**In the 21st century, the database is the marketplace. - Stan Rapp**

Prasyarat DFDS-01

ADSP-01 : https://tau-data.id/adsp/ (Memahami dasar Python dengan baik)
Kalkulus Dasar

dfds-01

tau-data Indonesia

Pendahuluan SQL dasar untuk Data Science - 01

https://tau-data.id/dfds-01/

Instalasi:¶

XAMPP: https://www.apachefriends.org/download.html
IBM Watson: https://www.ibm.com/cloud/sql-query

PHPMyAdmin: Obat Sakit Kepala¶

http://localhost/phpmyadmin/index.php

In [1]:

!pip install --upgrade mysql-connector-python

Requirement already up-to-date: mysql-connector-python in c:\winpython\python-3.6.8.amd64\lib\site-packages (8.0.21)
Requirement already satisfied, skipping upgrade: protobuf>=3.0.0 in c:\winpython\python-3.6.8.amd64\lib\site-packages (from mysql-connector-python) (3.12.2)
Requirement already satisfied, skipping upgrade: six>=1.9 in c:\winpython\python-3.6.8.amd64\lib\site-packages (from protobuf>=3.0.0->mysql-connector-python) (1.12.0)
Requirement already satisfied, skipping upgrade: setuptools in c:\winpython\python-3.6.8.amd64\lib\site-packages (from protobuf>=3.0.0->mysql-connector-python) (49.2.0)

In [2]:

import mysql.connector as mysql
import pandas as pd
from time import time

Koneksi ke MySQL lokal¶

In [3]:

par = {'db_': 'fga', 'usr':'root', 'pas':'', 'hst':'localhost'}

In [4]:

par['db_'], par['hst']

Out[4]:

('fga', 'localhost')

In [5]:

def conMql(dbPar, maxTry=3):
    try_ = 0
    while try_<maxTry:
        try:
            return mysql.connect(host=dbPar['hst'],user=dbPar['usr'],passwd=dbPar['pas'],db=dbPar['db_'])
        except (mysql.Error) as e:      
            print ("Error Connecting to MySQL %d: %s, please wait retrying" % (e.args[0],e.args[1]))
            try_ += 1; time.sleep(1)

In [6]:

db = conMql(par)
db

Out[6]:

<mysql.connector.connection.MySQLConnection at 0x2984b284f98>

Catatan:¶

Buka PHPMyAdmin ==> Generate Query untuk membantu pemula di Query Dasar
"Bisa karena terbiasa" ==> jangan hawatir untuk "mengingat"

In [7]:

qry = "CREATE TABLE `mahasiswa` ( `nim` VARCHAR(12) NOT NULL COMMENT 'Nomer Induk Mahasiswa' , \
       `nama_lengkap` VARCHAR(255) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT \
       'Nama Lengkap' , `jenis_kelamin` VARCHAR(1) CHARACTER SET ascii COLLATE ascii_general_ci \
       NOT NULL COMMENT 'Jenis Kelamin P atau W' , `angkatan` YEAR NOT NULL COMMENT \
       'Tahun Masuk UIN Jakarta' , PRIMARY KEY (`nim`)) \
       ENGINE = MyISAM CHARSET=utf8 COLLATE utf8_general_ci COMMENT = 'Tabel Mahasiswa UIN Jakarta';"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
cur.close()
db.close()
res

In [8]:

qry = "SHOW TABLES"
db = conMql(par)
cur = db.cursor()
cur.execute(qry)
data = cur.fetchall() # lakukan Pagination untuk data yang sangat besar
cur.close(); db.close()

data

Out[8]:

[('mahasiswa',)]

Catatan:¶

Di dunia nyata "jenis kelamin"/gender .. Most likely Boolean.
Module ini untuk Data Scientist (bukan Data Engineer), untuk sementara waktu kita hiraukan terlebih dahulu.

Well-known Engine:¶

MyISAM
InnoDB
memory

In [9]:

qry = "INSERT INTO `mahasiswa` (`nim`, `nama_lengkap`, `angkatan`, `jenis_kelamin`) \
       VALUES ('1234', 'bambang', '2016', 'P'), ('1235', 'Wati', '2017', 'W'), \
       ('1239', 'Iwan', '2017', 'P');"
db = conMql(par)
cur = db.cursor()
result = cur.execute(qry)
cur.close()
db.close()

In [10]:

qry = "SELECT * FROM mahasiswa"

db = conMql(par)
cur = db.cursor()
cur.execute(qry)
data = cur.fetchall() # lakukan Pagination untuk data yang sangat besar
cur.close(); db.close()

In [11]:

data

Out[11]:

[('1234', 'bambang', 'P', 2016),
 ('1235', 'Wati', 'W', 2017),
 ('1239', 'Iwan', 'P', 2017)]

More Practical Way untuk Data Scientist/Analyst: Use Pandas¶

In [12]:

qry = "SELECT * FROM mahasiswa"

db = conMql(par)
data = pd.read_sql(qry, db)

In [13]:

data.head()

Out[13]:

	nim	nama_lengkap	jenis_kelamin	angkatan
0	1234	bambang	P	2016
1	1235	Wati	W	2017
2	1239	Iwan	P	2017

Contoh Data¶

In [14]:

# Create Table 2
qry = "CREATE TABLE committees (committee_id INT AUTO_INCREMENT, name VARCHAR(100), PRIMARY KEY (committee_id));"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
cur.close(); db.close()
'Done'

Out[14]:

'Done'

In [15]:

# Create Table 1
qry = "CREATE TABLE members (member_id INT AUTO_INCREMENT, name VARCHAR(100), PRIMARY KEY (member_id))"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
cur.close(); db.close()
'Done'

Out[15]:

'Done'

In [16]:

# Insert Data 2
qry = "INSERT INTO committees (name) VALUES ('John'), ('Mary'), ('Amelia'), ('Joe')"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
db.commit() # Hati-hati ... Butuh tambahan perintah ini!!!
cur.close(); db.close()
'Done'

Out[16]:

'Done'

In [17]:

# Insert Data 1
qry = "INSERT INTO members (name) VALUES('John'),('Jane'),('Mary'),('David'),('Amelia');"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
db.commit() # Hati-hati ... Butuh tambahan perintah ini!!!
cur.close(); db.close()
'Done'

Out[17]:

'Done'

In [18]:

# Query to DataFrame
db = conMql(par)
query = "SELECT * FROM committees" # or members
data = pd.read_sql(query, db)
data.head()

Out[18]:

	committee_id	name
0	1	John
1	2	Mary
2	3	Amelia
3	4	Joe

In [19]:

# order by
db = conMql(par)
query = "SELECT * FROM committees ORDER BY name"
data = pd.read_sql(query, db)
data.head()

Out[19]:

	committee_id	name
0	3	Amelia
1	4	Joe
2	1	John
3	2	Mary

In [20]:

# add new names
qry = "INSERT INTO committees (name) VALUES('John'),('Mary'),('Amelia'),('Bambang');"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
db.commit() # Hati-hati ... Butuh tambahan perintah ini!!!
cur.close(); db.close()

In [21]:

# order by
db = conMql(par)
query = "SELECT * FROM committees ORDER BY name"
data = pd.read_sql(query, db)
data.head(10)

Out[21]:

	committee_id	name
0	3	Amelia
1	7	Amelia
2	8	Bambang
3	4	Joe
4	1	John
5	5	John
6	2	Mary
7	6	Mary

In [22]:

# group by
db = conMql(par)
query = "SELECT * FROM committees GROUP BY name" # or members
data = pd.read_sql(query, db)
data.head(10)

Out[22]:

	committee_id	name
0	3	Amelia
1	8	Bambang
2	4	Joe
3	1	John
4	2	Mary

In [23]:

# Select Distinct
db = conMql(par)
query = "SELECT DISTINCT(name) FROM committees" # or members
data = pd.read_sql(query, db)
data.head(10)

Out[23]:

	name
0	John
1	Mary
2	Amelia
3	Joe
4	Bambang

In [24]:

# WHERE clause
db = conMql(par)
query = "SELECT * FROM committees WHERE committee_id>4" # or members
data = pd.read_sql(query, db)
data.head(10)

Out[24]:

	committee_id	name
0	5	John
1	6	Mary
2	7	Amelia
3	8	Bambang

Search with index - Magick command¶

In [25]:

%lsmagic

Out[25]:

Available line magics:
%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %conda  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode

Available cell magics:
%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile

Automagic is ON, % prefix IS NOT needed for line magics.

In [26]:

%%timeit

db = conMql(par)
query = "SELECT * FROM committees WHERE name='John'" # or members
data = pd.read_sql(query, db)

4.17 ms ± 182 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [27]:

# Hati-hati var "data" tidak disimpan ke memori akibat penggunaan Magic Command "timeit"

Sekarang kita coba tambahkan index di field "name"¶

In [28]:

qry = "ALTER TABLE committees ADD FULLTEXT name (name);"
db = conMql(par)
cur = db.cursor()
res = cur.execute(qry)
cur.close();db.close()
'Done'

Out[28]:

'Done'

Sekarang kita coba Query lagi dan analisa performanya¶

Catt: pada data yang kecil mungkin tidak signifikan, tapi pada data yang besar sangat besar pengaruhnya.

In [29]:

%%timeit

db = conMql(par)
query = "SELECT * FROM committees WHERE name LIKE '%ohn%'" # or members
data = pd.read_sql(query, db)

4.13 ms ± 69.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [30]:

data.head()

Out[30]:

	committee_id	name
0	5	John
1	6	Mary
2	7	Amelia
3	8	Bambang

SQL Joins¶

Perintah JOIN dalam SQL digunakan untuk menampilkan data pada table yang saling berhubungan atau berelasi. Artinya kita dapat menampilkan data dalam beberapa table hanya dengan satu kali perintah.

1. inner Join (irisan) - Join Predicate¶

Membandingkan setiap baris di Tabel 1 & 2 kemudian menambahkan jika kondisi join keduanya benar (True).¶

In [31]:

# Query to DataFrame Kita lihat dulu datanya
db = conMql(par)
dt1 = pd.read_sql("SELECT * FROM committees", db)
dt2 = pd.read_sql("SELECT * FROM members", db)

pd.concat([dt1.reset_index(drop=1),dt2.reset_index(drop=1)], axis=1)

Out[31]:

	committee_id	name	member_id	name
0	1	John	1.0	John
1	2	Mary	2.0	Jane
2	3	Amelia	3.0	Mary
3	4	Joe	4.0	David
4	5	John	5.0	Amelia
5	6	Mary	NaN	NaN
6	7	Amelia	NaN	NaN
7	8	Bambang	NaN	NaN

In [32]:

db = conMql(par)
query = "SELECT m.member_id, m.name member, c.committee_id, c.name committee \
        FROM members m \
        INNER JOIN committees c \
        ON c.name = m.name"
data = pd.read_sql(query, db)
data.head(10)

Out[32]:

	member_id	member	committee_id	committee
0	1	John	1	John
1	3	Mary	2	Mary
2	5	Amelia	3	Amelia
3	1	John	5	John
4	3	Mary	6	Mary
5	5	Amelia	7	Amelia

LEFT JOIN¶

Untuk setiap data dari tabel pertama (kiri/Left), buat data baru hanya jika ada padanannya di tabel kanan.¶

In [33]:

# Query to DataFrame Kita lihat dulu datanya
db = conMql(par)
dt1 = pd.read_sql("SELECT * FROM committees", db)
dt2 = pd.read_sql("SELECT * FROM members", db)
pd.concat([dt1.reset_index(drop=1),dt2.reset_index(drop=1)], axis=1)

Out[33]:

	committee_id	name	member_id	name
0	1	John	1.0	John
1	2	Mary	2.0	Jane
2	3	Amelia	3.0	Mary
3	4	Joe	4.0	David
4	5	John	5.0	Amelia
5	6	Mary	NaN	NaN
6	7	Amelia	NaN	NaN
7	8	Bambang	NaN	NaN

In [34]:

query = "SELECT m.member_id, m.name member, c.committee_id, c.name committee\
        FROM members m\
        LEFT JOIN committees c USING(name)"
db = conMql(par)
data = pd.read_sql(query, db)
data.head(10)

Out[34]:

	member_id	member	committee_id	committee
0	1	John	1.0	John
1	3	Mary	2.0	Mary
2	5	Amelia	3.0	Amelia
3	1	John	5.0	John
4	3	Mary	6.0	Mary
5	5	Amelia	7.0	Amelia
6	2	Jane	NaN	None
7	4	David	NaN	None

Bisa juga¶

Catt hati-hati performa query
ini left join

In [35]:

query = "SELECT member_id, name\
        FROM members \
        WHERE name IN\
        (SELECT name FROM committees)"
db = conMql(par)
data = pd.read_sql(query, db)
data.head(10)

Out[35]:

	member_id	name
0	1	John
1	3	Mary
2	5	Amelia

CROSS JOIN¶

Tidak seperti inner, left/right, Cross Join tidak punya kondisi. CJ, menggabungkan setiap kolom di kiri dan kanan tabel.¶

Kalau tabel 1 ada N baris dan Tabel 2 ada M baris, maka CJ menghasilkan NxM baris.
Null tidak ikut CJ

In [36]:

# Query to DataFrame Kita lihat dulu datanya
db = conMql(par)
dt1 = pd.read_sql("SELECT * FROM committees", db)
dt2 = pd.read_sql("SELECT * FROM members", db)
pd.concat([dt1.reset_index(drop=1),dt2.reset_index(drop=1)], axis=1)

Out[36]:

	committee_id	name	member_id	name
0	1	John	1.0	John
1	2	Mary	2.0	Jane
2	3	Amelia	3.0	Mary
3	4	Joe	4.0	David
4	5	John	5.0	Amelia
5	6	Mary	NaN	NaN
6	7	Amelia	NaN	NaN
7	8	Bambang	NaN	NaN

In [37]:

query = "SELECT m.member_id, m.name member, c.committee_id, c.name committee\
        FROM members m\
        CROSS JOIN committees c"
db = conMql(par)
data = pd.read_sql(query, db)
data.head(30)

Out[37]:

	member_id	member	committee_id	committee
0	1	John	1	John
1	2	Jane	1	John
2	3	Mary	1	John
3	4	David	1	John
4	5	Amelia	1	John
5	1	John	2	Mary
6	2	Jane	2	Mary
7	3	Mary	2	Mary
8	4	David	2	Mary
9	5	Amelia	2	Mary
10	1	John	3	Amelia
11	2	Jane	3	Amelia
12	3	Mary	3	Amelia
13	4	David	3	Amelia
14	5	Amelia	3	Amelia
15	1	John	4	Joe
16	2	Jane	4	Joe
17	3	Mary	4	Joe
18	4	David	4	Joe
19	5	Amelia	4	Joe
20	1	John	5	John
21	2	Jane	5	John
22	3	Mary	5	John
23	4	David	5	John
24	5	Amelia	5	John
25	1	John	6	Mary
26	2	Jane	6	Mary
27	3	Mary	6	Mary
28	4	David	6	Mary
29	5	Amelia	6	Mary

End of Module¶

Code Lesson DFDS-01

Code dari lesson ini dapat di akses di Link berikut (wajib login ke Google/Gmail): Code DFDS-01
Di link tersebut anda langsung bisa merubah code dan menjalankannya. Keterangan lebih lanjut di Video DFDS-01.
Sangat disarankan untuk membuka code dan video "side-by-side" untuk mendapatkan pengalaman belajar yang baik (Gambar dibawah). SIlahkan modifikasi (coba-coba) hal lain, selain yang ditunjukkan di video untuk mendapatkan pengalaman belajar yang lebih mendalam. Tentu saja juga silahkan akses berbagai referensi lain untuk memperkaya pengetahuan lalu diskusikan di forum yang telah disediakan.

"**Side-by-Side**": Ilustrasi bagaimana menggunakan code dan video dalam pembelajaran di tau-data. untuk mendapatkan pengalaman belajar yang baik.

Video Lesson DFDS-01 (Coming Soon)

Referensi DFDS:

SQL Notes for Professionals, 2019, GoalKickers
Silberschatz, A., Korth, H. F., & Sudarshan, S. (1997). Database system concepts(Vol. 4). New York: McGraw-Hill.
DeBarros, A. (2018). Practical SQL: A Beginner’s Guide to Storytelling with Data. No Starch Press.

Top Links Menu

DFDS-01: Pendahuluan SQL untuk Data Science

Prasyarat DFDS-01

tau-data Indonesia

Pendahuluan SQL dasar untuk Data Science - 01

https://tau-data.id/dfds-01/

Instalasi:¶

PHPMyAdmin: Obat Sakit Kepala¶

Koneksi ke MySQL lokal¶

Catatan:¶

Catatan:¶

Well-known Engine:¶

More Practical Way untuk Data Scientist/Analyst: Use Pandas¶

Contoh Data¶

Search with index - Magick command¶

Sekarang kita coba tambahkan index di field "name"¶

Sekarang kita coba Query lagi dan analisa performanya¶

SQL Joins¶

1. inner Join (irisan) - Join Predicate¶

Membandingkan setiap baris di Tabel 1 & 2 kemudian menambahkan jika kondisi join keduanya benar (True).¶

LEFT JOIN¶

Untuk setiap data dari tabel pertama (kiri/Left), buat data baru hanya jika ada padanannya di tabel kanan.¶

Bisa juga¶

CROSS JOIN¶

Tidak seperti inner, left/right, Cross Join tidak punya kondisi. CJ, menggabungkan setiap kolom di kiri dan kanan tabel.¶

End of Module¶

Code Lesson DFDS-01

Video Lesson DFDS-01 (Coming Soon)

Referensi DFDS:

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