Appendix B: Python Quick Reference

Panduan Cepat Python untuk Machine Learning

Appendix B: Python Quick Reference

Catatan

Appendix ini adalah referensi cepat untuk konsep Python essensial yang digunakan dalam kursus Machine Learning. Untuk penjelasan mendalam, kunjungi dokumentasi resmi Python.

B.1 Tipe Data Fundamental

Tipe Data Dasar

Tipe Deskripsi Contoh
int Bilangan bulat x = 42
float Bilangan desimal y = 3.14
str Teks name = "Python"
bool Nilai logika flag = True
None Nilai kosong value = None

Tipe Data Koleksi

# List (terurut, dapat diubah)
fruits = ['apple', 'banana', 'cherry']
fruits[0]          # 'apple'
fruits.append('orange')

# Tuple (terurut, tidak dapat diubah)
coordinates = (10, 20)
x, y = coordinates  # unpacking

# Dictionary (key-value pairs)
person = {'name': 'Alice', 'age': 30, 'city': 'Jakarta'}
person['name']       # 'Alice'
person['age'] = 31   # update value

# Set (unik, tidak terurut)
unique_numbers = {1, 2, 3, 3, 2}  # {1, 2, 3}
unique_numbers.add(4)

B.2 Variabel dan Operator

Operator Aritmetika

a = 10
b = 3
print(a + b)    # 13 (penjumlahan)
print(a - b)    # 7 (pengurangan)
print(a * b)    # 30 (perkalian)
print(a / b)    # 3.333... (pembagian)
print(a // b)   # 3 (pembagian integer)
print(a % b)    # 1 (modulo/sisa)
print(a ** b)   # 1000 (pangkat)

Operator Perbandingan & Logika

x = 5
print(x == 5)      # True
print(x != 5)      # False
print(x > 3 and x < 10)   # True
print(x < 0 or x > 0)     # True
print(not x == 10)        # True

B.3 Control Flow

If-Elif-Else

score = 85
if score >= 90:
    grade = 'A'
elif score >= 80:
    grade = 'B'
else:
    grade = 'C'

Loop (For & While)

# For loop
for i in range(5):
    print(i)  # 0, 1, 2, 3, 4

for fruit in ['apple', 'banana', 'cherry']:
    print(fruit)

# While loop
count = 0
while count < 3:
    print(count)
    count += 1

# List comprehension (lebih Pythonic)
squares = [x**2 for x in range(5)]  # [0, 1, 4, 9, 16]

B.4 Functions dan Lambda

Fungsi Biasa

def calculate_mean(values):
    """Menghitung rata-rata dari list nilai."""
    return sum(values) / len(values)

mean = calculate_mean([10, 20, 30])  # 20

# Fungsi dengan default arguments
def greet(name, greeting="Hello"):
    return f"{greeting}, {name}!"

greet("Alice")           # "Hello, Alice!"
greet("Bob", "Hi")       # "Hi, Bob!"

Lambda (Anonymous Functions)

# Fungsi lambda untuk operasi sederhana
square = lambda x: x ** 2
print(square(5))  # 25

# Berguna dengan map, filter, sorted
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, numbers))  # [1, 4, 9, 16, 25]
evens = list(filter(lambda x: x % 2 == 0, numbers))  # [2, 4]

B.5 List Comprehensions

List comprehensions adalah cara ringkas membuat lists:

# Syntax: [expression for item in iterable if condition]

# Contoh sederhana
squares = [x**2 for x in range(10)]

# Dengan kondisi
even_squares = [x**2 for x in range(10) if x % 2 == 0]

# Nested comprehension
matrix = [[j for j in range(3)] for i in range(3)]
# [[0, 1, 2], [0, 1, 2], [0, 1, 2]]

# Dict comprehension
word_lengths = {word: len(word) for word in ['apple', 'banana', 'cherry']}
# {'apple': 5, 'banana': 6, 'cherry': 6}

B.6 NumPy Essentials

NumPy adalah library fundamental untuk komputasi numerik:

import numpy as np

# Membuat arrays
arr = np.array([1, 2, 3, 4, 5])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
zeros = np.zeros((3, 3))
ones = np.ones((2, 4))
range_arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]

# Shape dan indexing
print(matrix.shape)      # (2, 3)
print(matrix[0, 1])      # 2 (baris 0, kolom 1)

# Slicing
print(arr[1:4])          # [2 3 4]
print(matrix[:, 1])      # [2 5] (semua baris, kolom 1)

# Broadcasting (operasi otomatis pada dimensi berbeda)
a = np.array([1, 2, 3])
b = np.array([[1], [2], [3]])
result = a + b  # shape akan otomatis menyesuaikan

# Operasi matematika
print(np.sum(arr))       # 15
print(np.mean(arr))      # 3.0
print(np.std(arr))       # deviasi standar
print(np.max(arr))       # 5
print(arr * 2)           # [2  4  6  8 10]

B.7 Pandas Essentials

Pandas untuk manipulasi dan analisis data tabelar:

import pandas as pd

# Membuat DataFrame
data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 28],
    'score': [85, 92, 78]
}
df = pd.DataFrame(data)

# Membaca/menyimpan data
df = pd.read_csv('data.csv')
df.to_csv('output.csv', index=False)
df.to_excel('output.xlsx')

# Inspeksi data
print(df.head())         # 5 baris pertama
print(df.info())         # tipe dan non-null count
print(df.describe())     # statistik deskriptif

# Seleksi kolom
print(df['name'])        # Series
print(df[['name', 'age']])  # DataFrame

# Filtering
adults = df[df['age'] > 25]
high_scorers = df[df['score'] >= 85]

# Groupby operations
by_age = df.groupby('age')['score'].mean()
group_stats = df.groupby('age').agg({'score': ['mean', 'max'], 'age': 'count'})

# Menangani missing values
df.fillna(0)             # ganti NaN dengan 0
df.dropna()              # hapus baris dengan NaN

B.8 File I/O

Membaca & Menulis File Teks

# Membaca file
with open('file.txt', 'r') as f:
    content = f.read()

# Menulis file
with open('output.txt', 'w') as f:
    f.write('Hello, World!')

# Membaca line by line
with open('file.txt', 'r') as f:
    for line in f:
        print(line.strip())

Bekerja dengan JSON

import json

# Membaca JSON
with open('data.json', 'r') as f:
    data = json.load(f)

# Menulis JSON
data = {'name': 'Alice', 'age': 30}
with open('output.json', 'w') as f:
    json.dump(data, f, indent=2)

B.9 Exception Handling

Menangani error dengan graceful:

try:
    x = int("abc")  # ValueError
except ValueError:
    print("Invalid input!")
except Exception as e:
    print(f"Error: {e}")
finally:
    print("Cleanup code here")

# Raise exception
if x < 0:
    raise ValueError("x harus positif!")

B.10 String Operations

text = "Hello, World!"

# Basic operations
print(len(text))             # panjang string
print(text.upper())          # HELLO, WORLD!
print(text.lower())          # hello, world!
print(text.replace("World", "Python"))  # Hello, Python!

# String slicing
print(text[0:5])             # "Hello"
print(text[-6:])             # "World!"

# F-strings (Python 3.6+) - REKOMENDASI
name = "Alice"
age = 30
message = f"Nama: {name}, Umur: {age}"

# String methods
words = text.split(", ")     # ["Hello", "World!"]
joined = "-".join(words)     # "Hello-World!"

# Checking
print("Hello" in text)       # True
print(text.startswith("Hello"))  # True

B.11 Date & Time Operations

from datetime import datetime, timedelta

# Current date/time
now = datetime.now()
print(now)  # 2024-12-07 10:30:45.123456

# Create specific date
date = datetime(2024, 12, 7)

# Date arithmetic
tomorrow = now + timedelta(days=1)
next_week = now + timedelta(weeks=1)

# String formatting
print(now.strftime("%Y-%m-%d"))      # 2024-12-07
print(now.strftime("%d/%m/%Y %H:%M")) # 07/12/2024 10:30

# Parsing string to datetime
date_str = "2024-12-07"
date = datetime.strptime(date_str, "%Y-%m-%d")

B.12 Built-in Functions (Berguna untuk ML)

Fungsi Deskripsi Contoh
len() Panjang objek len([1,2,3]) → 3
max()/min() Nilai terbesar/terkecil max([1,5,3]) → 5
sum() Jumlah elemen sum([1,2,3]) → 6
abs() Nilai absolut abs(-5) → 5
round() Pembulatan round(3.14159, 2) → 3.14
enumerate() Index + value for i, v in enumerate(['a','b'])
zip() Kombinasi iterables zip([1,2], ['a','b']) → [(1,‘a’), (2,‘b’)]
sorted() Sorting sorted([3,1,2]) → [1,2,3]
reversed() Reverse list(reversed([1,2,3])) → [3,2,1]
isinstance() Type checking isinstance(5, int) → True

B.13 Common Pitfalls & Best Practices

Pitfall 1: Mutable Default Arguments

# SALAH
def add_to_list(item, my_list=[]):
    my_list.append(item)
    return my_list

# BENAR
def add_to_list(item, my_list=None):
    if my_list is None:
        my_list = []
    my_list.append(item)
    return my_list

Pitfall 2: Off-by-One Errors

# List index dimulai dari 0
arr = [1, 2, 3, 4, 5]
print(arr[0])      # 1 (elemen pertama)
print(arr[-1])     # 5 (elemen terakhir)
print(arr[:3])     # [1, 2, 3] (tidak termasuk index 3)

Pitfall 3: Shallow Copy vs Deep Copy

import copy

original = [[1, 2], [3, 4]]
shallow = original.copy()  # Hanya copy level pertama
deep = copy.deepcopy(original)  # Copy semua level

shallow[0][0] = 999
print(original)  # [[999, 2], [3, 4]] - BERUBAH!
print(deep)      # [[1, 2], [3, 4]] - tidak berubah

B.14 ML-Relevant Python Patterns

Iterating dengan enumerate

# Useful untuk training loops
for epoch in enumerate(range(num_epochs)):
    loss = train_step()
    print(f"Epoch {epoch}: Loss = {loss}")

Unpacking Values

# Useful untuk preprocessing data
X, y = features, labels
train_X, test_X = split_data(X)

Using with Statement

# Automatic resource management
with open('model.pkl', 'rb') as f:
    model = pickle.load(f)
# File otomatis tertutup setelah blok

Dictionary Get dengan Default

# Useful untuk hyperparameter handling
config = {'lr': 0.001}
batch_size = config.get('batch_size', 32)  # default 32 jika tidak ada

Referensi Lengkap