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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. List
2.3.1. Get Elements
2.3.2. Unpack Iterables
2.3.3. Join Iterables
2.3.4. Interaction Between 2 Lists
2.3.5. Apply Functions to Elements in a List
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
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. Work with String
4.11. Style a DataFrame
4.12. Test
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. Tools for Best Python Practices
6.12. Better Pandas
6.13. Testing
6.14. SQL Libraries
6.15. PySpark
6.16. Large Language Model (LLM)
7. Cool Tools
7.1. Alternative Approach
7.2. Workflow Automation
7.3. Code Review
7.4. Logging and Debugging
7.5. Better Outputs
7.6. Git and GitHub
7.7. Environment Management
8. Jupyter Notebook
8.1. Jupyter Notebook
Repository
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Data Science Tools
6.
Data Science Tools
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This chapter covers some useful tools for a data science project.
