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ClickHouse/docs/ja/integrations/sql-clients/jupysql.md

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add translated Japanese docs
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---
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)
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