![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])](https://image.slidesharecdn.com/dynamicpresentationnumpypandas-241203175509-a2d6406b/85/python_Dynamic_Presentation_NumPy_Pandas-pptx-3-320.jpg)
![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)](https://image.slidesharecdn.com/dynamicpresentationnumpypandas-241203175509-a2d6406b/85/python_Dynamic_Presentation_NumPy_Pandas-pptx-5-320.jpg)
![Grouping and Aggregating Data
• Group and aggregate data effectively with
Pandas.
df.groupby('Category')['Value'].sum()
df.agg({'Value': ['sum', 'mean']})](https://image.slidesharecdn.com/dynamicpresentationnumpypandas-241203175509-a2d6406b/85/python_Dynamic_Presentation_NumPy_Pandas-pptx-6-320.jpg)
![Interactive Exercise
• Practice what you've learned with these
exercises:
# Filter rows where Age > 30
df[df['Age'] > 30]](https://image.slidesharecdn.com/dynamicpresentationnumpypandas-241203175509-a2d6406b/85/python_Dynamic_Presentation_NumPy_Pandas-pptx-9-320.jpg)
python_Dynamic_Presentation_NumPy_Pandas.pptx
- 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.