# Multiple assignments
a, b, c = 1, 2, 3

# Anonymous function
square = lambda x: x * x

# Using lambda in built-in functions
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x * x, numbers))  # [1, 4, 9, 16, 25]
even_nums = list(filter(lambda x: x % 2 == 0, numbers))  # [2, 4]

# Basic decorator
def my_decorator(func):
    def wrapper():
        print("Something before the function call")
        func()
        print("Something after the function call")
    return wrapper

# Using the decorator
@my_decorator
def say_hello():
    print("Hello!")

# Decorators with arguments
def repeat(n):
    def decorator(func):
        def wrapper(*args, **kwargs):
            for _ in range(n):
                result = func(*args, **kwargs)
            return result
        return wrapper
    return decorator

@repeat(3)
def greet(name):
    print(f"Hello {name}")

class Employee(Person):
    # Constructor with parent's fields and new fields
    def __init__(self, name, age, employee_id):
        # Call parent's constructor
        super().__init__(name, age)
        self.employee_id = employee_id
    
    # Override parent's method
    def greet(self):
        return f"{super().greet()} and I work here"

# Multiple inheritance
class Student(Person):
    def __init__(self, name, age, student_id):
        super().__init__(name, age)
        self.student_id = student_id

class TeachingAssistant(Employee, Student):
    def __init__(self, name, age, employee_id, student_id):
        Employee.__init__(self, name, age, employee_id)
        self.student_id = student_id

# Basic file reading
with open("file.txt", "r") as file:
    content = file.read()  # Read entire file

# Reading line by line
with open("file.txt", "r") as file:
    for line in file:
        print(line.strip())

# Reading all lines at once
with open("file.txt", "r") as file:
    lines = file.readlines()  # Returns a list of lines

    # Writing to a file
with open("output.txt", "w") as file:
    file.write("Hello, World!")

# Appending to a file
with open("output.txt", "a") as file:
    file.write("\nAppended line")

# Writing multiple lines
lines = ["Line 1", "Line 2", "Line 3"]
with open("output.txt", "w") as file:
    file.writelines([line + "\n" for line in lines])

# Basic try-except
try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

# Multiple exceptions
try:
    num = int("abc")
except ValueError:
    print("Invalid conversion")
except (TypeError, ZeroDivisionError):
    print("Another error occurred")

# try-except-else-finally
try:
    file = open("file.txt", "r")
except FileNotFoundError:
    print("File not found")
else:
    content = file.read()  # Runs if no exception
    file.close()
finally:
    print("This always executes")  # Cleanup code

# Basic try-except
try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

# Multiple exceptions
try:
    num = int("abc")
except ValueError:
    print("Invalid conversion")
except (TypeError, ZeroDivisionError):
    print("Another error occurred")

# try-except-else-finally
try:
    file = open("file.txt", "r")
except FileNotFoundError:
    print("File not found")
else:
    content = file.read()  # Runs if no exception
    file.close()
finally:
    print("This always executes")  # Cleanup code

import numpy as np

# Basic 

a = np.array([2, 3, 4])         # Output: [2, 3, 4]
a = np.zeros((3, 2))            # Output: [[0. 0.], [0. 0.], [0. 0.]]
a = np.ones((2, 3), dtype=float)# Output: [[1. 1.], [1. 1.], [1. 1.]]
a = np.arange(3)                # Output: [0, 1, 2]
a = np.arange(10, 30, 5)        # Output: [10 15 20 25]
a = np.arange(6).reshape(3, 2)  # Output: [[0, 1], [2, 3], [4, 5]]
a = np.arange(6).reshape(3, -1) # Output: [[0, 1], [2, 3], [4, 5]], Automated
a = np.linspace(0, 2, 9)        # Output: [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2.]
a = np.random.random((2, 1))    # Output: [[0.79498573], [0.22507543]] 

a = np.array( [[1,1],[0,1]] )
b = np.array( [[2,0],[3,4]] )
print(a + b)                    # Elementwise addition
print(a * b)                    # Elementwise product
print(np.sin(a))                # Radian sine to each element
print(np.cos(a))                # Radian cosine to each element
print(np.exp(a))                # Exponential to each element
print(np.sqrt(a))               # Square root to each element
print(np.dot(a, b))             # Matrix product
print(np.concatenate((a, b)))   # Concatenate
print(a is b)                   # True if both have exactly the same element

# Indexing and Iterating
a = np.arange(10) ** 2
print(a[-1])    # Last element
print(a[2:5])   # Element indexed 2 to 4
print(a[::-1])  # Reverse iteration
a[:6:2] = 1000  # Set every 2nd element to 1000
print(a)
b = np.array([1,3,5,7,9])
print(a[b])     # Elements indexed by b


# Multidimentional Array
def f(x, y):
    return 10 * x + y
a = np.fromfunction(f, (5, 4), dtype=int)
print(a)
print(a[0:5, 1])  # Each row in the second column of a
print(a[1:3, :])  # Each column in the second and third row of a

# Dots (...)
a = np.arange(12).reshape(2,2,3)
print(a)
print(a[1, ...])  # Output: [[ 6  7  8][ 9 10 11]].Same as c[1, :, :] or c[1]
print(a[..., 2])  # Output: [[ 2  5] [ 8 11]]. Same as c[:, :, 2]

for element in a.flat:  # Iterate over all elements instead of array entries
    print(element, end=' ')
print('\n', a.ravel())  # Flatten the array

a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a.ndim)       # Number of dimensions
print(a.shape)      # Shape of the array
print(a.size)       # Number of elements
print(a.dtype)      # Data type
print(a.itemsize)   # Size of each element
print(a.nbytes)     # Total size of the array

# Stacking Array
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
print(np.vstack((a, b)))  # Output: [[1 2][3 4][5 6][7 8]], same as np.row_stack((a, b))
print(np.hstack((a, b)))  # Output: [[1 2 5 6][3 4 7 8]], same as np.column_stack((a, b))
print(np.r_[a[0], b[0], 1:3, 8])    # Output: [1 2 5 6 1 2 8]
print(np.c_[a[0], b[0], 7:9])       # Output: [[1 2 5 6][3 4 7 8]]

# Splitting Array
a = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
b = np.arange(8, 0, -1).reshape(2, 4)
print(a)
print(b)

print(np.hsplit(a, 2))  # Split each row into 2 array
# Output: [array([[0, 1],[4, 5]]), array([[2, 3], [6, 7]])]

print(np.hsplit(a, (1, 3)))  # Split a at column 2 and 5
# Output: [array([[0], [4]]), array([[1, 2], [5, 6]]), array([[3], [7]])]

print(np.vsplit(a, 2))  # Split each column into 2 array
# Output: [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]])]

# Matrix
from numpy import matrix
from numpy import linalg

a = matrix('1 2 3; 4 5 6; 7 8 0')
b = matrix('1; 2; 3')
print(a * a)                # Matrix multiplication
print(a.T)                  # Transpose
print(linalg.det(a))        # Determinant 
print(linalg.inv(a))        # Inverse
print(linalg.pinv(a))       # Pseudo-inverse
print(linalg.solve(a, b))   # Solve linear system

import pandas as pd

s = pd.Series([1, 3, np.nan, "HI"])
print(s)

dates = pd.date_range("20130101", periods=6)
print(dates)
dates

df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=s)
print(df)
print(df.index)
print(df.columns)

df2 = pd.DataFrame(
    {
        "A": 1.0,
        "B": pd.Timestamp("20130102"),
        "C": pd.Series(1, index=list(range(4)), dtype="float32"),
        "D": np.array([3] * 4, dtype="int32"),
        "E": pd.Categorical(["test", "train", "test", "train"]),
        "F": "foo",
    }
)
print(df2)
print(df2.dtypes)

%matplotlib inline  # Allow notebook to display plots
import matplotlib.pyplot as plt
import numpy as np

x = [1, 2, 3, 4, 5]
y = [10, 16, 4, 8, 6]
y1 = [1, 2, 3, 4, 5]
y2 = [5, 4, 3, 2, 1]

plt.plot(x, y, label='a', color='b', linestyle='-', marker='o')
plt.plot(x, y1, label='b', color='r', linestyle='-', marker='o') 
plt.plot(x, y2, label='c', color='g', linestyle='-', marker='o')
plt.xlabel('Label X-Axis')
plt.ylabel('Label Y-Axis')
plt.title('Title of Plot')
plt.legend(loc='upper left', fontsize='small', frameon=True, title='Legend Title')
plt.show()