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

slug	sidebar_label	description
/ja/integrations/jupysql	Jupyter notebooks	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。

# 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。

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、こちらのガイドをご覧ください。

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

%%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.

%%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.

%sql SELECT count() FROM trips limit 5;

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

count()
1999657

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

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

pickup_ntaname
Morningside Heights
Hudson Yards-Chelsea-Flatiron-Union Square
Midtown-Midtown South
SoHo-TriBeCa-Civic Center-Little Italy
Murray Hill-Kips Bay

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

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

round(avg(tip_amount), 2)
1.68

%%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.

passenger_count	average_total_amount
0	22.69
1	15.97
2	17.15
3	16.76
4	17.33
5	16.35
6	16.04
7	59.8
8	36.41
9	9.81

%%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.

pickup_date	pickup_ntaname	number_of_trips
2015-07-01	Bushwick North	2
2015-07-01	Brighton Beach	1
2015-07-01	Briarwood-Jamaica Hills	3
2015-07-01	Williamsburg	1
2015-07-01	Queensbridge-Ravenswood-Long Island City	9

# %sql DESCRIBE trips;

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

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

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

%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'>

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")