Skip to main content
Back to top
Ctrl
+
K
What Should You Expect From This Book?
1. How to Read This Book
2. Python Built-in Methods
2.1. String
2.2. Number
2.3. Set
2.4. Dictionary
2.5. Function
2.6. Classes
2.7. Datetime
2.8. Code Speed
2.9. Good Python Practices
2.10. New Features in Python
3. Python Utility Libraries
3.1. Collections
3.2. Itertools
3.3. Functools
3.4. Pydash
3.5. SymPy
3.6. Operator
3.7. Data Classes
3.8. Typing
3.9. pathlib
3.10. Pydantic
4. Pandas
4.1. Change Values
4.2. Get Certain Values From a DataFrame
4.3. Work with Datetime
4.4. Transform a DataFrame
4.5. Create a DataFrame
4.6. Combine Multiple DataFrames
4.7. Filter Rows or Columns
4.8. Manipulate a DataFrame Using Data Types
4.9. Sort Rows or Columns of a DataFrame
4.10. String
4.11. Style a DataFrame
4.12. Testing
5. NumPy
5.1. NumPy
6. Data Science Tools
6.1. Feature Extraction
6.2. Feature Engineer
6.3. Get Data
6.4. Manage Data
6.5. Machine Learning
6.6. Natural Language Processing
6.7. Time Series
6.8. Sharing and Downloading
6.9. Tools to Speed Up Code
6.10. Visualization
6.11. Better Pandas
6.12. Testing
6.13. SQL Libraries
6.14. Polars
6.15. PySpark
6.16. Delta Lake
6.17. Large Language Model (LLM)
7. Cool Tools
7.1. Tools for Best Python Practices
7.2. Alternative Approach
7.3. Workflow Automation
7.4. isort: Automatically Sort your Python Imports in 1 Line of Code
7.15. rich.inspect: Produce a Beautiful Report on any Python Object
7.29. Better Outputs
7.30. Git and GitHub
7.31. Environment Management
8. Jupyter Notebook
8.1. Jupyter Notebook
Repository
Open issue
.md
.pdf
Python Built-in Methods
2.
Python Built-in Methods
#
This chapter covers some useful Python built-in methods and libraries.
