Pandas DataFrame Transformations

import pandas as pd

df = pd.read_csv("data.csv")

# Rename columns
df = df.rename(columns={"old_name": "new_name", "col2": "column_two"})

# Add computed column
df["total"] = df["price"] * df["quantity"]

# Apply function to column
df["name"] = df["name"].str.strip().str.title()

# Type casting
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")

# Conditional column
df["status"] = df["total"].apply(
    lambda x: "high" if x > 1000 else "medium" if x > 100 else "low"
)

# Drop columns and duplicates
df = df.drop(columns=["unused_col"]).drop_duplicates(subset=["id"])

# Fill missing values
df["category"] = df["category"].fillna("unknown")
df["amount"] = df["amount"].fillna(df["amount"].median())

# Sort and reset index
df = df.sort_values("date", ascending=False).reset_index(drop=True)

print(df.info())
print(df.head())