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https://github.com/saadsalmanakram/data-processing
This repo is focused on all key frameworks, libraries or tools use for Data Processing
https://github.com/saadsalmanakram/data-processing
big-data pandas polars spark
Last synced: about 14 hours ago
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This repo is focused on all key frameworks, libraries or tools use for Data Processing
- Host: GitHub
- URL: https://github.com/saadsalmanakram/data-processing
- Owner: saadsalmanakram
- Created: 2024-10-05T11:16:21.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T04:48:19.000Z (18 days ago)
- Last Synced: 2025-01-28T05:26:00.325Z (18 days ago)
- Topics: big-data, pandas, polars, spark
- Homepage:
- Size: 1000 Bytes
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# 📊 Data Processing: Mastering Spark, Pandas, and Polars
## 📝 Introduction
**Data Processing** is at the core of every data-driven application. This repository serves as a **comprehensive guide** to **Spark, Pandas, and Polars**, helping learners efficiently handle, transform, and analyze large-scale datasets.
📌 **Learn efficient data handling techniques** with Pandas and Polars
📌 **Scale to big data** with Apache Spark
📌 **Master data transformations, filtering, and aggregations**
📌 **Optimize performance** using parallelization, lazy evaluation, and vectorized operations
📌 **Handle structured and unstructured data** (CSV, JSON, Parquet, Databases)
---
## 🚀 Features
- 🐼 **Pandas**: DataFrames, GroupBy, Joins, Pivot Tables
- ⚡ **Polars**: Lazy Execution, Parallelized Data Processing
- 🔥 **Apache Spark**: Distributed Computing, SQL Queries, ML Pipelines
- 📊 **Data Cleaning & Transformation**
- 🚀 **Performance Optimization Techniques**
- 📂 **Working with Large Datasets (CSV, JSON, Parquet, SQL)**
---
## 📌 Prerequisites
Before diving in, make sure you have:
- **Python 3.x** installed → [Download Here](https://www.python.org/downloads/)
- Libraries: Pandas, Polars, PySpark, SQLAlchemy
- Jupyter Notebook for interactive exploration
---
## 📂 Repository Structure
```
Data-Processing/
│── pandas/ # Pandas DataFrame operations
│── polars/ # Polars for high-performance data processing
│── spark/ # Apache Spark for big data analytics
│── README.md # Documentation
└── requirements.txt # Python dependencies
```
---
## 🏆 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/Data-Processing.git
cd Data-Processing
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run an Example Notebook
Launch Jupyter Notebook and open one of the provided notebooks:
```bash
jupyter notebook
```
---
## 🔍 Topics Covered
### 🐼 **Pandas Essentials**
- Creating & Manipulating DataFrames
- Filtering, Sorting, and Grouping
- Handling Missing Data
- Merging and Joining Data
- Time Series Analysis
### ⚡ **Polars - The Fast Alternative to Pandas**
- Lazy Evaluation & Parallel Processing
- Query Optimization
- Working with Large Datasets
- Expressive Query Syntax
### 🔥 **Apache Spark - Distributed Data Processing**
- Introduction to PySpark
- Spark DataFrame API
- SQL Queries in Spark
- Working with RDDs
- Performance Optimization (Caching, Partitioning)
### 📊 **Performance Optimization Techniques**
- **Vectorized Operations** vs. Loops
- **Lazy Execution** in Polars & Spark
- **Parallel Processing** for large datasets
- **Memory-efficient Data Handling**
---
## 🖥 Example: Pandas vs. Polars Performance Benchmark
```python
import pandas as pd
import polars as pl
import time
# Sample dataset
data = {"A": range(1_000_000), "B": range(1_000_000)}
# Pandas
start = time.time()
df_pd = pd.DataFrame(data)
df_pd["C"] = df_pd["A"] + df_pd["B"]
end = time.time()
print(f"Pandas Execution Time: {end - start:.4f} seconds")
# Polars
start = time.time()
df_pl = pl.DataFrame(data)
df_pl = df_pl.with_columns((df_pl["A"] + df_pl["B"]).alias("C"))
end = time.time()
print(f"Polars Execution Time: {end - start:.4f} seconds")
```
💡 **Key Takeaway**: Polars executes much faster than Pandas due to **parallelization and lazy evaluation**.
---
## 🚀 Real-World Applications
This repository includes practical **real-world projects**, such as:
📌 **ETL Pipelines** → Extract, Transform, and Load large datasets
📌 **Stock Market Data Analysis** → Process millions of rows efficiently
📌 **Log File Processing** → Analyze terabytes of server logs using Spark
📌 **SQL-like Querying on Big Data** → Run SQL queries on distributed datasets
---
## 🔥 Working with Apache Spark
Create a **Spark DataFrame** and run SQL queries:
```python
from pyspark.sql import SparkSession
# Initialize Spark
spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
# Load CSV into Spark DataFrame
df = spark.read.csv("datasets/large_data.csv", header=True, inferSchema=True)
# Run SQL Query
df.createOrReplaceTempView("data_table")
result = spark.sql("SELECT category, COUNT(*) FROM data_table GROUP BY category")
result.show()
```
---
## 🏆 Contributing
Contributions are welcome! 🚀
🔹 **Fork** the repository
🔹 Create a new branch (`git checkout -b feature-name`)
🔹 Commit changes (`git commit -m "Added Pandas optimization example"`)
🔹 Push to your branch (`git push origin feature-name`)
🔹 Open a pull request
---
## 📜 License
This project is licensed under the **MIT License** – feel free to use, modify, and share the code.
---
## 📬 Contact
For queries or collaboration, reach out via:
📧 **Email:** [email protected]
🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
---
⚡ **Master Data Processing and Scale to Big Data Efficiently!** ⚡
---