import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "salary": [50000, 60000, 55000]
})

# Add a new column
df["bonus"] = df["salary"] * 0.1
print("After adding bonus column:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "price": [100, 200, 150],
    "quantity": [5, 3, 4],
    "discount": [0.1, 0.15, 0.05]
})

# Calculate total with discount
df["total"] = df["price"] * df["quantity"] * (1 - df["discount"])
print("With calculated total:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "x": [1, 2, 3],
    "y": [4, 5, 6]
})

# Using assign (returns new DataFrame)
df_new = df.assign(z=lambda x: x["x"] + x["y"])
print("Using assign():")
print(df_new)

import pandas as pd

df = pd.DataFrame({
    "price": [100, 200, 150],
    "quantity": [5, 3, 4]
})

# Modify existing column
df["price"] = df["price"] * 1.1  # 10% increase
print("After price increase:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "salary": [50000, 60000, 55000]
})

# Update based on condition
df.loc[df["age"] > 30, "salary"] = df.loc[df["age"] > 30, "salary"] * 1.05
print("After salary increase for age > 30:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35]
})

# Apply function to a column
df["age_group"] = df["age"].apply(lambda x: "Young" if x < 30 else "Old")
print("With age groups:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "a": [1, 2, 3],
    "b": [4, 5, 6],
    "c": [7, 8, 9]
})

# Apply function to each row
df["sum"] = df.apply(lambda row: row["a"] + row["b"] + row["c"], axis=1)
print("With row sum:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "grade": ["A", "B", "C", "A", "B"]
})

# Map values
grade_map = {"A": 4.0, "B": 3.0, "C": 2.0}
df["gpa"] = df["grade"].map(grade_map)
print("With GPA mapping:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "status": ["active", "inactive", "pending", "active"]
})

# Replace values
df["status"] = df["status"].replace({"active": 1, "inactive": 0, "pending": 2})
print("After replacement:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "name": ["alice smith", "BOB JONES", "charlie brown"]
})

# Capitalize
df["name_capitalized"] = df["name"].str.capitalize()
print("Capitalized:")
print(df[["name", "name_capitalized"]])

# Title case
df["name_title"] = df["name"].str.title()
print("\nTitle case:")
print(df[["name", "name_title"]])

import pandas as pd

df = pd.DataFrame({
    "full_name": ["Alice Smith", "Bob Jones", "Charlie Brown"]
})

# Split into first and last name
df[["first_name", "last_name"]] = df["full_name"].str.split(" ", expand=True)
print("After splitting:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "first": ["Alice", "Bob", "Charlie"],
    "last": ["Smith", "Jones", "Brown"],
    "age": [25, 30, 35]
})

# Concatenate strings
df["full_name"] = df["first"] + " " + df["last"]
df["description"] = df["full_name"] + " is " + df["age"].astype(str) + " years old"
print("With concatenated columns:")
print(df[["full_name", "description"]])

import pandas as pd

df = pd.DataFrame({
    "date_str": ["2024-01-15", "2024-02-20", "2024-03-10"]
})

# Convert to datetime
df["date"] = pd.to_datetime(df["date_str"])
print("After conversion:")
print(df)
print(f"\nDate type: {df['date'].dtype}")

import pandas as pd

df = pd.DataFrame({
    "date": pd.date_range("2024-01-01", periods=3, freq="M")
})

# Extract components
df["year"] = df["date"].dt.year
df["month"] = df["date"].dt.month
df["day"] = df["date"].dt.day
df["day_name"] = df["date"].dt.day_name()

print("With date components:")
print(df)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Diana"],
    "age": [35, 25, 30, 28],
    "salary": [55000, 50000, 60000, 58000]
})

# Sort by age (ascending)
df_sorted = df.sort_values("age")
print("Sorted by age:")
print(df_sorted)

# Sort by salary (descending)
df_sorted2 = df.sort_values("salary", ascending=False)
print("\nSorted by salary (descending):")
print(df_sorted2)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Diana"],
    "department": ["Sales", "IT", "Sales", "IT"],
    "salary": [50000, 60000, 55000, 58000]
})

# Sort by department, then salary
df_sorted = df.sort_values(["department", "salary"], ascending=[True, False])
print("Sorted by department, then salary:")
print(df_sorted)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NYC", "SF", "Seattle"],
    "salary": [50000, 60000, 55000]
})

# Reorder columns
df_reordered = df[["name", "city", "age", "salary"]]
print("Reordered columns:")
print(df_reordered)

import pandas as pd

df = pd.DataFrame({
    "date": ["2024-01-01", "2024-01-01", "2024-01-02", "2024-01-02"],
    "product": ["A", "B", "A", "B"],
    "sales": [100, 150, 120, 180]
})

# Pivot
df_pivot = df.pivot(index="date", columns="product", values="sales")
print("Pivoted data:")
print(df_pivot)

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob"],
    "Q1": [100, 150],
    "Q2": [120, 180],
    "Q3": [130, 160]
})

# Melt
df_melted = df.melt(id_vars=["name"], 
                    value_vars=["Q1", "Q2", "Q3"],
                    var_name="quarter",
                    value_name="sales")
print("Melted data:")
print(df_melted)

import pandas as pd

df = pd.DataFrame({
    "product": ["Laptop", "Mouse", "Keyboard"],
    "price": [1200, 25, 75],
    "quantity": [5, 50, 30],
    "discount": [0.1, 0.05, 0.15]
})

# Calculate:
# 1. Revenue before discount (price * quantity)
# 2. Discount amount (price * quantity * discount)
# 3. Final revenue (revenue - discount)
# 4. Add a column indicating if product is expensive (> $100)

df["revenue_before"] = df["price"] * df["quantity"]
df["discount_amount"] = df["revenue_before"] * df["discount"]
df["final_revenue"] = df["revenue_before"] - df["discount_amount"]
df["is_expensive"] = df["price"] > 100

print(df)