参照(Reference) : 「データサイエンティスト協会スキル定義委員」の「データサイエンス100本ノック（構造化データ加工編）」

The Data Scientist Society Github :

GitHub - The-Japan-DataScientist-Society/100knocks-preprocess: データサイエンス100本ノック（構造化データ加工編）

データサイエンス100本ノック（構造化データ加工編）. Contribute to The-Japan-DataScientist-Society/100knocks-preprocess development by creating a...

Data Science 100 Knocks (Structured Data Processing) URL :

100knocks-preprocess/docker/work/answer/ans_preprocess_knock_SQL.ipynb at master · The-Japan-DataScientist-Society/100knocks-preprocess

データサイエンス100本ノック（構造化データ加工編）. Contribute to The-Japan-DataScientist-Society/100knocks-preprocess development by creating a...

Note: This is an ipynb file originally created by The Data Scientist Society(データサイエンティスト協会スキル定義委員) and translated from Japanese to English by DeepL.
The reason I updated this file is to spread this practice, which is useful for everyone who wants to practice SQL, from beginners to advanced engineers.
Since this data was created for Japanese, you may face language problems when practicing. But do not worry, it will not affect much.

You can get preprocess_knock files to practice python, SQL, R from my github account

ChanhiYasutomi - Repositories

ChanhiYasutomi has 17 repositories available. Follow their code on GitHub.

Introduction
How to use
100 data processing knocks

Introduction

The database is PostgreSQL13
First, execute the following cell
You can issue SQL by writing %%sql in the cell
You cannot check the table structure with the describe command from jupyter, so if you want to check the table structure, use a SELECT with a limit, for example.
You can use any familiar SQL client (connection information is as follows)
- IP address: localhost for Docker Desktop, 192.168.99.100 for Docker toolbox
- Port:5432
- Database name: dsdojo_db
- User name: padawan
- Password: padawan12345
It is recommended to limit the number of outputs, as Jupyter may freeze if a large amount of outputs are performed (the number of outputs is also mentioned in the question).
- Data processing also requires the ability to control the amount of results to be displayed for confirmation and to make light work of them.
If a large number of results are output, the file may become so heavy that it cannot be opened afterwards.
- In this case, the results of your work will be lost, but you can retrieve the file from GitHub.
- You can also delete large output ranges using a vim editor, for example.
Names, addresses etc. are dummy data and do not exist.

In [ ]:

%load_ext sql
import os

pgconfig = {
    'host': 'db',
    'port': os.environ['PG_PORT'],
    'database': os.environ['PG_DATABASE'],
    'user': os.environ['PG_USER'],
    'password': os.environ['PG_PASSWORD'],
}
dsl = 'postgresql://{user}:{password}@{host}:{port}/{database}'.format(**pgconfig)

# Set up Magic commands to write SQL
%sql $dsl

Out[ ]:

'Connected: padawan@dsdojo_db'

How to use

You can execute SQL from Jupyter against PostgreSQL by writing %%sql at the beginning of the cell and SQL in the second and subsequent lines.

In [ ]:

%%sql
SELECT 'You can run it like this' AS sample;

 * postgresql://padawan:***@db:5432/dsdojo_db
1 rows affected.

Out[ ]:

sample
You can run it like this

100 data processing knocks

S-001: Display the first 10 items of all items from the receipt details data (df_receipt) and visually check what data is held.

In [ ]:

%%sql

SELECT
  *
FROM 
  receipt
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

sales_ymd	sales_epoch	store_cd	receipt_no	receipt_sub_no	customer_id	product_cd	quantity	amount
20181103	1541203200	S14006	112	1	CS006214000001	P070305012	1	158
20181118	1542499200	S13008	1132	2	CS008415000097	P070701017	1	81
20170712	1499817600	S14028	1102	1	CS028414000014	P060101005	1	170
20190205	1549324800	S14042	1132	1	ZZ000000000000	P050301001	1	25
20180821	1534809600	S14025	1102	2	CS025415000050	P060102007	1	90
20190605	1559692800	S13003	1112	1	CS003515000195	P050102002	1	138
20181205	1543968000	S14024	1102	2	CS024514000042	P080101005	1	30
20190922	1569110400	S14040	1102	1	CS040415000178	P070501004	1	128
20170504	1493856000	S13020	1112	2	ZZ000000000000	P071302010	1	770
20191010	1570665600	S14027	1102	1	CS027514000015	P071101003	1	680

Commentary :

This code is written in SQL (Structured Query Language), a programming language used for managing and manipulating relational databases.

The code is querying a table named "receipt" and selecting all columns and rows from the table using the asterisk (*) wildcard character. The "LIMIT 10" clause at the end of the query limits the result set to the first 10 rows of the table.

In simpler terms, this code is retrieving the first 10 rows of data from the "receipt" table in a database. The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-002: Display 10 items from the receipt details data (df_receipt), specifying the columns in the order of sales_date (sales_ymd), customer_id (customer_id), product_code (product_cd) and sales amount (amount).

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd,
  amount
FROM 
  receipt
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	amount
20181103	CS006214000001	P070305012	158
20181118	CS008415000097	P070701017	81
20170712	CS028414000014	P060101005	170
20190205	ZZ000000000000	P050301001	25
20180821	CS025415000050	P060102007	90
20190605	CS003515000195	P050102002	138
20181205	CS024514000042	P080101005	30
20190922	CS040415000178	P070501004	128
20170504	ZZ000000000000	P071302010	770
20191010	CS027514000015	P071101003	680

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The specific columns "sales_ymd", "customer_id", "product_cd", and "amount" are selected using the SELECT statement. These columns represent various pieces of information related to a purchase transaction, such as the date of the sale, the ID of the customer who made the purchase, the code of the product that was purchased, and the amount of the purchase.

The "LIMIT 10" clause at the end of the query limits the result set to the first 10 rows of the table, which means that the query will only return data for the first 10 transactions recorded in the "receipt" table.

In summary, this code is retrieving specific pieces of information about the first 10 transactions recorded in the "receipt" table in a database, using the columns "sales_ymd", "customer_id", "product_cd", and "amount". The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-003: From the receipt details data (df_receipt), specify the columns in order of sales_date (sales_ymd), customer_id (customer_id), product_code (product_cd) and sales_amount (amount) and display 10 items. However, extract while renaming the item from sales_ymd to sales_date.

In [ ]:

%%sql

SELECT
  sales_ymd AS sales_date,
  customer_id,
  product_cd,
  amount 
FROM 
  receipt
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

sales_date	customer_id	product_cd	amount
20181103	CS006214000001	P070305012	158
20181118	CS008415000097	P070701017	81
20170712	CS028414000014	P060101005	170
20190205	ZZ000000000000	P050301001	25
20180821	CS025415000050	P060102007	90
20190605	CS003515000195	P050102002	138
20181205	CS024514000042	P080101005	30
20190922	CS040415000178	P070501004	128
20170504	ZZ000000000000	P071302010	770
20191010	CS027514000015	P071101003	680

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The SELECT statement is used to retrieve specific columns of data from the "receipt" table. However, in this case, the column names are being modified using the AS keyword. The first column is being renamed from "sales_ymd" to "sales_date". The other columns, "customer_id", "product_cd", and "amount", are being retained with their original names.

The "LIMIT 10" clause at the end of the query limits the result set to the first 10 rows of the table, which means that the query will only return data for the first 10 transactions recorded in the "receipt" table.

In summary, this code is retrieving specific columns of information about the first 10 transactions recorded in the "receipt" table in a database, using the modified column names "sales_date", "customer_id", "product_cd", and "amount". The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-004: From receipt details data (df_receipt), specify columns in the order of sales_date (sales_ymd), customer ID (customer_id), product code (product_cd) and sales amount (amount) and extract data satisfying the following conditions.

Customer ID (customer_id) is “CS018205000001”

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd,
  amount
FROM
  receipt
WHERE
  customer_id = 'CS018205000001'
;

 * postgresql://padawan:***@db:5432/dsdojo_db
12 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	amount
20180911	CS018205000001	P071401012	2200
20180414	CS018205000001	P060104007	600
20170614	CS018205000001	P050206001	990
20170614	CS018205000001	P060702015	108
20190216	CS018205000001	P071005024	102
20180414	CS018205000001	P071101002	278
20190226	CS018205000001	P070902035	168
20190924	CS018205000001	P060805001	495
20190226	CS018205000001	P071401020	2200
20180911	CS018205000001	P071401005	1100
20190216	CS018205000001	P040101002	218
20190924	CS018205000001	P091503001	280

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The SELECT statement is used to retrieve specific columns of data from the "receipt" table. The columns being selected are "sales_ymd", "customer_id", "product_cd", and "amount". These columns represent various pieces of information related to a purchase transaction, such as the date of the sale, the ID of the customer who made the purchase, the code of the product that was purchased, and the amount of the purchase.

The FROM statement specifies the "receipt" table from which the data is being retrieved.

The WHERE clause is used to filter the data based on a specific condition. In this case, the data is being filtered based on the condition that the "customer_id" column is equal to 'CS018205000001'. This means that the query will only retrieve data for transactions that were made by the customer with ID 'CS018205000001'.

In summary, this code is retrieving specific columns of information from the "receipt" table in a database, but only for transactions made by a specific customer with the ID 'CS018205000001'. The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-005: Extract data from receipt details data (df_receipt), specifying columns in the order of sales_date (sales_ymd), customer ID (customer_id), product code (product_cd) and sales amount (amount) and satisfying all the following conditions.

Customer ID (customer_id) is “CS018205000001”.

Sales amount (amount) is 1,000 or more.

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd,
  amount
FROM
  receipt
WHERE
  customer_id = 'CS018205000001'
AND 
  amount >= 1000
;

 * postgresql://padawan:***@db:5432/dsdojo_db
3 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	amount
20180911	CS018205000001	P071401012	2200
20190226	CS018205000001	P071401020	2200
20180911	CS018205000001	P071401005	1100

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The SELECT statement is used to retrieve specific columns of data from the "receipt" table. The columns being selected are "sales_ymd", "customer_id", "product_cd", and "amount". These columns represent various pieces of information related to a purchase transaction, such as the date of the sale, the ID of the customer who made the purchase, the code of the product that was purchased, and the amount of the purchase.

The FROM statement specifies the "receipt" table from which the data is being retrieved.

The WHERE clause is used to filter the data based on specific conditions. In this case, the data is being filtered based on two conditions:

The "customer_id" column must be equal to 'CS018205000001'.
The "amount" column must be greater than or equal to 1000.

This means that the query will only retrieve data for transactions that were made by the customer with ID 'CS018205000001' and have an amount greater than or equal to 1000.

In summary, this code is retrieving specific columns of information from the "receipt" table in a database, but only for transactions made by a specific customer with the ID 'CS018205000001' and with an amount greater than or equal to 1000. The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-006: Extract data from receipt details data (df_receipt), specifying the columns in the order of sales_date (sales_ymd), customer ID (customer_id), product code (product_cd), sales quantity (quantity) and sales amount (amount) and satisfying all the following conditions.

Customer ID (customer_id) is “CS018205000001”.

Sales amount (amount) is 1,000 or more or sales quantity (quantity) is 5 or more.

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd,
  quantity,
  amount
FROM
  receipt
WHERE
  customer_id = 'CS018205000001'
AND
  (
     amount >= 1000 OR quantity >= 5
  )
;

 * postgresql://padawan:***@db:5432/dsdojo_db
5 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	quantity	amount
20180911	CS018205000001	P071401012	1	2200
20180414	CS018205000001	P060104007	6	600
20170614	CS018205000001	P050206001	5	990
20190226	CS018205000001	P071401020	1	2200
20180911	CS018205000001	P071401005	1	1100

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The SELECT statement is used to retrieve specific columns of data from the "receipt" table. The columns being selected are "sales_ymd", "customer_id", "product_cd", "quantity", and "amount". These columns represent various pieces of information related to a purchase transaction, such as the date of the sale, the ID of the customer who made the purchase, the code of the product that was purchased, the quantity of the product purchased, and the amount of the purchase.

The FROM statement specifies the "receipt" table from which the data is being retrieved.

The WHERE clause is used to filter the data based on specific conditions. In this case, the data is being filtered based on two conditions:

The "customer_id" column must be equal to 'CS018205000001'.
The transaction must meet at least one of the following two conditions: a. The "amount" column must be greater than or equal to 1000. b. The "quantity" column must be greater than or equal to 5.

This means that the query will only retrieve data for transactions that were made by the customer with ID 'CS018205000001' and either have an amount greater than or equal to 1000 or a quantity greater than or equal to 5.

In summary, this code is retrieving specific columns of information from the "receipt" table in a database, but only for transactions made by a specific customer with the ID 'CS018205000001' and with either an amount greater than or equal to 1000 or a quantity greater than or equal to 5. The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-007: From the receipt details data (df_receipt), specify the columns in order of sales date (sales_ymd), customer ID (customer_id), product code (product_cd) and sales amount (amount) and extract data that satisfy all the following conditions.

Customer ID (customer_id) is “CS018205000001”.

Sales amount (amount) is between 1,000 and 2,000.

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd,
  amount
FROM
  receipt
WHERE
  customer_id = 'CS018205000001'
AND 
  amount BETWEEN 1000 AND 2000
;

 * postgresql://padawan:***@db:5432/dsdojo_db
1 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	amount
20180911	CS018205000001	P071401005	1100

Commentary :

This code is also written in SQL (Structured Query Language) and it is querying a table named "receipt".

The SELECT statement is used to retrieve specific columns of data from the "receipt" table. The columns being selected are "sales_ymd", "customer_id", "product_cd", and "amount". These columns represent various pieces of information related to a purchase transaction, such as the date of the sale, the ID of the customer who made the purchase, the code of the product that was purchased, and the amount of the purchase.

The FROM statement specifies the "receipt" table from which the data is being retrieved.

The WHERE clause is used to filter the data based on specific conditions. In this case, the data is being filtered based on two conditions:

The "customer_id" column must be equal to 'CS018205000001'.
The "amount" column must be between 1000 and 2000, inclusive.

This means that the query will only retrieve data for transactions that were made by the customer with ID 'CS018205000001' and have an amount between 1000 and 2000, inclusive.

The BETWEEN operator in the WHERE clause is used to filter results based on a range of values. In this case, it is used to retrieve transactions where the "amount" column falls between 1000 and 2000, inclusive.

In summary, this code is retrieving specific columns of information from the "receipt" table in a database, but only for transactions made by a specific customer with the ID 'CS018205000001' and with an amount between 1000 and 2000, inclusive. The exact meaning and structure of the data in the "receipt" table depends on the database schema and the information stored in it.

S-008: From the receipt details data (df_receipt), specify the columns in order of sales date (sales_ymd), customer ID (customer_id), product code (product_cd) and sales amount (amount), and extract data satisfying all the following conditions.

Customer ID (customer_id) is “CS018205000001”.

Product code (product_cd) is other than “P071401019”.

In [ ]:

%%sql

SELECT
  sales_ymd,
  customer_id,
  product_cd, amount
FROM
  receipt
WHERE
  customer_id = 'CS018205000001'
AND 
  product_cd != 'P071401019'
;

 * postgresql://padawan:***@db:5432/dsdojo_db
12 rows affected.

Out[ ]:

sales_ymd	customer_id	product_cd	amount
20180911	CS018205000001	P071401012	2200
20180414	CS018205000001	P060104007	600
20170614	CS018205000001	P050206001	990
20170614	CS018205000001	P060702015	108
20190216	CS018205000001	P071005024	102
20180414	CS018205000001	P071101002	278
20190226	CS018205000001	P070902035	168
20190924	CS018205000001	P060805001	495
20190226	CS018205000001	P071401020	2200
20180911	CS018205000001	P071401005	1100
20190216	CS018205000001	P040101002	218
20190924	CS018205000001	P091503001	280

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select data from the "receipt" table.

The columns that are being selected are: "sales_ymd", "customer_id", "product_cd", and "amount".

The query is filtering the data by only selecting records where the "customer_id" is equal to 'CS018205000001' AND the "product_cd" is NOT equal to 'P071401019'.

This means that the query will return all records for customer_id 'CS018205000001' except for records where the product_cd is 'P071401019'.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-009: In the following process, rewrite OR to AND without changing the output result.

SELECT * FROM store WHERE NOT (prefecture_cd = '13' OR floor_area > 900)

In [ ]:

%%sql

SELECT 
  * 
FROM 
  store 
WHERE 
  prefecture_cd != '13' 
AND 
  floor_area <= 900
;

 * postgresql://padawan:***@db:5432/dsdojo_db
3 rows affected.

Out[ ]:

store_cd	store_name	prefecture_cd	prefecture	address	address_kana	tel_no	longitude	latitude	floor_area
S14046	北山田店	14	神奈川県	神奈川県横浜市都筑区北山田一丁目	カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ	045-123-4049	139.5916	35.56189	831.0
S14011	日吉本町店	14	神奈川県	神奈川県横浜市港北区日吉本町四丁目	カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ	045-123-4033	139.6316	35.54655	890.0
S12013	習志野店	12	千葉県	千葉県習志野市芝園一丁目	チバケンナラシノシシバゾノイッチョウメ	047-123-4002	140.022	35.66122	808.0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "store" table.

The query is filtering the data by only selecting records where the "prefecture_cd" is NOT equal to '13' AND the "floor_area" is less than or equal to 900.

This means that the query will return all records from the "store" table where the "prefecture_cd" is not equal to 13 and the "floor_area" is less than or equal to 900.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-010: From the shop data (df_store), extract all items whose shop code (store_cd) starts with “S14” and display 10 items.

In [ ]:

%%sql

SELECT
  *
FROM 
  store
WHERE
  store_cd LIKE 'S14%'
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

store_cd	store_name	prefecture_cd	prefecture	address	address_kana	tel_no	longitude	latitude	floor_area
S14010	菊名店	14	神奈川県	神奈川県横浜市港北区菊名一丁目	カナガワケンヨコハマシコウホククキクナイッチョウメ	045-123-4032	139.6326	35.50049	1732.0
S14033	阿久和店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4043	139.4961	35.45918	1495.0
S14036	相模原中央店	14	神奈川県	神奈川県相模原市中央二丁目	カナガワケンサガミハラシチュウオウニチョウメ	042-123-4045	139.3716	35.57327	1679.0
S14040	長津田店	14	神奈川県	神奈川県横浜市緑区長津田みなみ台五丁目	カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ	045-123-4046	139.4994	35.52398	1548.0
S14050	阿久和西店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4053	139.4961	35.45918	1830.0
S14028	二ツ橋店	14	神奈川県	神奈川県横浜市瀬谷区二ツ橋町	カナガワケンヨコハマシセヤクフタツバシチョウ	045-123-4042	139.4963	35.46304	1574.0
S14012	本牧和田店	14	神奈川県	神奈川県横浜市中区本牧和田	カナガワケンヨコハマシナカクホンモクワダ	045-123-4034	139.6582	35.42156	1341.0
S14046	北山田店	14	神奈川県	神奈川県横浜市都筑区北山田一丁目	カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ	045-123-4049	139.5916	35.56189	831.0
S14022	逗子店	14	神奈川県	神奈川県逗子市逗子一丁目	カナガワケンズシシズシイッチョウメ	046-123-4036	139.5789	35.29642	1838.0
S14011	日吉本町店	14	神奈川県	神奈川県横浜市港北区日吉本町四丁目	カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ	045-123-4033	139.6316	35.54655	890.0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "store" table.

The query is filtering the data by only selecting records where the "store_cd" column starts with the letters 'S14' using the LIKE operator with the wildcard character '%'.

The query is also using the LIMIT clause to limit the number of returned records to 10.

This means that the query will return up to 10 records from the "store" table where the "store_cd" starts with 'S14'.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-011: From the customer data (df_customer), extract all items whose customer ID (customer_id) ends with 1, and display 10 items.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
WHERE 
  customer_id LIKE '%1' 
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS037613000071	六角雅彦	9	不明	1952-04-01	66	136-0076	東京都江東区南砂**********	S13037	20150414	0-00000000-0
CS028811000001	堀井かおり	1	女性	1933-03-27	86	245-0016	神奈川県横浜市泉区和泉町**********	S14028	20160115	0-00000000-0
CS040412000191	川井郁恵	1	女性	1977-01-05	42	226-0021	神奈川県横浜市緑区北八朔町**********	S14040	20151101	1-20091025-4
CS028314000011	小菅あおい	1	女性	1983-11-26	35	246-0038	神奈川県横浜市瀬谷区宮沢**********	S14028	20151123	1-20080426-5
CS039212000051	藤島恵梨香	1	女性	1997-02-03	22	166-0001	東京都杉並区阿佐谷北**********	S13039	20171121	1-20100215-4
CS015412000111	松居奈月	1	女性	1972-10-04	46	136-0071	東京都江東区亀戸**********	S13015	20150629	0-00000000-0
CS004702000041	野島洋	0	男性	1943-08-24	75	176-0022	東京都練馬区向山**********	S13004	20170218	0-00000000-0
CS041515000001	栗田千夏	1	女性	1967-01-02	52	206-0001	東京都多摩市和田**********	S13041	20160422	E-20100803-F
CS029313000221	北条ひかり	1	女性	1987-06-19	31	279-0011	千葉県浦安市美浜**********	S12029	20180810	0-00000000-0
CS034312000071	望月奈央	1	女性	1980-09-20	38	213-0026	神奈川県川崎市高津区久末**********	S14034	20160106	0-00000000-0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is filtering the data by only selecting records where the "customer_id" column ends with the number '1' using the LIKE operator with the wildcard character '%'.

The query is also using the LIMIT clause to limit the number of returned records to 10.

This means that the query will return up to 10 records from the "customer" table where the "customer_id" ends with the number '1'.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-012: From shop data (df_store), display all items with “横浜市(Yokohama-city)” in the address.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  store 
WHERE 
  address LIKE '%横浜市%'
;

 * postgresql://padawan:***@db:5432/dsdojo_db
11 rows affected.

Out[ ]:

store_cd	store_name	prefecture_cd	prefecture	address	address_kana	tel_no	longitude	latitude	floor_area
S14010	菊名店	14	神奈川県	神奈川県横浜市港北区菊名一丁目	カナガワケンヨコハマシコウホククキクナイッチョウメ	045-123-4032	139.6326	35.50049	1732.0
S14033	阿久和店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4043	139.4961	35.45918	1495.0
S14040	長津田店	14	神奈川県	神奈川県横浜市緑区長津田みなみ台五丁目	カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ	045-123-4046	139.4994	35.52398	1548.0
S14050	阿久和西店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4053	139.4961	35.45918	1830.0
S14028	二ツ橋店	14	神奈川県	神奈川県横浜市瀬谷区二ツ橋町	カナガワケンヨコハマシセヤクフタツバシチョウ	045-123-4042	139.4963	35.46304	1574.0
S14012	本牧和田店	14	神奈川県	神奈川県横浜市中区本牧和田	カナガワケンヨコハマシナカクホンモクワダ	045-123-4034	139.6582	35.42156	1341.0
S14046	北山田店	14	神奈川県	神奈川県横浜市都筑区北山田一丁目	カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ	045-123-4049	139.5916	35.56189	831.0
S14011	日吉本町店	14	神奈川県	神奈川県横浜市港北区日吉本町四丁目	カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ	045-123-4033	139.6316	35.54655	890.0
S14048	中川中央店	14	神奈川県	神奈川県横浜市都筑区中川中央二丁目	カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ	045-123-4051	139.5758	35.54912	1657.0
S14042	新山下店	14	神奈川県	神奈川県横浜市中区新山下二丁目	カナガワケンヨコハマシナカクシンヤマシタニチョウメ	045-123-4047	139.6593	35.43894	1044.0
S14006	葛が谷店	14	神奈川県	神奈川県横浜市都筑区葛が谷	カナガワケンヨコハマシツヅキククズガヤ	045-123-4031	139.5633	35.53573	1886.0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "store" table.

The query is filtering the data by only selecting records where the "address" column contains the string '横浜市' using the LIKE operator with the wildcard character '%'.

This means that the query will return all records from the "store" table where the "address" column contains the string '横浜市'.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-013: From the customer data (df_customer), extract all data whose status code (status_cd) begins with the letters A to F and display 10 items.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
WHERE 
  status_cd ~ '^[A-F]' 
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS031415000172	宇多田貴美子	1	女性	1976-10-04	42	151-0053	東京都渋谷区代々木**********	S13031	20150529	D-20100325-C
CS015414000103	奥野陽子	1	女性	1977-08-09	41	136-0073	東京都江東区北砂**********	S13015	20150722	B-20100609-B
CS011215000048	芦田沙耶	1	女性	1992-02-01	27	223-0062	神奈川県横浜市港北区日吉本町**********	S14011	20150228	C-20100421-9
CS029415000023	梅田里穂	1	女性	1976-01-17	43	279-0043	千葉県浦安市富士見**********	S12029	20150610	D-20100918-E
CS035415000029	寺沢真希	9	不明	1977-09-27	41	158-0096	東京都世田谷区玉川台**********	S13035	20141220	F-20101029-F
CS031415000106	宇野由美子	1	女性	1970-02-26	49	151-0053	東京都渋谷区代々木**********	S13031	20150201	F-20100511-E
CS029215000025	石倉美帆	1	女性	1993-09-28	25	279-0022	千葉県浦安市今川**********	S12029	20150708	B-20100820-C
CS033605000005	猪股雄太	0	男性	1955-12-05	63	246-0031	神奈川県横浜市瀬谷区瀬谷**********	S14033	20150425	F-20100917-E
CS033415000229	板垣菜々美	1	女性	1977-11-07	41	246-0021	神奈川県横浜市瀬谷区二ツ橋町**********	S14033	20150712	F-20100326-E
CS008415000145	黒谷麻緒	1	女性	1977-06-27	41	157-0067	東京都世田谷区喜多見**********	S13008	20150829	F-20100622-F

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is filtering the data by only selecting records where the "status_cd" column matches the regular expression pattern '^[A-F]' using the ~ operator.

The regular expression pattern '^[A-F]' matches any value in the "status_cd" column that starts with the letters A through F. The ^ character in the pattern indicates the start of the string, and the [A-F] specifies a character range.

The query is also using the LIMIT clause to limit the number of returned records to 10.

This means that the query will return up to 10 records from the "customer" table where the "status_cd" column starts with the letters A through F.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-014: From the customer data (df_customer), extract all data items whose status code (status_cd) ends with the numbers 1 to 9, and display 10 items.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
WHERE 
  status_cd ~ '[1-9]$' 
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS001215000145	田崎美紀	1	女性	1995-03-29	24	144-0055	東京都大田区仲六郷**********	S13001	20170605	6-20090929-2
CS033513000180	安斎遥	1	女性	1962-07-11	56	241-0823	神奈川県横浜市旭区善部町**********	S14033	20150728	6-20080506-5
CS011215000048	芦田沙耶	1	女性	1992-02-01	27	223-0062	神奈川県横浜市港北区日吉本町**********	S14011	20150228	C-20100421-9
CS040412000191	川井郁恵	1	女性	1977-01-05	42	226-0021	神奈川県横浜市緑区北八朔町**********	S14040	20151101	1-20091025-4
CS009315000023	皆川文世	1	女性	1980-04-15	38	154-0012	東京都世田谷区駒沢**********	S13009	20150319	5-20080322-1
CS015315000033	福士璃奈子	1	女性	1983-03-17	36	135-0043	東京都江東区塩浜**********	S13015	20141024	4-20080219-3
CS023513000066	神戸そら	1	女性	1961-12-17	57	210-0005	神奈川県川崎市川崎区東田町**********	S14023	20150915	5-20100524-9
CS035513000134	市川美帆	1	女性	1960-03-27	59	156-0053	東京都世田谷区桜**********	S13035	20150227	8-20100711-9
CS001515000263	高松夏空	1	女性	1962-11-09	56	144-0051	東京都大田区西蒲田**********	S13001	20160812	1-20100804-1
CS040314000027	鶴田きみまろ	9	不明	1986-03-26	33	226-0027	神奈川県横浜市緑区長津田**********	S14040	20150122	2-20080426-4

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is filtering the data by only selecting records where the "status_cd" column matches the regular expression pattern '[1-9]$' using the ~ operator.

The regular expression pattern '[1-9]$' matches any value in the "status_cd" column that ends with a digit from 1 through 9. The $ character in the pattern indicates the end of the string, and the [1-9] specifies a character range.

The query is also using the LIMIT clause to limit the number of returned records to 10.

This means that the query will return up to 10 records from the "customer" table where the "status_cd" column ends with a digit from 1 through 9.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-015: From the customer data (df_customer), extract all data items whose status code (status_cd) begins with the letters A to F and ends with the numbers 1 to 9, and display 10 items.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
WHERE 
  status_cd ~ '^[A-F].*[1-9]$' 
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS011215000048	芦田沙耶	1	女性	1992-02-01	27	223-0062	神奈川県横浜市港北区日吉本町**********	S14011	20150228	C-20100421-9
CS022513000105	島村貴美子	1	女性	1962-03-12	57	249-0002	神奈川県逗子市山の根**********	S14022	20150320	A-20091115-7
CS001515000096	水野陽子	9	不明	1960-11-29	58	144-0053	東京都大田区蒲田本町**********	S13001	20150614	A-20100724-7
CS013615000053	西脇季衣	1	女性	1953-10-18	65	261-0026	千葉県千葉市美浜区幕張西**********	S12013	20150128	B-20100329-6
CS020412000161	小宮薫	1	女性	1974-05-21	44	174-0042	東京都板橋区東坂下**********	S13020	20150822	B-20081021-3
CS001215000097	竹中あさみ	1	女性	1990-07-25	28	146-0095	東京都大田区多摩川**********	S13001	20170315	A-20100211-2
CS035212000007	内村恵梨香	1	女性	1990-12-04	28	152-0023	東京都目黒区八雲**********	S13035	20151013	B-20101018-6
CS002515000386	野田コウ	1	女性	1963-05-30	55	185-0013	東京都国分寺市西恋ケ窪**********	S13002	20160410	C-20100127-8
CS001615000372	稲垣寿々花	1	女性	1956-10-29	62	144-0035	東京都大田区南蒲田**********	S13001	20170403	A-20100104-1
CS032512000121	松井知世	1	女性	1962-09-04	56	210-0011	神奈川県川崎市川崎区富士見**********	S13032	20150727	A-20100103-5

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is filtering the data by only selecting records where the "status_cd" column matches the regular expression pattern '^[A-F].*[1-9]$' using the ~ operator.

The regular expression pattern '^[A-F].*[1-9]$' matches any value in the "status_cd" column that starts with the letters A through F, followed by any number of characters, and ends with a digit from 1 through 9. The ^ character in the pattern indicates the start of the string, and the $ character indicates the end of the string.

The query is also using the LIMIT clause to limit the number of returned records to 10.

This means that the query will return up to 10 records from the "customer" table where the "status_cd" column starts with the letters A through F and ends with a digit from 1 through 9.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-016: From the shop data (df_store), display all items of data where the telephone number (tel_no) is 3 digits – 3 digits – 4 digits.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  store 
WHERE 
  tel_no ~ '^[0-9]{3}-[0-9]{3}-[0-9]{4}$'
;

 * postgresql://padawan:***@db:5432/dsdojo_db
34 rows affected.

Out[ ]:

store_cd	store_name	prefecture_cd	prefecture	address	address_kana	tel_no	longitude	latitude	floor_area
S12014	千草台店	12	千葉県	千葉県千葉市稲毛区千草台一丁目	チバケンチバシイナゲクチグサダイイッチョウメ	043-123-4003	140.118	35.63559	1698.0
S13002	国分寺店	13	東京都	東京都国分寺市本多二丁目	トウキョウトコクブンジシホンダニチョウメ	042-123-4008	139.4802	35.70566	1735.0
S14010	菊名店	14	神奈川県	神奈川県横浜市港北区菊名一丁目	カナガワケンヨコハマシコウホククキクナイッチョウメ	045-123-4032	139.6326	35.50049	1732.0
S14033	阿久和店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4043	139.4961	35.45918	1495.0
S14036	相模原中央店	14	神奈川県	神奈川県相模原市中央二丁目	カナガワケンサガミハラシチュウオウニチョウメ	042-123-4045	139.3716	35.57327	1679.0
S14040	長津田店	14	神奈川県	神奈川県横浜市緑区長津田みなみ台五丁目	カナガワケンヨコハマシミドリクナガツタミナミダイゴチョウメ	045-123-4046	139.4994	35.52398	1548.0
S14050	阿久和西店	14	神奈川県	神奈川県横浜市瀬谷区阿久和西一丁目	カナガワケンヨコハマシセヤクアクワニシイッチョウメ	045-123-4053	139.4961	35.45918	1830.0
S13052	森野店	13	東京都	東京都町田市森野三丁目	トウキョウトマチダシモリノサンチョウメ	042-123-4030	139.4383	35.55293	1087.0
S14028	二ツ橋店	14	神奈川県	神奈川県横浜市瀬谷区二ツ橋町	カナガワケンヨコハマシセヤクフタツバシチョウ	045-123-4042	139.4963	35.46304	1574.0
S14012	本牧和田店	14	神奈川県	神奈川県横浜市中区本牧和田	カナガワケンヨコハマシナカクホンモクワダ	045-123-4034	139.6582	35.42156	1341.0
S14046	北山田店	14	神奈川県	神奈川県横浜市都筑区北山田一丁目	カナガワケンヨコハマシツヅキクキタヤマタイッチョウメ	045-123-4049	139.5916	35.56189	831.0
S14022	逗子店	14	神奈川県	神奈川県逗子市逗子一丁目	カナガワケンズシシズシイッチョウメ	046-123-4036	139.5789	35.29642	1838.0
S14011	日吉本町店	14	神奈川県	神奈川県横浜市港北区日吉本町四丁目	カナガワケンヨコハマシコウホククヒヨシホンチョウヨンチョウメ	045-123-4033	139.6316	35.54655	890.0
S13016	小金井店	13	東京都	東京都小金井市本町一丁目	トウキョウトコガネイシホンチョウイッチョウメ	042-123-4015	139.5094	35.70018	1399.0
S14034	川崎野川店	14	神奈川県	神奈川県川崎市宮前区野川	カナガワケンカワサキシミヤマエクノガワ	044-123-4044	139.5998	35.57693	1318.0
S14048	中川中央店	14	神奈川県	神奈川県横浜市都筑区中川中央二丁目	カナガワケンヨコハマシツヅキクナカガワチュウオウニチョウメ	045-123-4051	139.5758	35.54912	1657.0
S12007	佐倉店	12	千葉県	千葉県佐倉市上志津	チバケンサクラシカミシヅ	043-123-4001	140.1452	35.71872	1895.0
S14026	辻堂西海岸店	14	神奈川県	神奈川県藤沢市辻堂西海岸二丁目	カナガワケンフジサワシツジドウニシカイガンニチョウメ	046-123-4040	139.4466	35.32464	1732.0
S13041	八王子店	13	東京都	東京都八王子市大塚	トウキョウトハチオウジシオオツカ	042-123-4026	139.4235	35.63787	810.0
S14049	川崎大師店	14	神奈川県	神奈川県川崎市川崎区中瀬三丁目	カナガワケンカワサキシカワサキクナカゼサンチョウメ	044-123-4052	139.7327	35.53759	962.0
S14023	川崎店	14	神奈川県	神奈川県川崎市川崎区本町二丁目	カナガワケンカワサキシカワサキクホンチョウニチョウメ	044-123-4037	139.7028	35.53599	1804.0
S13018	清瀬店	13	東京都	東京都清瀬市松山一丁目	トウキョウトキヨセシマツヤマイッチョウメ	042-123-4017	139.5178	35.76885	1220.0
S14027	南藤沢店	14	神奈川県	神奈川県藤沢市南藤沢	カナガワケンフジサワシミナミフジサワ	046-123-4041	139.4896	35.33762	1521.0
S14021	伊勢原店	14	神奈川県	神奈川県伊勢原市伊勢原四丁目	カナガワケンイセハラシイセハラヨンチョウメ	046-123-4035	139.3129	35.40169	962.0
S14047	相模原店	14	神奈川県	神奈川県相模原市千代田六丁目	カナガワケンサガミハラシチヨダロクチョウメ	042-123-4050	139.3748	35.55959	1047.0
S12013	習志野店	12	千葉県	千葉県習志野市芝園一丁目	チバケンナラシノシシバゾノイッチョウメ	047-123-4002	140.022	35.66122	808.0
S14042	新山下店	14	神奈川県	神奈川県横浜市中区新山下二丁目	カナガワケンヨコハマシナカクシンヤマシタニチョウメ	045-123-4047	139.6593	35.43894	1044.0
S12030	八幡店	12	千葉県	千葉県市川市八幡三丁目	チバケンイチカワシヤワタサンチョウメ	047-123-4005	139.924	35.72318	1162.0
S14025	大和店	14	神奈川県	神奈川県大和市下和田	カナガワケンヤマトシシモワダ	046-123-4039	139.468	35.43414	1011.0
S14045	厚木店	14	神奈川県	神奈川県厚木市中町二丁目	カナガワケンアツギシナカチョウニチョウメ	046-123-4048	139.3651	35.44182	980.0
S12029	東野店	12	千葉県	千葉県浦安市東野一丁目	チバケンウラヤスシヒガシノイッチョウメ	047-123-4004	139.8968	35.65086	1101.0
S12053	高洲店	12	千葉県	千葉県浦安市高洲五丁目	チバケンウラヤスシタカスゴチョウメ	047-123-4006	139.9176	35.63755	1555.0
S14024	三田店	14	神奈川県	神奈川県川崎市多摩区三田四丁目	カナガワケンカワサキシタマクミタヨンチョウメ	044-123-4038	139.5424	35.6077	972.0
S14006	葛が谷店	14	神奈川県	神奈川県横浜市都筑区葛が谷	カナガワケンヨコハマシツヅキククズガヤ	045-123-4031	139.5633	35.53573	1886.0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "store" table.

The query is filtering the data by only selecting records where the "tel_no" column matches the regular expression pattern '^[0-9]{3}-[0-9]{3}-[0-9]{4}$' using the ~ operator.

The regular expression pattern '^[0-9]{3}-[0-9]{3}-[0-9]{4}$' matches any value in the "tel_no" column that follows the format of a valid phone number with the format ###-###-####, where each # represents a digit. The ^ character in the pattern indicates the start of the string, and the $ character indicates the end of the string. The {3} and {4} are quantifiers that specify the exact number of digits in each section of the phone number.

This means that the query will return all records from the "store" table where the "tel_no" column matches the format of a valid phone number.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-017: Sort customer data (df_customer) by birth_day(birth_day) in order of age and display all 10 items from the top.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
ORDER BY 
  birth_day 
LIMIT 10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS003813000014	村山菜々美	1	女性	1928-11-26	90	182-0007	東京都調布市菊野台**********	S13003	20160214	0-00000000-0
CS026813000004	吉村朝陽	1	女性	1928-12-14	90	251-0043	神奈川県藤沢市辻堂元町**********	S14026	20150723	0-00000000-0
CS018811000003	熊沢美里	1	女性	1929-01-07	90	204-0004	東京都清瀬市野塩**********	S13018	20150403	0-00000000-0
CS027803000004	内村拓郎	0	男性	1929-01-12	90	251-0031	神奈川県藤沢市鵠沼藤が谷**********	S14027	20151227	0-00000000-0
CS013801000003	天野拓郎	0	男性	1929-01-15	90	274-0824	千葉県船橋市前原東**********	S12013	20160120	0-00000000-0
CS001814000022	鶴田里穂	1	女性	1929-01-28	90	144-0045	東京都大田区南六郷**********	S13001	20161012	A-20090415-7
CS016815000002	山元美紀	1	女性	1929-02-22	90	184-0005	東京都小金井市桜町**********	S13016	20150629	C-20090923-C
CS009815000003	中田里穂	1	女性	1929-04-08	89	154-0014	東京都世田谷区新町**********	S13009	20150421	D-20091021-E
CS012813000013	宇野南朋	1	女性	1929-04-09	89	231-0806	神奈川県横浜市中区本牧町**********	S14012	20150712	0-00000000-0
CS005813000015	金谷恵梨香	1	女性	1929-04-09	89	165-0032	東京都中野区鷺宮**********	S13005	20150506	0-00000000-0

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is sorting the data in ascending order based on the "birth_day" column using the ORDER BY clause.

The LIMIT clause is used to limit the number of returned records to 10.

This means that the query will return the first 10 records from the "customer" table when the data is sorted in ascending order based on the "birth_day" column.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-018: Sort customer data (df_customer) by birth_day(birth_day) in descending order, displaying all 10 items from the top.

In [ ]:

%%sql

SELECT 
  * 
FROM 
  customer 
ORDER BY 
  birth_day DESC 
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
CS035114000004	大村美里	1	女性	2007-11-25	11	156-0053	東京都世田谷区桜**********	S13035	20150619	6-20091205-6
CS022103000002	福山はじめ	9	不明	2007-10-02	11	249-0006	神奈川県逗子市逗子**********	S14022	20160909	0-00000000-0
CS002113000009	柴田真悠子	1	女性	2007-09-17	11	184-0014	東京都小金井市貫井南町**********	S13002	20160304	0-00000000-0
CS004115000014	松井京子	1	女性	2007-08-09	11	165-0031	東京都中野区上鷺宮**********	S13004	20161120	1-20081231-1
CS002114000010	山内遥	1	女性	2007-06-03	11	184-0015	東京都小金井市貫井北町**********	S13002	20160920	6-20100510-1
CS025115000002	小柳夏希	1	女性	2007-04-18	11	245-0018	神奈川県横浜市泉区上飯田町**********	S14025	20160116	D-20100913-D
CS002113000025	広末まなみ	1	女性	2007-03-30	12	184-0015	東京都小金井市貫井北町**********	S13002	20171030	0-00000000-0
CS033112000003	長野美紀	1	女性	2007-03-22	12	245-0051	神奈川県横浜市戸塚区名瀬町**********	S14033	20150606	0-00000000-0
CS007115000006	福岡瞬	1	女性	2007-03-10	12	285-0845	千葉県佐倉市西志津**********	S12007	20151118	F-20101016-F
CS014113000008	矢口莉緒	1	女性	2007-03-05	12	260-0041	千葉県千葉市中央区東千葉**********	S12014	20150622	3-20091108-6

Commentary :

This code is written in SQL language and is querying a database.

The query is using the SELECT statement to select all columns from the "customer" table.

The query is sorting the data in descending order based on the "birth_day" column using the ORDER BY clause.

The LIMIT clause is used to limit the number of returned records to 10.

This means that the query will return the first 10 records from the "customer" table when the data is sorted in descending order based on the "birth_day" column.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-019: Rank the receipt details data (df_receipt) in descending order of sales amount per item (amount) and display 10 items from the top. The items shall display the customer ID (customer_id), sales amount (amount) and the rank assigned. If the sales amount (amount) is equal, the same rank shall be assigned.

In [ ]:

%%sql

SELECT
  customer_id,
  amount,
  RANK() OVER(ORDER BY amount DESC) AS ranking 
FROM 
  receipt
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	amount	ranking
CS011415000006	10925	1
ZZ000000000000	6800	2
CS028605000002	5780	3
ZZ000000000000	5480	4
ZZ000000000000	5480	4
CS015515000034	5480	4
ZZ000000000000	5440	7
CS021515000089	5440	7
CS020414000081	5280	9
ZZ000000000000	5280	9

Commentary :

This code is written in SQL language and is querying a database.

The query is selecting the "customer_id" and "amount" columns from the "receipt" table.

The query is also using a window function called RANK(), which is being used to calculate the ranking of each record based on the "amount" column. The RANK() function calculates the rank of each record by assigning the same rank to equal values and leaving gaps between the ranks for non-equal values. The window function is defined using the OVER() clause, which specifies the order in which to apply the ranking and any partitioning or filtering criteria.

The query is using the ORDER BY clause to order the data in descending order based on the "amount" column.

The query is using the AS keyword to alias the result of the RANK() function as "ranking".

The LIMIT clause is used to limit the number of returned records to 10.

This means that the query will return the first 10 records from the "receipt" table, with each record including the "customer_id", "amount", and "ranking" columns. The "ranking" column will contain the ranking of each record based on the "amount" column, with the highest amount having a ranking of 1.

This code is being executed within a Jupyter notebook using the SQL magic command "%%sql". This command allows SQL code to be executed directly within the notebook.

S-020: For receipt details data (df_receipt), assign ranks in descending order of sales amount (amount) per item and display the first 10 items. Items shall display the customer ID (customer_id), sales amount (amount) and the rank assigned. Even if the sales amount (amount) is equal, a different rank should be assigned.

In [ ]:

%%sql

SELECT
  customer_id,
  amount,
  ROW_NUMBER() OVER(ORDER BY amount DESC) AS ranking 
FROM 
  receipt
LIMIT 
  10
;

 * postgresql://padawan:***@db:5432/dsdojo_db
10 rows affected.

Out[ ]:

customer_id	amount	ranking
CS011415000006	10925	1
ZZ000000000000	6800	2
CS028605000002	5780	3
ZZ000000000000	5480	4
CS015515000034	5480	5
ZZ000000000000	5480	6
ZZ000000000000	5440	7
CS021515000089	5440	8
CS001412000160	5280	9
CS020515000102	5280	10

Commentary :

This code is a SQL query being executed in a Jupyter notebook using the magic command "%%sql".

The query selects three columns from the "receipt" table: "customer_id", "amount", and a new column "ranking". The "customer_id" and "amount" columns are straightforward and simply retrieve the values for these two fields. The "ranking" column is a derived column created using a window function called ROW_NUMBER().

The ROW_NUMBER() function generates a unique sequential number for each row in the result set based on the ORDER BY clause provided. In this case, the function is being used to assign a rank to each row based on the value in the "amount" column. The ORDER BY clause is ordering the rows in descending order based on the "amount" column, so the highest amount will be ranked 1, the second-highest amount will be ranked 2, and so on.

The AS keyword is used to give the new column an alias of "ranking".

Finally, the LIMIT clause is used to limit the number of rows returned by the query to 10.

Therefore, the result of this query will be the top 10 rows from the "receipt" table, with columns for "customer_id", "amount", and "ranking". The ranking column will contain a sequential number assigned to each row based on the order of the "amount" column, with the highest amount having a ranking of 1.

Data Science 100 Knocks (Structured Data Processing) - SQL

This is an ipynb file originally created by The Data Scientist Society(データサイエンティスト協会スキル定義委員) and translated from Japanese to English by DeepL. The reason I updated this file is to spread this practice, which is useful for everyone who wants to practice SQL, from beginners to advanced engineers. Since this data is created for Japanese, you may face language problems when practicing. But do not worry, it will not affect much.

Data Science 100 Knocks (Structured Data Processing) - SQL part1 (Q1 to Q20)

Data Science 100 Knocks (Structured Data Processing) - SQL part2 (Q21 to Q40)

Data Science 100 Knocks (Structured Data Processing) - SQL part3 (Q41 to Q60)

Data Science 100 Knocks (Structured Data Processing) - SQL part4 (Q61 to Q80)

Data Science 100 Knocks (Structured Data Processing) - SQL part5 (Q81 to Q100)

Data Science 100 Knocks (Structured Data Processing)

This is an ipynb file originally created by The Data Scientist Society(データサイエンティスト協会スキル定義委員) and translated from Japanese to English by DeepL. The reason I updated this file is to spread this practice, which is useful for everyone who wants to practice Python, SQL, R, from beginners to advanced engineers. Since this data is created for Japanese, you may face language problems when practicing. But do not worry, it will not affect much.