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431 lines
9.1 KiB
Markdown
431 lines
9.1 KiB
Markdown
---
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slug: /ja/integrations/jupysql
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sidebar_label: Jupyter notebooks
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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。
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---
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# ClickHouseでJupySQLを使用する
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このガイドでは、ClickHouseとの統合について説明します。
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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。
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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。
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```python
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# 必要なパッケージをインストール
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%pip install --quiet jupysql clickhouse_sqlalchemy
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```
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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。
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```python
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import pandas as pd
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from sklearn_evaluation import plot
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# jupysql Jupyter拡張機能をインポートしてSQLセルを作成
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%load_ext sql
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%config SqlMagic.autocommit=False
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```
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**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。**
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**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。
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```python
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%sql clickhouse://default:@localhost:8123/default
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```
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```sql
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%%sql
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CREATE TABLE trips
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(
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`trip_id` UInt32,
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`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),
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`pickup_date` Date,
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`pickup_datetime` DateTime,
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`dropoff_date` Date,
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`dropoff_datetime` DateTime,
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`store_and_fwd_flag` UInt8,
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`rate_code_id` UInt8,
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`pickup_longitude` Float64,
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`pickup_latitude` Float64,
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`dropoff_longitude` Float64,
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`dropoff_latitude` Float64,
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`passenger_count` UInt8,
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`trip_distance` Float64,
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`fare_amount` Float32,
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`extra` Float32,
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`mta_tax` Float32,
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`tip_amount` Float32,
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`tolls_amount` Float32,
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`ehail_fee` Float32,
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`improvement_surcharge` Float32,
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`total_amount` Float32,
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`payment_type` Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),
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`trip_type` UInt8,
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`pickup` FixedString(25),
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`dropoff` FixedString(25),
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`cab_type` Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),
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`pickup_nyct2010_gid` Int8,
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`pickup_ctlabel` Float32,
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`pickup_borocode` Int8,
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`pickup_ct2010` String,
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`pickup_boroct2010` String,
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`pickup_cdeligibil` String,
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`pickup_ntacode` FixedString(4),
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`pickup_ntaname` String,
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`pickup_puma` UInt16,
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`dropoff_nyct2010_gid` UInt8,
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`dropoff_ctlabel` Float32,
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`dropoff_borocode` UInt8,
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`dropoff_ct2010` String,
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`dropoff_boroct2010` String,
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`dropoff_cdeligibil` String,
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`dropoff_ntacode` FixedString(4),
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`dropoff_ntaname` String,
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`dropoff_puma` UInt16
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)
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ENGINE = MergeTree
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PARTITION BY toYYYYMM(pickup_date)
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ORDER BY pickup_datetime;
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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</tr>
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</table>
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```sql
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%%sql
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INSERT INTO trips
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SELECT * FROM s3(
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'https://datasets-documentation.s3.eu-west-3.amazonaws.com/nyc-taxi/trips_{1..2}.gz',
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'TabSeparatedWithNames', "
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`trip_id` UInt32,
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`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),
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`pickup_date` Date,
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`pickup_datetime` DateTime,
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`dropoff_date` Date,
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`dropoff_datetime` DateTime,
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`store_and_fwd_flag` UInt8,
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`rate_code_id` UInt8,
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`pickup_longitude` Float64,
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`pickup_latitude` Float64,
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`dropoff_longitude` Float64,
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`dropoff_latitude` Float64,
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`passenger_count` UInt8,
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`trip_distance` Float64,
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`fare_amount` Float32,
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`extra` Float32,
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`mta_tax` Float32,
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`tip_amount` Float32,
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`tolls_amount` Float32,
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`ehail_fee` Float32,
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`improvement_surcharge` Float32,
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`total_amount` Float32,
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`payment_type` Enum8('UNK' = 0, 'CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4),
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`trip_type` UInt8,
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`pickup` FixedString(25),
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`dropoff` FixedString(25),
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`cab_type` Enum8('yellow' = 1, 'green' = 2, 'uber' = 3),
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`pickup_nyct2010_gid` Int8,
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`pickup_ctlabel` Float32,
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`pickup_borocode` Int8,
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`pickup_ct2010` String,
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`pickup_boroct2010` String,
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`pickup_cdeligibil` String,
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`pickup_ntacode` FixedString(4),
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`pickup_ntaname` String,
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`pickup_puma` UInt16,
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`dropoff_nyct2010_gid` UInt8,
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`dropoff_ctlabel` Float32,
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`dropoff_borocode` UInt8,
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`dropoff_ct2010` String,
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`dropoff_boroct2010` String,
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`dropoff_cdeligibil` String,
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`dropoff_ntacode` FixedString(4),
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`dropoff_ntaname` String,
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`dropoff_puma` UInt16
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") SETTINGS input_format_try_infer_datetimes = 0
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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</tr>
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</table>
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```python
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%sql SELECT count() FROM trips limit 5;
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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<th>count()</th>
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</tr>
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<tr>
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<td>1999657</td>
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</tr>
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</table>
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```python
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%sql SELECT DISTINCT(pickup_ntaname) FROM trips limit 5;
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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<th>pickup_ntaname</th>
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</tr>
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<tr>
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<td>Morningside Heights</td>
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</tr>
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<tr>
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<td>Hudson Yards-Chelsea-Flatiron-Union Square</td>
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</tr>
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<tr>
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<td>Midtown-Midtown South</td>
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</tr>
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<tr>
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<td>SoHo-TriBeCa-Civic Center-Little Italy</td>
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</tr>
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<tr>
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<td>Murray Hill-Kips Bay</td>
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</tr>
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</table>
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```python
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%sql SELECT round(avg(tip_amount), 2) FROM trips
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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<th>round(avg(tip_amount), 2)</th>
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</tr>
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<tr>
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<td>1.68</td>
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</tr>
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</table>
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```sql
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%%sql
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SELECT
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passenger_count,
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ceil(avg(total_amount),2) AS average_total_amount
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FROM trips
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GROUP BY passenger_count
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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<th>passenger_count</th>
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<th>average_total_amount</th>
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</tr>
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<tr>
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<td>0</td>
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<td>22.69</td>
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</tr>
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<tr>
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<td>1</td>
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<td>15.97</td>
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</tr>
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<tr>
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<td>2</td>
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<td>17.15</td>
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</tr>
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<tr>
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<td>3</td>
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<td>16.76</td>
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</tr>
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<tr>
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<td>4</td>
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<td>17.33</td>
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</tr>
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<tr>
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<td>5</td>
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<td>16.35</td>
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</tr>
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<tr>
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<td>6</td>
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<td>16.04</td>
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</tr>
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<tr>
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<td>7</td>
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<td>59.8</td>
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</tr>
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<tr>
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<td>8</td>
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<td>36.41</td>
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</tr>
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<tr>
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<td>9</td>
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<td>9.81</td>
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</tr>
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</table>
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```sql
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%%sql
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SELECT
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pickup_date,
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pickup_ntaname,
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SUM(1) AS number_of_trips
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FROM trips
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GROUP BY pickup_date, pickup_ntaname
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ORDER BY pickup_date ASC
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limit 5;
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```
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* clickhouse://default:***@localhost:8123/default
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Done.
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<table>
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<tr>
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<th>pickup_date</th>
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<th>pickup_ntaname</th>
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<th>number_of_trips</th>
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</tr>
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<tr>
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<td>2015-07-01</td>
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<td>Bushwick North</td>
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<td>2</td>
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</tr>
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<tr>
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<td>2015-07-01</td>
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<td>Brighton Beach</td>
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<td>1</td>
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</tr>
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<tr>
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<td>2015-07-01</td>
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<td>Briarwood-Jamaica Hills</td>
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<td>3</td>
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</tr>
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<tr>
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<td>2015-07-01</td>
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<td>Williamsburg</td>
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<td>1</td>
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</tr>
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<tr>
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<td>2015-07-01</td>
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<td>Queensbridge-Ravenswood-Long Island City</td>
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<td>9</td>
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</tr>
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</table>
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```python
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# %sql DESCRIBE trips;
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```
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```python
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# %sql SELECT DISTINCT(trip_distance) FROM trips limit 50;
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```
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```sql
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%%sql --save short-trips --no-execute
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SELECT *
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FROM trips
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WHERE trip_distance < 6.3
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```
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* clickhouse://default:***@localhost:8123/default
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Skipping execution...
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```python
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%sqlplot histogram --table short-trips --column trip_distance --bins 10 --with short-trips
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```
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<AxesSubplot: title={'center': "'trip_distance' from 'short-trips'"}, xlabel='trip_distance', ylabel='Count'>
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```python
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ax = %sqlplot histogram --table short-trips --column trip_distance --bins 50 --with short-trips
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ax.grid()
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ax.set_title("Trip distance from trips < 6.3")
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_ = ax.set_xlabel("Trip distance")
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```
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