NumPy and Pandas Data Manipulation for E-Commerce Analysis

Table of Contents

• Project Overview
• Technologies Used
• Features
• Getting Started
• Data Sources
• Usage
• Examples
• Contributing
• License
• Contact

Project Overview

This repository contains a comprehensive project focused on data manipulation using NumPy and Pandas. It showcases advanced techniques for data analysis, cleaning, and business intelligence, specifically applied to e-commerce sales data. By utilizing these powerful libraries, you can extract insights, visualize trends, and make informed decisions based on data.

You can find the latest releases and download files here.

Technologies Used

• Python: The main programming language used for data manipulation.
• NumPy: A library for numerical operations.
• Pandas: A library for data manipulation and analysis.
• Matplotlib: A library for data visualization.
• Seaborn: A library for statistical data visualization.
• Jupyter Notebook: An interactive environment for running Python code.

Features

• Data Cleaning: Tools to clean and preprocess e-commerce sales data.
• Data Analysis: Functions to analyze sales trends and customer behavior.
• Business Intelligence: Techniques to derive insights from data for strategic decisions.
• Visualizations: Graphs and charts to represent data visually.
• Machine Learning: Basic models for predicting sales trends.

Getting Started

To get started with this project, follow these steps:

1. Clone the Repository: Use the command below to clone the repository to your local machine.

   git clone https://github.com/Lovish123456/numpy-pandas-data-manipulation.git

2. Navigate to the Directory: Change to the project directory.

   cd numpy-pandas-data-manipulation

3. Install Dependencies: Make sure you have the required libraries installed. You can use pip to install them.

   pip install -r requirements.txt

4. Run the Jupyter Notebook: Start Jupyter Notebook to explore the project.

   jupyter notebook

Data Sources

The project uses synthetic e-commerce sales data. You can generate your own data or modify the existing dataset to fit your needs. The dataset includes:

• Sales transactions
• Customer information
• Product details
• Date and time of purchases

Usage

After setting up the project, you can start analyzing the data. The Jupyter Notebook contains various sections, each focusing on a specific aspect of data manipulation. You can modify the code to suit your requirements.

Example Code Snippet

Here's a simple example of how to load and analyze data using Pandas:

import pandas as pd

# Load the dataset
data = pd.read_csv('ecommerce_sales_data.csv')

# Display the first few rows
print(data.head())

# Analyze total sales
total_sales = data['Sales'].sum()
print(f'Total Sales: ${total_sales}')

Examples

Data Cleaning

Cleaning data is essential for accurate analysis. The project includes functions to handle missing values, remove duplicates, and standardize formats.

# Remove duplicates
data = data.drop_duplicates()

# Fill missing values
data['Customer_Age'].fillna(data['Customer_Age'].mean(), inplace=True)

Data Visualization

Visualizing data helps in understanding trends and patterns. The project uses Matplotlib and Seaborn for this purpose.

import matplotlib.pyplot as plt
import seaborn as sns

# Plot total sales by month
monthly_sales = data.groupby('Month')['Sales'].sum()
sns.lineplot(x=monthly_sales.index, y=monthly_sales.values)
plt.title('Total Sales by Month')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

Contributing

Contributions are welcome! If you have suggestions or improvements, feel free to create a pull request. Make sure to follow the coding standards and include tests for new features.

Steps to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push to your branch.
5. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or feedback, feel free to reach out via GitHub issues or contact me directly.

You can find the latest releases and download files here.

Explore the power of data manipulation with NumPy and Pandas!