"""
Topic 08: Introduction to Data Science (NumPy & Pandas) - HARD Exercises (10)
"""
# Exercise 1: Matrix Operations - Create a random 4x4 matrix and find its transpose, determinant, and inverse.
# (Hint: Use numpy.linalg)
# Write your code below:
# Exercise 2: Data Cleaning Project - Create a messy DataFrame (missing values, duplicate rows, incorrect types).
# Perform a full cleanup: drop duplicates, fill missing values, and convert types.
# Write your code below:
# Exercise 3: Pivot Tables - Create a DataFrame with sales data (Date, Region, Product, Sales).
# Create a pivot table showing total sales by Region and Product.
# Write your code below:
# Exercise 4: Time Series - Create a Pandas DataFrame with a 'Date' index for the month of January 2024.
# Generate random "Price" data for each day.
# Write your code below:
# Exercise 5: Correlation Matrix - Given a DataFrame with several numeric columns,
# compute the correlation matrix.
# Write your code below:
# Exercise 6: Normalization - Write a function that takes a NumPy array and normalizes its values
# between 0 and 1.
# Write your code below:
# Exercise 7: Merging - Create two DataFrames: one with "Employee_ID" and "Name", the other with
# "Employee_ID" and "Department". Merge them using an inner join.
# Write your code below:
# Exercise 8: Custom Apply - Create a function that categorizes age (Child, Adult, Senior)
# and apply it to a column 'Age' in a DataFrame using .apply().
# Write your code below:
# Exercise 9: Outlier Detection - Find values in a NumPy array that are more than 3 standard deviations away from the mean.
# Write your code below:
# Exercise 10: Multi-Index - Create a DataFrame with a Multi-Index (Hierarchical indexing)
# for "Year" and "Quarter".
# Write your code below: