The document discusses converting data into information using NumPy and Pandas Python libraries. It covers topics like arrays and matrices, different data formats, NumPy operations for linear algebra and math, and Pandas for working with labeled data and performing analyses like sorting, filtering, and correlations. The goal is to understand how to structure and analyze data using these Python tools.

1. Data to
Information:
NumPy and
Pandas
“From bits to information”

2. Outline
• Data to Information.
• Arrays and Matrices.
• Python: Arrays, Lists and Dictionaries.
• NumPy.
• Pandas.
• Tuples

3. Data to Information

4. Data formats
• Data: Collection, Preservation, Analysis and Reporting.

5. Arrays and
Matrices
“From bits to information”

6. Arrays

7. Arrays

8. 2D Array

9. 2D Arrays

10. Dictionaries

11. Dictionaries

12. Arrays, Lists and Dictionaries: Using NumPy

13. Arrays, Lists and Dictionaries: Using a Dataframe

14. NumPy
“From bits to information”

15. NumPy

16. NumPy: Linear Equation Solving

17. NumPy Array Operations

18. NumPy Maths Operations

19. NumPy Array Operations

20. NumPy: Matrix Operations

21. NumPy: Bitwise

22. Pandas
“From bits to information”

23. Pandas

24. Pandas: Data Types

25. Pandas: Len() and Columns

26. Pandas: Columns

27. Pandas: Describe()

28. Pandas: Sorting

29. Pandas: Correlate()

30. Pandas: Filtering

31. Tuples
“From bits to information”

32. Tuples

33. Data to
Information:
NumPy and
Pandas
“From bits to information”