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Effective Python for Data Scientists
Efficient Python Tricks and Tools for Data Scientists
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. Tuple
2.5. Dictionary
2.6. Function
2.7. Classes
2.8. Datetime
2.9. Code Speed
2.10. Good Python Practices
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
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
9. Insights From Data
9.1. Analyze Data Science Market
9.2. Programming Languages by Age
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Insights From Data
9.
Insights From Data
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This chapter shows some interesting visualizations using information extracted from data.
