# Variable assignment
x = 10
name = "Alice"
is_active = True

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

# Swap variables
a, b = b, a

# Type conversion
num_str = "123"
num = int(num_str)

# If-elif-else
if x > 10:
    print("Greater than 10")
elif x == 10:
    print("Equal to 10")
else:
    print("Less than 10")

# Ternary operator
result = "Even" if x % 2 == 0 else "Odd"

# For loop
for i in range(5):  # 0 to 4
    print(i)

# Iterate through list
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)

# With index
for idx, fruit in enumerate(fruits):
    print(f"{idx}: {fruit}")

# While loop
count = 0
while count < 5:
    print(count)
    count += 1

# Loop control
for num in range(10):
    if num == 3:
        continue  # Skip this iteration
    if num == 7:
        break     # Exit loop
    print(num)

# Basic function
def greet(name):
    return f"Hello, {name}!"

# Default arguments
def power(base, exponent=2):
    return base ** exponent

# Variable arguments
def sum_all(*args):
    return sum(args)

# Keyword arguments
def create_user(**kwargs):
    print(kwargs)  # Dictionary of arguments

# Lambda functions
square = lambda x: x * x
sorted_names = sorted(names, key=lambda x: x.lower())

# String creation
s1 = 'Single quotes'
s2 = "Double quotes"
s3 = """Multi-line
string"""

# String operations
name = "Alice"
greeting = f"Hello, {name}!"  # f-string (Python 3.6+)
length = len(name)
substring = name[1:3]  # 'li'

# Common methods
"hello".upper()       # 'HELLO'
"HELLO".lower()       # 'hello'
" hello ".strip()     # 'hello'
"a,b,c".split(',')    # ['a', 'b', 'c']
'-'.join(['a', 'b'])  # 'a-b'

# String formatting
"{} + {} = {}".format(1, 2, 3)  # '1 + 2 = 3'
"%d + %d = %d" % (1, 2, 3)      # Old style

# List creation
numbers = [1, 2, 3]
mixed = [1, "two", 3.0]

# List operations
numbers.append(4)       # [1, 2, 3, 4]
numbers.insert(1, 1.5)  # [1, 1.5, 2, 3, 4]
numbers.remove(2)       # [1, 1.5, 3, 4]
popped = numbers.pop()  # 4, list is [1, 1.5, 3]

# List comprehensions
squares = [x**2 for x in range(5)]  # [0, 1, 4, 9, 16]
evens = [x for x in range(10) if x % 2 == 0]  # [0, 2, 4, 6, 8]

# Slicing
nums = [0, 1, 2, 3, 4]
nums[1:3]    # [1, 2]
nums[:3]     # [0, 1, 2]
nums[2:]     # [2, 3, 4]
nums[::-1]   # Reverse list [4, 3, 2, 1, 0]

# Dictionary creation
person = {"name": "Alice", "age": 30}

# Dictionary operations
person["email"] = "alice@example.com"  # Add key
age = person.get("age")                # Get value
email = person.pop("email")            # Remove key

# Dictionary comprehension
squares = {x: x*x for x in range(5)}  # {0:0, 1:1, 2:4, 3:9, 4:16}

# Iterating
for key, value in person.items():
    print(f"{key}: {value}")

# Tuples (immutable)
point = (10, 20)
x, y = point  # Unpacking

# Sets (unique elements)
primes = {2, 3, 5, 7}
primes.add(11)          # Add element
evens = {2, 4, 6, 8}
union = primes | evens  # {2, 3, 4, 5, 6, 7, 8, 11}

import numpy as np

# Creating arrays
arr1 = np.array([1, 2, 3])          # 1D array
arr2 = np.array([[1, 2], [3, 4]])   # 2D array
zeros = np.zeros((3, 3))            # 3x3 zeros
ones = np.ones((2, 4))              # 2x4 ones
identity = np.eye(3)                # 3x3 identity
range_arr = np.arange(0, 10, 2)     # [0, 2, 4, 6, 8]

# Array operations
arr = np.array([1, 2, 3])
arr + 1               # [2, 3, 4]
arr * 2               # [2, 4, 6]
np.sqrt(arr)          # [1., 1.414, 1.732]

# Indexing and slicing
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
matrix[1, 2]          # 6 (second row, third column)
matrix[:, 1]          # [2, 5, 8] (second column)

# Useful functions
arr = np.array([3, 1, 4, 2])
arr.sum()             # 10
arr.mean()            # 2.5
arr.max()             # 4
np.sort(arr)          # [1, 2, 3, 4]

import matplotlib.pyplot as plt

# Simple line plot
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y)
plt.title("Simple Plot")
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.show()

# Scatter plot
plt.scatter(x, y, color='red', marker='o')
plt.show()

# Bar chart
labels = ['A', 'B', 'C']
values = [10, 20, 15]
plt.bar(labels, values)
plt.show()

# Histogram
data = np.random.normal(0, 1, 1000)
plt.hist(data, bins=30)
plt.show()

# Subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
ax1.plot(x, y)
ax2.scatter(x, y)

import pandas as pd

# Series (1D)
s = pd.Series([1, 3, 5, np.nan, 6, 8])

# DataFrame (2D)
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': pd.Timestamp('20230101'),
    'C': pd.Series(1, index=list(range(4)), dtype='float32'),
    'D': np.array([3] * 4, dtype='int32'),
    'E': ['test', 'train', 'test', 'train'],
    'F': 'foo'
})

# Read CSV
data = pd.read_csv('data.csv')

# Basic operations
df.head()       # First 5 rows
df.describe()   # Summary statistics
df['A'].mean()  # Mean of column A
df[df['A'] > 2]  # Filtering

# Grouping
df.groupby('E').sum()  # Group by column E and sum