Python
Install
sudo pacman -S python
python --version
Libraries and Executable path:
import sys
print ('\n'.join(sys.path))
# /usr/lib/python312.zip
# /usr/lib/python3.12
# /usr/lib/python3.12/lib-dynload
# /home/mlibre/.local/lib/python3.12/site-packages
# /usr/lib/python3.12/site-packages
print (sys.executable)
# /usr/bin/python
help("a".find)
# Help on built-in function find:
# find(...) method of builtins.str instance
# S.find(sub[, start[, end]]) -> int
# Return the lowest index in S where substring sub is found,
# such that sub is contained within S[start:end]. Optional
# arguments start and end are interpreted as in slice notation.
# Return -1 on failure.
Jupyter Notebook
pip install notebook --break-system-packages
jupyter notebook
You can also open http://localhost:8888/tree or http://localhost:8888/lab
Concepts
String
text = "abcd"
print ("thank :) %s" % text) # abcd
print (f"thank :) {text[0:3]}") # abc
print (f'''thanks
:)
{text[1:]} # bcd
{text[-1]} # d
''')
text2 = "pi is {pi:.2f}"
print(text2.format(pi = 3.144444)) # pi is 3.14
text3 = "pi is {} or {}"
print(text3.format(3.14, 3)) # pi is 3.14 or 3
text4 = "pi is {0} or {1}"
print(text4.format(3.14, 3)) # pi is 3.14 or 3
text5 = "a big number {0:,.3f}"
print(text5.format(3000.14567)) # a big number 3,000.146
text6 = "a big number {:,.3f}"
print(text6.format(3000.14567)) # a big number 3,000.146
text7 = "pi is a number"
isLoc, isLoc2 = text7.find("is"), text2.index("is")
print(isLoc, isLoc2) # 3 3
Number
piString = "3.14"
pi = float(piString) # 3.14
b = int(pi) # 3
print(f'''
Multiplication: {pi * b}
Division: {pi / b}
Floor division: {pi // b}
Modulus: {pi % b}
Exponentiation: {pi ** b}
''')
List
mixed = ["apple", 2, 3.5, True]
mixed.append("orange")
mixed.remove(3.5)
print(mixed) # ['apple', 2, True, 'orange']
squares = [x ** 2 for x in range(10)]
print(squares) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Tuple
fruits = ("apple", "banana", "cherry")
# Tuples are immutable, so you can't modify them directly
fruits[0] = "kiwi" # would raise an error
Dictionary
person = {
"name": "Alice",
"age": 25,
"city": "New York"
}
print(person) # {'name': 'Alice', 'age': 25, 'city': 'New York'}
print(person["name"]) # Alice
print(person.keys()) # dict_keys(['name', 'age', 'city'])
print(person.items()) # dict_items([('name', 'Alice'), ('age', 25), ('city', 'New York')])
Loop
text = "abcd"
for i in text:
print(i)
# a b c d
for i in range(5):
print(i)
# 0 1 2 3 4
# Looping through a list
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
print(fruit)
# apple banana cherry
# Looping through a dictionary
person = {"name": "Alice", "age": 25, "city": "New York"}
for key, value in person.items():
print(f"{key}: {value}")
# name: Alice
# age: 25
# city: New York
Functions
def greet(name="Guest"):
return f"Hello, {name}!"
print(greet()) # Hello, Guest!
print(greet("Bob")) # Hello, Bob!
def sum_all(*args):
return sum(args)
print(sum_all(1, 2, 3, 4)) # 10
def describe_person(**kwargs):
for key, value in kwargs.items():
print(f"{key}: {value}")
describe_person(name="Alice", age=25, city="New York")
# name: Alice
# age: 25
# city: New York
add = lambda x, y: x + y
print(add(3, 5)) # 8
# Using lambda with sort
points = [(2, 3), (1, 2), (4, 1)]
secondValue = lambda point: point[1] # always returns second value
points.sort(key=secondValue)
print(points) # [(4, 1), (1, 2), (2, 3)]
def my_decorator(func):
def wrapper():
print("Something is happening before the function is called.")
func()
print("Something is happening after the function is called.")
return wrapper
@my_decorator
def say_hello():
print("Hello!")
say_hello()
# Output:
# Something is happening before the function is called.
# Hello!
# Something is happening after the function is called.
class
class Person:
def __init__(self, name):
self._name = name
@property
def name(self):
return self._name
@name.setter
def name(self, value): # now we can set and get with "name" not "_name"
self._name = value
class Employee(Person): # inherits from Person
species = "Homo sapiens" # Class variable
def __init__(self, name, employee_id):
super().__init__(name)
self.employee_id = employee_id
self._test1 = "test1" # Protected attribute
self.__test2 = "test2" # Private attribute
def get_employee_details(self):
return f"Name: {self.name}, Employee ID: {self.employee_id}"
# Creating an instance of the subclass
employee1 = Employee("Bob", "E1234")
print(employee1.get_employee_details())
# Name: Bob, Age: 30, Employee ID: E1234
employee1.name = "Alice"
print(employee1.get_employee_details())
# Name: Alice, Age: 30, Employee ID: E1234
employee1.employee_id = "E1235"
print(employee1.get_employee_details())
Error handling
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
except ValueError:
print("Invalid value!")
else:
print("error is not ValueError or ZeroDivisionError")
finally:
print("This will execute no matter what")
# Rasing and error
def check_age(age):
if age < 5:
raise ValueError("Age must be 5 or older")
return "Access granted"
try:
check_age(4)
except ValueError as e:
print(e)
Math
import math as m
m.pi # 3.141592653589793
from math import *
print(pi)
import random as r
print(r.random())
NumPy
pip install numpy --break-system-packages
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr) # [1 2 3 4 5]
# Creating a 2D array
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2d)
# [[1 2 3]
# [4 5 6]]
print(arr2d.shape) # (2, 3)
# Creating a 2D array
arr2d = np.array([[1, 2, 3], [4, 5, 6, 7]])
# Raises ValueError. The requested array has an inhomogeneous shape after 1 dimensions
arr = np.array([1, 2, 3, 4, 5])
# Mean
print(np.mean(arr)) # 3.0
# Sum
print(np.sum(arr)) # 15
# Standard deviation
print(np.std(arr)) # 1.4142135623730951
# Minimum and Maximum
print(np.min(arr)) # 1
print(np.max(arr)) # 5
# Dot for 1d array
vector_a = np.array([1, 2, 3])
vector_b = np.array([4, 5, 6])
dot_product = np.dot(vector_a, vector_b) # 1∗4 + 2∗5 + 3∗6 = 4+10+18=32
print("Dot product of vectors:", dot_product) # Dot product of vectors: 32
# Dot for 2d array
matrix_a = np.array([[1, 2], [3, 4]])
matrix_b = np.array([[5, 6], [7, 8]])
matrix_product = np.dot(matrix_a, matrix_b)
print("Dot product of matrices:\n", matrix_product)
# [ [19 22], [43 50] ]
# Save to a file
np.save('array.npy', arr)
# Load from a file
loaded_arr = np.load('array.npy')
print(loaded_arr) # [1 2 3 4 5]
Pandas
pip install pandas --break-system-packages
import pandas as pd
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)
print(df)
# Name Age City
# 0 Alice 25 New York
# 1 Bob 30 Los Angeles
# 2 Charlie 35 Chicago
df['Age'] = df['Age'] + 1
print(df['Age']) # Age column
# 0 26
# 1 31
# 2 36
print(df.head(2)) # First And Second rows
# Name Age City
# 0 Alice 25 New York
# 1 Bob 30 Los Angeles
print(df.iloc[0]) # First row
# Name Alice
# Age 25
# City New York
df1 = pd.DataFrame({
'Name': ['Alice', 'Bob'],
'Age': [25, 30]
})
df2 = pd.DataFrame({
'Name': ['Alice', 'David'],
'Age': [35, 40]
})
# Concatenating DataFrames
df_concat = pd.concat([df1, df2])
# Merging DataFrames on a key
df3 = pd.DataFrame({
'Name': ['Alice', 'Bob', 'Charlie'],
'City': ['New York', 'Los Angeles', 'Chicago']
})
merged_df = pd.merge(df_concat, df3, on='Name')
print(merged_df)
# Name Age City
# 0 Alice 25 New York
# 1 Bob 30 Los Angeles
# 2 Alice 35 New York
Matplotlib
pip install matplotlib --break-system-packages
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plt.plot(x, y)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Simple Line Plot')
plt.show()