https://github.com/newking9088/python_for_data_scientist
A detailed reference covering Python's essential tools for data science - from basic data structures (lists, tuples, sets, dictionaries) to advanced libraries (NumPy, Pandas) and visualization techniques (Matplotlib, Seaborn, Plotly). Includes practical examples, best practices, and optimization tips for working with data at scale.
https://github.com/newking9088/python_for_data_scientist
Last synced: 3 months ago
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A detailed reference covering Python's essential tools for data science - from basic data structures (lists, tuples, sets, dictionaries) to advanced libraries (NumPy, Pandas) and visualization techniques (Matplotlib, Seaborn, Plotly). Includes practical examples, best practices, and optimization tips for working with data at scale.
- Host: GitHub
- URL: https://github.com/newking9088/python_for_data_scientist
- Owner: newking9088
- Created: 2025-02-20T03:16:23.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-20T03:18:39.000Z (3 months ago)
- Last Synced: 2025-02-20T04:25:05.969Z (3 months ago)
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python Data Structures and Visualization Guide for Data Scientist
A detailed reference covering Python's essential tools for data science - from basic data structures (lists, tuples, sets, dictionaries) to advanced libraries (NumPy, Pandas) and visualization techniques (Matplotlib, Seaborn, Plotly). Includes practical examples, best practices, and optimization tips for working with data at scale.
## Table of Contents
### 1. Python Data Structures
- **Lists**: Mutable sequences with comprehensive methods for data manipulation
- **Tuples**: Immutable sequences ideal for fixed data structures
- **Sets**: Unordered collections for unique elements and set operations
- **Dictionaries**: Key-value pairs for efficient data mapping
- **Strings**: Text processing and manipulation
- **min/max Functions**: Efficient ways to find extremes in data
### 2. NumPy Fundamentals
- Array creation and manipulation
- Mathematical operations
- Statistical functions
- Linear algebra operations
- File I/O operations
- Performance optimization techniques
### 3. Pandas for Data Analysis
- **Data Structures**
- Series and DataFrame fundamentals
- Index manipulation
- **Data Operations**
- Basic data access and cleaning
- Advanced data manipulation
- GroupBy operations
- Merging and joining datasets
- **Time Series**
- DateTime functionality
- Time zone handling
- Resampling and rolling windows
- **Advanced Features**
- Window functions
- Complex grouping operations
- Memory optimization
### 4. Data Visualization
- **Pandas Plotting**: Quick visualizations from DataFrames
- **Matplotlib**: Customizable static visualizations
- **Seaborn**: Statistical data visualization
- **Plotly**: Interactive web-based visualizations
## Key Features
- 📊 Comprehensive examples for each concept
- 🔧 Practical implementations and best practices
- 💡 Performance optimization tips
- 🎯 Common interview questions and solutions
## Getting Started
```python
# Required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
```
## Usage Examples
Each section includes practical examples showing real-world applications:
```python
# Example: Advanced DataFrame Operation
df = pd.DataFrame({
'date': pd.date_range('2023-01-01', periods=10),
'value': np.random.randn(10)
})
# Calculate 7-day rolling average
df['rolling_avg'] = df['value'].rolling(window=7).mean()
```
## Best Practices
- Efficient data structure selection
- Memory optimization techniques
- Performance considerations
- Visualization guidelines
## Contributing
Feel free to contribute to this repository by:
1. Forking the project
2. Creating your feature branch
3. Committing your changes
4. Pushing to the branch
5. Opening a pull request