https://github.com/venkat-a/exploratory-data-analysis-eda-using-pyspark
Leverage the power of Apache Spark for large-scale data processing and analysis
https://github.com/venkat-a/exploratory-data-analysis-eda-using-pyspark
dataframes descriptive-statistics hadoop-hdfs matplotlib plotly-express pyspark-python seaborn sql statistical-analysis visualization
Last synced: over 1 year ago
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Leverage the power of Apache Spark for large-scale data processing and analysis
- Host: GitHub
- URL: https://github.com/venkat-a/exploratory-data-analysis-eda-using-pyspark
- Owner: venkat-a
- License: mit
- Created: 2024-03-13T09:01:22.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-21T02:54:55.000Z (about 2 years ago)
- Last Synced: 2025-01-08T04:36:29.575Z (over 1 year ago)
- Topics: dataframes, descriptive-statistics, hadoop-hdfs, matplotlib, plotly-express, pyspark-python, seaborn, sql, statistical-analysis, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 147 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Exploratory-Data-Analysis-EDA-using-PySpark
This repository contains a comprehensive Jupyter notebook guide for performing Exploratory Data Analysis (EDA) using PySpark, with a focus on the necessary steps to install Java, Spark, and Findspark in your environment. This guide is structured to provide a seamless introduction to working with big data using PySpark, offering insights into its advantages over traditional data analysis tools like pandas.
The guide further delves into practical EDA techniques, comparisons between pandas and Spark, and visualizations to uncover insights from big data. It's designed for beginners and intermediate users who are looking to enhance their data analysis skills with PySpark."
## Description
This guide starts with the essentials of installing Java, Spark, and Findspark, setting the stage for complex data analysis tasks. It transitions into detailed exploratory data analysis, showcasing the power of Spark for handling large datasets efficiently.
## Sections
The notebook is structured into multiple sections, each focusing on a specific aspect of the EDA process with PySpark. Here are some highlighted sections:
Steps 1 through 29: These steps cover everything from initial setup to advanced data manipulation and visualization techniques.
"Difference between pandas and spark": A comparative analysis showcasing the strengths and limitations of pandas and Spark for data analysis.
Key Features
## Comprehensive Guide:
From installation to advanced analysis, this notebook serves as an end-to-end guide for EDA with PySpark.
## Hands-on Examples:
Includes practical examples and code snippets to illustrate how PySpark can be used to analyze large datasets.
## Comparative Analysis:
Offers insights into how PySpark compares to pandas, helping users make informed choices about the right tool for their data analysis tasks.
## Prerequisites
## To follow along with this guide, you will need:
Python 3.x installed on your machine.
Basic understanding of Python programming and data analysis concepts.
Installation
## The following Python libraries are used in this guide:
findspark
matplotlib
pyspark
seaborn
You can install these libraries using pip:
## bash
pip install findspark matplotlib pyspark seaborn