
このガイドでは、ClickHouseとの統合について説明します。

Jupysqlを使ってClickHouse上でクエリを実行します。データがロードされた後、SQLプロットでデータを可視化します。

JupysqlとClickHouseの統合は、clickhouse_sqlalchemyライブラリを使用することで可能になります。このライブラリは、両システム間のコミュニケーションを容易にし、ClickHouseに接続してSQL方言を渡すことを可能にします。接続されたら、ClickhouseのネイティブUIまたはJupyterノートブックから直接SQLクエリを実行できます。

```python
# 必要なパッケージをインストール
%pip install --quiet jupysql clickhouse_sqlalchemy
```

    注: 更新されたパッケージを使用するにはカーネルを再起動する必要があるかもしれません。



```python
import pandas as pd
from sklearn_evaluation import plot

# jupysql Jupyter拡張機能をインポートしてSQLセルを作成
%load_ext sql
%config SqlMagic.autocommit=False
```

**次の段階に進むには、Clickhouseが起動してアクセス可能であることを確認してください。ローカル版またはクラウド版のどちらでも利用できます。**

**注:** 接続文字列は、接続しようとしているインスタンスタイプ（URL、ユーザー、パスワード）に応じて調整する必要があります。以下の例ではローカルインスタンスを使用しています。詳しくは、[こちらのガイド](https://clickhouse.com/docs/ja/getting-started/quick-start)をご覧ください。

```python
%sql clickhouse://default:@localhost:8123/default
```

```sql
%%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
    Done.





<table>
    <tr>
    </tr>
</table>




```sql
%%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
    Done.





<table>
    <tr>
    </tr>
</table>




```python
%sql SELECT count() FROM trips limit 5;
```

    *  clickhouse://default:***@localhost:8123/default
    Done.





<table>
    <tr>
        <th>count()</th>
    </tr>
    <tr>
        <td>1999657</td>
    </tr>
</table>




```python
%sql SELECT DISTINCT(pickup_ntaname) FROM trips limit 5;
```

    *  clickhouse://default:***@localhost:8123/default
    Done.





<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
%sql SELECT round(avg(tip_amount), 2) FROM trips
```

    *  clickhouse://default:***@localhost:8123/default
    Done.





<table>
    <tr>
        <th>round(avg(tip_amount), 2)</th>
    </tr>
    <tr>
        <td>1.68</td>
    </tr>
</table>




```sql
%%sql
SELECT
    passenger_count,
    ceil(avg(total_amount),2) AS average_total_amount
FROM trips
GROUP BY passenger_count
```

    *  clickhouse://default:***@localhost:8123/default
    Done.





<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
%%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
    Done.





<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
# %sql DESCRIBE trips;
```


```python
# %sql SELECT DISTINCT(trip_distance) FROM trips limit 50;
```


```sql
%%sql --save short-trips --no-execute
SELECT *
FROM trips
WHERE trip_distance < 6.3
```

    *  clickhouse://default:***@localhost:8123/default
    Skipping execution...



```python
%sqlplot histogram --table short-trips --column trip_distance --bins 10 --with short-trips
```




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





![histogram example](images/jupysql-plot-1.png)




```python
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")
```



![histogram second example](images/jupysql-plot-1.png)