The document provides an overview of advanced data science techniques using NumPy and Pandas in Python. Key topics include advanced matrix operations, data grouping, aggregation, linear algebra, and the creation of pivot tables. It emphasizes NumPy for efficient numerical computations and Pandas for simplifying data manipulation.

1. Mastering Data Science with
NumPy and Pandas
Advanced Python Programming
Techniques
By Anurag

2. Agenda
• 1. Introduction to NumPy
• 2. Advanced Matrix Operations
• 3. Introduction to Pandas
• 4. Data Grouping & Aggregation
• 5. Advanced Linear Algebra with NumPy
• 6. Pivot Tables in Pandas

3. Introduction to NumPy
• NumPy is the backbone of numerical
computations in Python.
• Why NumPy is faster than lists:
import numpy as np
arr = np.array([1, 2, 3, 4])

4. Advanced NumPy - Matrix
Operations
• Perform advanced matrix operations with
NumPy.
matrix.T # Transpose
np.dot(matrix1, matrix2) # Matrix Multiplication

5. Introduction to Pandas
• Pandas simplifies data manipulation with
Series and DataFrames.
import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

6. Grouping and Aggregating Data
• Group and aggregate data effectively with
Pandas.
df.groupby('Category')['Value'].sum()
df.agg({'Value': ['sum', 'mean']})

7. Advanced NumPy - Linear Algebra
• Perform advanced linear algebra operations
with NumPy.
np.linalg.eig(matrix) # Eigenvalues and Eigenvectors
np.linalg.svd(matrix) # SVD Decomposition

8. Pivot Tables in Pandas
• Reshape and summarize data using pivot
tables in Pandas.
pd.pivot_table(df, values='Value', index='Category', aggfunc='sum')

9. Interactive Exercise
• Practice what you've learned with these
exercises:
# Filter rows where Age > 30
df[df['Age'] > 30]

10. Summary
• Key takeaways:
• - NumPy: Fast numerical computations.
• - Pandas: Easy data manipulation.
• - Advanced operations enhance efficiency.

11. Questions and Closing
• What concepts would you like to explore
further?
• Practice and explore real-world datasets.