UsefulLinks
Computer Science
Data Science
Pandas Library
1. Introduction to Pandas
2. Core Data Structures
3. Data Loading and Saving
4. Indexing and Data Selection
5. Data Cleaning and Preparation
6. Combining and Reshaping Data
7. Grouping and Aggregation
8. Working with Text Data
9. Working with Time Series Data
10. Multi-level Indexing
11. Data Visualization
12. Advanced Topics and Performance
12.
Advanced Topics and Performance
12.1.
Categorical Data Type
12.1.1.
Creating Categorical Data
12.1.2.
.cat Accessor
12.1.2.1.
Renaming Categories
12.1.2.2.
Reordering Categories
12.1.2.3.
Adding Categories
12.1.2.4.
Removing Categories
12.1.3.
Benefits of Categorical Data
12.1.3.1.
Memory Efficiency
12.1.3.2.
Improved Performance
12.1.3.3.
Sorting and Grouping with Categoricals
12.2.
Performance Optimization
12.2.1.
Memory Usage Analysis
12.2.1.1.
memory_usage() Method
12.2.2.
Efficient Data Types
12.2.2.1.
Downcasting Numeric Types
12.2.2.2.
Using Categorical Types
12.2.2.3.
String vs Object Types
12.2.3.
File Format Optimization
12.2.3.1.
Parquet Format Benefits
12.2.3.2.
Feather Format Benefits
12.2.3.3.
Compression Options
12.2.4.
Working with Large Datasets
12.2.4.1.
Chunking Data
12.2.4.2.
Memory-efficient Operations
12.3.
Method Chaining
12.3.1.
Writing Readable Chained Operations
12.3.2.
pipe() Method
12.3.3.
Best Practices for Method Chaining
12.4.
Configuration and Settings
12.4.1.
pd.set_option() Function
12.4.2.
Display Options
12.4.2.1.
Maximum Rows and Columns
12.4.2.2.
Column Width
12.4.2.3.
Precision and Formatting
12.4.3.
Performance-related Options
12.4.4.
Resetting Options to Default
12.4.4.1.
pd.reset_option() Function
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11. Data Visualization
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1. Introduction to Pandas